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Cross-Setting Discharge Function Score for Inpatient Rehabilitation Facilities

CBE ID
4630
1.1 New or Maintenance
Is Under Review
Yes
1.3 Measure Description

This outcome measure estimates the percentage of Inpatient Rehabilitation Facility (IRF) Medicare patient stays that meet or exceed an expected discharge function score. The expected discharge function score is a risk-adjusted estimate that accounts for patient characteristics. The measure includes patients who are 18 years of age or older and the timeframe for the measure is 12 months.

        • 1.5 Measure Type
          1.6 Composite Measure
          No
          1.7 Electronic Clinical Quality Measure (eCQM)
          1.8 Level Of Analysis
          1.10 Measure Rationale

          Measuring functional status of inpatient rehabilitation facility (IRF) patients can provide valuable information about a IRF’s quality of care. A patient’s functional status may be associated with adverse health outcomes such as falls, fractures, exacerbation of chronic conditions, and a higher risk of readmissions following IRF care. Predictors of poorer recovery in function include greater age, complications after hospital discharge, and residence in a nursing home. Understanding factors associated with poorer functional recovery facilitates the ability to estimate expected functional outcome recovery for patients, based on their personal characteristics.

          IRFs can positively impact their patients’ functional outcomes. During a IRF stay, the goals of treatment include fostering the patient’s ability to manage their daily activities so that the patient can complete functional (i.e., self-care and mobility) activities as independently as possible and, if feasible, return to a safe, active and productive life in a community-based setting. 

          The Cross-Setting Discharge Function Score (Discharge Function) measure determines how successful each IRF is at achieving or exceeding an expected level of functional ability for its patients at discharge. An expectation for discharge function score is built for each IRF stay by accounting for patient characteristics that impact their functional status. The Cross-Setting Discharge Function for a given IRF is the proportion of that IRF’s stays where a patient’s observed discharge function score meets or exceeds their expected discharge function score. IRFs with low percentage indicate that they are not achieving the functional gains at discharge that are expected based upon patient characteristics and patient status at admission for a larger share of their patients. The measure provides information to IRFs that has the potential to hold providers accountable for functional outcomes and encourages them to improve the quality of care they deliver. This measure also promotes patient wellness, encourages adequate nursing and therapy services to help prevent adverse outcomes (e.g., potentially preventable hospitalization) and increases the transparency of quality of care in the IRF setting. Discharge Function adds value to the IRF QRP function measure portfolio by using specifications that allow for better comparisons across Post-Acute Care  settings, considering both self-care and mobility activities in the function score, and refining the approach to addressing missing activity scores including those coded with activity not attempted codes.

          1.20 Testing Data Sources
          1.25 Data Sources

          The Inpatient Rehabilitation Facility Patient Assessment Instrument (IRF-PAI) data are collected on all Medicare patients who receive services from IRFs More information about the IRF-PAI is available at: https://www.cms.gov/medicare/payment/prospective-payment-systems/inpatient-rehabilitation/pai

        • 1.14 Numerator

          The number of Medicare IRF stays in the denominator with a discharge function score that is equal to or higher than the calculated expected discharge function score.  

          The function items used to determine the observed function score are: Eating (GG0130A3), Oral Hygiene (GGO130B3), Toileting Hygiene (GG0130C3), Roll left and right (GG0170A3), Lying to sitting on side of bed (GG0170C3), Sit to stand (GG0170D3), Chair/bed-to-chair transfer (GG0170E3), Toilet transfer (GG0170F3), and Walk 10 feet (GG0170I3) and Walk 50 feet with two turns (GG0170J3) if the patient walks, or Wheel 50 feet with two turns (GG0170R3) if the patient does not walk and uses a wheelchair.  The specifications for calculation of the function score are provided in the following manual report: 

          https://www.cms.gov/files/document/irf-qm-calculations-and-reporting-users-manual-v60.pdf

          1.14a Numerator Details

          The numerator is the number of IRF stays during the reporting period in which the observed discharge function score (Section 1) for select GG function activities is equal to or greater than the expected discharge function score (Section 2).

          Section 1. The observed discharge function score is the sum of individual function activities at discharge. The section in each PAC assessment instrument titled Section GG, Functional Ability and Goals, includes standardized patient assessment data elements that measure mobility and self-care functional status. The Discharge Function measure focuses on these standardized activities that are currently available across all PAC settings (listed below). Valid responses for the standardized functional items/activities are reported below,

          The function items used to determine the observed function score are the standardized functional items/activities that are currently available across all PAC settings: 

          • Eating (GG0130A), 
          • Oral Hygiene (GGO130B), 
          • Toileting Hygiene (GG0130C), 
          • Roll left and right (GG0170A), 
          • Lying to sitting on side of bed (GG0170C), 
          • Sit to stand (GG0170D), 
          • Chair/bed-to-chair transfer (GG0170E), 
          • Toilet transfer (GG0170F), 
          • Walk 10 feet (GG0170I), 
          • Walk 50 feet with two turns (GG0170J), 
          • Wheel 50 feet with two turns (GG0170R) 

          Standardized Functional Assessment (GG) Item Responses:

          • Six-level rating scale:
            • 06 = Independent
            • 05 = Setup or clean-up assistance
            • 04 = Supervision or touching assistance
            • 03 = Partial/moderate assistance
            • 02 = Substantial/maximal assistance
            • 01 = Dependent
          • Activity Not Attempted Codes:
            • 07 = Patient refused
            • 09 = Not applicable
            • 10 = Not attempted due to environmental limitations
            • 88 = Not attempted due to medical condition or safety concerns
            • ^ = Skip pattern
          • Missing data
            • - = Not assessed/no information

          The following steps are used to determine the observed discharge function score for each stay: 

          • Step 1: If the code for an activity is between 06 (independent) to 01 (most independent), then use code as the score for that activity. 
          • Step 2: If code for an item is 07, 09, 10, 88, dashed (-), skipped (^), or missing, then the score for that activity is estimated with statistical imputation (see Section 3.5).
          • Step 3: Sum scores across all items to calculate the total observed discharge function score. 
          • Step 4: Round the observed discharge function score to the fourth decimal place.

          If the patient does not walk at the time of admission and discharge, different mobility item scores are used. 

          Use 2 * the Wheel 50 Feet with 2 Turns (GG0170R) score to calculate the total observed discharge function score for stays where (i) Walk 10 Feet (GG0170I) has an activity not attempted (ANA) code at both admission and discharge and (ii) either Wheel 50 Feet with 2 Turns (GG0170R) or Wheel 150 Feet (GG0170S) has a code between 1 and 6 at either admission or discharge. For all other patient stays, use Walk 10 Feet (GG0170I) and Walk 50 Feet with 2 Turns (GG0170J) to calculate the total observed discharge function score. 

          For all cases, 10 activities are used to calculate a patient’s total observed discharge score and score values range from 10 – 60. 

          Section 2. The expected discharge function score is determined by applying the regression equation determined from risk adjustment to each IRF stay using admission Inpatient Rehabilitation Facility Patient Assessment Instrument (IRF-PAI) data. Risk adjustment controls for patient characteristics such as the admission function score, age, and clinical conditions. Refer to Section ‎4.4 for details on risk adjustment. For consistent comparison against the observed discharge function score, the expected discharge function score is also rounded to the fourth decimal place.

        • 1.15 Denominator

          The total number of Medicare IRF stays, except those that meet the exclusion criteria. 

          1.15a Denominator Details

          The denominator is total number of Medicare IRF stays with an IRF-PAI discharge record in the measure reporting period, which do not meet the exclusion criteria. The reporting period for the measure is 12 months (four quarters). Documentation on how IRF stays are constructed is available in the IRF Quality Reporting Program Measure Calculations and Reporting User’s Manual: Version 6.0.

          1.15d Age Group
          Adults (18-64 years)
          Older Adults (65 years and older)
        • 1.15b Denominator Exclusions

          Medicare IRF stays are excluded from measure calculations if: 

          1) The Medicare IRF Stay is an incomplete stay: Patients with incomplete stays are identified based on the following criteria: 

          • Discharge to acute care that results in the patient’s absence from the IRF for longer than three calendar days; or 
          • Died while in IRF or 
          • Discharged against medical advice or 
          • Length of stay is less than three days

          2) The patient has the following medical conditions: Coma, persistent vegetative state, complete tetraplegia, locked-in syndrome, or severe anoxic brain damage, cerebral edema or compression of brain.

          3) The patient is younger than age 18

          4) The patient is discharged to hospice or received hospice while a patient

          1.15c Denominator Exclusions Details

          Medicare IRF stays are excluded from measure calculations if:

          1) The IRF stay is an incomplete stay: Patients with incomplete stays (incomplete = [1]) are identified based on the following criteria using the specified data elements:

          • Discharge to acute care that results in the patient’s absence from the IRF for longer than three calendar days: Discharge destination/Living setting (Item 44D = [02, 63, 65, 66]) OR
          • Died while in IRF (Item 44C = [0]) OR
          • Discharged against medical advice (Item 41 = [1]); OR
          • Length of stay is less than three days; Item 40 (Discharge Date) – Item 12 (Admission Date) is less than three days. 

          2) The patient has any of the following medical conditions at the time of admission: Coma, persistent vegetative state, complete tetraplegia, severe brain damage, locked-in syndrome, or severe anoxic brain damage, cerebral edema or compression of brain, as identified by: 

          • Impairment Group 0004.1221 or 0004.1222 or 0004.2221 or 0004.2222 on Item 21A; or 
          • ICD-10-CM codes on Item 22 or Item 24:
            • Coma = B15.0, B16.0, B16.2, B17.11, B19.0, B19.11, B19.21, E03.5, E08.01, E08.11, E08.641, E09.01, E09.11, E09.641, E10.11, E10.641, E11.11, E11.01, E11.641, E13.01 ,E13.11, E13.641, E15, K70.41, K71.11, K72.01, K72.11, K72.91, P91.5, R40.20, R40.2113, R40.2114, R40.2123, R40.2124, R40.2213, R40.2214, R40.2223, R40.2224, R40.2313, R40.2314, R40.2323, R40.2324, R40.2333, R40.2334, R40.2343, R40.2344, R40.2433, R40.2434, R40.2443, R40.2444 
            • Persistent vegetative state = R40.3 
            • Severe brain damage = S06.A1XA, S06.A1XD, S06.A1XS 
            • Complete tetraplegia = G82.51, G82.52, G82.53, S14.111AS, 14.111DS1, 4.112A, S14.112D, S14.113A, S14.113D, S14.114A, S14.114D, S14.115A, S14.115D, S14.116A, S14.116D, S14.117A, S14.117D, S14.118A, S14.118D, S14.119A, S14.119D
            • Locked-in state G83.5 
            • Severe anoxic brain damage, edema or compression G93.1 G93.5 G93.6

          3) The patient is younger than age 18: Truncate (Item 12 (Admission Date) – Item 6 (Birth Date)).

           

          4) The patient is discharged to hospice or received hospice while a patient: Item 44D (Discharge destination/Living setting) = [50, 51]. 

        • 1.13a Data dictionary not attached
          Yes
          1.16 Type of Score
          1.17 Measure Score Interpretation
          Better quality = Higher score
          1.18 Calculation of Measure Score

          Discharge Function measure is the proportion of IRF stays in which the observed discharge function score is equal to or greater than an expected discharge function score. A higher score indicates better performance in functional outcomes. For each IRF stay, observed discharge function score and expected discharge function score are determined. For each IRF, Discharge Function is the proportion of quality episodes where the observed discharge function score is greater than or equal to the expected discharge function score.

          The Cross-Setting Discharge Function Score measure focuses on standardized functional assessment items listed below (the same set of items listed in in the numerator description in Section 1.14a) that are currently available across all PAC settings:

          • GG0130A: Eating
          • GG0130B: Oral Hygiene
          • GG0130C: Toileting Hygiene
          • GG0170A: Roll Left and Right
          • GG0170C: Lying to Sitting on Side
          • GG0170D: Sit to Stand
          • GG0170E: Chair/Bed-to-Chair Transfer
          • GG0170F: Toilet Transfer
          • GG0170I: Walk 10 Feet
          • GG0170J: Walk 50 Feet with 2 Turns
          • GG0170R: Wheel 50 Feet with 2 Turns

          Valid Responses for the Standardized Functional Assessment Items: The response categories of the standardized functional items/activities reflect the level of assistance required by the patient to perform these activities:

          • Patient Functional Status Assessed:
            • 6: Independent
            • 5: Setup or clean-up assistance
            • 4: Supervision or touching assistance
            • 3: Partial/moderate assistance
            • 2: Substantial/maximal assistance
            • 1: Dependent
          • Activity Not Attempted (ANA) Codes:
            • 7: Patient refused
            • 9: Not applicable
            • 10: Not attempted due to environmental limitations
            • 88: Not attempted due to medical condition or safety concerns
          • Other NA Codes:
            • ^: Skip pattern
            • -: Not assessed/no information available

          The process for calculating Discharge Function can be divided into two phases. In the first phase, standardized functional items/activities at admission and at discharge that have an Activity Not Attempted (ANA) code of 07, 09, 10, or 88, a dash (-), or a skip (^) recorded (hereafter referred to as NA) are estimated with statistical imputation methods. The estimation models include the predictors used in risk adjustment and covariates for scores on other standardized functional items/activities. Notably, the estimation process uses all GG items available in IRFs to estimate the NA scores for the subset of standardized functional items/activities used for the Discharge Function numerator. See the Attached file for more details on the estimation process. In the second phase, the calculation of Discharge Function continues. The steps below describe how to calculate the Discharge Function score.

          Step1: For each IRF stay, calculate the observed discharge function score by summing the individual standardized functional items/activities. If the standardized functional items/activities  has a score of 1 − 6, then use the score for that item. If the standardized functional item/activity has an NA value recorded, then use the imputed score.

          A patient is determined to be a wheelchair user if (i) Walk 10 Feet (GG0170I) has an ANA code at both admission and discharge and (ii) either Wheel 50 Feet with 2 Turns (GG0170R) has a code between 01 and 06 at either admission or discharge. 

          For the patients who are wheelchair users, the observed discharge function score is calculated as sum(GG0130A, GG0130B, GG0130C, GG0170A, GG0170C, GG0170D, GG0170E, GG0170F, (2×GG0170R)). For all other patients, the observed discharge function score is calculated as sum(GG0130A, GG0130B, GG0130C, GG0170A, GG0170C, GG0170D, GG0170E, GG0170F, GG0170I, GG0170J).

          Since there are 10 GG items included in the observed discharge function score, each patient’s total observed discharge score will range from 10 – 60.

          Step 2: Identify excluded IRF stays. Excluded IRF stays are those that are incomplete stays. Also excluded are IRF stays where the patient has a diagnosis indicating coma, persistent vegetative state, complete tetraplegia, severe brain damage, locked-in syndrome, or severe anoxic brain damage, cerebral edema or compression of brain. IRF stays for patients who are younger than 18 are excluded. Finally, IRF stays where the patient is discharged to hospice (home or institutional facility) are also excluded.

          Step 3: For each IRF stay, calculate the expected discharge function score. The risk adjustment model is an ordinary least squares linear regression model, which estimates the relationship between discharge function score and a set of risk adjustors. 

          The risk adjustment model is run on all IRF stays to determine the model intercept () and risk adjustor coefficients (). Expected discharge function scores are calculated by applying the regression equation to each IRF stay at admission.

          Note that any expected discharge function score greater than the maximum (i.e., 60) would be recoded to the maximum score.

          Step 4: Calculate the difference in observed and expected discharge function scores. For each IRF stay which does not meet the exclusion criteria, compare each patient’s observed discharge function score (Step 1) and expected discharge function score (Step 3) and classify the difference as one of the following:

          Observed discharge function score is equal to or greater than the expected discharge function score.

          Observed discharge function score is lower than the expected discharge function score.

          This is listed under Activity Not Attempted Codes in IRF.

          Step 5: Determine the denominator count. Determine the total number of IRF stays with a discharge date in the measure reporting period, which do not meet the exclusion criteria.

          Step 6: Determine the numerator count. The numerator for this quality measure is the number of IRF stays in which the observed discharge function score (rounded to four decimal places) is the equal to or greater than the expected discharge function score (rounded to four decimal places).

          Step 7: Calculate the IRF-level discharge function percent. Divide the IRF’s numerator count (Step 6) by its denominator count (Step 5) to obtain the IRF-level discharge function percent, then multiply by 100 to obtain a percent value.

          Step 8: Round the percent value to two decimal places. If the digit in the third decimal place is 5 or greater, add 1 to the second decimal place, otherwise leave the second decimal place unchanged. Drop all the digits following the second decimal place.

          1.18a Attach measure score calculation diagram, if applicable
          1.19 Measure Stratification Details

          Not applicable

          1.26 Minimum Sample Size

          At least 20 eligible IRF stays are required for the Discharge Function measure in the reporting period. In FY 2023, 98.7% (n=1,150) of all IRFs (n=1,165) met this threshold and accounted for almost 100% (n=496,510) of all eligible IRF stays among all providers.

        • Steward
          Centers for Medicare & Medicaid Services
          Steward Organization POC Email
          Steward Organization Copyright

          yes

          Measure Developer Secondary Point Of Contact

          Anne Deutsch
          RTI international
          3040 Cornwallis Rd
          P.O. Box 12194
          Research Triangle Park, NC 27709
          United States

          • 2.1 Attach Logic Model
            2.2 Evidence of Measure Importance

            IRF care includes the provision of intensive rehabilitation therapy to patients who experienced functional limitations due to an illness or injury. Research examining functional outcomes has included analyses focused on physical functioning, which encompasses self-care and mobility. Physical function is a modifiable predictor of several outcomes including successful discharge to the community [1], functional recovery [2, 3], and re-hospitalization rates [4, 5].  Evidence suggests that IRF care can improve functional outcomes [6, 17] and that outcomes vary across IRFs, which provides an opportunity to monitor provider-level variation through the Discharge Function Score measure. Higher functional status at discharge is associated with better quality of life and fewer complications post-rehabilitation. Patients with greater independence are also more likely to be discharged to the community. IRF care has been shown to improve patient functional status. Several studies have reported that IRF care improved patients’ physical functioning at discharge for patients with various diagnoses, including traumatic brain injury [7, 8], stroke [3, 6, 9, 20, 21], and lower extremity joint surgery [8, 9]. For example, a pilot observational cohort study for IRF patients with stroke assessed functional improvements during rehabilitation and found that over 75% of the participants had meaningful improved in their function abilities [21]. An additional retrospective analysis identified significant functional mobility improvement among IRF patients that survived severe COVID-19 [11].  

            Functional improvement at discharge can vary based on the facility, indicating an opportunity to measure facility-level differences in patient outcomes. For example, two retrospective cohort studies including over 1,000 IRFs found that variation in functional improvement among patients recovering from hip fracture [12] and stroke [13] was more strongly related to differences on facility-level factors than by region. Functional outcomes can depend on whether providers develop person-centered care plans that are unique to each patient’s clinical needs. For example, a retrospective cohort study by Cogan et al. (2020) found that the rate of recovery and length of stay were significantly associated with functional improvement and emphasized the need to evaluate each patient’s rate of functional gain and cater therapy intensity and time accordingly [16]. Differences in facility profit status may relate to changes in functional outcomes. A retrospective database review of 877 IRF facilities between 2016 and 2018 identified that patients at for-profit and not-for-profit facilities had significant differences in outcomes due to differences in patient selection, coding and/or billing, or other unreported factors [19]. 

            Patient characteristics are important predictors of discarge functional status. Evidence suggests that patient case-mix is associated with functional improvement [14, 15, 18]. Evans et al. (2021) observed that patients with better motor and cognition scores at admission and lower comorbidity burden were more likely to improve mobility at discharge [7]. An observational study with nearly 500,000 IRF patients found that IRF patients made significant functional improvement in mobility and self-care between admission and discharge, but the degree of improvement was negatively associated with the number of comorbidities [2].  Kao et al. (2022) found that traumatic spinal cord injury  patients with obesity had less functional improvement than underweight or average weight patients and that among traumatic spinal cord injury patients with obesity, those with longer stays at an IRF showed greater improvements in functional outcome that those with the shortest stays [22]. Researchers suggest that obesity is negatively associated with functional improvement while IRF length of stay may be positively associated with functional improvement [22]. 

            Overall, the literature provides evidence that IRFs can affect functional outcomes at discharge. As such, variations in functional status of IRF patients at discharge could be measured and monitored through the Discharge Function Score measure. Because function outcomes vary based on patient characteristics, the Discharge Function Score measure adjusts for relevant risk factors [20]. 

            Furthermore, the Discharge Function Score measure aligns well with value-based care models, which prioritize patient outcomes over service volume. Studies show that this measure serves as a reliable metric for value-based care initiatives, as it reflects functional improvement, a key indicator of quality care. This evidence supports health policies that promote independence and patient-centered outcomes as primary indicators of IRF care effectiveness.

            Monitoring discharge function scores also has significant implications for healthcare cost reduction. Patients discharged with higher functional status are less likely to experience complications or require readmission, which reduces costs for both patients and the healthcare system overall. CMS data support that successful discharge outcomes correlate with lower post-discharge expenses, reducing long-term healthcare expenditures.

             

            References 

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            2. Deutsch A, Palmer L, Vaughan M, Schwartz C, McMullen T. Inpatient Rehabilitation Facility Patients' Functional Abilities and Validity Evaluation of the Standardized Self-Care and Mobility Data Elements. Arch Phys Med Rehabil. 2022 Feb 11:S0003-9993(22)00205-2. doi: 10.1016/j.apmr.2022.01.147. Epub ahead of print. PMID: 35157893. 
            3. Hong I, Goodwin JS, Reistetter TA, Kuo YF, Mallinson T, Karmarkar A, Lin YL, Ottenbacher KJ. Comparison of Functional Status Improvements Among Patients With Stroke Receiving Postacute Care in Inpatient Rehabilitation vs Skilled Nursing Facilities. JAMA Netw Open. 2019 Dec 2;2(12):e1916646. doi: 10.1001/jamanetworkopen.2019.16646. PMID: 31800069; PMCID: PMC6902754. 
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            5. Middleton A, Graham JE, Lin YL, Goodwin JS, Bettger JP, Deutsch A, Ottenbacher KJ. Motor and Cognitive Functional Status Are Associated with 30-day Unplanned Rehospitalization Following Post-Acute Care in Medicare Fee-for-Service Beneficiaries. J Gen Intern Med. 2016 Dec;31(12):1427-1434. doi: 10.1007/s11606-016-3704-4. Epub 2016 Jul 20. PMID: 27439979; PMCID: PMC5130938. 
            6. Yu-Chien, C., Lin, H., Yu-Fu, C., Hong-Yaw, C., Yu-Tsz Shiu, & Hon-Yi, S. (2023). Minimal clinically important difference (MCID) in the functional status measures in patients with stroke: Inverse probability treatment weighting. Journal of Clinical Medicine, 12(18), 5828. doi:https://doi.org/10.3390/jcm12185828 
            7. Evans E, Krebill C, Gutman R, Resnik L, Zonfrillo MR, Lueckel SN, Zhang W, Kumar RG, Dams-O'Connor K, Thomas KS. Functional motor improvement during inpatient rehabilitation among older adults with traumatic brain injury. PM R. 2021 May 21:10.1002/pmrj.12644. doi: 10.1002/pmrj.12644. Epub ahead of print. PMID: 34018693; PMCID: PMC8606011. 
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            10. Cogan AM, Weaver JA, McHarg M, Leland NE, Davidson L, Mallinson T. Association of Length of Stay, Recovery Rate, and Therapy Time per Day With Functional Outcomes After Hip Fracture Surgery. JAMA Netw Open. 2020 Jan 3;3(1):e1919672. doi: 10.1001/jamanetworkopen.2019.19672. PMID: 31977059; PMCID: PMC6991278. 
            11. Olezene CS, Hansen E, Steere HK, et al. Functional outcomes in the inpatient rehabilitation setting following severe COVID-19 infection. PLoS One. 2021;16(3):e0248824. Published 2021 Mar 31. doi:10.1371/journal.pone.0248824 
            12. Teppala, S., Ottenbacher, K. J., Eschbach, K., Kumar, A., Al Snih, S., Chan, W. J., & Reistetter, T. A. (2017). Variation in Functional Status After Hip Fracture: Facility and Regional Influence on Mobility and Self-Care. The journals of gerontology. Series A, Biological sciences and medical sciences, 72(10), 1376–1382. https://doi.org/10.1093/gerona/glw249 
            13. Reistetter TA, Kuo YF, Karmarkar AM, et al. Geographic and facility variation in inpatient stroke rehabilitation: multilevel analysis of functional status. Arch Phys Med Rehabil. 2015;96(7):1248-1254. doi:10.1016/j.apmr.2015.02.020 
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            16. Cogan AM, Weaver JA, McHarg M, Leland NE, Davidson L, Mallinson T. Association of Length of Stay, Recovery Rate, and Therapy Time per Day With Functional Outcomes After Hip Fracture Surgery. JAMA Netw Open. 2020 Jan 3;3(1):e1919672. doi: 10.1001/jamanetworkopen.2019.19672. PMID: 31977059; PMCID: PMC6991278. 
            17. Osundolire, S., Mbrah, A., Liu, S., & Lapane, K. L. (2024/01//). Association between patient and facility characteristics and rehabilitation outcomes after joint replacement surgery in different rehabilitation settings for older adults: A systematic review. Journal of Geriatric Physical Therapy (2001), 47(1), E1-E18. doi:https://doi.org/10.1519/JPT.0000000000000369. 
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            20. Janet Herbold, Theodore O'Brien, Karrah Peters, Andrea Sanichar, Suzanne Babyar, Responsiveness of Section GG Scores in Tracking Post-Stroke Functional Recovery From Inpatient Rehabilitation Admission to 90-Day Follow-Up, Archives of Physical Medicine and Rehabilitation, Volume 104, Issue 12, 2023, Pages 2002-2010, ISSN 0003-9993, https://doi.org/10.1016/j.apmr.2023.07.013. 
            21. Erin Y. Harmon, Jerome Niyirora, Amy E. Teale, Matthew B. Sonagere, Mark A. Linsenmeyer, Lynne Nicolson, Assessing Clinically Important Differences During Rehabilitation for Stroke: A Pilot Study Evaluating Anchor and Distribution Derived Estimates of Physical Function Change in Classically Summed and Rasch Models of Section GG of the Inpatient Rehabilitation Facility Patient Assessment Instrument, Archives of Physical Medicine and Rehabilitation, 2024, ISSN 0003-9993, https://doi.org/10.1016/j.apmr.2024.02.721. 
            22. Yu-Hsiang Kao, Yuying Chen, Anne Deutsch, Huacong Wen, Tung-Sung Tseng, Rehabilitation Length of Stay, Body Mass Index, and Functional Improvement Among Adults With Traumatic Spinal Cord Injury, Archives of Physical Medicine and Rehabilitation, Volume 103, Issue 4, 2022, Pages 657-664, ISSN 0003-9993, https://doi.org/10.1016/j.apmr.2021.09.017.
          • 2.3 Anticipated Impact

            Physical function is a modifiable factor associated with several outcomes, including successful discharge to the community, and re-hospitalization rates [1, 2, 3]. Thus, the Discharge Function Measure can improve patient outcomes in post-acute care by promoting functional independence, reducing adverse events, and lowering healthcare costs. 

            The cross-setting Discharge Function Score measure determines how successful each IRF is at achieving or exceeding an expected level of functional ability for its patients at discharge. An expectation for discharge function score is built for each IRF stay by accounting for patient characteristics that impact their functional status. The final cross-setting Discharge Function for a given IRF is the proportion of that IRF’s stays where a patient’s observed discharge function score meets or exceeds their expected discharge function score. IRFs with low scores indicate that they are not achieving the functional gains at discharge that are expected based upon patient characteristics and patient status at admission for a larger share of their patients. The measure provides information to IRFs that has the potential to hold providers accountable for functional outcomes and encourages them to improve the quality of care they deliver. This measure also promotes patient wellness, encourages adequate nursing and therapy services to help prevent adverse outcomes (e.g., potentially preventable hospitalization) and increases the transparency of quality of care in the IRF setting. 

            Discharge Function adds value to the IRF QRP function measure portfolio by using specifications that allow for better comparisons across post-acute care settings, considering both self-care and mobility activities in the function score, and refining the approach to addressing items coded with activity not attempted codes

            One concern about unintended consequences with the Cross-Setting Discharge Function Score is that the measure may lead IRFs to selectively admit patients, either by encouraging or avoiding admission of certain types of patients and patients with certain characteristics. To address this, providers’ performance is evaluated among their peers after adjusting for difference in patient case-mix across IRFs. The risk adjustment methodology applied to this measure will help reduce providers’ incentive to selectively admit patients. Therefore, providers’ performance on this measure will be adjusted for the characteristics of their patient population and “level the playing field” across providers. The detailed risk-adjustment strategy will be publicly available, allowing providers to understand that those who provide care for more “high risk” patients are not at a disadvantage given their patient case-mix. 

            Another potential concern about the cross-setting Discharge Function Score measure could be that it focuses on a subset of the available GG items. If the items are not included in this publicly reported measure, it could reduce the incentive to complete those items and could result in higher levels of ANAs. However, the GG items excluded from the Cross-Setting Discharge Function Score measure are used in other IRF Quality Reporting Program measures and Section GG items are used in the IRF Prospective payment system. Together, these circumstances should provide an incentive for continued reporting of these GG items

            Another possibility related to increased ANA rates is that providers could strategically code ANAs in an attempt to game the estimated values from the statistical imputation models. For instance, IRFs could record ANA codes for patients who did not improve by discharge if the discharge estimation models would predict higher scores based on that patient’s characteristics. However, this type of gaming, where providers are determining in real-time which patients would perform better with statistical estimation than a true discharge score, would require sophisticated understanding and application of the estimation methodology.The Cross-Setting Discharge Function Score measure will be monitored to identify unintended consequences, including patient selection patterns or changes in ANA coding, which could lead to future re-specification of the measure as needed.

             

            References:

            1. Gustavson, A. M., Malone, D. J., Boxer, R. S., Forster, J. E., & Stevens-Lapsley, J. E. (2020). Application of High-Intensity Functional Resistance Training in a Skilled Nursing Facility: An Implementation Study. Physical therapy, 100(10), 1746–1758. https://doi.org/10.1093/ptj/pzaa126. 
            2. Sarguni Singh, Elizabeth Molina, Elisabeth Meyer, Sung-Joon Min, Stacy Fischer, Post-Acute Care Outcomes and Functional Status Changes of Adults with New Cancer Discharged to Skilled Nursing Facilities, Journal of the American Medical Directors Association, Volume 23, Issue 11, 2022, Pages 1854-1860, ISSN 1525-8610, https://doi.org/10.1016/j.jamda.2022.02.010. 
            3. Brian Downer, Ioannis Malagaris, Chih-Ying Li, Mi Jung Lee, Rachel Deer, The Influence of Prior Functional Status on Self-Care Improvement During a Skilled Nursing Facility Stay, Journal of the American Medical Directors Association, Volume 23, Issue 11, 2022, Pages 1861-1867, ISSN 1525-8610, https://doi.org/10.1016/j.jamda.2022.03.003 
            2.5 Health Care Quality Landscape

            The Improving Medicare Post Acute Care Transformation (IMPACT) Act of 2014 requires the collection of standardized data across post-acute care providers and required the Centers for Medicare & Medicaid Services to develop and implement quality measures, including quality measures addressing self-care and mobility function.

            The Cross Setting DC Function measure was developed based on input obtained during two Technical Expert Panel (TEP) meetings (July 2021 and January 2022). During these meetings, panelists expressed that:

            1. The IRF QRP would benefit from having a cross-setting functional outcome measure to use instead  function process measure (Application of Percent of Long-Term Care Hospital (LTCH) Patients With an Admission and Discharge Functional Assessment and a Care Plan That Addresses Function that was recently removed from the IRF QRP. The Cross-Setting Discharge Function measure has higher variation in provider performance and offers more informative comparisons between IRF for patients, caregivers, and stakeholders.
            2. The Cross-Setting Discharge Function Score measure benefits from being specified to align across PAC settings (IRF, LTCH, SNF, HHA). Due to limited GG item availability in LTCH, only a subset of items can be used to produce measure scores that could be computed identically in each PAC setting. We calculated measure scores with all GG items available in IRF v. the subset available in LTCH. Panelists reviewed comparisons between provider scores and model fit and found that the narrower set of GG items provides similar capture of functional status. [1] 
            3. The Activity Not Attempted (ANA) codes are used frequently on assessments for certain GG items, and statistical imputation should be used as the method to estimate resulting missing item scores. 

             

            [1] https://www.cms.gov/sites/default/files/2022-04/PAC-Function-TEP-Summary-Report-Jul2021.pdf

            2.6 Meaningfulness to Target Population

            Functional status, including ability to perform daily activities, is important from patient and caregiver perspectives, with functional goal-setting being an important focus of patient- and family- centered care. For the majority of patients in post-acute care, promoting functional independence and setting functional goals to facilitate return to community living is a primary goal of care. For patients receiving home health services, functional assessment and goal-setting are also a primary focus to attain independent functioning in the home and community, return to or surpass prior level of functioning, maintain current level of functioning, or slow the process of functional decline. In LTCH settings, where patients are medically complex and sometimes referred to as chronically critically ill, promoting physical function is particularly important to mitigate functional deterioration, morbidity, and medical complications from prolonged bedrest and hospitalization. From a caregiver perspective, focus on functional status and functional goal-setting is important to reduce caregiver burden, and minimize need for assistance at home.

            CMS convened a Patient and Family Engagement Listening Session to discuss this measure with patients and their caregivers. The Patient and Family Engagement Listening Session demonstrated that the measure concept resonates with patients and caregivers. Participants’ views of self-care and mobility were aligned with the functional domains captured by the measure, and they found them to be critical aspects of care. Participants emphasized the importance of measuring functional outcomes and were specifically interested in metrics that show how many patients discharged from particular facilities made improvements in self-care and mobility.

            The Discharge Function Measure directly reflects the priorities of post-acute care patients, who value functional independence, quality of life, and avoiding rehospitalization or institutionalization. 

            Below are key points supported by peer-reviewed literature:

            1. Patients Value Functional Independence:

            Studies show that post-acute care patients prioritize functional recovery (e.g., mobility, self-care) as the most important outcome following discharge. Functional independence enables patients to return home and manage daily life without relying on long-term institutional care or home health services.

            Source: Graham, J. E., et al. (2016). "Patients' perspectives on discharge from post-acute care settings: Priorities for functional recovery." Archives of Physical Medicine and Rehabilitation. 

            2. Improved Quality of Life:

            Health-related quality of life encompasses patients’ physical health perceptions and functional status. Patients who regain independence in activities of daily living report higher satisfaction with their health and overall life post-discharge. They value avoiding dependency on caregivers, especially for basic tasks like toileting and dressing.

            Source: Greenfield, S., and Nelson, E. (2020). "The influence of functional independence on quality of life in post-acute care patients." Quality of Life Research.

            Additionally, researchers exploring patient and consumer perspectives on function have reported that functional status and functional outcomes are important from the patient and consumer perspective (Stineman 2009, Kramer 1997, Kurz 2008). These studies show that patients place a value on their functional outcomes and rehabilitation goals mostly through research that examines how patients can categorize their functional goals in hierarchies of what they perceive as the most important to least important functional outcomes for the purpose of their own quality of life. Stineman’s research shows patients and consumers value their functional outcomes although inpatient rehabilitation patients may have different perspectives on what is important for them to gain from their rehabilitation compared to community dwelling consumers. One study, specifically focused on patients undergoing rehabilitation in IRFs (n=79) found that eating was the most valued functional activity for them, followed by bathing, toileting, and bowel/bladder function (Stineman 2009).

            Sources:

            Kramer AM. (1997) Rehabilitation care and outcomes from the patient's perspective. Medical care. 35(6):JS48-57.

            Kurz AE, Saint-Louis N, Burke JP, Stineman MG. (2008) Exploring the personal reality of disability and recovery: A tool for empowering the rehabilitation process. Qualitative Health Research. 18(1):90-105.

            Stineman MG, Rist PM, Kurichi JE, Maislin G. (2009) Disability meanings according to patients and clinicians: imagined recovery choice pathways. Quality of Life Research. 18:389-98.

            3. Reduction in Hospital Readmissions:

            Patients view avoiding rehospitalization as crucial to their recovery. Research demonstrates that patients who regain functional independence are less likely to be readmitted, an outcome patients find meaningful as it reduces the emotional and physical stress of hospitalization.

            Source: Ouslander, J.G., and Berenson, R.A. (2011). "Reducing Unnecessary Hospitalizations of Nursing Home Residents." The New England Journal of Medicine .

            4. Desire to Return Home:

            A primary goal for many PAC patients is to return home after their rehabilitation. Being discharged with higher functional ability is highly valued because it enables patients to live in their communities, reducing the need for institutional care or home health services.

            Source: Harrison, S., et al. (2017). "Patient priorities in post-acute care: Returning home with functional independence." Journal of Aging & Health .

            5. Patients Want to Avoid Institutional Long-Term Care:

            Patients fear the loss of autonomy associated with long-term care facilities and express a strong preference for achieving the functional status that allows them to avoid this outcome. Functional independence is a top priority for maintaining control over their living situation.

            Source: Kane, R.A. (2001). "Long-Term Care and Patient Preferences: Achieving Independence and Control." The Gerontologist .

            In summary, input from a variety of stakeholders has been taken into consideration throughout the measure development process. Feedback was sought and considered from patients and caregivers on the salience of the measure concept and from Technical Expert Panels (TEPs) on the appropriate specifications for the cross-setting measure.

          • 2.4 Performance Gap

            There is evidence of a performance gap and variability in performance for this quality measure. In FY 2023, we examined variability in provider measure results for provider with at least twenty stays after applying denominator exclusion criteria. This testing included 496,510 stays treated in 1,150 IRFs. The mean measure results among these IRFs was 55.1% (median: 56.4%, IQR: 42.4% to 69.4%). Final scores ranged from a minimum of 0% to a maximum score of 94.8%. This wide variation indicates there is a performance gap in Discharge Function Scores across IRFs.

            Table 1. Performance Scores by Decile
            Performance Gap
            Overall Minimum Decile_1 Decile_2 Decile_3 Decile_4 Decile_5 Decile_6 Decile_7 Decile_8 Decile_9 Decile_10 Maximum
            Mean Performance Score 55.1 0 21.1 35.1 42.2 48.2 53.9 59.6 64.7 69.4 74.6 82.6 94.8
            N of Entities 1,150 1 115 115 115 116 114 115 115 115 115 115 1
            N of Persons / Encounters / Episodes 496,510 55 26,264 39,811 46,022 47,432 44,996 47,404 54,476 70,440 71,513 48,152 240
            • 3.1 Feasibility Assessment

              A feasibility assessment was not necessary. The data elements used for measure construction are part of the standard data collection processes for IRF providers and are already used in existing IRF QRP measures. 

              3.3 Feasibility Informed Final Measure

              The feasibility assessment was not necessary (see explanation in 3.1 above) therefore no changes were made to the measure specification.

            • 3.4 Proprietary Information
              Not a proprietary measure and no proprietary components
              • 4.1.3 Characteristics of Measured Entities

                All testing used IRF stays completed in FY 2023. A total of 1,165 IRFs submitted IRF-PAI data for complete stays in the 12-month testing period. We identified providers that met the reportability threshold of at least twenty stays after applying denominator exclusion criteria. After applying denominator exclusion criteria and the reportability threshold of 20 stays, this testing ultimately included 496,510 stays spread across 1,150 IRFs. The included IRFs were geographically diverse, with no one region containing more than 25% of the IRFs. The IRFs were divided about equally between not-for-profit (43%) and For-profit entities (43%) and located in urban areas (88%). Facility size is presented based on the number of patient stays. A quarter (25%) of the IRFs were large with 581-3780 stays in FY 2023, and 51% of the included IRFs were medium sized with 163-580 stays in FY 2023. Only 24% were small IRFs with 20-162 stays in the FY 2023. Note that providers with less than 20 stays during the 12-month testing period are excluded from facility-level analyses presented below.

                The provider-level characteristics give insight into the distribution of stays and providers across categories such as stay count, profit status, rurality, region, and provider type for publicly reportable providers. Here’s a summary:

                Overall Distribution: The below data captures publicly reportable providers, covering 496,510 stays across 1,150 providers

                Stay Count: Providers are categorized by the number of stays, indicating their size. Most providers fall into the medium size category, though large providers account for the majority of stays, reflecting a higher volume of care provided by a smaller subset of providers.

                • Large (581–3,780 stays): 58% of stays, 25% of providers.
                • Medium (163–580 stays): 36% of stays, 51% of providers.
                • Small (20–162 stays): 6% of stays, 24% of providers.

                Profit Status: For-profit and not-for-profit providers are evenly split in terms of provider count, but for-profit providers handle a higher percentage of stays, indicating a potentially larger scale of operations. Providers’ profit status distribution is as follows:

                • For-Profit: 59% of stays, 43% of providers.
                • Not-For-Profit: 32% of stays, 43% of providers.
                • Government: 5% of stays, 5% of providers.
                • Unknown: 4% of stays, 6% of providers.

                Rurality: The vast majority of care is provided in urban settings, with rural providers making up a small fraction of both stays and provider counts, which may highlight challenges in rural healthcare access and coverage.

                • Urban: 95% of stays, 88% of providers.
                • Rural: 5% of stays, 12% of providers.

                Region: The highest concentration of stays and providers is in the South - West South Central and South Atlantic regions, possibly reflecting higher demand or provider capacity in these areas. In contrast, the West regions (Mountain and Pacific) have fewer providers and stays, which may indicate regional disparities in provider availability. Providers and stays are distributed across U.S. regions as follows:

                • South - West South Central: 22% of stays, 19% of providers.
                • Midwest - East North Central: 13% of stays, 16% of providers.
                • South - South Atlantic: 22% of stays, 17% of providers.
                • Other regions with lower representation include West - Pacific (8% of stays, 6% of providers) and West - Mountain (7% of stays, 6% of providers).
                4.1.1 Data Used for Testing

                The data used report results in this form are derived from several sources. The primary source of data for the measure is Inpatient Rehabilitation Facility Patient Assessment Instrument (IRF-PAI) data from Fiscal Year (FY) 2023. An IRF PAI record represents an IRF stays. AN IRF stay may include interrupted stays lasting 3 calendar days or less. The target period for the measure is 12 months (4 quarters). If a patient has multiple eligible SNF stays with a discharge date within the target period, then each eligible stay is included in the measure. 

                For analyses related to health equity, we also used Medicare administrative data to determine dual eligibility status for Medicare and Medicaid and Area Deprivation Index (ADI) data, derived from American Community Survey data. The ADI is presented as an index ranging from zero to 100, designed to represent neighborhood socioeconomic disadvantage, with 100 representing the most disadvantaged neighborhoods nationwide.

                4.1.4 Characteristics of Units of the Eligible Population

                All testing used IRF stays completed in FY 2023. IRFs submitted a total of 571,948 completed stays that ended in FY 2023. After applying denominator exclusion criteria and applying the reportability threshold of 20 stays, we ultimately included 496,510 stays in the measure population and testing. For included IRF stays, 90.4% were for patients who were over the age of 65 and the majority were female (52.5%) and white (79.6%). The Area Deprivation Index (Neighborhood Atlas - Home (wisc.edu)) included 14% of included stays with an ADI of more than 85. The primary medical condition of included stays was varied – 21% had a stroke, 14.4% had fractures and other traumas, and the rest were scattered among a range of diagnoses. 

                The stay-level characteristics provides a large sample of stays, ensuring a comprehensive overview of patient characteristics across multiple demographics and conditions. 

                Overall:

                • Total eligible stays: 496,510 (100%)

                Race: The majority of stays involve White patients (80%), with limited representation from other racial and ethnic groups, indicating potential disparities in access or reporting among diverse populations.

                • White: 80% (399,624)
                • Black: 10% (48,969)
                • Hispanic/Latinx: 6% (28,622)
                • Asian: 2% (8,746)
                • American Indian/Alaska Native: <1% (1,994)
                • Native Hawaiian/Pacific Islander: <1% (526)
                • Multiple Race/Ethnicity: <1% (712)
                • No Information Available: 1% (7,317)

                Sex: There is a fairly even split between male and female patients, with a slightly higher proportion of stays among females (53%).

                • Male: 47% (232,290)
                • Female: 53% (264,220)

                Age: The majority of stays are among older adults, particularly those aged 65–84 years (71%), highlighting the predominance of elderly patients in need of post-acute care.

                • <35 years: <1% (1,288)
                • 35–44 years: 1% (3,849)
                • 45–54 years: 2% (10,267)
                • 55–64 years: 6% (31,769)
                • 65–74 years: 34% (168,028)
                • 75–84 years: 37% (185,575)
                • 85–90 years: 14% (67,468)
                • 90 years: 6% (28,266)

                Payer: Most stays are covered by Medicare (83%), with a notable minority of patients being dual-eligible, indicating the significant role of public insurance in post-acute care financing.

                • Medicare only: 83% (412,164)
                • Dual (Medicare & Medicaid): 17% (84,274)
                • Unknown: <1% (66)

                Area Deprivation Index (ADI): Patients are distributed across various socioeconomic levels, with a significant portion (14%) from highly disadvantaged areas (ADI ≥ 85), indicating the need for tailored support for socioeconomically vulnerable populations.

                • ADI ≥ 85: 14% (69,229)
                • ADI 75–84: 10% (49,313)
                • ADI 50–74: 27% (136,152)
                • ADI 25–49: 27% (135,993)
                • ADI 0–24: 18% (88,278)
                • Unknown: 4% (17,545)

                Health-Related Social Needs/SDOH Items: A significant portion of patients (26%) require health literacy support, emphasizing the need for accessible and understandable health information in patient care.

                • Interpreter need: 3% (12,435)
                • Transportation need: 3% (14,439)
                • Health literacy need: 26% (131,044)
                • Social isolation: 9% (46,468)

                Primary Diagnosis Group: Stroke and cardiorespiratory conditions make up a substantial portion of stays (40%), indicating a focus on complex rehabilitation needs in IRFs for these conditions.

                • Hip/Knee Replacement: 4% (17,997)
                • Stroke: 21% (104,879)
                • Non-Traumatic Brain Dysfunction: 7% (36,080)
                • Traumatic Brain Dysfunction: 4% (21,545)
                • Non-Traumatic Spinal Cord Dysfunction: 4% (21,100)
                • Traumatic Spinal Cord Dysfunction: 1% (4,169)
                • Progressive Neurological Conditions: 2% (10,537)
                • Other Neurological Conditions: 11% (56,995)
                • Fractures and Other Multiple Trauma: 14% (69,276)
                • Amputation: 3% (12,701)
                • Other Orthopedic Conditions: 8% (39,128)
                • Debility, Cardiorespiratory Conditions: 19% (95,311)
                • Medically Complex Conditions: 1% (6,792)
                4.1.2 Differences in Data

                The sample remained the same for all aspects of testing. For testing of differences in performance scores across socio-contextual variables, including race, ethnicity and socio-economics status (see Section 5. Equity), we used additional data sources to incorporate ADI, derived from census data, and dual eligibility for Medicare and Medicaid from CMS administrative data. 

              • 4.2.2 Method(s) of Reliability Testing

                We report testing results throughout this document at two levels: 1) data elements (i.e., items) and the function scale (i.e., summed value derived from item codes) and 2) the computed quality measure result. 

                To assist the reader in understanding the testing analysis and results, we begin by providing a brief overview of these components of the performance measure:

                1. Data Elements:

                a. Clinicians code 11 motor function data elements included in Section GG of each PAC assessment instrument. One is a wheelchair data elements used for patients who do not walk as part of the recoding approach. Depending on the context, we sometimes refer to these data elements as “items” or “activities.”   

                b. The motor function data elements are collected at the time of admission and discharge using a 6-level rating scale (01 to 06), or activity not attempted codes if, for example, the activity was not attempted due to medical or safety concerns.

                c. Higher scores indicate higher ability (i.e., more independence)

                d. For the performance measure calculation, data element activity not attempted codes and missing data are recoded using statistical imputation to estimate the item score. 

                e. A discharge function scale score is created by summing the data element scores, after re-coding. The range of the discharge function score is 10 to 60 units.

                f. For the Discharge Function Score, a score of 10 indicates the patient is dependent on a helper to perform all activities (i.e., data elements) and a score of 60 means the patient is independent on all activities.

                2. Calculated Performance Measure Score: The Percentage of IRF Patient stays that Meet or Exceed an Expected Discharge Function Score

                a. The calculated performance measure score is the percentage of IRF patient stays that meet or exceed an expected discharge function score within an IRF.  The risk-adjustment procedure used to calculate the expected score is described  in Section 4.4. 

                b. This performance measure estimates the percentage of IRF patient stays that meet or exceed an expected discharge function score.

                Reliability testing of the items was conducted when the items were initially developed and a summary of the inter-rater rater and the video reliability studies are described in the attachment focused on reliability.  

                Internal Consistency of the Items (unit of analysis is patient assessments): We examined the internal consistency of the function scale/instrument scores for each patient stay. Internal consistency provides a general assessment of how well the function data elements interrelate within the mobility scale/instrument. This internal consistency analysis is an indicator of the reliability of the function scale/instrument.

                Internal consistency was assessed using the Cronbach’s alpha coefficient, which is the average correlation of all possible half-scale divisions. Cronbach’s alpha is a statistic frequently calculated when testing instrument or scale psychometrics. The Cronbach’s alpha reliability estimate ranges from zero to one, with an estimate of zero indicating that there is no consistency of measurement among the items, and one indicating perfect consistency. Many cutoff criteria exist to determine whether or not a scale shows good consistency or whether the items “hang together” well. General consensus is that Cronbach’s alpha should be at least 0.70 for an adequate scale for group-level decisions, and alphas closer to 1 indicate a good scale 

                Aron A, Aron EN Statistics for Psychology. 2nd ed. Upper Saddle River, NJ: Prentice Hall, 1999

                 

                Computed Performance Measure Score Reliability – Split-half Reliability (unit of analysis of providers): 

                Split-half reliability was used to examine the reliability of the computed performance measure scores. We used measure scores only for IRFs that met the reportability threshold of 20 stays after denominator exclusions are applied. We conducted split-half reliability by randomly splitting each provider’s patient stays into two groups and calculating correlations between the computed performance measure scores of the randomly divided groups. When a provider’s data, after being randomly divided into two groups, show similar scores to one another, the performance measure score is more likely to reflect systematic differences in IRF providers quality rather than random variation. The Intraclass Correlation Coefficient (ICC) were used to measure internal reliability. We calculated Shrout-Fleiss intraclass correlation coefficients ICC(2,1)*  between the split-half scores to measure reliability. Because each half of each facility’s split-half pair has – by definition – half as many beneficiaries, the measurement of each half-facility is less statistically reliable than a measurement of a full facility would be. Therefore, after calculating ICC estimates, we then applied the Spearman-Brown formula to correct for the attenuation bias that stems from splitting samples. Intraclass correlations were also calculated by facility volume (quartile of stay count, with quartiles 2 and 3 grouped into the "Medium" category) to examine whether there were differences in performance measure reliability by IRF size. Guidelines for interpretation vary, but largely agree a value of 0.75 indicates acceptable or good reliability and values exceeding 0.9 indicate excellent reliability (Adams, 2009; Cicchetti, 2001; Koo & Li, 2016; Portney & Watkins, 2009)

                Adams JL. (2009). The Reliability of Provider Profiling: A Tutorial. Santa Monica, CA: RAND Corporation. https://www.rand.org/pubs/technical_reports/TR653.html

                Cicchetti, D. V. (2001). The precision of reliability and validity estimates re-visited: Distinguishing between clinical and statistical significance of sample size requirements. Journal of Clinical and Experimental Neuropsychology, 23(5), 695–700.
                Koo, T. K., & Li, M. Y. (2016). A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of chiropractic medicine, 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012

                Portney, L. G., & Watkins, M. P. (2009). Foundations of clinical research: applications to practice (Vol. 892, pp. 11-15). Upper Saddle River, NJ: Pearson/Prentice Hall.

                Shrout, Patrick E. and Fleiss, Joseph L. Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin, 1979, 86, 420-3428.).

                 

                Signal to Noise (unit of analysis of providers): If a measure is reliable, then true differences in providers should explain a substantial proportion of the variance in quality measure scores. The computed performance measure scores are the risk-adjusted discharge function scores. We used measure scores only for IRFs that met the reportability threshold of 20 stays after denominator exclusions are applied. Reliability was assessed by following the RAND methodology*. The RAND methodology (Adams, 2009) estimates a reliability statistic  for each measured entity; that statistic evaluates the within-provider variance in the context of the overall between-providers variance of the measure. The method assumes a beta-binomial model for patient outcomes. The beta-binomial assumption implies that providers vary with regard to the probability of observed outcomes (in this case, the probability that a given beneficiary will have a discharge function score greater than or equal to the expected discharge score): higher-quality providers produce higher probabilities of positive outcomes relative to lower-quality providers. Those probabilities are assumed to be sampled from a beta distribution. Within each provider, the provider-specific probabilities result in binary outcomes that are binomially-distributed, and the observed proportions of outcomes represent quality measure scores.  Adams (2009) notes that values of  that are between 0.7 and 0.8 indicate acceptable reliability. We used the Betabin SAS macro described in the paper to estimate Signal-to-Noise Reliability (CBB) in by using CBB method for the provider-to-provider variance. As a robustness check, we also estimated Signal-to-Noise Reliability (VAR) using the sample variance to estimate the provider-to-provider variance (not presented in the table in 4.2.3 but discussed in 4.2.4)

                Adams JL. (2009). The Reliability of Provider Profiling: A Tutorial. Santa Monica, CA: RAND Corporation. https://www.rand.org/pubs/technical_reports/TR653.html

                4.2.3 Reliability Testing Results

                Summary of critical data element reliability testing: 

                As reported in the attached document 4.2.3, inter-rater and the video reliability study found good to very good reliability testing. 

                Scale Analysis- Internal Consistency (unit of analysis is patient assessments): 

                The attached table 4.3.3A-1 includes Cronbach Alpha results for the discharge assessment for non-wheelchair and wheelchair users using the estimated values.

                Computed Quality Measure Score Reliability

                Split-half Reliability (unit of analysis of providers): The attached table 4.2.3A-2 includes the split-half reliability (unit of analysis of providers) as described above.

                Signal to Noise (unit of analysis of providers): Signal-to-noise testing (Table 2 below) suggests that a high proportion of the variation in quality measure scores is due to differences in provider quality measure results rather than variation within providers.

                4.2.3a Attach Additional Reliability Testing Results
                Table 2. Accountable Entity–Level Reliability Testing Results by Denominator-Target Population Size
                Accountable Entity-Level Reliability Testing Results
                &nbsp; Overall Minimum Decile_1 Decile_2 Decile_3 Decile_4 Decile_5 Decile_6 Decile_7 Decile_8 Decile_9 Decile_10 Maximum
                Reliability 0.97 0.73 0.97 0.96 0.96 0.96 0.96 0.96 0.97 0.98 0.98 0.98 1.00
                Mean Performance Score 0.55 0 0.21 0.35 0.42 0.48 0.54 0.60 0.65 0.69 0.75 0.83 0.95
                N of Entities 1,150 1 115 115 115 116 114 115 115 115 115 115 1
                N of Persons / Encounters / Episodes 496,510 55 26,264 39,811 46,022 47,432 44,996 47,404 54,476 70,440 71,513 48,152 240
                4.2.4 Interpretation of Reliability Results

                Cronbach’s alpha (unit of analysis of assessment data)

                Cronbach's alpha results show the items measure the concept of function in a consistent manner.

                Split-half Reliability (unit of analysis of providers)

                Split-half analysis results indicated strong, positive correlations between the IRF providers’ randomly divided groups’ computed performance measure scores, providing strong evidence of measure reliability with an ICC of 0.97 overall. ICCs were exceptionally strong across providers regardless of volume, with ICC of 0.94 even among the smallest providers (20-162 discharges).

                Signal to Noise (unit of analysis of providers)

                Signal to Noise Testing suggests very strong reliability across providers, with a reliability statistic of 0.97. Robustness checks in which we calculated the Signal-to-Noise Reliability (VAR) using the sample variance also gave an overall statistic of 0.97. Both of these pass the threshold of acceptable reliability.  

              • 4.3.3 Method(s) of Validity Testing

                We report testing results throughout this document at two levels: 1) data elements/scale and 2)  Computed Quality Measure results. 

                 

                1. Critical Data Elements/Scale

                Several studies have examined the validity of the data elements by examining the relationship between the items and length of stay, discharge to community rates and risk of falls.

                 

                2. Computed Quality Measure Score

                Convergent Validity: To evaluate convergent validity of measure scores, we measured Spearman’s rank correlation between the Cross-Setting Discharge Function Score measure and other SNF QRP measures (See Table 4.3.3iin the attachment). The analysis used FY 2023 data and only included data from SNFs with at least 20 stays. 

                Face validity of performance measure score:

                To assess face validity of the Cross-Setting Discharge Function Score measure, two Technical Expert Panel (TEP) meetings (July 2021 and January 2022), as well as a Patient and Family Engagement Listening Session, were convened. TEP members showed strong support for the face validity of this measure. Though a vote was not taken at the meeting, the TEP agreed with the conceptual and operational definition of the measure. Many patients enter rehabilitation care after an acute event for the purpose of regaining function. This measure directly assesses the ability of providers to enable patients to reach their highest level of function possible.  Panelists reviewed the validity analyses described herein and agreed they demonstrated measure validity. 

                The Patient and Family Engagement Listening Session demonstrated that the measure concept resonates with patients and caregivers. Participants’ views of self-care and mobility were aligned with the functional domains captured by the measure, and they found them to be critical aspects of care. Participants emphasized the importance of measuring functional outcomes and were specifically interested in metrics that show how many patients discharged from particular facilities made improvements in self-care and mobility.

                In developing the measures, statistical imputation was implemented to estimate item scores for patients where a GG item was NA using models that adjust for patient clinical characteristics. We evaluated the empiric validity of our estimation methodology using the following analyses. 

                 

                1. We estimated admission and discharge scores for each GG item used in measure construction. To evaluate model fit of estimation models, we calculated C-statistics for each of the 22 estimation models. C-statistics ranged from 0.83-0.99, and the mean C-statistic was 0.94..

                2. A bootstrapping method was used to measure bias and mean squared error (MSE) in the estimation method that uses statistical imputation compared to the recode approach used in the self-care and mobility functional outcome measures. Bias measures the average amount by which the estimated value differs from the true value. Bias is signed, with a positive amount meaning that the estimated values were higher, on average, than were the true values. MSE measures how far away the method is, on average from the truth. It is unsigned and can be positive even if bias is zero. In IRFs, statistical estimation also resulted in lower levels of bias (-0.39 at admission; -0.07 at discharge) and MSE (2.17 at admission; 0.50 at discharge) compared to the bias (-1.43 at admission; -0.51 at discharge) and MSE (6.99 at admission; 2.58 at discharge) produced from the recode approach, which supports the validity of the statistical estimation method in this setting. 

                3. We calculated the difference in discharge function between episodes that have bona fide item scores at admission and stays with NA codes at admission where we estimate the item score. This difference provides a metric of how accurately estimated item scores reflect true patient function. For all 11 items, the difference was lower than if these ANAs were recoded to the most dependent level of functional status. This result indicates that statistical estimation produced more accurate results. 

                4.3.4 Validity Testing Results

                1. Critical Data Elements/Scale

                The validity of the items used to calculate the quality measure has been evaluated by examining the relationship between each item and other indicators. Results of these analyses showed that higher admission function item scores, indicating higher functional ability, were associated with shorter inpatient stays, as expected. Further, higher admission function item scores, indicating higher functional ability, were associated with higher rates of community discharge. 

                These studies also examine content validity of the standardized functional assessment items that are used to calculate the measure and found the self-care and mobility activities (items) used in the DC Function measure are items used in many other functional assessment instruments.

                Sources:

                Toth M, Palmer L, Marino ME, Smith A, Schwartz C, Deutsch A, McMullen T. Validation of the Standardized Function Data Elements among Medicare Skilled Nursing Facility Residents, Journal of the American Medical Directors Association. 24(3): 307-313.e1, 2023, https://doi.org/10.1016/j.jamda.2022.12.014.

                Deutsch A, Palmer L, Vaughan M, Schwartz C, McMullen T. Inpatient Rehabilitation Facility Patients' Functional Abilities and Validity Evaluation of the Standardized Self-Care and Mobility Data Elements. Arch Phys Med Rehabil. 2022 Jun; 103(6): 1070-1084.e3. https://doi.org/10.1016/j.apmr.2022.01.147   

                 

                2. Computed Quality Measure Result

                Face Validity Based on TEP Feedback

                A Technical Expert Panel provided feedback on the Cross Setting Discharge Function measure representing face validity.

                1. Expert Consensus on Discharge Function Score
                • The discharge function measure was reviewed and supported by a multi-disciplinary panel of experts, including persons with lived experience.
                • Evidence: The panelists favored reporting discharge function measures due to their ability to reflect patient recovery at discharge. They preferred reporting discharge function rather than change in function measures because it better captures patient status at the point of leaving the provider.

                "Panelists from the July 2021 TEP favored Discharge Function Score measures over Change in Function Score measures and recommended moving forward with Discharge Function Score for the cross-setting measure."

                Source: PAC Function TEP Summary Report – July 2021 and PAC Function TEP Summary Report – January 2022.

                Robust Risk Adjustment for Fair Comparisons

                • The measure uses a robust risk adjustment methodology, which supports fair comparisons of measure results across providers by accounting for differences in patient age, clinical characteristics and comorbidities.
                • Evidence: This ensures that providers are compared on a level playing field, taking into account the complexity of patients treated at each provider.

                "Calculate expected Discharge Function Mobility Score for each eligible stay using risk adjustment coefficients, including demographics, health characteristics, and admission function score."

                Source: PAC Function TEP Summary Report – January 2022.

                Handling of Activities Not Attempted codes

                • The discharge function measure incorporates statistical imputation to address that not all patients can complete each of the functional activities and are thus coded using the Activities Not Attempted codes. This supports measure validity even when certain activities cannot be completed during the patient's stay.
                • Evidence: The TEP strongly favored using statistical imputation over simply coding missing data as "dependent," ensuring that the discharge function measure more accurately reflects the patient's true capabilities.

                "Panelists tended to favor statistical imputation with continued refinement to improve cross-setting performance. Panelists agreed that the current recode could be improved upon."

                Source: PAC Function TEP Summary Report – July 2021 and PAC Function TEP Summary Report – January 2022.

                Alignment with Patient-Centered Outcomes

                • The discharge function measure is designed to reflect patient-centered goals, focusing on the safe and functional transition of patients back to their community or home setting.
                • Evidence: Functional outcomes at discharge are aligned with patient goals of regaining independence, which is a key measure of quality in post-acute care.

                "The discharge function score is designed to reflect the ability of post-acute care providers to successfully rehabilitate patients, ensuring they regain functional independence at discharge and beyond."

                Source: PAC Function TEP Summary Report – January 2022.

                Interested Parties Engaged and Broad Support

                • The measure was reviewed by a diverse group of interested parties with broad support and clinical relevance across different care settings.
                • Evidence: Clinicians, policy experts, and performance measurement specialists contributed their feedback, ensuring that the measure is relevant and usable across different PAC settings.

                "The PAC QRP Functions TEP comprised 15 stakeholders with diverse perspectives and areas of expertise, including clinical, policy and program, measures development, and technical expertise."

                Source: PAC Function TEP Summary Report – January 2022.

                 

                Convergent Validity. 

                Measure validity was assessed by comparing the Discharge Function measure with other QRP measures using Spearman (rank) correlations between provider’s performance scores presented in attached Table 4.3.4a.

                4.3.4a Attach Additional Validity Testing Results
                4.3.5 Interpretation of Validity Results

                1. Critical Data Elements

                We reviewed and the described results from several published studies that examined the validity of the function items. 

                 

                2.         Computed Quality Measure Score

                Convergent Validity. First, as expected, scores for the Discharge Function Score measure correlated well but not perfectly with the Cross-Setting Discharge Function Score measures including Discharge Self-Care (0.91) and Discharge Mobility (0.93). This is expected because the IRF QRP self-care and mobility functional outcome measures use overlapping but not identical GG items and a different method for handling NA codes. We observed a weak positive correlation with the Discharge to Community measure (0.30). There was also a weak correlation with the Potentially Preventable Readmissions within 30-Days Post-Discharge measure (0.13) and Falls with Major Injury (0.13). The measure had weaker associations with measures in which the stay ended with acute events (Potentially Preventable Readmissions within stay), which would be excluded from this measure’s denominator, and measures with a rare outcome (new/worsened pressure ulcers/injuries).  

              • 4.4.1 Methods used to address risk factors
                4.4.2 Conceptual Model Rationale

                The rationale for risk adjustment is to account for differences in patient populations. By risk adjusting, the performance measure assesses providers based on their quality of care and not the underlying health of the population. Providers are not penalized for serving patients with greater clinical need, and fair comparisons can be made across providers.

                The performance measure is cross-setting, calculated for inpatient rehabilitation (IRF), skilled nursing (SNF), long-term care hospital (LTCH), and home health (HH). The development team sought to align risk factors across settings as much as possible. The team presented to a TEP an overview of the availability of clinically meaningful risk factors in each setting. TEP members supported setting-specific parameters for risk adjustment since there are different data points available as well as clinical considerations for each setting. 

                The development team also presented to the TEP the conceptual model shown below in 4.4.2a. TEP members agreed that the conceptual model presented does represent the salient points about the relationship between social risk factors (SRFs), patient functional outcomes, and provider quality. TEP members provided examples of ways in which providers are able to, and should be expected to, mitigate the influence of SRFs on patient outcomes.

                TEP members supported further analysis to understand the effect of measurable SRFs. Specifically, the TEP cited the following as potential measurable SRFs that can impact functional outcomes: dual enrollment, ADI, and race/ethnicity (although noting that these are impacted by provider bias).

                Below are the currently measurable SRFs included in risk adjustment testing, but not used in the final risk adjustment model. Health-related social needs items are not yet available cross-setting but can be tested for inclusion in the future.

                Social Risk Factors (SRFs) Included in Risk Adjustment Testing

                Dual Enrollment:

                • Medicare (reference group)
                • Dual
                • Medicaid
                • Neither Medicare nor Medicaid
                • Unknown Payer

                Race:

                • American Indian or Alaska Native
                • Asian
                • Black
                • Hispanic or Latino
                • Multiple Race
                • Native Hawaiian or Other Pacific Islander
                • No Race/Ethnicity Information Available
                • White (reference group)

                Area Deprivation Index (ADI):

                • ADI (≥85)
                • ADI Missing
                4.4.2a Attach Conceptual Model
                4.4.3 Risk Factor Characteristics Across Measured Entities

                Table 4.4.4A-1 in the attached file shows the number, percent, and average observed score of quality episodes that have the associated risk factor covariate. The table presents information for each risk factor covariate in the final model plus the additional SRF risk factors considered but not used in the final risk adjustment model are listed in Table 4.4.4A-2.

                4.4.4 Risk Adjustment Modeling and/or Stratification Results

                Discharge Function is a cross-setting performance measure calculated for IRF, SNF, LTCH, and HH. Because different data elements are collected across the assessment instruments for each setting, the development team aligned clinically meaningful covariates as much as possible. 

                The development team then presented to a TEP an overview of the data availability in each setting, shown below, and solicited feedback on which covariates should be included in the cross-setting measure risk adjustment model.

                Covariate Availability Across Settings of Care for Discharge Function Measure

                1. Age: LTCH, IRF, SNF, HH
                2. Admission Mobility Score: LTCH, IRF, SNF, HH
                3. Primary Medical Condition Category (PMCC): LTCH, IRF, SNF, HH
                4. Interaction of Admission Mobility Score and PMCC: LTCH, IRF, SNF, HH
                5. Prior Function/Device Use: LTCH, IRF, SNF
                6. Pressure Ulcers: LTCH, SNF
                7. Cognitive Function: LTCH, IRF, SNF, HH
                8. Communication Impairment: LTCH, IRF, SNF, HH
                9. Incontinence: LTCH, IRF, SNF, HH
                10. Falls: LTCH, IRF, SNF, HH
                11. Nutritional Approach: LTCH, IRF, SNF
                12. Comorbidities: LTCH, IRF, SNF, HH
                13. Ventilation Status: LTCH, IRF
                14. Availability of Assistance: LTCH, IRF, SNF
                15. Living Arrangements: LTCH, IRF, SNF, HH

                The TEP members expressed support for setting-specific models since there are different data points available as well as different clinical considerations for each setting. The panelists suggested additional risk adjustors to consider, including Prior living site; Prior hospitalization; Chronic conditions; Obesity; Severity of health condition(s); Low BMI; Pain; Wound infection; Transportation; and Health literacy.

                Below is a listing of the covariate groups included in the final risk adjustment model for IRF. Information on the covariates were obtained from the IRF-PAI.

                The final risk adjustment approach includes adjusting for the following patient characteristics:

                • Age Category: Age was calculated as of the admission date of the IRF stay using the beneficiary’s date of birth.
                • Admission Function Score: Sum of admission scores for function items included in the discharge score (Section 3.4.1), which can range from 10-60, with a higher score indicating greater independence. ANAs in the admission item scores are treated the same way as NAs in the discharge item scores, with ANAs replaced with imputed scores (Appendix). Walking items and wheeling item are used in the same manner as for the discharge score (See Appendix). Admission score squared is also included as a risk adjustor.
                • Primary Diagnosis Group: Primary diagnosis is the principal reason for admitting the patient into IRF care
                • Prior surgery: This covariate captures whether or not the patient had prior surgery
                • Prior Function/Device Use: These covariates capture patient’s functional status prior to the stay. 
                • Pressure Ulcers: These covariates capture the presence of pressure ulcer(s) at different stages. 
                • Cognitive Function: These covariates capture the patient’s cognitive function by assessing whether or not the patient’s mental status at admission is impaired, and if impaired, at what level.
                • Communication impairment: These covariates capture the patient’s communication function, and indicate whether or not the patient’s communication status at admission is impaired, and if impaired, at what level. 
                • Incontinence These covariates indicate the patient’s level of bladder and bowel incontinence. 
                • Nutritional Status: These covariates indicate patient’s swallowing ability at IRF admission and patient’s body mass index
                • History of Falls: This covariate indicates a history of falls prior to the IRF admission
                • Comorbidities: Comorbidities are obtained from Item 24 in the IRF-PAI. Comorbidities are grouped using CMS Hierarchical Condition Categories (HCC) software version 24.

                Significantly, discharge Function is a cross-setting performance measure calculated for IRFs , SNFs, LTCHs, and HH. Because different data elements are collected across the assessment instruments for each setting, the development team aligned clinically meaningful covariates as much as possible.

                The risk adjustment model is an ordinary least squares (OLS) linear regression. It estimates the relationship between discharge function score and the set of risk adjustors. The risk adjustment model is run on all stays to determine the model intercept  and risk adjustor coefficients. 

                Table 4.4.4A-1 in the attached file presents the model results for the final risk adjustment model and the alternative risk adjustment model with additional SRF covariates options is in Table 4.4.4A-2.

                4.4.4a Attach Risk Adjustment Modeling and/or Stratification Specifications
                4.4.5 Calibration and Discrimination

                A well-calibrated model demonstrates good predictive ability to distinguish high-risk from low-risk patients. To assess risk adjustment model calibration, we divided our dataset into deciles of expected values and calculated the ratio of average expected discharge score to average observed discharge score within each decile. A ratio of 1 would indicate perfect agreement between average expected and observed discharge function scores. We expect that the risk adjusted model performance will be stable among IRFs regardless of whether they have patients with low or high expected discharge scores on average.

                As seen in Table 4.4.5A-2, the average expected to observed discharge score ratios within each decile approximated 1.0, with a range of 0.99 to 1.01, validating model performance. There was little variability in average expected to observed discharge score ratios across deciles, supporting model stability across the range of expected discharge function scores and across the sample. 

                We analyzed model fit using adjusted R-squared to determine if the risk adjustment model can accurately predict discharge function while controlling for patient case-mix. The adjusted R-squared value was 0.49, which suggests good model discrimination. Please see Table 4.4.5A-1 in the attached file.

                4.4.5a Attach Calibration and Discrimination Testing Results
                4.4.6 Interpretation of Risk Factor Findings

                Risk factors were chosen based on clinical relevance to Discharge Function performance. Risk factors were recommended by clinician members of the measure development team and by the TEP. The Final risk adjustment model has an adjusted R-squared value of 0.49. 

                4.4.7 Final Approach to Address Risk Factors
                Risk adjustment approach
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                Risk adjustment approach
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                Conceptual model for risk adjustment
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                Conceptual model for risk adjustment
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                • 5.1 Contributions Towards Advancing Health Equity

                  The measure provides a means for assessing the impact of provider performance on patients who experience social risk factors (SRF) to a greater degree than those who have fewer SRFs. For example, dual-eligible patients tend to experience worse socioeconomic circumstances than other patients. These circumstances can negatively impact health outcomes. Some of the disparity in outcomes between dual and non-dual patients can be explained through differences in prevalence of clinical conditions addressed through risk adjustment. However, even after risk adjustment, dual patients fare worse, on average, than non-duals for all settings. One contributing factor could be that there are socioeconomic drivers of health disparities in dual patients beyond what is captured through risk adjustment. This raises the concern that providers who serve these populations are unduly penalized in quality measurement when dual-eligibility is not included in the risk adjustment model. 

                   

                  We tested three SRFs of interest:

                  1. Medicare vs. dually enrolled (patient is dually enrolled at any time during the quality episode)

                  2. Race/ethnicity

                  3. ADI

                  We used several approaches to test differences in performance scores across multiple SRFs and to consider some SRFs for inclusion in the risk adjustment model. First, we constructed alternative risk adjustment models that included additional covariates for payer, race/ethnicity, and ADI, and examined the impact on provider performance. 

                  We find that across most of the alternative risk adjustment models considered, the SRF covariates are significant but small, and have little to no impact on model fit. The details of the alternative risk adjustment models are shown in the attachment for Section 4.4.4a.

                  Second, we stratified the performance scores by SRFs. Using the current model, we calculated provider scores for patients with and without SRFs and grouped SNFs into quintiles based on their proportion of Black/non-White, dual, and dual and high ADI patients. We then examined whether performance declines with the proportion of patients with SRFs, and whether this impacts patients both with and without SRFs. 

                  Across SNFs, we compared Discharge Function FY 2023 performance by subgroups of agencies based on the percentage of patients who are Black, Non-White, Medicaid or Dual-eligible, and Dual-eligible or living in a neighborhood with ADI ≥ 85. To be more specific, we defined subgroups of agencies based on quintiles of the percentage of patients within the agency who have the SRF. For race and ethnicity characteristics, we used the IRF-PAI item to identify patient’s race/ethnicity as Black or Non-White. 

                  As shown in Table 5-1A in the supplemental files. we observed lower patient-level performance on this measure for patients with some SRFs. 

                  In addition, in Table 5.1-B in the Supplemental Files we see that providers that treat a larger proportion of patients with these identified SRFs also had differences in their outcomes. We compared Discharge Function performance based on the percentage of IRFs patients who are Black, Non-White, Medicaid or Dual-eligible, and Dual-eligible or living in a neighborhood with ADI ≥ 85. IRFs that serve 16-86.8% of patients with a Black race have an average 54.5% performance score compared to a 61.7% performance score for IRFs that serve few patients with a Black race. However, patients with dual status had a lower mean score (56.5%) compared to Medicare only patients (59.3%).

                  Risk adjusting for these characteristics may mask systematic disparities in care that should be examined and ultimately providers for which should be held accountable.

                  • 6.2.1 Actions of Measured Entities to Improve Performance

                    All IRFs with at least 20 qualifying stays receive quarterly measure reports on all their publicly reported measures. In addition, providers can run on-demand, confidential reports showing individual measure results and national averages, through CMS’ iQIES. 

                    There is an email box that IRFs may submit questions to as well as a website on which the latest measure updates are posted. The IRF Quality Reporting Quality Measure User’s Manual describes the provider reports that are available. IRFs make use of these reports to monitor and improve the quality of care. 

                    6.2.2 Feedback on Measure Performance

                    IRFs receive quarterly measure reports on all their measures. There is an email box that IRFs may submit questions to as well as a website on which the latest measure updates are posted. CMS makes available information about risk models and covariates on its website.

                    6.2.3 Consideration of Measure Feedback

                    No measure specifications changes requested or made.

                    6.2.4 Progress on Improvement

                    This measure is too new to provide an assessment on impacts on improvement.

                    6.2.5 Unexpected Findings

                    None

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                      First Name
                      Olivia
                      Last Name
                      Giles

                      Submitted by Olivia on Mon, 11/25/2024 - 11:49

                      Permalink

                      Hello! This is Troy Hillman, Director of Quality and Health Policy at the American Medical Rehabilitation Providers Association (AMRPA). AMRPA is the national trade association representing over 800 inpatient rehab facilities, which includes both freestanding inpatient rehab facilities and rehabilitation units of acute care hospitals. AMRPA appreciates the opportunity to comment on CBE #4630: Cross-Setting Discharge Function Score for Inpatient Rehabilitation Facilities. As we have stated in previous comments as part of the Fiscal Year 2024 IRF Proposed Rule, AMRPA did not support the adoption of this measure into the IRF QRP, and the concerns that we had about this measure at that time are consistent with the concerns we have today about this measure being considered for endorsement.

                       

                      First, this measure should be identified as competing with two currently endorsed measures: CBE #2635: Discharge Self-Care Score, as well as CBE: #2636: Discharge Mobility Score. As identified in the PQM Measure Evaluation Rubric, the measure developer must indicate how the new measure is superior to existing measures as part of the importance criteria for endorsement, and we ask that the PQM E&M Committee require an evaluation of competing measures as part of the consideration for endorsement of this measure.

                       

                      AMPRA also has concerns about the imputation method utilized for calculating assessment data. The development of this measure for use in the IRF QRP suggested that the imputation method was necessary for those instances where functional assessment data was considered “missing.” Except for the dash value, where no information was available for the assessment, all other “activity not attempted” codes are indicative of an affirmative functional assessment value and should not be considered as missing. Instead, each code represents a circumstance where a patient is not capable of performing the activity, suggesting that their performance should be considered at least as the most severe level, or code 01 “dependent.” AMRPA recommends that the PQM E&M Committee evaluate the imputation method, and whether this accurately represents the patient's functional status at the time of assessment.

                       

                      AMRPA also asks that you consider whether this measure meets the feasibility criteria required for endorsement, where the measure developer considered the people, tools, tasks, and technologies necessary to implement this measure. While the data elements utilized in the measure calculations are already part of the IRF-PAI, IRFs would require a significant amount of investment in technology to implement the imputation methodology required to manage the performance of this measure. Providers that are unable to calculate imputed values on their own have limited ability to identify what the patient's measure scores may be in order to determine what risk adjusted target value is required to contribute towards a positive result.

                       

                      Finally, AMRPA members are concerned about the suggestion that this is a cross-setting measure and the unintended consequences that may result from such a designation. This measure differs across post-acute care settings as the risk adjustment methodology utilizes setting-specific covariates and setting-specific coefficients. For example, each setting has a model intercept for the expected discharge function score for each patient to meet or exceed, in order to perform well on this measure. For IRFs, that intercept is a little over 34, while for SNFs the model intercept is 30, and for LTACs the model intercept is 14. This suggests that depending on a setting, a patient with the exact same patient characteristics will need to meet or exceed different expectations, with IRF patients having the highest expectation, and LTACs having the lowest expectation. To truly be considered a cross-setting measure, AMRPA members believe that the patient expectations should be consistent, regardless of setting. 

                       

                      Additionally, in suggesting this as a cross-setting measure that is publicly reported, these measures have the unintended consequence of potentially being used to limit patient access to certain settings based upon performance results. While CMS and the measure developers documented concerns about providers denying access to certain patients who may not perform well on this measure, AMRPA questions whether consideration was given for referral sources utilizing this information to direct patients to alternative settings which may not provide the appropriate level of services to produce high quality outcomes. We ask that the committee consider these unintended consequences when evaluating this measure for endorsement. Again, AMPRA appreciates the opportunity to comment on these, and will be submitting additional written comments for consideration of this measure for endorsement. Thank you very much.

                      Organization
                      Troy Hillman, American Medical Rehabilitation Providers Association (AMRPA)

                       We agree that the “Activity Not Attempted” codes are used when the patient was not able to complete an activity.  Missing data, which are indicated with a dash, are rare.  The imputation estimates the likely score for the Activity Not Attempted codes for each activity based on the patterns and averages observed for similar cases. Testing has shown that the imputation approach shows less bias in function measure scores and more precise estimates for item scores than the approach of replacing all “Activity Not Attempted” values with the value of 1 (most dependent). A bootstrapping method was used to measure bias and mean squared error (MSE) in the statistical imputation method compared to the ‘recode to 1’ approach. The goal of the bootstrapping method was to determine how similar imputed values were to the true item score. The average MSE and bias statistics for statistical imputation vs. recoding to 1 were: 

                      IRF: 

                      1. Statistical imputation:  
                        1. Mean squared error: 1.41 at admission and 0.52 at discharge 
                        2. Bias: -0.23 at admission and -0.07 at discharge 
                      2. Recode to 1: 
                        1. Mean squared error: 5.95 at admission and 3.58 at discharge 
                        2. Bias: -1.33 at admission and -0.58 at discharge 

                       

                      CMS and their contractors calculate the measure results, so providers do not need to invest in technology to produce the results.  CMS does publish the model coefficients so providers do have access to information about how patient characteristics are used to estimate “Activity Not Attempted” codes.

                       

                       The term “cross-setting” is used in the measure title to convey that the quality measures are aligned across the four post-acute care settings. The measures for each setting are calculated separately because populations treated in each setting vary. This includes the calculation of the risk-adjustment models. Thus, the regression coefficients are different across the different settings.   The measure results are reported separately on the Compare website by program (i.e., separately for IRFs and SNFs). 

                       

                      This measure is an important addition to the PAC QRPs as it is both (A) cross-setting and (B) an outcome measure.  This measure fulfills statutory requirements as specified in the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014. The IMPACT Act includes a mandate that CMS develop and report cross-setting measures. It specifically requires CMS to develop cross-setting quality measures that assess functional status and changes in function. This measure fulfills these statutory requirements. The contractor developing the home health measure worked with the contractor developing this IRF measure.

                       

                      With regard to IRFs potentially avoiding patients with complex needs, risk-adjustment and the application of exclusion criteria should reduce this concern. The risk adjustment methodology is used for the measure to account for differences in case mix across IRFs. Specifically, providers’ performance on this measure are adjusted for the characteristics of their patient population at admission and “level the playing field” across providers.  Further, exclusion criteria are applied to also reduce the potential impact of patients with complex needs (patients with severe brain injury and tetraplegia complete) affecting the measure results. 

                      Organization
                      Centers for Medicare & Medicaid Services/RTI International

                      Submitted by Janet Pue (not verified) on Fri, 12/13/2024 - 17:28

                      Permalink

                      On behalf of Carolinas Rehabilitation-Patient Safety Organization (PSO), commonly referred to as EQUADRSM, we appreciate the opportunity to comment on CBE #4630 – Cross-Setting Discharge Function Score for Inpatient Rehabilitation Facilities (IRF).  EQUADRSM is a PSO supporting the performance improvement of inpatient rehabilitation hospitals across the country.

                       

                      This measure does not meet the requirements for endorsement as the measure is administratively difficult to manage, produces results inconsistent with other endorsed measures, and could produce unintended consequences impacting access to IRF care.  The concerns are detailed below for PQM E&M Committee’s consideration of these reasons when evaluating this measure for endorsement.

                       

                      First, clinicians are struggling to understand and manage performance on this measure.  While the data elements utilized in the ‎measure are part of the Inpatient Rehabilitation Facility – Patient Assessment Instrument (IRF-PAI), the imputation methodology to manage ‎the performance of this measure requires an infeasible ‎investment in technology and training to implement.  Providers that are unable to calculate imputed values on ‎their own have limited ability to identify what the patients’ measure scores are ‎and what the risk-adjusted target values are to create a positive ‎result.  ‎ The imputation method is unnecessary, as existing endorsed measures (CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients) do not require imputed values and instead utilize values that are representative of the patient’s functional status at admission and discharge.  This measure does not meet the feasibility criteria required for ‎endorsement, as this measure requires impractical investment in the people, tools, tasks, and ‎technologies necessary to implement this measure. 

                       

                      Second, this measure produces results that are inconsistent with two currently endorsed functional measures (CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients).  The existing endorsed measures utilize all the functional items in the Self-Care and Mobility domains and produce an accurate representation of the functional improvement and discharge functional status of every IRF patient.  The Discharge Function Score measure utilizes 3 of the 7 Self-Care items and 7 of the 17 Mobility items included in the functional assessment of IRF patients.  Utilizing less than half of all functional items included in the IRF-PAI and existing endorsed measures produces values that are inconsistent and are not representative of the functional improvements and discharge functional status the patients that achieve as part of their IRF care. We do not believe that the new measure is superior to existing measures and should not replace the existing measures.  This measure does not meet the importance criteria required for ‎endorsement.

                       

                      Finally, there are unintended consequences that may result from the endorsement of this measure.  This measure is identified as “Cross-Setting”, yet it differs significantly across post-acute care settings.  While the functional items included in this measure are similar across post-acute care settings, the measure calculations and risk-adjusted patient expectations are very different.  The measure considered for endorsement for Home Health has a different developer and different imputation and risk-adjustment methodology.  Depending on post-acute care setting, a patient with the same patient characteristics will need to meet or exceed different expectations, with IRF patients having the highest expectation and Long-term acute care hospital (LTCH) patients having the lowest expectation.  To be considered a “cross-setting” measure, the measure calculations and patient expectations should be standardized regardless of setting. Additionally, since these measures are publicly reported on Care Compare, there is the unintended consequence of measure values being used to limit patient access to certain settings based upon reported results.  The characterization of that this measure as cross-setting measures implies that comparison of results can be made between IRFs, SNFs, LTCHs and Home Health providers.  Payers and referral sources already are using this information to direct patients to alternative settings which may not provide the appropriate services to produce high quality outcomes.  This measure does not meet the use and usability criteria required for ‎endorsement.

                       

                      As detailed above, this measure does not meet all the criteria necessary for endorsement, and EQUADRSM therefore asks the PQM E&M Committee to not support the endorsement of this measure.  

                      Organization
                      EQUADR (Exchanged Quality Data for Rehabilitation) Patient Safety Organization

                      As noted in the prior response, testing has shown that the imputation approach shows less bias in function measure scores and more precise estimates for item scores than the approach of replacing all “Activity Not Attempted” values with the value of 1 (most dependent). A bootstrapping method was used to measure bias and mean squared error (MSE) in the statistical imputation method compared to the ‘recode to 1’ approach. The goal of the bootstrapping method was to determine how similar imputed values were to the true item score. The average MSE and bias statistics for statistical imputation vs. recoding to 1 were: 

                      IRF: 

                      1. Statistical imputation:  
                        1. Mean squared error: 1.41 at admission and 0.52 at discharge 
                        2. Bias: -0.23 at admission and -0.07 at discharge 
                      2. Recode to 1: 
                        1. Mean squared error: 5.95 at admission and 3.58 at discharge 
                        2. Bias: -1.33 at admission and -0.58 at discharge 

                       

                      As noted in the prior response, CMS and their contractors calculate the measure results, so providers do not need to invest in technology to produce the results.  CMS does publish the model coefficients so providers do have access to information about how patient characteristics are used to estimate “Activity Not Attempted” codes.

                       

                      As noted in the prior response, the term “cross-setting” is used in the measure title to convey that the quality measures are aligned across the four post-acute care settings. The measures for each setting are calculated separately because populations treated in each setting vary. This includes the calculation of the risk-adjustment models. Thus, the regression coefficients are different across the different settings.   The measure results are reported separately on the Compare website by program (i.e., separately for IRFs and SNFs). 

                       

                      The Cross-Setting measure was developed so it is aligned across IRFs, SNFs, LTCHs and HHAs and the function items in the measures’ specifications are the items included in all these post-acute care settings. For the IRF setting, the Spearman correlation coefficient between this Cross Setting Discharge Function measure and the Discharge Self-Care measure and Discharge Mobility measure are .91 and .93, respectively.

                       

                      The Cross Setting Discharge Function measures were developed to be aligned across settings and the risk-adjustment and imputation approach are the same across all the measures. As noted above, the measure results are reported separately for IRFs and other post-acute care settings. The Centers for Medicare & Medicaid Services expects that providers deliver care based on patient needs, not based on quality measure scores or payment.

                      Organization
                      Centers for Medicare & Medicaid Services/RTI International

                      Submitted by Deborah Head (not verified) on Fri, 12/13/2024 - 19:52

                      Permalink

                      On behalf of Gundersen Health System, we appreciate the opportunity to comment on CBE #4630 – Cross-Setting Discharge Function Score for Inpatient Rehabilitation Facilities (IRF).  

                       

                      This measure does not meet the requirements for endorsement as the measure.  It is administratively difficult to manage, produces results inconsistent with other endorsed measures, and could produce unintended consequences impacting access to IRF care.  Please consider details below for PQM E&M Committee’s consideration of these reasons when evaluating this measure for endorsement.

                       

                      Performance on this measure is difficult to understand. The imputation methodology is not able to be replicated nor are we able to pinpoint patient specifics to fully understand how improvement is able to be made on an individual patient level.  This makes it meaningless as a measure to work toward improvement. The Federal Register Final Rule explains the imputation recodes missing functional status data to the most likely value and produces an estimate of scores on items with missing values.  This data is not representative of the patients’ actual function and has the risk of jeopardizing representation of the functional benefit inpatient rehabilitation had on patients served by inpatient rehabilitation. The time, resources and technologies needed to implement and understand this measure would be far higher than the value it would provide to meeting a cross-setting metric.

                       

                      In addition, this measure utilizes only a fraction of the functional items included in the IRF-PAI.  Therefore, the measure is not representative of the functional improvements and discharge functional status of patients receiving care in an Inpatient Rehabilitation Facility.  This only serves to severely undercut the value of IRF level of care.  It also does not have a cognitive or neuropsychology element that many times can be a primary focus of IRF level of care.  The public reporting of this measure on Care Compare does not represent accurately the benefit of the functional gains our patients make on our Rehab Unit.  The unintended consequences of displaying this information for consumers and other public entities such as insurance companies can lead to barriers to access the most appropriate level of care. Payers and referral sources are already using information to direct patients to alternative settings which might not be in the patients’ best interest. When it is difficult to understand, replicate and doesn’t represent the actual benefits of care provided.  There are varied measure calculations and risk-adjusted patient expectations in the various post-acute care settings.  Depending on the post-acute care setting, a patient with the same patient characteristics will need to meet or exceed different expectations and the IRF setting patients having the highest expectation.  This does not serve to be a cross-setting measure. The risk to limit patient access to care is a big concern and should be considered as a reason not to endorse this measure. 

                       

                      As I outlined here, this measure does not meet all the criteria necessary for endorsement, and Gundersen Health System therefore asks the PQM E&M Committee to not support the endorsement of this measure. 

                       

                      Sincerely,

                      Deborah Head, OTR/L Rehab Program Manager

                      Gundersen Health System  

                      Organization
                      Gundersen Health System
                      First Name
                      Anne
                      Last Name
                      Deutsch

                      Submitted by RTI_QM on Mon, 12/23/2024 - 13:14

                      In reply to by Deborah Head (not verified)

                      Permalink

                      As noted above, testing has shown that the imputation approach shows less bias in function measure scores and more precise estimates for item scores than the approach of replacing all “Activity Not Attempted” values with the value of 1 (most dependent). A bootstrapping method was used to measure bias and mean squared error (MSE) in the statistical imputation method compared to the ‘recode to 1’ approach. The goal of the bootstrapping method was to determine how similar imputed values were to the true item score. The average MSE and bias statistics for statistical imputation vs. recoding to 1 were: 

                      IRF: 

                      1. Statistical imputation:  
                        1. Mean squared error: 1.41 at admission and 0.52 at discharge 
                        2. Bias: -0.23 at admission and -0.07 at discharge 
                      2. Recode to 1: 
                        1. Mean squared error: 5.95 at admission and 3.58 at discharge 
                        2. Bias: -1.33 at admission and -0.58 at discharge 

                      As noted above, CMS and their contractors calculate the measure results, so providers do not need to invest in technology to produce the results.  CMS does publish the model coefficients so providers do have access to information about how patient characteristics are used to estimate “Activity Not Attempted” codes.

                       

                      As noted above, the term “cross-setting” is used in the measure title to convey that the quality measures are aligned across the four post-acute care settings. The measures for each setting are calculated separately because populations treated in each setting vary. This includes the calculation of the risk-adjustment models. Thus, the regression coefficients are different across the different settings.   The measure results are reported separately on the Compare website by program (i.e., separately for IRFs and SNFs). 

                       

                      As noted above, this measure is an important addition to the PAC QRPs as it is both (A) cross-setting and (B) an outcome measure.  This measure fulfills statutory requirements as specified in the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014. The IMPACT Act includes a mandate that CMS develop and report cross-setting measures. It specifically requires CMS to develop cross-setting quality measures that assess functional status and changes in function. This measure fulfills these statutory requirements. 

                       

                      With regard to IRFs potentially avoiding patients with complex needs, risk-adjustment and the application of exclusion criteria should reduce this concern. The risk adjustment methodology is used for the measure to account for differences in case mix across IRFs. Specifically, providers’ performance on this measure are adjusted for the characteristics of their patient population at admission and “level the playing field” across providers.  Further, exclusion criteria are applied to also reduce the potential impact of patients with complex needs (patients with severe brain injury and tetraplegia complete) affecting the measure results. 

                       

                      As noted above, the Cross-Setting measure was developed so it is aligned across IRFs, SNFs, LTCHs and HHAs and the function items in the measures’ specifications are the items included in all these post-acute care settings. For the IRF setting, the Spearman correlation coefficient between this Cross Setting Discharge Function measure and the Discharge Self-Care measure and Discharge Mobility measure are .91 and .93, respectively.

                       

                      As noted above, the Cross Setting Discharge Function measures were developed to be aligned across settings and the risk-adjustment and imputation approach are the same across all the measures. As noted above, the measure results are reported separately for IRFs and other post-acute care settings. The Centers for Medicare & Medicaid Services expects that providers deliver care based on patient needs, not based on quality measure scores or payment.

                       

                       With regard to cognition and neuropsychology, separate data elements on the IRF-PAI measure communication and cognitive function. The Cross Setting Discharge Function measure focuses on motor or physical functioning, which covers self-care and mobility activities. 

                      Organization
                      Centers for Medicare & Medicaid Services/RTI International

                      Submitted by A Esquenazi (not verified) on Fri, 12/13/2024 - 20:07

                      Permalink

                      On behalf of MossRehab we appreciate the opportunity to comment on CBE #4630 – Cross-Setting Discharge Function Score for Inpatient Rehabilitation Facilities (IRF). 

                       

                      This measure does not meet the requirements for endorsement as the measure is administratively difficult to manage,  results produced are inconsistent with other endorsed measures, and could produce unintended consequences impacting access to IRF care.  

                       

                      Clinicians are struggling to understand and manage performance on this measure.  While the data elements utilized in the ‎measure are part of the Inpatient Rehabilitation Facility – Patient Assessment Instrument (IRF-PAI), the imputation methodology to manage ‎the performance of this measure requires an infeasible ‎investment in technology and training to implement.  Providers that are unable to calculate imputed values on ‎their own have limited ability to identify what the patients’ measure scores are ‎and what the risk-adjusted target values are to create a positive ‎result.  ‎ The imputation method is unnecessary, as existing endorsed measures (CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients) do not require imputed values and instead utilize values that are representative of the patient’s functional status at admission and discharge.  This measure does not meet the feasibility criteria required for ‎endorsement, as this measure requires impractical investment in the people, tools, tasks, and ‎technologies necessary to implement this measure. 

                       

                      This measure produces results that are inconsistent with two currently endorsed functional measures (CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients).  The existing endorsed measures utilize all the functional items in the Self-Care and Mobility domains and produce an accurate representation of the functional improvement and discharge functional status of every IRF patient.  The Discharge Function Score measure utilizes 3 of the 7 Self-Care items and 7 of the 17 Mobility items included in the functional assessment of IRF patients.  Utilizing less than half of all functional items included in the IRF-PAI and existing endorsed measures produces values that are inconsistent and are not representative of the functional improvements and discharge functional status the patients that achieve as part of their IRF care.  Additionally, the public reporting of this measure on Care Compare shows that measure performance is inconsistent with the two other endorsed measures, and some IRFs are reporting that patients who meet expectations for the Discharge Self-Care and Discharge Mobility measures are identified as not meeting expectations for the Discharge Function Score measure.  Because of these inconsistencies, we do not believe that the new measure is superior to existing measures and should not replace the existing measures.  This measure does not meet the importance criteria required for ‎endorsement.

                       

                      There are potential unintended consequences that may result from the endorsement of this measure.  This measure is identified as “Cross-Setting”, yet it differs significantly across post-acute care settings.  While the functional items included in this measure are similar across post-acute care settings, the measure calculations and risk-adjusted patient expectations are very different.   The measure considered for endorsement for Home Health has a different developer and different imputation and risk-adjustment methodology.  Depending on post-acute care setting, a patient with the same patient characteristics will need to meet or exceed different expectations, with IRF patients having the highest expectation and Long-term acute care hospital (LTCH) patients having the lowest expectation.  To be considered a “cross-setting” measure, the measure calculations and patient expectations should be standardized regardless of setting. Additionally, since these measures are publicly reported on Care Compare, there is the unintended consequence of measure values being used to limit patient access to certain settings based upon reported results.  The characterization of that this measure as cross-setting measures implies that comparison of results can be made between IRFs, SNFs, LTCHs and Home Health providers.  Payers and referral sources already are using this information to direct patients to alternative settings which may not provide the appropriate services to produce high quality outcomes.  This measure does not meet the use and usability criteria required for ‎endorsement.

                       

                      As detailed above, this measure does not meet all the criteria necessary for endorsement, and MossRehab therefore asks the PQM E&M Committee to not support the endorsement of this measure. 

                       

                      Organization
                      MossRehab

                      As noted above, testing has shown that the imputation approach shows less bias in function measure scores and more precise estimates for item scores than the approach of replacing all “Activity Not Attempted” values with the value of 1 (most dependent). A bootstrapping method was used to measure bias and mean squared error (MSE) in the statistical imputation method compared to the ‘recode to 1’ approach. The goal of the bootstrapping method was to determine how similar imputed values were to the true item score. The average MSE and bias statistics for statistical imputation vs. recoding to 1 were: 

                      IRF: 

                      1. Statistical imputation:  
                        1. Mean squared error: 1.41 at admission and 0.52 at discharge 
                        2. Bias: -0.23 at admission and -0.07 at discharge 
                      2. Recode to 1: 
                        1. Mean squared error: 5.95 at admission and 3.58 at discharge 
                        2. Bias: -1.33 at admission and -0.58 at discharge 

                      As noted above, CMS and their contractors calculate the measure results, so providers do not need to invest in technology to produce the results.  CMS does publish the model coefficients so providers do have access to information about how patient characteristics are used to estimate “Activity Not Attempted” codes.

                       

                      As noted above, the term “cross-setting” is used in the measure title to convey that the quality measures are aligned across the four post-acute care settings. The measures for each setting are calculated separately because populations treated in each setting vary. This includes the calculation of the risk-adjustment models. Thus, the regression coefficients are different across the different settings.   The measure results are reported separately on the Compare website by program (i.e., separately for IRFs and SNFs). 

                       

                      As noted above, this measure is an important addition to the PAC QRPs as it is both (A) cross-setting and (B) an outcome measure.  This measure fulfills statutory requirements as specified in the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014. The IMPACT Act includes a mandate that CMS develop and report cross-setting measures. It specifically requires CMS to develop cross-setting quality measures that assess functional status and changes in function. This measure fulfills these statutory requirements. The contractor developing the home health measure worked with the contractor developing this IRF measure.

                       

                      As noted above, with regard to IRFs potentially avoiding patients with complex needs, risk-adjustment and the application of exclusion criteria should reduce this concern. The risk adjustment methodology is used for the measure to account for differences in case mix across IRFs. Specifically, providers’ performance on this measure are adjusted for the characteristics of their patient population at admission and “level the playing field” across providers.  Further, exclusion criteria are applied to also reduce the potential impact of patients with complex needs (patients with severe brain injury and tetraplegia complete) affecting the measure results. 

                       

                      As noted above, the Cross-Setting measure was developed so it is aligned across IRFs, SNFs, LTCHs and HHAs and the function items in the measures’ specifications are the items included in all these post-acute care settings. For the IRF setting, the Spearman correlation coefficient between this Cross Setting Discharge Function measure and the Discharge Self-Care measure and Discharge Mobility measure are .91 and .93, respectively.

                       

                      As noted above, the Cross Setting Discharge Function measures were developed to be aligned across settings and the risk-adjustment and imputation approach are the same across all the measures. As noted above, the measure results are reported separately for IRFs and other post-acute care settings. The Centers for Medicare & Medicaid Services expects that providers deliver care based on patient needs, not based on quality measure scores or payment.

                      Organization
                      Centers for Medicare & Medicaid Services/RTI International

                      Submitted by Shannon Tucker (not verified) on Sat, 12/14/2024 - 07:15

                      Permalink

                      I appreciate the opportunity to comment on CBE #4630 – Cross-Setting Discharge Function Score for Inpatient Rehabilitation Facilities (IRF).  

                       

                      This measure does not meet the requirements for endorsement as the measure is administratively difficult to manage, produces results inconsistent with other endorsed measures, and could produce unintended consequences impacting access to IRF care.  The concerns are detailed below for PQM E&M Committee’s consideration of these reasons when evaluating this measure for endorsement.

                       

                      First, clinicians are struggling to understand and manage performance on this measure.  While the data elements utilized in the ‎measure are part of the Inpatient Rehabilitation Facility – Patient Assessment Instrument (IRF-PAI), the imputation methodology to manage ‎the performance of this measure requires an infeasible ‎investment in technology and training to implement.  Providers that are unable to calculate imputed values on ‎their own have limited ability to identify what the patients’ measure scores are ‎and what the risk-adjusted target values are to create a positive ‎result.  ‎ The imputation method is unnecessary, as existing endorsed measures (CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients) do not require imputed values and instead utilize values that are representative of the patient’s functional status at admission and discharge.  This measure does not meet the feasibility criteria required for ‎endorsement, as this measure requires impractical investment in the people, tools, tasks, and ‎technologies necessary to implement this measure. 

                       

                      Second, this measure produces results that are inconsistent with two currently endorsed functional measures (CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients).  The existing endorsed measures utilize all the functional items in the Self-Care and Mobility domains and produce an accurate representation of the functional improvement and discharge functional status of every IRF patient.  The Discharge Function Score measure utilizes 3 of the 7 Self-Care items and 7 of the 17 Mobility items included in the functional assessment of IRF patients.  Utilizing less than half of all functional items included in the IRF-PAI and existing endorsed measures produces values that are inconsistent and are not representative of the functional improvements and discharge functional status the patients that achieve as part of their IRF care.  Additionally, the public reporting of this measure on Care Compare shows that measure performance is inconsistent with the two other endorsed measures, and some IRFs are reporting that patients who meet expectations for the Discharge Self-Care and Discharge Mobility measures are identified as not meeting expectations for the Discharge Function Score measure.  Because of these inconsistencies, we do not believe that the new measure is superior to existing measures and should not replace the existing measures.  This measure does not meet the importance criteria required for ‎endorsement.

                       

                      Finally, there are unintended consequences that may result from the endorsement of this measure.  This measure is identified as “Cross-Setting”, yet it differs significantly across post-acute care settings.  While the functional items included in this measure are similar across post-acute care settings, the measure calculations and risk-adjusted patient expectations are very different.   The measure considered for endorsement for Home Health has a different developer and different imputation and risk-adjustment methodology.  Depending on post-acute care setting, a patient with the same patient characteristics will need to meet or exceed different expectations, with IRF patients having the highest expectation and Long-term acute care hospital (LTCH) patients having the lowest expectation.  To be considered a “cross-setting” measure, the measure calculations and patient expectations should be standardized regardless of setting. Additionally, since these measures are publicly reported on Care Compare, there is the unintended consequence of measure values being used to limit patient access to certain settings based upon reported results.  The characterization of that this measure as cross-setting measures implies that comparison of results can be made between IRFs, SNFs, LTCHs and Home Health providers.  Payers and referral sources already are using this information to direct patients to alternative settings which may not provide the appropriate services to produce high quality outcomes.  This measure does not meet the use and usability criteria required for ‎endorsement.

                       

                      If representing your IRF: 

                      As detailed above, this measure does not meet all the criteria necessary for endorsement, and [Insert hospital/corporation name] therefore asks the PQM E&M Committee to not support the endorsement of this measure.  

                       

                      If making an individual statement: 

                      As detailed above, this measure does not meet all the criteria necessary for endorsement, and therefore ask the PQM E&M Committee to not support the endorsement of this measure.

                      Organization
                      HCA

                      As noted above, testing has shown that the imputation approach shows less bias in function measure scores and more precise estimates for item scores than the approach of replacing all “Activity Not Attempted” values with the value of 1 (most dependent). A bootstrapping method was used to measure bias and mean squared error (MSE) in the statistical imputation method compared to the ‘recode to 1’ approach. The goal of the bootstrapping method was to determine how similar imputed values were to the true item score. The average MSE and bias statistics for statistical imputation vs. recoding to 1 were: 

                      IRF: 

                      1. Statistical imputation:  
                        1. Mean squared error: 1.41 at admission and 0.52 at discharge 
                        2. Bias: -0.23 at admission and -0.07 at discharge 
                      2. Recode to 1: 
                        1. Mean squared error: 5.95 at admission and 3.58 at discharge 
                        2. Bias: -1.33 at admission and -0.58 at discharge 

                       

                      As noted above, CMS and their contractors calculate the measure results, so providers do not need to invest in technology to produce the results.  CMS does publish the model coefficients so providers do have access to information about how patient characteristics are used to estimate “Activity Not Attempted” codes.

                       

                      As noted above, the term “cross-setting” is used in the measure title to convey that the quality measures are aligned across the four post-acute care settings. The measures for each setting are calculated separately because populations treated in each setting vary. This includes the calculation of the risk-adjustment models. Thus, the regression coefficients are different across the different settings.   The measure results are reported separately on the Compare website by program (i.e., separately for IRFs and SNFs). 

                       

                      As noted above, this measure is an important addition to the PAC QRPs as it is both (A) cross-setting and (B) an outcome measure.  This measure fulfills statutory requirements as specified in the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014. The IMPACT Act includes a mandate that CMS develop and report cross-setting measures. It specifically requires CMS to develop cross-setting quality measures that assess functional status and changes in function. This measure fulfills these statutory requirements. The contractor developing the home health measure worked with the contractor developing this IRF measure.

                       

                      As noted above, with regard to IRFs potentially avoiding patients with complex needs, risk-adjustment and the application of exclusion criteria should reduce this concern. The risk adjustment methodology is used for the measure to account for differences in case mix across IRFs. Specifically, providers’ performance on this measure are adjusted for the characteristics of their patient population at admission and “level the playing field” across providers.  Further, exclusion criteria are applied to also reduce the potential impact of patients with complex needs (patients with severe brain injury and tetraplegia complete) affecting the measure results. 

                       

                      As noted above, the Cross-Setting measure was developed so it is aligned across IRFs, SNFs, LTCHs and HHAs and the function items in the measures’ specifications are the items included in all these post-acute care settings. For the IRF setting, the Spearman correlation coefficient between this Cross Setting Discharge Function measure and the Discharge Self-Care measure and Discharge Mobility measure are .91 and .93, respectively.

                       

                      As noted above, the Cross Setting Discharge Function measures were developed to be aligned across settings and the risk-adjustment and imputation approach are the same across all the measures. As noted above, the measure results are reported separately for IRFs and other post-acute care settings. The Centers for Medicare & Medicaid Services expects that providers deliver care based on patient needs, not based on quality measure scores or payment.

                      Organization
                      Centers for Medicare & Medicaid Services/RTI International

                      Submitted by Kara Simpson, MPT (not verified) on Sat, 12/14/2024 - 16:54

                      Permalink

                      Kara Simpson, VP Clinical Operations for Ernest Health, representing 30 inpatient rehabilitation hospitals and 7 LTACHs.  On behalf of our organization, we fully agree with all of the points brought forward by Troy and AMRPA with the setting of the Discharge Function Score in QRP across settings.  We do not endorse this for all of the same reasonings.  Thank you.

                      Organization
                      Ernest Health

                      Submitted by Chloe Slocum, … (not verified) on Mon, 12/16/2024 - 09:08

                      Permalink

                      I appreciate the opportunity to comment on CBE #4630 – Cross-Setting Discharge Function Score for Inpatient Rehabilitation Facilities (IRF).  

                       

                      This measure does not meet the requirements for endorsement as the measure is administratively difficult to manage, produces results inconsistent with other endorsed measures, and could produce unintended consequences impacting access to IRF care.  The concerns are detailed below for PQM E&M Committee’s consideration of these reasons when evaluating this measure for endorsement.

                       

                      First, clinicians are struggling to understand and manage performance on this measure.  While the data elements utilized in the ‎measure are part of the Inpatient Rehabilitation Facility – Patient Assessment Instrument (IRF-PAI), the imputation methodology to manage ‎the performance of this measure requires an infeasible ‎investment in technology and training to implement.  Providers that are unable to calculate imputed values on ‎their own have limited ability to identify what the patients’ measure scores are ‎and what the risk-adjusted target values are to create a positive ‎result.  ‎ The imputation method is unnecessary, as existing endorsed measures (CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients) do not require imputed values and instead utilize values that are representative of the patient’s functional status at admission and discharge.  This measure does not meet the feasibility criteria required for ‎endorsement, as this measure requires impractical investment in the people, tools, tasks, and ‎technologies necessary to implement this measure. 

                       

                      Second, this measure produces results that are inconsistent with two currently endorsed functional measures (CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients).  The existing endorsed measures utilize all the functional items in the Self-Care and Mobility domains and produce an accurate representation of the functional improvement and discharge functional status of every IRF patient.  The Discharge Function Score measure utilizes 3 of the 7 Self-Care items and 7 of the 17 Mobility items included in the functional assessment of IRF patients.  Utilizing less than half of all functional items included in the IRF-PAI and existing endorsed measures produces values that are inconsistent and are not representative of the functional improvements and discharge functional status the patients that achieve as part of their IRF care.  Additionally, the public reporting of this measure on Care Compare shows that measure performance is inconsistent with the two other endorsed measures, and some IRFs are reporting that patients who meet expectations for the Discharge Self-Care and Discharge Mobility measures are identified as not meeting expectations for the Discharge Function Score measure.  Because of these inconsistencies, we do not believe that the new measure is superior to existing measures and should not replace the existing measures.  This measure does not meet the importance criteria required for ‎endorsement.

                       

                      Finally, there are unintended consequences that may result from the endorsement of this measure.  This measure is identified as “Cross-Setting”, yet it differs significantly across post-acute care settings.  While the functional items included in this measure are similar across post-acute care settings, the measure calculations and risk-adjusted patient expectations are very different.  The measure considered for endorsement for Home Health has a different developer and different imputation and risk-adjustment methodology.  Depending on post-acute care setting, a patient with the same patient characteristics will need to meet or exceed different expectations, with IRF patients having the highest expectation and Long-term acute care hospital (LTCH) patients having the lowest expectation.  To be considered a “cross-setting” measure, the measure calculations and patient expectations should be standardized regardless of setting. Additionally, since these measures are publicly reported on Care Compare, there is the unintended consequence of measure values being used to limit patient access to certain settings based upon reported results.  The characterization of that this measure as cross-setting measures implies that comparison of results can be made between IRFs, SNFs, LTCHs and Home Health providers.  Payers and referral sources already are using this information to direct patients to alternative settings which may not provide the appropriate services to produce high quality outcomes.  This measure does not meet the use and usability criteria required for ‎endorsement.

                       

                      As detailed above, this measure does not meet all the criteria necessary for endorsement, and therefore ask the PQM E&M Committee to not support the endorsement of this measure.

                      Organization
                      Spaulding Rehabilitation Hospital / Harvard Medical School

                      As noted above,  testing has shown that the imputation approach shows less bias in function measure scores and more precise estimates for item scores than the approach of replacing all “Activity Not Attempted” values with the value of 1 (most dependent). A bootstrapping method was used to measure bias and mean squared error (MSE) in the statistical imputation method compared to the ‘recode to 1’ approach. The goal of the bootstrapping method was to determine how similar imputed values were to the true item score. The average MSE and bias statistics for statistical imputation vs. recoding to 1 were: 

                      IRF: 

                      1. Statistical imputation:  
                        1. Mean squared error: 1.41 at admission and 0.52 at discharge 
                        2. Bias: -0.23 at admission and -0.07 at discharge 
                      2. Recode to 1: 
                        1. Mean squared error: 5.95 at admission and 3.58 at discharge 
                        2. Bias: -1.33 at admission and -0.58 at discharge 

                       

                       

                      As noted above, CMS and their contractors calculate the measure results, so providers do not need to invest in technology to produce the results.  CMS does publish the model coefficients so providers do have access to information about how patient characteristics are used to estimate “Activity Not Attempted” codes.

                       

                      As noted above, the term “cross-setting” is used in the measure title to convey that the quality measures are aligned across the four post-acute care settings. The measures for each setting are calculated separately because populations treated in each setting vary. This includes the calculation of the risk-adjustment models. Thus, the regression coefficients are different across the different settings.   The measure results are reported separately on the Compare website by program (i.e., separately for IRFs and SNFs). 

                       

                      As noted above, this measure is an important addition to the PAC QRPs as it is both (A) cross-setting and (B) an outcome measure.  This measure fulfills statutory requirements as specified in the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014. The IMPACT Act includes a mandate that CMS develop and report cross-setting measures. It specifically requires CMS to develop cross-setting quality measures that assess functional status and changes in function. This measure fulfills these statutory requirements. The contractor developing the home health measure worked with the contractor developing this IRF measure.

                       

                      as noted above, with regard to IRFs potentially avoiding patients with complex needs, risk-adjustment and the application of exclusion criteria should reduce this concern. The risk adjustment methodology is used for the measure to account for differences in case mix across IRFs. Specifically, providers’ performance on this measure are adjusted for the characteristics of their patient population at admission and “level the playing field” across providers.  Further, exclusion criteria are applied to also reduce the potential impact of patients with complex needs (patients with severe brain injury and tetraplegia complete) affecting the measure results. 

                       

                      As noted above, the Cross-Setting measure was developed so it is aligned across IRFs, SNFs, LTCHs and HHAs and the function items in the measures’ specifications are the items included in all these post-acute care settings. For the IRF setting, the Spearman correlation coefficient between this Cross Setting Discharge Function measure and the Discharge Self-Care measure and Discharge Mobility measure are .91 and .93, respectively.

                       

                      As noted above, the Cross Setting Discharge Function measures were developed to be aligned across settings and the risk-adjustment and imputation approach are the same across all the measures. As noted above, the measure results are reported separately for IRFs and other post-acute care settings. The Centers for Medicare & Medicaid Services expects that providers deliver care based on patient needs, not based on quality measure scores or payment.

                      Organization
                      Centers for Medicare & Medicaid Services/RTI International

                      Submitted by Danni Grotzky (not verified) on Mon, 12/16/2024 - 09:23

                      Permalink

                      On behalf of Madonna Rehabilitation Hospital, we appreciate the opportunity to comment on CBE #4630 – Cross-Setting Discharge Function Score for Inpatient Rehabilitation Facilities (IRF).  

                       

                      This measure does not meet the requirements for endorsement as the measure is administratively difficult to manage, produces results inconsistent with other endorsed measures, and could produce unintended consequences impacting access to IRF care.  The concerns are detailed below for PQM E&M Committee’s consideration of these reasons when evaluating this measure for endorsement.

                       

                      First, clinicians are struggling to understand and manage performance on this measure.  While the data elements utilized in the ‎measure are part of the Inpatient Rehabilitation Facility – Patient Assessment Instrument (IRF-PAI), the imputation methodology to manage ‎the performance of this measure requires an infeasible ‎investment in technology and training to implement.  Providers that are unable to calculate imputed values on ‎their own have limited ability to identify what the patients’ measure scores are ‎and what the risk-adjusted target values are to create a positive ‎result.  ‎ The imputation method is unnecessary, as existing endorsed measures (CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients) do not require imputed values and instead utilize values that are representative of the patient’s functional status at admission and discharge.  This measure does not meet the feasibility criteria required for ‎endorsement, as this measure requires impractical investment in the people, tools, tasks, and ‎technologies necessary to implement this measure. 

                       

                      Second, this measure produces results that are inconsistent with two currently endorsed functional measures (CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients).  The existing endorsed measures utilize all the functional items in the Self-Care and Mobility domains and produce an accurate representation of the functional improvement and discharge functional status of every IRF patient.  The Discharge Function Score measure utilizes 3 of the 7 Self-Care items and 7 of the 17 Mobility items included in the functional assessment of IRF patients.  Utilizing less than half of all functional items included in the IRF-PAI and existing endorsed measures produces values that are inconsistent and are not representative of the functional improvements and discharge functional status the patients that achieve as part of their IRF care.  Additionally, the public reporting of this measure on Care Compare shows that measure performance is inconsistent with the two other endorsed measures, and some IRFs are reporting that patients who meet expectations for the Discharge Self-Care and Discharge Mobility measures are identified as not meeting expectations for the Discharge Function Score measure.  Because of these inconsistencies, we do not believe that the new measure is superior to existing measures and should not replace the existing measures.  This measure does not meet the importance criteria required for ‎endorsement.

                       

                      Finally, there are unintended consequences that may result from the endorsement of this measure.  This measure is identified as “Cross-Setting”, yet it differs significantly across post-acute care settings.  While the functional items included in this measure are similar across post-acute care settings, the measure calculations and risk-adjusted patient expectations are very different.   The measure considered for endorsement for Home Health has a different developer and different imputation and risk-adjustment methodology.  Depending on post-acute care setting, a patient with the same patient characteristics will need to meet or exceed different expectations, with IRF patients having the highest expectation and Long-term acute care hospital (LTCH) patients having the lowest expectation.  To be considered a “cross-setting” measure, the measure calculations and patient expectations should be standardized regardless of setting. Additionally, since these measures are publicly reported on Care Compare, there is the unintended consequence of measure values being used to limit patient access to certain settings based upon reported results.  The characterization of that this measure as cross-setting measures implies that comparison of results can be made between IRFs, SNFs, LTCHs and Home Health providers.  Payers and referral sources already are using this information to direct patients to alternative settings which may not provide the appropriate services to produce high quality outcomes. Madonna Rehabilitation Hospital houses several levels of care, and this measure will make it hard for them to distinguish outcomes between the levels if all the outcomes are being figured differently.  This measure does not meet the use and usability criteria required for ‎endorsement.

                       

                      As detailed above, this measure does not meet all the criteria necessary for endorsement, and Madonna Rehabilitation Hospital therefore asks the PQM E&M Committee to not support the endorsement of this measure.  

                       

                      Organization
                      Madonna Rehabilitation Hospital
                      First Name
                      Anne
                      Last Name
                      Deutsch

                      Submitted by RTI_QM on Mon, 12/23/2024 - 13:22

                      In reply to by Danni Grotzky (not verified)

                      Permalink

                      As noted above, CMS and their contractors calculate the measure results, so providers do not need to invest in technology to produce the results.  CMS does publish the model coefficients so providers do have access to information about how patient characteristics are used to estimate “Activity Not Attempted” codes.

                       

                       As noted above, the term “cross-setting” is used in the measure title to convey that the quality measures are aligned across the four post-acute care settings. The measures for each setting are calculated separately because populations treated in each setting vary. This includes the calculation of the risk-adjustment models. Thus, the regression coefficients are different across the different settings.   The measure results are reported separately on the Compare website by program (i.e., separately for IRFs and SNFs). 

                       

                      As noted above, this measure is an important addition to the PAC QRPs as it is both (A) cross-setting and (B) an outcome measure.  This measure fulfills statutory requirements as specified in the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014. The IMPACT Act includes a mandate that CMS develop and report cross-setting measures. It specifically requires CMS to develop cross-setting quality measures that assess functional status and changes in function. This measure fulfills these statutory requirements. The contractor developing the home health measure worked with the contractor developing this IRF measure.

                       

                      As noted above, with regard to IRFs potentially avoiding patients with complex needs, risk-adjustment and the application of exclusion criteria should reduce this concern. The risk adjustment methodology is used for the measure to account for differences in case mix across IRFs. Specifically, providers’ performance on this measure are adjusted for the characteristics of their patient population at admission and “level the playing field” across providers.  Further, exclusion criteria are applied to also reduce the potential impact of patients with complex needs (patients with severe brain injury and tetraplegia complete) affecting the measure results. 

                      Organization
                      Centers for Medicare & Medicaid Services/RTI International

                      Submitted by Mary Ellen DeB… (not verified) on Mon, 12/16/2024 - 13:04

                      Permalink

                      On behalf of Encompass Health, we appreciate the opportunity to comment on CBE #4630 – Cross-Setting Discharge Function Score for Inpatient Rehabilitation Facilities (IRF).  

                       

                      We have serious concerns with the proposed Discharge Function measure, both with the recoding of the “NA” IRF-PAI section GG items for use in the measure calculation and the proposed combination of self-care and mobility into a single measure.  We do not support this proposed Discharge Function measure and respectfully urge that it not be finalized but instead be subject to further consideration and refinement prior to being endorsed by the PQM E&M Committee.

                       

                      Recoding ANA codes by using an algorithm-based imputation model to calculate the Discharge Function measure would supersede the clinical judgement of caregivers when assessing their patients, pursuant to current CMS guidance. It is not at all surprising that patients who are required to benefit from intensive rehabilitative therapy during their stay at a rehabilitation hospital may not be able to attempt certain self-care and mobility functional tasks safely at the time of their admission. 

                       

                      In addition to our concern that the new Discharge Function measure’s recoding of ANA codes runs counter to IRF caregivers’ clinical judgement, we also have concerns on the use of the bootstrapping sample to develop the Discharge Function measure imputation model and risk adjustment.  Acumen’s report noted “clinical and function characteristics was different between observations with and without NAs.”  A model built solely upon the functional abilities of patients who had no NA assessments should not be imposed on patients who had NA assessments.  

                       

                      Furthermore, the different codes combined into the model as NA codes: Activity not attempted (codes 07, 09, 10, and 88), a dash (-), and a skip (^) have significantly different CMS guidance and specifications for their use.  Codes 07, 09, 10, 88, or skip (^) do not represent “missing” data as described in the Proposed Rule.  Rather, these codes represent clinical information that the patient was incapable of performing a task for reasons specified by CMS in the IRF-PAI manual. The only “missing” functional status data is represented by a dash (-), where the item was unable to be scored at all. These dashes are typically used for patients who had a short stay or unplanned discharge, and an item could not be assessed (with nonsensical responses to the BIMS assessment, as instructed by CMS, being the exception).  The other activity not attempted codes were assessed by a clinician and coded appropriately, so the data are not “missing.” 

                       

                      Additionally, we do not support the combination of self-care and mobility items into a single discharge function score, rather they should be assessed and reported independently to provide the most accurate assessment of a patient’s abilities and disabilities.  Furthermore, these measures were approved for use in the IRF setting as separate measures compiled of a different set of GG items (NQF #2635 and #2636). Further, some patients have an imbalance of impairment between upper mobility and lower mobility.  For example, a frontal stroke patient would have more limited function in their lower extremities and be disadvantaged in their Mobility scores, while a central spinal cord syndrome patient would have more limited function in their upper extremities and be disadvantaged in their Self-Care scores.

                       

                      For a true “cross-setting” functional measure that yields truly comparable results, more must be done to standardize data across PAC settings and the measure set must be able to collect and differentiate patients’ abilities and disabilities in a wide range of functional levels, such as patients who may be more functionally independent in the HH setting and LTCH patients who are more functionally dependent.  The measure should prioritize ensuring that these distinctions in patient characteristics are preserved.

                       

                      Finally, to our knowledge this is the first time CMS will be implementing a quality measure score with data not actually collected by clinicians but imputed based on the model developed.  CMS and Acumen should release more of the data and methodology so the PQM E&M Committee may verify the model outcomes appropriately. In particular, the report is somewhat unclear how walk versus wheelchair patients for purposes of assessing ANA status were accounted for.  In our analyses, depending on how the model handles dashes and ANA codes for walk and wheel patients, the sample of patients without an NA can range from over 60 percent to over 90 percent.  This wide discrepancy shows the complexity of developing this measure and in verifying its results. 

                       

                      We are concerned that the outcomes calculated in this measure will be inaccurate and not represent the true functional gain achieved by patients during their course of treatment in a rehabilitation hospital, as the expected discharge function score will be calculated for patients with NA codes based on the function items of patients without NA codes. 

                       

                      To summarize, we are concerned that the Discharge Function measure has not been appropriately developed or tested, will supersede the clinical judgement of providers in assessing their patients’ functional capabilities, and may not be equitable among IRF patients.  We do not support the PQM E&M committee endorsing this measure and recommend CMS engage with providers to better understand the assessment of patients in a clinical setting before moving any further. 

                       

                       

                      As detailed above, this measure does not meet all the criteria necessary for endorsement, and Encompass Health, therefore asks the PQM E&M Committee to not support the endorsement of this measure.  

                      Organization
                      Encompass Health

                      As noted above, CMS and their contractors calculate the measure results, so providers do not need to invest in technology to produce the results.  CMS does publish the model coefficients so providers do have access to information about how patient characteristics are used to estimate “Activity Not Attempted” codes.

                       

                       As noted above, the term “cross-setting” is used in the measure title to convey that the quality measures are aligned across the four post-acute care settings. The measures for each setting are calculated separately because populations treated in each setting vary. This includes the calculation of the risk-adjustment models. Thus, the regression coefficients are different across the different settings.   The measure results are reported separately on the Compare website by program (i.e., separately for IRFs and SNFs). 

                       

                      As noted above, this measure is an important addition to the PAC QRPs as it is both (A) cross-setting and (B) an outcomemeasure.  This measure fulfills statutory requirements as specified in the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014. The IMPACT Act includes a mandate that CMS develop and report cross-setting measures. It specifically requires CMS to develop cross-setting quality measures that assess functional status and changes in function. This measure fulfills these statutory requirements. 

                       

                      As noted above, with regard to IRFs potentially avoiding patients with complex needs, risk-adjustment and the application of exclusion criteria should reduce this concern. The risk adjustment methodology is used for the measure to account for differences in case mix across IRFs. Specifically, providers’ performance on this measure are adjusted for the characteristics of their patient population at admission and “level the playing field” across providers.  Further, exclusion criteria are applied to also reduce the potential impact of patients with complex needs (patients with severe brain injury and tetraplegia complete) affecting the measure results. 

                       

                      As noted above, the Cross-Setting measure was developed so it is aligned across IRFs, SNFs, LTCHs and HHAs and the function items in the measures’ specifications are the items included in all these post-acute care settings. For the IRF setting, the Spearman correlation coefficient between this Cross Setting Discharge Function measure and the Discharge Self-Care measure and Discharge Mobility measure are .91 and .93, respectively.

                       

                      As noted above, the Cross Setting Discharge Function measures were developed to be aligned across settings and the risk-adjustment and imputation approach are the same across all the measures. As noted above, the measure results are reported separately for IRFs and other post-acute care settings. The Centers for Medicare & Medicaid Services expects that providers deliver care based on patient needs, not based on quality measure scores or payment.

                       

                      The Quality Measure Calculation and Reporting User’s Manual provides details about the measure calculation and is posted on the CMS website: https://www.cms.gov/files/document/irf-qm-calculations-and-reporting-users-manual-v60.pdf.  The inclusion of the wheelchair mobility activities reflects our interest in including patients who use a wheelchair for mobility and patients’ skills in learning and improving those skills. 

                       

                      With regard to your suggestion for a true “cross-setting” functional measure that yields truly comparable results, thank you for this feedback. We will consider this feedback for future measure development.

                      Organization
                      Centers for Medicare & Medicaid Services/RTI International

                      Submitted by Joyal Pavey (not verified) on Mon, 12/16/2024 - 16:05

                      Permalink

                      On behalf of Mary Free Bed Rehabilitation Hospital, we appreciate the opportunity to comment on CBE #4630 – Cross-Setting Discharge Function Score for Inpatient Rehabilitation Facilities (IRF).  

                       

                       

                      This measure does not meet the requirements for endorsement as the measure could be administratively difficult to manage (feasibility), does not appear to produce new information (importance), and could produce unintended consequences impacting access to IRF care (usability).  Our concerns regarding the PQM E&M Committee’s consideration of endorsement for this measure are detailed below .

                       

                      First, responsibility for imputation and scoring is unclear.  While the data elements utilized in the ‎measure are part of the Inpatient Rehabilitation Facility – Patient Assessment Instrument (IRF-PAI), the imputation methodology to manage ‎the performance of this measure comes with burden. The report is not clear on whether CMS would incorporate imputation and scoring algorithms into their reporting systems or if providers are responsible for doing so. If this becomes the providers’ responsibility there is room for error in submitting scores if the algorithms are not properly implemented. ‎In addition, evidence to support use of a new imputation method which differs from the method used for the existing Self-Care and Mobility scores (CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients) is insufficient. Although the new imputation method demonstrated reductions in bias and error, the report did not provide information on whether the new method would change results relative to current procedure. And, if the new procedure is better, should it not be implemented for all quality measures to ensure consistency in reporting? Thus, we feel this measure does not meet the feasibility criteria required for ‎endorsement, as this measure may place unnecessary burden on providers for scoring and imputation and may perform different from existing imputation methods. 

                       

                      Second, this measure produces results that are paradoxically both similar  and inconsistent with two currently endorsed functional measures (CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients).  The existing endorsed measures utilize all the functional items in the Self-Care and Mobility domains and produce an accurate representation of the functional improvement and discharge functional status of every IRF patient.  The Discharge Function Score creates a composite measure by utilizing 3 of the 7 Self-Care items and 7 of the 17 Mobility items included in the functional assessment of IRF patients. The report notes high correlations (r=0.85-0.91) between the new measure and the existing Self-Care and Mobility discharge and change scores as one would expect for a measure derived from these existing measures. Of concern is that these correlations are not independent and possibly inflates as the scores based in part items that are in both comparators (3 Self-Care items and 7 mobility items). However, the correlation with Discharge to Community (NQF #3479) was only 0.24, indicating a positive by weak association with this item. Another problem arises from the inclusion of facilities with as few as 20 cases in the model. While results for facilities with large samples might be stable, facilities with small samples could be biased or fluctuate widely from one quarter to another. One solution would be to include patient data from earlier than the past year, although this would increase the duration of time over which patient outcomes are reflected in their quality scores. Despite similarities in scores by correlation, the public reporting of this measure on Care Compare shows that measure performance is inconsistent with the two other endorsed measures, and some IRFs are reporting that patients who meet expectations for the Discharge Self-Care and Discharge Mobility measures are identified as not meeting expectations for the Discharge Function Score measure. This discrepancy could create problems with interpretation of the Self-Care, Mobility, and the Discharge Function Score collectively, erode confidence in the QRP measures, or complicate decisions made based on these scores. At a minimum, we would like to see a comparison of the new and existing measures to determine where and to what extent discrepancies arise. If discrepancies are limited to only facilities with small samples, the new measure may be comparable to existing measures. However, if discrepancies also occur with large facilities, the impact of implementation on providers should be examined and reported. Further, if the objective is to replace the existing measures, why include only a subset of each of the Self-Care and Mobility items. As noted, the full item sets are already measured so why include only a portion of the available information? For these reasons, we do not believe that the new measure is superior to existing measures and thus does not meet the importance criteria required for ‎endorsement.

                       

                      Finally, there are unintended consequences that may result from the endorsement of this measure.  This measure is identified as “Cross-Setting”, yet it differs significantly across post-acute care settings.  While the functional items included in this measure are similar across post-acute care settings, the measure calculations and risk-adjusted patient expectations are very different.  The measure considered for endorsement for Home Health has a different developer and different imputation and risk-adjustment methodology.  Depending on post-acute care setting, a patient with the same patient characteristics will need to meet or exceed different expectations established by setting-specific models. Although the patient populations of IRF, long-term acute care hospitals (LTCH), skilled nursing facilities (SNFs), and Home Health Associations may overlap, they have differences imposed on them by their respective eligibility criteria and/or timelines for admission. Thus, creating separate expectations based on regression equations that only represent patients from within each setting does not permit comparisons for facilities from different settings.  To be considered a “cross-setting” measure, the measure calculations and patient expectations must be standardized regardless of setting. As these measures are publicly reported on Care Compare, there is potential for misuse, whether unintended or intended, of measure values being used to limit patient access to certain settings based upon reported results.  The characterization of that this measure as cross-setting measures implies that comparison of results can be made between IRFs, SNFs, LTCHs and Home Health providers which the statistical methods do not support.  Payers and referral sources already are using this information to direct patients to alternative settings which may not provide the appropriate services to produce high quality outcomes.  Thus, we feel this measure does not meet the use and usability criteria required for ‎endorsement.

                      As detailed above, this measure does not meet all the criteria necessary for endorsement, and Mary Free Bed Rehabilitation Hospital therefore asks the PQM E&M Committee to not support the endorsement of this measure.  

                       

                      Organization
                      Mary Free Bed

                      As noted above, CMS and their contractors calculate the measure results, so providers do not need to invest in technology to produce the results.  CMS does publish the model coefficients so providers do have access to information about how patient characteristics are used to estimate “Activity Not Attempted” codes.

                       

                      As noted above, the term “cross-setting” is used in the measure title to convey that the quality measures are aligned across the four post-acute care settings. The measures for each setting are calculated separately because populations treated in each setting vary. This includes the calculation of the risk-adjustment models. Thus, the regression coefficients are different across the different settings.   The measure results are reported separately on the Compare website by program (i.e., separately for IRFs and SNFs). 

                       

                      As noted above, this measure is an important addition to the PAC QRPs as it is both (A) cross-setting and (B) an outcome measure.  This measure fulfills statutory requirements as specified in the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014. The IMPACT Act includes a mandate that CMS develop and report cross-setting measures. It specifically requires CMS to develop cross-setting quality measures that assess functional status and changes in function. This measure fulfills these statutory requirements. The contractor developing the home health measure worked with the contractor developing this IRF measure.

                       

                      As noted above, with regard to IRFs potentially avoiding patients with complex needs, risk-adjustment and the application of exclusion criteria should reduce this concern. The risk adjustment methodology is used for the measure to account for differences in case mix across IRFs. Specifically, providers’ performance on this measure are adjusted for the characteristics of their patient population at admission and “level the playing field” across providers.  Further, exclusion criteria are applied to also reduce the potential impact of patients with complex needs (patients with severe brain injury and tetraplegia complete) affecting the measure results. 

                       

                      As noted above, the Cross-Setting measure was developed so it is aligned across IRFs, SNFs, LTCHs and HHAs and the function items in the measures’ specifications are the items included in all these post-acute care settings. For the IRF setting, the Spearman correlation coefficient between this Cross Setting Discharge Function measure and the Discharge Self-Care measure and Discharge Mobility measure are .91 and .93, respectively.

                       

                      As noted above, the Cross Setting Discharge Function measures were developed to be aligned across settings and the risk-adjustment and imputation approach are the same across all the measures. As noted above, the measure results are reported separately for IRFs and other post-acute care settings. The Centers for Medicare & Medicaid Services expects that providers deliver care based on patient needs, not based on quality measure scores or payment.

                       

                      As noted above, the Quality Measure Calculation and Reporting User’s Manual provides details about the measure calculation and is posted on the CMS website: https://www.cms.gov/files/document/irf-qm-calculations-and-reporting-users-manual-v60.pdf The inclusion of the wheelchair mobility activities reflects our interest in including patients who use a wheelchair for mobility and patients’ skills in learning and improving those skills. 

                       

                      The weak correlation between the Discharge Function measure and the Discharge to Community measure may reflect in part the exclusion criteria that are applied to the Discharge Function measure. More specifically, the Discharge function excludes all patients with incomplete stays, which includes patients discharged to acute care.

                      Organization
                      Centers for Medicare & Medicaid Services/RTI International

                      Submitted by American Medic… (not verified) on Mon, 12/16/2024 - 17:44

                      Permalink

                      The American Medical Rehabilitation Providers Association (AMRPA) appreciates the opportunity to follow-up with additional detail on the verbal comments provided on November 21 related to CBE #4630 – Cross-Setting Discharge Function Score for Inpatient Rehabilitation Facilities. AMRPA is the national trade association representing over 800 inpatient rehabilitation facilities (IRFs), ‎including freestanding IRFs and rehabilitation units of acute care hospitals, which focus on the care and ‎functional recovery of some the most vulnerable Medicare beneficiaries – such as patients with ‎ stroke, brain injury, and spinal cord injury. ‎ 

                       

                      As noted in our prior verbal comments, AMRPA members believe that CBE #4630 – Cross-Setting Discharge Function Score for Inpatient Rehabilitation Facilities is a competing measure for two existing endorsed measures, CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients.  The two existing endorsed measures separate patient function into two separate domains, Self-Care and Mobility, and utilize all the functional status items included on the IRF-PAI.  Using separate measures and all the functional items for quality recognizes that IRF patients have significantly different functional abilities or disabilities requiring the intensive therapies provided only in the IRF setting.   The measure under consideration for endorsement combines these two domains of patient function and utilizes 3 of the 7 Self-Care items and 7 of the 17 Mobility items included in the functional ‎assessment of IRF patients.  Utilizing less than half of all functional items included in the IRF-PAI ‎and existing endorsed measures produces values that are inconsistent and are not ‎representative of the functional improvements and discharge functional status that patients ‎achieve as part of their IRF care‎.  

                       

                      Additionally, the public reporting of this measure on Care Compare shows that ‎performance on the Discharge Function Score measures is inconsistent with the two other endorsed measures.  For example, in the September 2024 Care Compare refresh, 153 of the over 1100 IRFs with reported values (roughly 14%) appear to have a value for the Discharge Function Score measures that is less than their performance on both existing endorsed measures that utilize all functional items.  Similarly, AMRPA member IRFs are reporting ‎that patients who meet expectations for the Discharge Self-Care and Discharge Mobility ‎measures are identified as not meeting expectations for the Discharge Function Score ‎measure.‎ As identified in the PQM Measure Evaluation Rubric, the measure developer must indicate how the new measure is superior to existing measures as part of the Importance criteria for endorsement.  Based upon the inconsistencies in the measure performance against two existing endorsed measures currently displayed in public reporting, AMRPA does not believe that this measure should be considered superior to the existing endorsed measures and therefore does not meet the Importance criteria necessary for endorsement.

                       

                      AMRPA also has concerns about the imputation method utilized for calculating assessment data.  As AMRPA stated in comments to the FY 2024 IRF Proposed Rule:

                       

                      The imputation method should not be used to recode instances where the assessment data is not “missing.” 

                       

                      CMS and the original measure developer (Acumen) suggest that “missing” functional status data include patients who are coded with the following assessment values:

                      • Code 07, Patient refused: if the patient refused to complete the activity.
                      • Code 09, Not applicable: if the patient did not attempt to perform the activity and did not perform this activity prior to the current illness, exacerbation, or injury.
                      • Code 10, Not attempted due to environmental limitations: if the patient did not attempt this activity due to environmental limitations. Examples include lack of equipment, weather constraints.
                      • Code 88, Not attempted due to medical condition or safety concerns: if the activity was not attempted due to medical condition or safety concerns.
                      • Skipped Value, Item was skipped based upon a prior response.
                      • Dash Value, Indicates “No information.”

                      Except for the Dash Value (where no information was available for the assessment), all other values are indicative of an affirmative functional assessment value and should not be considered as “missing.” Each code represents a circumstance where the patient is not capable of performing the activity, suggesting that their performance should be considered at least at the most severe level (Code 01 – Dependent, where if the activity was attempted it would require a helper to complete the entire activity for the patient or require two or more helpers to complete the activity). Code 07, Patient Refused rarely occurs and, in these situations, the refusal from the patient to perform the activity suggests that it would require a helper to complete the activity for the patient. Code 09, Not Applicable indicates that the patient did not perform the activity and did not perform the activity prior to the IRF stay, suggesting that it would require a helper to complete the activity for the patient. Code 10, Environmental Limitations indicates that if the activity was attempted without equipment or in a compromised weather situation, the patient would have required a helper to complete the activity for them. Code 88, Medical Condition or Safety Concerns indicates that, if the activity was attempted, it would impact the patient’s medical condition or put the patient in an unsafe situation, unless the activity was completed by a helper. Finally, Skipped Value indicates that the patient was unable to perform a prior activity of a similar nature, meaning that the patient would be similarly unable to perform this activity without a helper. In these scenarios, the data is not “missing” and, instead, the coded value represents a situation where the most dependent level is the only clinically relevant option. Therefore, the statistical imputation model should not be used for these code values and instead should only be used when the Dash Value, representing no information, is coded.

                       

                      The imputation method will be operationally difficult for clinicians to know the patient values and manage performance. 

                       

                      Without a technological/software solution or other means to calculate the imputed values in real-time, clinicians will be left to wonder what the patient’s function score will be at admission or discharge and would have to wait for CMS to publish measure data to know whether their patient met or exceeded the expected discharge score. Under these circumstances, where the values are not readily available, performance on this measure becomes guesswork for any patient requiring the use of an imputed value. Quality measures are meant to be actionable and meaningful; however, the imputation method will make it extremely difficult for IRFs to know what is needed to improve performance and provide meaningful results to patients.

                       

                      AMRPA also asks that you consider whether this measure meets the feasibility criteria required for endorsement. The measure developer must consider the people, tools, tasks, and technologies necessary to implement this measure.  As AMRPA noted in our comments to the FY 2024 IRF Proposed Rule:

                       

                      Costs associated with managing this measure have not been considered, such as software ‎updates associated with the various measure calculations and training/education ‎for clinicians.

                       

                      While CMS states that “…this measure adds no additional provider burden since it would be calculated using data from the IRF-PAI that IRFs are already required to collect,” IRFs will still need to educate and train their clinicians on the new measure, incorporate discussion of this measure into their interdisciplinary team meetings, and create a solution that will calculate imputation values and the risk-adjusted expected discharge function score values in order to manage performance. Clinicians will need to understand which items are included in this new discharge function score measure, the implications of the imputation method and the use of the “missing” codes, and how performance on this measure may differ from other quality measures. Clinicians will also need to understand how to review measure results on Provider Preview Reports and other CMS reporting tools. We would estimate that the time necessary to educate/train one clinician on this measure would take at least one hour, and the review of Provider Preview Reports and other CMS quality measure reports to take at least one hour every quarter, or 4 hours annually. Using the adjusted hourly wages provided in the proposed rule, the cost of educating, training, and managing this measure for one clinician would be between $250 (Licensed Practical and Licensed Vocational Nurses) to $450 (Physical Therapists) annually. Across the over 1,100 IRFs, this would represent an annual cost burden of between $275,000 and $495,000. Should additional education and training be provided to other clinicians, this will add another $50-90 per clinician.

                       

                      Technology-related costs should also be considered, as the imputation method and risk-adjusted expected scores require advanced calculations to be able to monitor patient progress toward their expectation. We will not speculate what the average cost for such software development may be, but instead note that any costs associated with these needs have not been considered as part of the proposed adoption of this measure. 

                       

                      AMRPA members believe that these costs must be considered, as they would significantly impact the values in the IRF QRP Impact Table, Accounting Statement Table, and conclusion of estimated payment changes per discharge displayed in the proposed rule. We further encourage CMS to consider these costs when assessing all new or modified measures in order to more accurately reflect the burden IRF QRP changes pose for IRFs and other providers.

                       

                      While the data elements utilized in the measure calculations are already part of the IRF-PAI, IRFs would require a significant investment in technology to implement the imputation methodology required to manage the performance of this measure.  Providers that are unable to calculate imputed values on their own will have limited ability to identify what the patients’ measures scores may be order determine what risk-adjusted target value is required to contribute towards a positive result.  For these reasons, AMRPA believes that this measure does not meet the feasibility criteria required for endorsement.

                       

                      Finally, AMRPA members are concerned about the suggestion that this is a cross-setting measure and the unintended consequences that may result from this designation.  This measure differs across post-acute care settings as the risk-adjustment methodology utilizes setting specific covariates and setting-specific coefficients.  For example, each setting has a model intercept for the expected discharge function score for each patient.  For IRFs the model intercept is 34.1701, for SNFs the model intercept is 30.0118, and for LTCHs the model intercept is 14.5276.  This suggests that depending on setting a patient with the same patient characteristics will need to meet or exceed different expectations, with IRF patients having the highest expectation and LTCH patients having the lowest expectation.  To be considered a cross-setting measure, AMRPA members believe that the patient expectations should be consistent regardless of setting.

                       

                      Additionally, in suggesting this as a cross-setting measure that is publicly reported, these measures have the unintended consequence of potentially being used to limit patient access to certain settings based upon results.  While CMS and the measure developers documented concerns about providers denying access to certain patients who may not perform well on this measure, AMRPA questions whether consideration was given for referral sources using this information to direct patients to alternative settings which may not provide the appropriate services to produce high quality outcomes.  We ask that the committee consider these unintended consequences when evaluating this measure for endorsement.

                       

                      As previously stated, AMRPA appreciates the opportunity to provide these additional detailed comments related to CBE #4630 – Cross-Setting Discharge Function Score ‎for Inpatient Rehabilitation Facilities. We ask that the PQM E&M Advanced Illness and Post-Acute Care Recommendation Committee consider our comments when evaluating this measure for endorsement.  Should the committee require any additional information related to our comments on the measure, we ask that you please reach out to AMRPA Director of Quality and Health Policy, Troy Hillman ([email protected]) or AMRPA President, Kate Beller ([email protected]).

                      Organization
                      American Medical Rehabilitation Providers Association (AMRPA)

                      As noted above, testing has shown that the imputation approach shows less bias in function measure scores and more precise estimates for item scores than the approach of replacing all “Activity Not Attempted” values with the value of 1 (most dependent). A bootstrapping method was used to measure bias and mean squared error (MSE) in the statistical imputation method compared to the ‘recode to 1’ approach. The goal of the bootstrapping method was to determine how similar imputed values were to the true item score. The average MSE and bias statistics for statistical imputation vs. recoding to 1 were: 

                      IRF: 

                      1. Statistical imputation:  
                        1. Mean squared error: 1.41 at admission and 0.52 at discharge 
                        2. Bias: -0.23 at admission and -0.07 at discharge 
                      2. Recode to 1: 
                        1. Mean squared error: 5.95 at admission and 3.58 at discharge 
                        2. Bias: -1.33 at admission and -0.58 at discharge 

                      As noted above, CMS and their contractors calculate the measure results, so providers do not need to invest in technology to produce the results.  CMS does publish the model coefficients so providers do have access to information about how patient characteristics are used to estimate “Activity Not Attempted” codes. The Quality Measure Calculation and Reporting User’s Manual provides details about the measure calculation and is posted on the CMS website: https://www.cms.gov/files/document/irf-qm-calculations-and-reporting-users-manual-v60.pdf

                       

                      As noted above, this measure is an important addition to the PAC QRPs as it is both (A) cross-setting and (B) an outcomemeasure.  This measure fulfills statutory requirements as specified in the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014. The IMPACT Act includes a mandate that CMS develop and report cross-setting measures. It specifically requires CMS to develop cross-setting quality measures that assess functional status and changes in function. This measure fulfills these statutory requirements. 

                       

                      As noted above, with regard to IRFs potentially avoiding patients with complex needs, risk-adjustment and the application of exclusion criteria should reduce this concern. The risk adjustment methodology is used for the measure to account for differences in case mix across IRFs. Specifically, providers’ performance on this measure are adjusted for the characteristics of their patient population at admission and “level the playing field” across providers.  Further, exclusion criteria are applied to also reduce the potential impact of patients with complex needs (patients with severe brain injury and tetraplegia complete) affecting the measure results. 

                       

                      As noted above, the Cross-Setting measure was developed so it is aligned across IRFs, SNFs, LTCHs and HHAs and the function items in the measures’ specifications are the items included in all these post-acute care settings. For the IRF setting, the Spearman correlation coefficient between this Cross Setting Discharge Function measure and the Discharge Self-Care measure and Discharge Mobility measure are .91 and .93, respectively.

                       

                      As noted above, the Cross Setting Discharge Function measures were developed to be aligned across settings and the risk-adjustment and imputation approach are the same across all the measures. As noted above, the measure results are reported separately for IRFs and other post-acute care settings. The Centers for Medicare & Medicaid Services expects that providers deliver care based on patient needs, not based on quality measure scores or payment.

                      Organization
                      Centers for Medicare & Medicaid Services/RTI International

                      Submitted by Bruce Pomeranz, M.D. (not verified) on Mon, 12/16/2024 - 20:21

                      Permalink

                      I appreciate the opportunity to comment on CBE #4630 – Cross-Setting Discharge Function Score for Inpatient Rehabilitation Facilities (IRF).  

                       

                      This measure does not meet the requirements for endorsement as the measure is administratively difficult to manage, produces results inconsistent with other endorsed measures, and could produce unintended consequences impacting access to IRF care.  The concerns are detailed below for PQM E&M Committee’s consideration of these reasons when evaluating this measure for endorsement.

                       

                      First, clinicians are struggling to understand and manage performance on this measure.  While the data elements utilized in the ‎measure are part of the Inpatient Rehabilitation Facility – Patient Assessment Instrument (IRF-PAI), the imputation methodology to manage ‎the performance of this measure requires an infeasible ‎investment in technology and training to implement.  Providers that are unable to calculate imputed values on ‎their own have limited ability to identify what the patients’ measure scores are ‎and what the risk-adjusted target values are to create a positive ‎result.  ‎ The imputation method is unnecessary, as existing endorsed measures (CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients) do not require imputed values and instead utilize values that are representative of the patient’s functional status at admission and discharge.  This measure does not meet the feasibility criteria required for ‎endorsement, as this measure requires impractical investment in the people, tools, tasks, and ‎technologies necessary to implement this measure. 

                       

                      Second, this measure produces results that are inconsistent with two currently endorsed functional measures (CBE #2635 Discharge Self-Care Score for Medical Rehabilitation Patients and CBE #2636 Discharge Mobility Score for Medical Rehabilitation Patients).  The existing endorsed measures utilize all the functional items in the Self-Care and Mobility domains and produce an accurate representation of the functional improvement and discharge functional status of every IRF patient.  The Discharge Function Score measure utilizes 3 of the 7 Self-Care items and 7 of the 17 Mobility items included in the functional assessment of IRF patients.  Utilizing less than half of all functional items included in the IRF-PAI and existing endorsed measures produces values that are inconsistent and are not representative of the functional improvements and discharge functional status the patients that achieve as part of their IRF care.  Additionally, the public reporting of this measure on Care Compare shows that measure performance is inconsistent with the two other endorsed measures, and some IRFs are reporting that patients who meet expectations for the Discharge Self-Care and Discharge Mobility measures are identified as not meeting expectations for the Discharge Function Score measure.  Because of these inconsistencies, we do not believe that the new measure is superior to existing measures and should not replace the existing measures.  This measure does not meet the importance criteria required for ‎endorsement.

                       

                      Finally, there are unintended consequences that may result from the endorsement of this measure.  This measure is identified as “Cross-Setting”, yet it differs significantly across post-acute care settings.  While the functional items included in this measure are similar across post-acute care settings, the measure calculations and risk-adjusted patient expectations are very different.   The measure considered for endorsement for Home Health has a different developer and different imputation and risk-adjustment methodology.  Depending on post-acute care setting, a patient with the same patient characteristics will need to meet or exceed different expectations, with IRF patients having the highest expectation and Long-term acute care hospital (LTCH) patients having the lowest expectation.  To be considered a “cross-setting” measure, the measure calculations and patient expectations should be standardized regardless of setting. Additionally, since these measures are publicly reported on Care Compare, there is the unintended consequence of measure values being used to limit patient access to certain settings based upon reported results.  The characterization of that this measure as cross-setting measures implies that comparison of results can be made between IRFs, SNFs, LTCHs and Home Health providers.  Payers and referral sources already are using this information to direct patients to alternative settings which may not provide the appropriate services to produce high quality outcomes.  This measure does not meet the use and usability criteria required for ‎endorsement.

                       

                      As detailed above, this measure does not meet all the criteria necessary for endorsement, and therefore I ask the PQM E&M Committee to not support the endorsement of this measure.

                      Organization
                      Kessler Institute for Rehabilitation

                      As noted above, testing has shown that the imputation approach shows less bias in function measure scores and more precise estimates for item scores than the approach of replacing all “Activity Not Attempted” values with the value of 1 (most dependent). A bootstrapping method was used to measure bias and mean squared error (MSE) in the statistical imputation method compared to the ‘recode to 1’ approach. The goal of the bootstrapping method was to determine how similar imputed values were to the true item score. The average MSE and bias statistics for statistical imputation vs. recoding to 1 were: 

                      IRF: 

                      1. Statistical imputation:  
                        1. Mean squared error: 1.41 at admission and 0.52 at discharge 
                        2. Bias: -0.23 at admission and -0.07 at discharge 
                      2. Recode to 1: 
                        1. Mean squared error: 5.95 at admission and 3.58 at discharge 
                        2. Bias: -1.33 at admission and -0.58 at discharge 

                      As noted above, CMS and their contractors calculate the measure results, so providers do not need to invest in technology to produce the results.  CMS does publish the model coefficients so providers do have access to information about how patient characteristics are used to estimate “Activity Not Attempted” codes.

                       

                      As noted above, the term “cross-setting” is used in the measure title to convey that the quality measures are aligned across the four post-acute care settings. The measures for each setting are calculated separately because populations treated in each setting vary. This includes the calculation of the risk-adjustment models. Thus, the regression coefficients are different across the different settings.   The measure results are reported separately on the Compare website by program (i.e., separately for IRFs and SNFs).  The Quality Measure Calculation and Reporting User’s Manual provides details about the measure calculation and is posted on the CMS website: https://www.cms.gov/files/document/irf-qm-calculations-and-reporting-users-manual-v60.pdf 

                       

                      As noted above, this measure is an important addition to the PAC QRPs as it is both (A) cross-setting and (B) an outcome measure.  This measure fulfills statutory requirements as specified in the Improving Medicare Post-Acute Care Transformation (IMPACT) Act of 2014. The IMPACT Act includes a mandate that CMS develop and report cross-setting measures. It specifically requires CMS to develop cross-setting quality measures that assess functional status and changes in function. This measure fulfills these statutory requirements. The contractor developing the home health measure worked with the contractor developing this IRF measure.

                       

                      As noted above, with regard to IRFs potentially avoiding patients with complex needs, risk-adjustment and the application of exclusion criteria should reduce this concern. The risk adjustment methodology is used for the measure to account for differences in case mix across IRFs. Specifically, providers’ performance on this measure are adjusted for the characteristics of their patient population at admission and “level the playing field” across providers.  Further, exclusion criteria are applied to also reduce the potential impact of patients with complex needs (patients with severe brain injury and tetraplegia complete) affecting the measure results. 

                       

                      As noted above, the Cross-Setting measure was developed so it is aligned across IRFs, SNFs, LTCHs and HHAs and the function items in the measures’ specifications are the items included in all these post-acute care settings. For the IRF setting, the Spearman correlation coefficient between this Cross Setting Discharge Function measure and the Discharge Self-Care measure and Discharge Mobility measure are .91 and .93, respectively.

                       

                      As noted above, the Cross Setting Discharge Function measures were developed to be aligned across settings and the risk-adjustment and imputation approach are the same across all the measures. As noted above, the measure results are reported separately for IRFs and other post-acute care settings. The Centers for Medicare & Medicaid Services expects that providers deliver care based on patient needs, not based on quality measure scores or payment.

                      Organization
                      Centers for Medicare & Medicaid Services/RTI International