This outcome measure estimates the percentage of Medicare Part A skilled nursing facility stays that meet or exceed an expected discharge function score. The expected discharge function score is a risk-adjusted estimate that accounts for resident characteristics. The measure includes patients who are 18 years of age or older and the measure timeframe is 12 months.
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1.5 Measure Type1.6 Composite MeasureNo1.7 Electronic Clinical Quality Measure (eCQM)1.8 Level Of Analysis1.9 Care Setting1.10 Measure Rationale
Measuring functional status of skilled nursing facility (SNF) patients can provide valuable information about a SNF’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 SNF 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.
SNFs can positively impact their patients’ functional outcomes. During a SNF 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 SNF 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 SNF stay by accounting for patient characteristics that impact their functional status. The Cross-Setting Discharge Function for a given SNF is the proportion of that SNF’s stays where a patient’s observed discharge function score meets or exceeds their expected discharge function score. SNFs 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 SNFs 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 SNF setting. Discharge Function adds value to the SNF 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.
1.11 Measure Webpage1.20 Testing Data Sources1.25 Data SourcesThe SNF MDS data are collected on all Medicare fee-for service patients who receive services from a skilled nursing facility in a nursing home or a swing bed provider. More information about the MDS is available at: https://www.cms.gov/medicare/quality/nursing-home-improvement/resident-assessment-instrument-manual
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1.14 Numerator
The number of Medicare Part A SNF 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 patients walks or Wheel 50 feet with two turns (GG0170R3) if the patient does not walk and users a wheelchair. The specifications for calculation of the function score are provided in the following manual: https://www.cms.gov/files/document/snf-qm-calculations-and-reporting-users-manual-v60.pdf
1.14a Numerator DetailsThe numerator is the number of SNF 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 GG items 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)
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 SNF stay using admission MDS 3.0 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.
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1.15 Denominator
The total number of Medicare Part A SNF stays, except those that meet the exclusion criteria.
1.15a Denominator DetailsThe denominator is total number of Medicare Part A SNF stays with an MDS 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 SNF stays are constructed is available in the Skilled Nursing Facility Quality Reporting Program Measure Calculations and Reporting User’s Manual: Version 6.0.
1.15d Age GroupAdults (18-64 years)Older Adults (65 years and older)
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1.15b Denominator Exclusions
Medicare Part A SNF Stays are excluded from the measure calculaton if:
1) The Medicare Part A SNF Stay is an incomplete stay: Patients with incomplete stays are identified based on the following criteria:
- Unplanned discharge, which would include discharge against medical advice
- Discharge to acute hospital, psychiatric hospital, long-term care hospital
- SNF PPS Part A stay less than 3 days
- The patient died during the SNF stay
2) The patient has the following medical conditions at the time of admission (i.e., on the 5-Day PPS assessment):
- 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 DetailsMedicare Part A SNF stays are excluded from the measure calculation if:
1) The Medicare Part A SNF stay is an incomplete stay: Patients with incomplete stays (incomplete = [1]) are identified based on the following criteria using the specified data elements:
- Unplanned discharge, which includes discharge against medical advice, indicated by A0310G (Type of Discharge) = 2 (Unplanned discharge) [as indicated on an OBRA Discharge (RFA: A0310F = [10, 11]) that has a discharge date (A2000) on the same day or the day after the End Date of Most Recent Medicare Stay (A2400C)]. OR
- Discharge to acute hospital, psychiatric hospital, long-term care hospital indicated by A2105 = [04, 05, 07]. [as indicated on an OBRA Discharge (RFA: A0310F = [10, 11]) that has a discharge date (A2000) that is on the same day or the day after the End Date of Most Recent Medicare Stay (A2400C)]. OR
- SNF PPS Part A stay less than 3 days ((A2400C minus A2400B) < 3 days) OR
- The patient died during the SNF stay (i.e., Type 2 SNF Stays). Type 2 SNF Stays are SNF stays with a PPS 5-Day Assessment (A0310B = [01]) and a matched Death in Facility Tracking Record (A0310F = [12]).
2) The patient has any of the following medical conditions at the time of admission (i.e., on the 5-Day PPS assessment): 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:
- B0100 (Comatose) = 1 or the following ICD-10 codes (Severe brain damage = S06.A1XA S06.A1XD S06.A1XS )
- Complete and severe tetraplegia = G82.51, G82.52, G82.53, S14.111A, S14.111D, S14.112A, 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: A1600 (Entry Date) – A0900 (Birth Date) is less than 18 years. Age is calculated in years based on the truncated differences between entry date (A1600) and birth date (A0900); i.e., the difference is not rounded to the nearest whole number
4) The patient is discharged to hospice or received hospice while a patient:
- A2105 (Discharge status) = [09, 10], as indicated on an OBRA Discharge (RFA: A0310F = [10, 11] that has a discharge date (A2000) on the same day or the day after the End Date of Most Recent Medicare Stay (A2400C) OR O0110K1b (Hospice while a Patient) = [1], as indicated at the time of admission (i.e., on the PPS 5-Day Assessment)
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1.13a Data dictionary not attachedYes1.16 Type of Score1.17 Measure Score InterpretationBetter quality = Higher score1.18 Calculation of Measure Score
Discharge Function measure is the proportion of SNF 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 SNF stay, observed discharge function score and expected discharge function score are determined. For each SNF, 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 the numerator description in Section 1.14a) 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)
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:
- 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 process for calculating Discharge Function can be divided into two phases. In the first phase, GG items 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 GG items. Notably, the estimation process uses all GG items available in SNFs to estimate the NA scores for the subset of GG 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.
Step 1: For each SNF stay, calculate the observed discharge function score by summing the individual GG items. If the GG item has a score of 1 − 6, then use the score for that item. If the GG item 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 SNF stays. Excluded SNF stays are those that are incomplete stays, and stays of patients younger than 18 years old. Also excluded are SNF stays where the patient has a diagnosis indicating coma, persistent vegetative state, complete tetraplegia, locked-in state, severe anoxic brain damage, cerebral edema, or compression of the brain. Finally, SNF stays where the patient is discharged to hospice (home or institutional facility) are also excluded.
Step 3: For each SNF 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 SNF stays to determine the model intercept and risk adjustor coefficients. Expected discharge function scores are calculated by applying the regression equation to each SNF 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 SNF 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.
Step 5: Determine the denominator count. Determine the total number of SNF stays with an MDS 3.0 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 SNF 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 SNF-level discharge function percent. Divide the SNF’s numerator count (Step 6) by its denominator count (Step 5) to obtain the SNF-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 applicable1.19 Measure Stratification DetailsThis measure is not stratified
1.26 Minimum Sample SizeAt least 20 stays are required for the Discharge Function measure in the reporting period. In FY 2023, 81.4% (11,976) of all SNFs (n=14,720) met this threshold and accounted for 97.1% of all eligible stays among all providers.
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7.1 Supplemental Attachment
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StewardCenters for Medicare & Medicaid ServicesSteward Organization POC EmailSteward Organization URLSteward Organization Copyright
yes
Measure Developer Secondary Point Of ContactAnne Deutsch
RTI international
3040 Cornwallis Road
P.O. Box 12194
Research Triangle Park, NC 27709
United StatesMeasure Developer Secondary Point Of Contact Email
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2.1 Attach Logic Model2.2 Evidence of Measure Importance
Patients who require additional inpatient care following a major illness or injury may receive care in a SNF. SNF care is comprised of several core services, including the provision of rehabilitation therapy to those experiencing functional limitations following discharge from a hospital stay [1]. Physical function is a modifiable factor associated with several outcomes, including successful discharge to the community, and re-hospitalization rates [2, 9, 13].
Patients’ functional outcomes vary based on rehabilitation treatments provided by the SNF. For example, one retrospective observational study examining 10 SNFs found that patients had significantly different functional recovery rates after controlling for patients’ demographic and clinical characteristics [3]. Another study among patients 65 years or older receiving rehabilitation following a hip fracture found that length of stay was associated with mobility and self-care scores at SNF discharge [4]. Variation in the functional outcomes of SNF patients may be monitored by the Discharge Function Score measure. Evidence suggests that variation in SNF functional outcomes is associated with several interventions and processes of care, such as the intensity, type, and amount of therapy provided, as well as the use of enhanced medical rehabilitation.
The method and structure of therapy that patients receive in the facility may be associated with changes in SNF functional outcomes, reflecting an opportunity to measure facility-level differences in patient outcomes. One randomized controlled trial emphasized the safety and feasibility of the implementation of a high-intensity resistance training framework in SNFs. Participants in the high-intensity rehabilitation program showed greater patient satisfaction, reduced length of stay, and faster gait speed change in comparison to the control group receiving usual care [2]. A retrospective cohort study among older adults with sepsis found that more hours of physical and occupational therapy during the first seven days of the SNF stay were associated with a significantly higher probability of improvement in functional outcomes [6]. Prusynski et al. found that SNF patients who receive 10 of fewer minutes of therapy above weekly reimbursement thresholds (Resource Utilization Groups) were more likely to improve in functional abilities and successfully discharge to the community, suggesting that even small amounts of extra therapy may have contributed positively to patients’ functional outcomes [10]. A cross-sectional study identified associations between the proportion of minutes of physical and occupational therapy that were received as multi-participant sessions and community discharge and functional improvement. Researchers found positive associations between low and medium multi-participant therapy levels and patient outcomes and suggested that SNFs continue to deliver the majority of therapy as individualized treatment [11].
SNF utilization of therapy interventions may also impact functional outcomes of SNF patients. Enhanced medical rehabilitation has also been associated with better functional outcomes. Enhanced medical rehabilitation differs from standard rehabilitation efforts in that it uses a patient-directed approach that links therapy activities to personal goals of the patient resulting in a more motivational approach. Older adults assigned to the enhanced medical rehabilitation group in one randomized controlled trial demonstrated 25% greater recovery of function compared to those assigned to the group receiving the standard of care [8]. In another randomized controlled trial, enhanced medical rehabilitation only benefitted individuals with relatively intact executive functioning [8].
Because patients’ age, cognitive function and comorbid conditions can affect their functional outcomes, the Discharge Function Score measure adjusts for these factors. Functional outcomes vary based on patients’ cognitive function, medical complexity, and patient case-mix. Patients with higher cognitive function at SNF admission achieved larger gains in functional status compared to patients with cognitive impairment [12]. Another study found that short-stay nursing home patients with conditions such as cognitive impairment, delirium, dementia, and stroke showed less functional improvement relative to patients without these conditions [5]. A retrospective cohort study among older adults with sepsis found that the probability of improvement in function decreased with more hospitalizations in the prior year, older age, and more severe cognitive impairment at SNF admission [6].
Overall, literature indicates that SNF processes and interventions may influence the functional outcomes of patients at discharge. Variations in the functional status of SNF patients could be measured and monitored through the Discharge Function Score measure. Since function outcomes vary based on patient characteristics, the Discharge Function Score measure adjusts for relevant risk factors.
References:
- Gustavson, A. M., Falvey, J. R., Forster, J. E., & Stevens-Lapsley, J. E. (2019). Predictors of Functional Change in a Skilled Nursing Facility Population. Journal of geriatric physical therapy (2001), 42(3), 189–195. https://doi.org/10.1519/JPT.0000000000000137
- 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
- Johnson J.K., Hohman J., Stilphen M., Bethoux F., Rothberg M.B. (2021). Functional Recovery Rate: A Feasible Method for Evaluating and Comparing Rehabilitation Outcomes Between Skilled Nursing Facilities. JAMDA, 22(8), P1633-1639.E3. https://doi.org/10.1016/j.jamda.2020.09.037
- Cogan A.M., Weaver J.A., McHarg M., Leland N.E., Davidson L., Mallinson T. (2020). Association of Length of Stay, Recovery Rate, and Therapy Time per Day With Functional Outcomes After Hip Fracture Surgery. JAMA Netw Open, 3(1), e1919672. https://doi.org/10.1001/jamanetworkopen.2019.19672
- Wysocki, A., Thomas, K. S., & Mor, V. (2015). Functional Improvement Among Short-Stay Nursing Home Residents in the MDS 3.0. Journal of the American Medical Directors Association, 16(6), 470–474. https://doi.org/10.1016/j.jamda.2014.11.018
- Downer, B., Pritchard, K., Thomas, K. S., & Ottenbacher, K. (2021). Improvement in Activities of Daily Living during a Nursing Home Stay and One-Year Mortality among Older Adults with Sepsis. Journal of the American Geriatrics Society, 69(4), 938–945. https://doi.org/10.1111/jgs.16915
- Lenze E.J., Lenard E., Bland M., Barco, P., Miller, J.P., Yingling, M., Lang, C.E., Morrow-Howell, N., Baum C.M., Binder, E.F., Rodebaugh, T.L. (2019). Effect of Enhanced Medical Rehabilitation on Functional Recovery in Older Adults Receiving Skilled Nursing Care After Acute Rehabilitation: A Randomized Clinical Trial. JAMA Netw Open, 2(7):e198199. https://doi.org/10.1001/jamanetworkopen.2019.8199
- Ercal, B., Rodebaugh, T. L., Bland, M. D., Barco, P., Lenard, E., Lang, C. E., Miller, J. P., Yingling, M., & Lenze, E. J. (2021). Executive Function Moderates Functional Outcomes of Engagement Strategies During Rehabilitation in Older Adults. American journal of physical medicine & rehabilitation, 100(7), 635–642. https://doi.org/10.1097/PHM.0000000000001739.
- 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.
- Rachel A. Prusynski, Bianca K. Frogner, Sean D. Rundell, Sujata Pradhan, Tracy M. Mroz, Is More Always Better? Financially Motivated Therapy and Patient Outcomes in Skilled Nursing Facilities, Archives of Physical Medicine and Rehabilitation, Volume 105, Issue 2, 2024, Pages 287-294, ISSN 0003-9993, https://doi.org/10.1016/j.apmr.2023.07.014.
- Prusynski, R. A., Rundell, S. D., Sujata, P., & Mroz, T. M. (2023///Oct-Dec). Some but not too much: Multiparticipant therapy and positive patient outcomes in skilled nursing facilities. Journal of Geriatric Physical Therapy, 46(4), 185-195. doi:https://doi.org/10.1519/JPT.000000000000036
- Katie A. Butera, Allison M. Gustavson, Jeri E. Forster, Daniel Malone, Jennifer E. Stevens-Lapsley, Admission Cognition and Function Predict Change in Physical Function Following Skilled Nursing Rehabilitation, Journal of the American Medical Directors Association, Volume 25, Issue 1, 2024, Pages 17-23, ISSN 1525-8610, https://doi.org/10.1016/j.jamda.2023.09.011.
- 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
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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 or maintenance, reducing adverse events, and lowering healthcare costs.
The cross-setting Discharge Function Score measure determines how successful each SNF 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 SNF stay by accounting for patient characteristics that impact their functional status. The final cross-setting Discharge Function for a given SNF is the proportion of that SNF’s stays where a patient’s observed discharge function score meets or exceeds their expected discharge function score. SNFs 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 SNFs 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 SNF setting.
Discharge Function adds value to the SNF QRP 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 SNFs 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 SNFs. 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. See https://www.cms.gov/files/document/snf-qm-calculations-and-reporting-users-manual-v60.pdf for more details on the risk adjustment methodology.
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 SNF Quality Reporting Program measures and Section GG items are used in the SNF 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, SNFs 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:
- 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
- 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.
- 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 LandscapeThe Improving Medicare Post Acute Care Transformation (IMPACT) Act of 2014 requires the collection of standardized data across post-acute care providers and also 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:
- The SNF 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 SNF QRP. The Cross-Setting Discharge Function measure has higher variation in provider performance and offers more informative comparisons between SNFs for patients, caregivers, and stakeholders.
- 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 SNF 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]
- 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.
In addition to the SNF QRP, the Cross-Setting Discharge Function Score measure has been adopted in the SNF Value Based-Purchasing Program and the Nursing Home Quality Initiative.
[1] https://www.cms.gov/sites/default/files/2022-04/PAC-Function-TEP-Summary-Report-Jul2021.pdf
2.6 Meaningfulness to Target PopulationFunctional 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 maintenance or functional independence and setting functional goals to facilitate return to community living is a primary goal of care. For patients receiving home care setting, functional assessment and goal-setting are also a primary focus to attain independent functioning, 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.
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2.4 Performance Gap
There is evidence of a performance gap and variability in performance for this quality measure.
Table 1 below reports on data for 11,976 SNFs that met the minimum threshold of SNF stays for public reporting of the Discharge Function measure (≥20) in the twelve-month reporting period of FY 2023. The data reported in Table 1 provides evidence of a performance gap among providers as performance ranges from 23.7% for the mean of the first decile to 76.2% for the mean for the top decile.
Table 1. Performance Scores by DecilePerformance 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 51.2 0.0 23.7 35.4 41.4 46.0 50.0 53.8 57.5 61.7 66.9 76.2 100 N of Entities 11,976 2 1,197 1,205 1,214 1,187 1,185 1,206 1,189 1,204 1,192 1,197 1 N of Persons / Encounters / Episodes 1,021,195 61 88,882 92,040 99,324 98,220 101,614 109,319 112,171 110,354 109,285 99,986 25
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3.1 Feasibility Assessment
A feasibility assessment was not necessary. The MDS data elements used for measure construction are part of the standard data collection processes for SNF providers and are already used in existing SNF QRP measures.
MDS 3.0 data collection and submission is a requirement of the Medicare Program. Functional assessment is conducted as part of usual clinical practice, and information on functional status used to calculate this measure is recorded in the relevant MDS items embedded in the provider’s clinical assessment. MDS data are collected by the SNF during the stay and submitted electronically to CMS via the Internet Quality Improvement and Evaluation System (iQIES). No issues regarding availability of data, missing data, timing or frequency of data collection, patient confidentiality or implementation have become apparent since the addition of these items on the MDS in 10/1/2016.
3.3 Feasibility Informed Final MeasureMDS data collection and submission is a requirement of the Medicare Program.
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3.4 Proprietary InformationNot a proprietary measure and no proprietary components
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4.1.3 Characteristics of Measured Entities
All testing used SNF stays completed in FY 2023. A total of 14,720 SNFs submitted MDS 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 1,021,195 stays in 11,976 SNFs.
The included SNFs were geographically diverse, with no one geographic region containing more than 21% of the included SNFs. The majority of the SNFs were for-profit entities (74%) that were freestanding (97%) and located in urban areas (76%). Facility size is presented based on the number of patient stays. Roughly a third of the SNFs were large with 106-1070 stays in FY 2023, and 62% of the included SNFs were medium sized with 38-105 stays in FY 2023. Only 8% were small SNFs with 20-37 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:
- The data includes a total of 1,021,195 stays across 11,976 providers.
- Stay Count:
- Large providers (106-1070 stays) account for the majority, with 619,479 stays (61%) across 3,678 providers (31%).
- Medium providers (38-105 stays) represent 381,714 stays (37%) across 7,390 providers (62%).
- Small providers (20-37 stays) comprise 20,002 stays (2%) among 908 providers (8%).
- Profit Status:
- For-profit providers dominate with 748,091 stays (73%) across 8,843 providers (74%).
- Not-for-profit providers account for 236,361 stays (23%) with 2,567 providers (21%).
- Government-affiliated providers contribute 36,295 stays (4%) across 556 providers (5%).
- Rurality:
- Urban providers comprise 85% of stays (865,854) and 76% of providers (9,086).
- Rural providers make up 15% of stays (155,341) across 2,890 providers (24%).
- Regional Distribution:
- The largest regional share comes from the South - South Atlantic with 211,963 stays (21%) and 2,100 providers (18%).
- Other significant regions include the Northeast - Middle Atlantic (175,287 stays, 17%) and the Midwest - East North Central (160,267 stays, 16%).
- Provider Type:
- Freestanding providers make up nearly all providers, with 988,190 stays (97%) across 11,621 providers (97%).
- Hospitals account for 28,848 stays (3%) across 284 providers (2%).
This distribution provides a comprehensive look at provider characteristics, showing that the majority of stays are within large, for-profit, urban, and freestanding facilities, with notable regional variations in provider concentration.
4.1.1 Data Used for TestingThe data used report results in this form are derived from several sources. The primary source of data for the measure is MDS assessment data from Fiscal Year (FY) 2023. The MDS assessments are combined into SNF stays.
Stays were defined using the following logic: A Medicare Part A SNF Stay includes consecutive time in the facility starting with the Medicare Part A Admission Record (PPS 5-Day assessment) through the Medicare Part A Discharge Record (PPS Discharge Assessment) or Death in Facility Tracking Record at the end the SNF stay and all intervening assessments. A Medicare Part A SNF Stay, thus defined, may include interrupted stays lasting 3 calendar days or less. Incomplete Medicare Part A SNF stays occur if the patient was discharged to an acute care setting (e.g., acute hospital, psychiatric hospital, or long-term care hospital), had an unplanned discharge, was discharged against medical advice, had a stay that was less than three days, or died while in the facility. All Medicare Part A SNF stays not meeting the criteria for incomplete stays will be considered complete stays.
The target date for an SNF MDS record reflects the timeframe in which the assessment as to be completed. The target period for the measure is 12 months (4 quarters). To construct the SNF stays, all SNF MDS records with a target date within the target period are selected. SNF MDS records are sorted by the unique patient identifier, start date of the most recent Medicare stay (item A2400B), target date, record type, and assessment internal ID. Record type is a categorical variable indicating whether an assessment is a death in facility tracking record (4), PPS discharge assessment (3), PPS 5-day assessment (2), or another record type (1). Records are sorted in descending order of start date of the most recent Medicare stay, target date, and record type. The most recent stay for each patient is selected first. Then, assessments falling between the start of the target period and the day before the patient’s most recent stay are identified, and stays are iteratively constructed in this manner. 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 PopulationAll testing used SNF stays completed in FY 2023. SNFs submitted a total of 1,051,424 stays that ended in FY 2023. After applying denominator exclusion criteria and applying the reportability threshold of 20 stays, the final included 1,021,195 stays in the measure population and testing. For included SNF stays, 93% were for patients who were over the age of 65 and the majority were female (61%) and white (80%). The Area Deprivation Index (Neighborhood Atlas - Home (wisc.edu)) with roughly 50% of included stays with an ADI of 0-50. The primary medical condition of included stays was varied – 43% had medically complex conditions, 16% had fractures and other traumas, and the rest were scattered among a range of diagnoses.
The stay-level characteristics provide a detailed profile of eligible stays, focusing on demographics, payer information, social determinants, and primary medical conditions. Here’s a breakdown:
- Overall Count:
- Total eligible stays: 1,021,195
- Race:
- White: Majority at 80% (811,962).
- Black: 9% (91,237).
- Hispanic/Latinx: 3% (31,919).
- Asian: 2% (19,170).
- American Indian/Alaska Native and Native Hawaiian/Pacific Islander: Less than 1% combined.
- No Information Available: 6% (59,208), reflecting gaps in race/ethnicity data.
- Sex:
- Female: 61% (623,501).
- Male: 39% (397,694).
- Age:
- 75-84 years: Largest age group at 36% (365,064).
- 65-74 years: 23% (237,097).
- 85-90 years: 20% (204,845).
- > 90 years: 14% (139,992).
- 55-64 years: 5% (54,647).
- ≤ 54 years: Smallest group at 2% (19,550).
- Payer:
- Medicare: Primary payer for 64% (654,253) of stays.
- Dual (Medicare and Medicaid): 36% (366,892).
- Neither Medicare nor Medicaid: Rare, with negligible counts.
- Area Deprivation Index (ADI):
- Largest proportion falls in 25-49 percentile: 27% (273,701).
- 50-74 percentile: 25% (251,313).
- Other ADI categories range from 12% to 24%, with 4% Unknown.
- Health-Related Social Need (SDOH):
- Interpreter Need: Present in 3% (27,680) of stays, indicating linguistic needs within the patient population.
- Primary Medical Condition:
- Medically Complex Conditions are the largest group, comprising 43% (434,611) of stays.
- Fractures and Other Multiple Trauma: 16% (163,483).
- Debility, Cardiorespiratory Conditions: 13% (132,957).
- Other Orthopedic Conditions: 8% (84,256).
- Other Neurological Conditions: 7% (71,503).
- Conditions with smaller representations include Hip and Knee Replacements (3%), Stroke (5%), and Amputation (1%).
The stay-level characteristics provide a comprehensive view of the eligible patient population, focusing on demographics, payer information, social determinants, and primary medical conditions across 1,021,195 stays.
4.1.2 Differences in DataThe 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 & Medicaid from CMS administrative data.
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4.2.1 Level(s) of Reliability Testing Conducted4.2.2 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 quality measure:
1. Data Elements:
a. Clinicians code 11 motor function data elements included in Section GG of each post-acute care assessment instrument. One is a wheelchair data element 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 quality 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 SNF Patient stays that Meet or Exceed an Expected Discharge Function Score
a. The calculated performance measure score is the percentage of SNF patient stays that meet or exceed an expected discharge function score within an SNF. The risk-adjustment procedure used to calculate the expected score is described in the Calculation Algorithm attachment.
b. This performance measure estimates the percentage of SNF 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.
We use three methods for reliability testing with the FY 2023 data: Cronbach’s alpha coefficient, split-sample reliability testing, and signal-to-noise ratio testing.
Cronbach’s alpha coefficient assesses the internal consistency of the function scale/instrument scores for each assessment. Internal consistency provides a general assessment of how well the function data elements interrelate within the function 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.1
Split-sample reliability testing examines the agreement between two performance measure scores for a SNF based on randomly split, independent subsets of patient quality episodes within the same measurement period. We randomly divided each SN’Fs FY 2023 patient stays into halves and calculated performance measure scores for each split-half sample using the same measure specification. We then calculated Shrout-Fleiss2 intraclass correlation coefficients (ICC[2,1]) between the split-half scores to measure reliability.
Signal-to-noise reliability testing examines the overall reliability of the measure scores by comparing the true effect (the signal) to the error (the noise). We estimated the signal-to-noise ratio in two ways. We first followed the RAND methodology which is reported below in 4.2.3. Then, as a robustness check, we also estimated the ratio by using the sample variance to estimate the provider-to-provider variance.
We performed reliability testing on all SNFs with 20 or more patient quality episodes. These patient quality episodes had complete data. Please see the attached Excel file.
[1] Aron A, Aron EN Statistics for Psychology. 2nd ed. Upper Saddle River, NJ: Prentice Hall, 1999
[2] McGraw, K. O., & Wong, S. P. (1996). Forming inferences about some intraclass correlation coefficients. Psychological methods, 1(1), 30.
4.2.3 Reliability Testing ResultsCritical data element reliability testing:
The attached document reports inter-rater and the video reliability study testing results from 2012.
Internal Consistency (unit of analysis is patient assessments): The table 4.3.3A-1 in the attachment reports Cronbach Alpha results for the discharge assessment for patients who walk (non-wheelchair users) 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 ResultsTable 2. Accountable Entity–Level Reliability Testing Results by Denominator-Target Population SizeAccountable Entity-Level Reliability Testing Results Overall Minimum Decile_1 Decile_2 Decile_3 Decile_4 Decile_5 Decile_6 Decile_7 Decile_8 Decile_9 Decile_10 Maximum Reliability 0.82 0.59 0.84 0.81 0.81 0.80 0.81 0.81 0.82 0.82 0.83 0.84 1.00 Mean Performance Score 0.51 0.00 0.24 0.35 0.41 0.46 0.50 0.54 0.57 0.62 0.67 0.76 1.00 N of Entities 11,976 1 1,197 1,205 1,214 1,187 1,185 1,206 1,189 1,204 1,192 1,197 1 N of Persons / Encounters / Episodes 1,021,195 30 88,882 92,040 99,324 98,220 101,614 109,319 112,171 110,354 109,285 99,986 25 4.2.4 Interpretation of Reliability ResultsCronbach’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 SNF providers’ randomly divided groups’ Computed Quality Measure scores, providing strong evidence of measure reliability with an ICC of 0.80 overall. As shown in the table in the attached file, ICCs were exceptionally strong for providers with higher volume, with ICC of 0.92 among the largest providers (106 - 1,070 discharges).
Signal to Noise (unit of analysis of providers)
Signal to Noise Testing suggests strong reliability across providers, with a reliability statistic of 0.82. Robustness checks in which we calculated the Signal-to-Noise Reliability (VAR) using the sample variance gave an overall statistic of 0.85. Both of these pass the threshold of acceptable reliability.
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4.3.1 Level(s) of Validity Testing Conducted4.3.2 Type of accountable entity-level validity testing conducted4.3.3 Method(s) of Validity Testing
We report testing results throughout this document at two levels: 1) data elements/scale and 2) Computed Quality Measure Scores.
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 4.3.3 in attached file). The analysis used FY2023 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 SNFs, statistical estimation resulted in lower levels of bias (-0.21 at admission; -0.17 at discharge) and MSE (1.71 at admission; 1.41 at discharge) compared to the bias (-1.24 at admission; -0.72 at discharge) and MSE (5.05 at admission; 4.18 at discharge) produced from the recode approach, which supports the validity of the statistical estimation method for 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 ResultsCritical 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 one set of analyses showed that higher admission function item scores, indicating higher functional ability, were associated with shorter inpatient stays, as expected. Other analyses showed higher admission function item scores, indicating higher functional ability, were associated with higher rates of community discharge.
These studies also examined content validity of the Section GG 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
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.
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 quality measures in the SNF Quality Reporting Program using Spearman (rank) correlations between provider’s performance scores presented in Table 4.3.4a.
4.3.4a Attach Additional Validity Testing Results4.3.5 Interpretation of Validity ResultsCritical Data Elements
The published studies demonstrate evidence of the validity of the function items.
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 (0.77) and Discharge Mobility (0.82). This is expected because the SNF QRP self-care and mobility functional outcome measures use overlapping but not identical GG items and a different method for handling Activity Not Attempted codes.
The Discharge function measure was weakly positively associated with Discharge to Community measure (0.17). Additionally, it was not correlated with the Potentially Preventable Readmissions within 30-Days Post-Discharge measure (-0.06) or the Medicare Spending Per Beneficiary measure (-0.04). The measure had weaker associations with measures in which the stay ended with acute events (healthcare acquired Infections, and new or worsened pressure ulcers/injuries).
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4.4.1 Methods used to address risk factors4.4.2 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 R/E Information Available
- White (reference group)
Area Deprivation Index (ADI)
- ADI (≥85)
- ADI Missing
4.4.2a Attach Conceptual Model4.4.3 Risk Factor Characteristics Across Measured EntitiesTable 4.4.4A-1 in the attached file shows the number, percent, and average observed score of SNF stays 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 ResultsDischarge Function is a cross-setting performance, standardized measure calculated for IRFs, SNFs, LTCHs, and HH. The different data elements are collected across the assessment instruments for each setting, and 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, and solicited feedback on which covariates should be included in the cross-setting measure risk adjustment model.
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 SNF. Information on the covariates were obtained from the MDS.
The final risk adjustment approach includes adjusting for the following patient characteristics:
- Age Category: Age was calculated as of the admission date of the SNF 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 (Section X). Walking items and wheeling item are used in the same manner as for the discharge score (See Section X). Admission score squared is also included as a risk adjustor.
- Primary Medical Condition Category: Primary Medical Condition is principal reason for admitting the patient into SNF care.
- Interaction between Primary Medical Condition Category and Admission Function Score: This covariate is the admission function score multiplied by primary diagnosis.
- 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 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 total parenteral nutrition status on SNF’s admission and patient’s body mass index.
- History of Falls: This covariate indicates a history of falls prior to the SNF admission.
- Hierarchical Condition Categories (HCC) Comorbidities: Comorbidities are obtained from Section I in SNF-MDS. Comorbidities are grouped using CMS Hierarchical Condition Categories (HCC) software version 24
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. Expected discharge function scores are calculated by applying the regression equation to each stay using admission data.
We also tested three SRFs of interest that ultimately were not included in the final risk adjustment model:
1. Medicare vs. dually enrolled (patient is dually enrolled at any time during the quality episode)
2. Race/ethnicity
3. ADI
We constructed alternative risk adjustment models that included additional covariates for payer, race/ethnicity, and ADI to consider these SRFs for inclusion in the risk adjustment model.
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 Specifications4.4.5 Calibration and DiscriminationA 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 of expected scores. 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 SNFs 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 0.98 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 0.62, which suggests good model discrimination. Please see Table 4.4.5A-1 in the attached file.
4.4.5a Attach Calibration and Discrimination Testing Results4.4.6 Interpretation of Risk Factor FindingsRisk 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 0.62.
We find that across 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. The attached file presents the model results for the final risk adjustment model and the alternative risk adjustment model with additional SRF covariates.
While we considered these SRFs for inclusion in the risk adjustment model, we ultimately decided against such inclusion, primarily for conceptual reasons. Including these SRFs in risk adjustment models runs the risk of adjusting for factors that providers could control and should improve on – like active/unconscious bias against particular patient populations. It effectively lowers the expected outcomes for high-SRF patients, making expectations easier to meet, without improving the actual outcomes or underlying treatments. Further, when measures are stratified by such SRFs (enabling identification of gaps in provider quality between, for example, dually and non-dually enrolled patients, as is done in confidential feedback reports to providers), adjusting for dual eligibility as a risk factor may diminish CMS’s ability to make such stratified information clear and useful to providers. Finally, assessment items released since measure development allow for the possibility of more refined measurement of social determinants of health (e.g., health literacy, transportation). These alternatives can be tested for future revisions of the Discharge Function measure.
4.4.7 Final Approach to Address Risk FactorsRisk adjustment approachOnRisk adjustment approachOffConceptual model for risk adjustmentOffConceptual model for risk adjustmentOn
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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.
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 measure 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 MDS 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 higher SRF. Within the same provider, patients who were black or non-white, dually eligible or with and ADI ≥ 85 were less likely to have an observed discharge function score that is higher than expected based on their clinical characteristics.
In addition, as shown in Table 5.1-B in the Supplemental Files, we observed that providers that treat a larger proportion of patients with these identified SRFs also had differences in their outcomes, though not as stark as the within-provider performance scores. We compared Discharge Function performance based on the percentage of SNF patients who are Black, Non-White, Medicaid or Dual-eligible, and Dual-eligible or living in a neighborhood with ADI ≥ 85. When examining performance by proportion of high-SRF patients served, Discharge Function scores for all patients (both high- and low-SRF) decrease as proportion of high-SRF patients served by the provider increases (see Table 5.1-B). For example, SNFs that serve 16 to 95.5% of patients with a Black race have an average 50.8% performance score compared to a 53.3% performance score for SNFs that serve no patients who identify as Black. Risk adjusting for these characteristics may mask systematic disparities in care that should be examined and ultimately providers for which should be held accountable.
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6.1.1 Current StatusYes6.1.2 Current or Planned Use(s)6.1.4 Program Details1) CMS Skilled Nursing Facility Quality Reporting Program; 2) CMS Nursing Home Quality Initiative; 3) CMS Skilled Nursing Facility Value Based Purchas, https://www.cms.gov/medicare/quality/snf-quality-reporting-program/measures-and-technical-information; 2) https://www.cms.gov/medicare/quality/nursing-home-improvement/quality-measures; 3) https://www.cms.gov/medicare/quality/nursing-home-improvement/value-based-purchasing/measures, https://www.cms.gov/medicare/quality/snf-quality-reporting-program; 2) https://www.cms.gov/medicare/quality/nursing-home-improvement; 3) https://www., Skilled Nursing Facilities in the United States, Level of Analysis: FacilityCare Setting: Skilled Nursing Facility Number of Skilled Nursing Facilities: 11,976 Number of Skilled Nursing Facility Pat
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6.2.1 Actions of Measured Entities to Improve Performance
All skilled nursing facilities 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 SNFs may submit questions to as well as a website on which the latest measure updates are posted. The SNF Quality Reporting Quality Measure User’s Manual describes the provider reports that are available. SNFs make use of these reports to monitor and improve the quality of care.
6.2.2 Feedback on Measure PerformanceSkilled nursing facilities receive quarterly measure reports on all their measures. There is an email box that SNFs 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 FeedbackNo measure specifications changes requested or made.
6.2.4 Progress on ImprovementThis measure is too new to provide an assessment on impacts on improvement.
6.2.5 Unexpected Findingsnone
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