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Improvement in Ambulation/locomotion

CBE ID
0167
1.4 Project
Advanced Illness and Post-Acute Care
1.1 New or Maintenance
Maintenance
E&M Cycle
Spring 2024
Is Under Review
No
1.3 Measure Description

Percentage of home health episodes of care during which the patient improved in ability to ambulate. This is a rate/proportion measure targeted at older adults with multiple chronic conditions during home health quality of care episodes. 

    • 1.5 Measure Type
      Outcome
      1.6 Composite Measure
      No
      1.7 Electronic Clinical Quality Measure (eCQM)
      No
      1.8 Level Of Analysis
      Facility
      1.9 Care Setting
      Home Health
      1.10 Measure Rationale

      Many patients who receive home health care are recovering from an injury or illness and may have difficulty walking or moving around safely. They may need help from a person or special equipment (like a walker or cane) to accomplish this activity. Home health care staff can encourage patients to be as independent as possible and can evaluate patients’ needs for, and teach them how to use, special devices or equipment to help increase their ability to perform some activities without the assistance of another person. Safe ambulation and mobility are critical to being able to remain at home. Improving functional status such as a patient’s ability to perform ambulation/locomotion, contributes to quality of life and allows them to live safely and as long as possible in their own environment. Getting better at walking or moving around may be a sign that they are meeting the goals of their care plan or that their health status is improving. Recovering independence in walking or moving around with assistive devices is often a rehabilitative goal for home health patients, making it a reasonable evaluation indicator of effective and high-value home health care.

      1.11 Measure Webpage
      https://www.cms.gov/medicare/quality/home-health/home-health-quality-measures
      1.20 Testing Data Sources
      Standardized Patient Assessments
      1.25 Data Sources

      https://www.cms.gov/medicare/quality/home-health/oasis-data-sets

       

      The reporting of quality data by home health agencies (HHAs) is mandated by Section 1895(b)(3)(B)(v)(II) of the Social Security Act (“the Act”).  Outcome and Assessment Information Set (OASIS) reporting is mandated in the Medicare regulations at 42 C.F.R.§484.250(a), which requires HHAs to submit OASIS assessments to meet the quality reporting requirements of section 1895(b)(3)(B)(v) of the Act. It is important to note that to calculate quality measures from OASIS data, there must be a complete quality episode, which requires both a Start of Care (initial assessment) or Resumption of Care OASIS assessment and a Transfer or Discharge OASIS assessment.

    • 1.14 Numerator

      Number of home health episodes of care where the value recorded on the discharge assessment indicates less impairment in ambulation locomotion at discharge than at start (or resumption) of care.

      1.14a Numerator Details

      The number of home health episodes of care from the denominator in which the value recorded for the OASIS item M1860 (“Ambulation/Locomotion”) on the discharge assessment is numerically less than the value recorded on the start (or resumption) of care assessment, indicating less impairment at discharge compared to start/resumption of care.

    • 1.15 Denominator

      Number of home health episodes of care ending with a discharge from the agency during the reporting period, other than those covered by generic or measure-specific exclusions.

      1.15a Denominator Details

      Home health episodes ending with a discharge during the reporting period (M0100[2]=09), other than those covered by generic or measure-specific exclusions.

    • 1.15b Denominator Exclusions

      All home health episodes for which the patient, at start/resumption of care, was able to ambulate/locomote independently (M1860[1] = 00), or the patient was nonresponsive (M1700[1] = 04 or M1710[1] = NA or M1720[1] = NA), or the episode is covered by the generic exclusions (see following section).

      1.15c Denominator Exclusions Details

      Home health episodes of care for which (1) at start/resumption of care, OASIS item M1860 "Ambulation/ Locomotion" = 0, indicating that the patient was able to ambulate independently; or (2) at start/resumption of care, OASIS-E item M1700 "Cognitive Functioning" is 4, or M1710 "When Confused" is NA, or M1720 "When Anxious" is NA, indicating the patient is non-responsive; or (3) The patient did not have a discharge assessment because the episode of care ended in transfer to inpatient facility or death at home; or (4) the episode is covered by one or more of the generic exclusions:

       

      1. Pediatric home health patients (less than 18 years of age).
      2. Home health patients receiving maternity care only. 
      3. Home health patients receiving non-skilled care only. 
      4. Home health patients for which neither Medicare nor Medicaid are a payment source. 
      5. The episode of care does not end during the reporting period. 
      6. If the home health agency sample includes fewer than 20 episodes after all other patient-level exclusions are applied, or if the agency has been in operation less than six months, then the data is suppressed from public reporting on Home Health Compare.
      7. Hospice exclusion: Episodes of care that end in a non-institutional hospice on or after January 1, 2023 are excluded: M2420 “Discharge Disposition” is 3 and M0100 “Reason For Assessment” is 9.

       

      Table 1 (see supplemental attachment page 3) provides the episode counts by exclusion criterion for episodes of care that started and ended in CY 2022. In CY 2022, 1,658,640 episodes of care were excluded from the denominator for Improvement in Ambulation/Locomotion (#0167) due to meeting at least one exclusion criterion. Approximately 94,000 episodes of care in CY 2022 ended in a discharge to non-institutional hospice. This exclusion criterion is only applicable to episodes of care ending on or after January 1, 2023 and is therefore not listed in Table 1 (see supplemental attachment page 3).

    • 1.18 Calculation of Measure Score

      1. Define an episode of care (the unit of analysis): Data from matched pairs of OASIS assessments for each episode of care (start or resumption of care paired with a discharge or transfer to inpatient facility) are used to calculate individual patient outcome measures.

       

      2. Identify target population: All quality episodes of care ending during a specified time interval (usually a period of twelve months), subject to generic and measure-specific exclusions. Cases meeting the target outcome are those where the patient is more independent in ambulation/mobility at discharge than at start/resumption of care: M1860_CRNT_AMBLTN [2] < M1860_CRNT_AMBLTN [1].

       

      3. Aggregate the Data: The observed outcome measure value for each home health agency is calculated as the percentage of cases meeting the target population (denominator) criteria that meet the target outcome (numerator) criteria.

       

      4. Risk Adjustment: The expected probability for a patient is calculated using the following formula:

      P(x) = 1/(1 + e-(a+Σbixi))

      Where:

      P(x) = predicted probability of achieving outcome x 

      a = constant parameter listed in the model documentation 

      bi = coefficient for risk factor i in the model documentation 

      xi = value of risk factor i for this patient

       

      Predicted probabilities for all patients included in the measure denominator are then averaged to derive an expected outcome value for the home health agency. This expected value is then used, together with the observed (unadjusted) outcome value and the expected value for the national population of patients for the same data collection period, to calculate a risk-adjusted outcome value for the home health agency. The formula for the adjusted value of the outcome measure is as follows:

      X(Ara) = X(Aobs) + X(Nexp) - X(Aexp)

      Where:

      X(Ara) = Agency risk-adjusted outcome measure value 

      X(Aobs) = Agency observed outcome measure value 

      X(Aexp) = Agency expected outcome measure value 

      X(Nexp) = National expected outcome measure value

      If the result of this calculation is a value greater than 100%, the adjusted value is set to 100%. Similarly, if the result is a negative number the adjusted value is set to zero.

      1.13a Data dictionary not attached
      Yes
      1.17 Measure Score Interpretation
      Better quality = Higher score
      OLD 1.12 MAT output not attached
      Attached
      1.26 Minimum Sample Size

      Not applicable.

      1.19 Measure Stratification Details

      The measure is not stratified. 

      1.16 Type of Score
      Rate/proportion
    • 2.1 Attach Logic Model
      0167_Logic Model.pdf
      2.2 Evidence of Measure Importance

      Many patients who receive home health care are recovering from injury, surgery or an illness that affects their ability to safely ambulate in their home environment and beyond. Decreased ability or difficulty with ambulation can lead to an increased risk of falls, hospitalization, and functional decline which also increases the risk of becoming homebound, particularly in older adults (Leppä et al., 2021; Meijers et al., 2012; Robinson et al., 2022). Difficulty with ambulation is one of the main reasons that patients are referred to post-acute care services like home health (Prvu Bettger et al., 2015). Mobility directly impacts performance of activities of daily living (ADLs) like transferring, toileting, and bathing as well as instrumental activities of daily living (IADLs) like shopping, preparing meals, and doing housework. Ambulation is an essential part of home health patients’ safety, contributes to quality of life and allows them to live as long as possible in their home environment. Interventions offered by home health such as physical and occupational therapy are effective strategies for helping patients maintain or improve their ability to ambulate safely in the home. The utilization of physical therapy services in home health is associated with an increased likelihood of being able to successfully be discharged into the community without hospital readmission (Knox et al., 2022). Measures related to ambulation and ADL function have been shown to be related to the overall quality of a home health agency and can help detect disparities in care for populations using home health services (Chase et al., 2018; Fashaw-Walters et al., 2022).

       

      Chase, J.-A. D., Huang, L., Russell, D., Hanlon, A., O’Connor, M., Robinson, K. M., & Bowles, K. H. (2018). Racial/ethnic disparities in disability outcomes among post-acute home care patients. Journal of aging and health, 30(9), 1406-1426. 

       

      Fashaw-Walters, S. A., Rahman, M., Gee, G., Mor, V., White, M., & Thomas, K. S. (2022). Out Of Reach: Inequities In The Use Of High-Quality Home Health Agencies: Study examines inequities in the use of high-quality home health agencies. Health Affairs, 41(2), 247-255. 

       

      Knox, S., Downer, B., Haas, A., & Ottenbacher, K. J. (2022). Home health utilization association with discharge to community for people with dementia. Alzheimer's & Dementia: Translational Research & Clinical Interventions, 8(1), e12341. 

       

      Leppä, H., Karavirta, L., Rantalainen, T., Rantakokko, M., Siltanen, S., Portegijs, E., & Rantanen, T. (2021). Use of walking modifications, perceived walking difficulty and changes in outdoor mobility among community-dwelling older people during COVID-19 restrictions. Aging Clinical and Experimental Research, 33(10), 2909-2916. 

       

      Meijers, J. M. M., Halfens, R., Neyens, J. C., Luiking, Y., Verlaan, G., & Schols, J. (2012). Predicting falls in elderly receiving home care: the role of malnutrition and impaired mobility. The journal of nutrition, health & aging, 16(7), 654-658. 

       

      Prvu Bettger, J., McCoy, L., Smith, E. E., Fonarow, G. C., Schwamm, L. H., & Peterson, E. D. (2015). Contemporary trends and predictors of postacute service use and routine discharge home after stroke. Journal of the American Heart Association, 4(2), e001038. 

       

      Robinson, T. N., Carmichael, H., Hosokawa, P., Overbey, D. M., Goode, C. M., Barnett Jr, C. C., . . . Jones, T. S. (2022). Decreases in daily ambulation forecast post-surgical re-admission. The American Journal of Surgery, 223(5), 857-862.

    • 2.6 Meaningfulness to Target Population

      The public can comment on the home health quality program when a notice of proposed rulemaking is published as well as through the consensus-based entity public commenting. No comments have been received during this time period regarding this measure. The target of this performance-based measure is the Medicare-certified home health agency. Functional status was confirmed as a domain of importance for quality measurement at a recent Technical Expert Panel (TEP).

    • 2.4 Performance Gap

      Improvement in Ambulation/Locomotion (#0167) is calculated using CMS’s Home Health Quality Reporting Program’s assessment tool, the Outcome and Assessment Information Set (OASIS). All components of the measure are defined using data from the OASIS, including the numerator, denominator, exclusions, and risk factors. The measure is risk adjusted to account for patient characteristics at the start of care or resumption of care (SOC/ROC). The denominator consists of unique quality episodes, i.e. a SOC/ROC assessment paired with an end of care (EOC) assessment.

       

      While all the data used to report results in this form are derived from the OASIS, the periods used to generate results vary. Trends are presented from calendar year 2019 (CY 2019) to CY 2022. We restrict descriptive characteristics, reliability, and validity to CY 2022, the most recent calendar year of data currently available. The results generated for risk adjustment use CY 2021 data, the data used during the most recent maintenance reevaluation and risk adjustment update.

       

      Table 2 (see supplemental attachment page 8) presents performance for Improvement in Ambulation/Locomotion (#0167) from CY 2019 to CY 2022 among home health agencies that exceed the public reporting threshold of at least 20 quality episodes of care. Overall, mean performance has been trending upwards, with a low of 0.760 in CY 2019 and a high of 0.798 in CY 2022. The lower and upper bounds of the interquartile range have also increased with each year. Despite the steady increases year-over-year, there remains a performance gap for Improvement in Ambulation/Locomotion (#0167). Fewer than 70 percent of quality episodes exhibit improvement among the lowest quartile of home health agencies, and between 20 and 25 percent of quality episodes fail to improve for the average home health agency (see Tables 2 and 3 on supplemental attachment page 8).

       

      Aside from CY 2020, which was affected by the COVID public health emergency reporting requirements, roughly 4.4 million quality episodes are used to score the measure. The measure is publicly reported for over 7,000 home health agencies.

       

      Table 3 (see supplemental attachment page 8) presents performance for Improvement in Ambulation/Locomotion (#0167) for CY 2022 by home health agency size among home health agencies that exceed the public reporting threshold. From bottom decile to top decile, the distribution in performance is tight with a minimum at Decile 1 of 0.678 and a maximum at Decile 9 of 0.863, a 0.185 difference in mean score. Smaller home health agencies perform worse on the measure, with Deciles 1 to 4 performing lower than the overall mean score and Deciles 5 to 10 performing higher.

      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 0.798 0.000 0.678 0.729 0.753 0.779 0.796 0.824 0.839 0.858 0.863 0.861 1.000
      N of Entities 7,628 776 757 770 749 762 765 762 763 762 762
      N of Persons / Encounters / Episodes 4,407,460 21,568 37,470 59,605 87,796 133,995 199,740 299,379 464,732 778,829 2,324,346
    • 3.1 Feasibility Assessment

      This is a long-standing measure in the Home Health Quality Reporting Program. We have not identified any feasibility issues for this measure. The Outcome and Assessment Information Set (OASIS) items for this measure must be completed as part of the OASIS assessment. Responses to the questions are required for data submission to the CMS system. The OASIS burden estimates are reported through rulemaking.

      3.3 Feasibility Informed Final Measure

      OASIS data collection and submission are a requirement of the Medicare Home Health Conditions of Participation. Functional assessment is conducted as part of usual clinical practice, and information on ambulation status used to calculate this measure is recorded in the relevant OASIS items embedded in the home health agency’s clinical assessment. OASIS data are collected by the home health agency during the care episode 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 OASIS-E was implemented January 1, 2023. No changes have been made to the measure specifications.

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

      Table 4 (see supplemental attachment page 10) identifies the publicly reporting home health agencies by size and Census region. This distribution of home health agencies is used for reliability and validity testing. 7,628 home health agencies have 20 or more quality episodes starting and ending in CY 2022. 

      4.1.1 Data Used for Testing

      While all the data used to report results in this form are derived from the OASIS, the periods used to generate results vary. We restrict descriptive characteristics, reliability, and validity to CY 2022, the most recent calendar year of data currently available, and the results generated for exclusions and risk adjustment use CY 2021 data, the data used during the most recent maintenance reevaluation and risk adjustment update.

      4.1.4 Characteristics of Units of the Eligible Population

      Table 5 (see supplemental attachment page 10) identifies the patient characteristics of quality episodes treated by publicly reporting home health agencies. Characteristics are reported by sex, race, age, and Census region. This distribution of quality episodes is used for reliability and validity testing. 4,407,460 quality episodes started and ended in CY 2022 and met the denominator exclusion and public reporting requirements. 

      4.1.2 Differences in Data

      Not applicable.

    • 4.2.1 Level(s) of Reliability Testing Conducted
      Patient or Encounter-Level (e.g., inter-abstractor reliability)
      Accountable Entity-Level (e.g., signal-to-noise analysis)
      4.2.2 Method(s) of Reliability Testing

      Below, we address reliability at two levels: (1) the performance measure and (2) the underlying data element: OASIS item M1860 (Ambulation/Locomotion: Current ability to walk safely, once in a standing position, or use a wheelchair, once in a seated position, on a variety of surfaces).

       

      Reliability of the Performance Measure Score: Abt measured the extent to which differences in each quality measure were due to actual differences in agency performance versus variation that arises from measurement error.  Statistically, reliability depends on performance variation for a measure across agencies, the random variation in performance for a measure within an agency’s panel of attributed beneficiaries, and the number of beneficiaries attributed to the agency.  High reliability for a measure suggests that comparisons of relative performance across agencies are likely to be stable over different performance periods, and that the performance of one agency on the quality measure can confidently be distinguished from another. Potential reliability values range from zero to one, where one (highest possible reliability) means that all variation in the measure’s rates is the result of variation in differences in performance across agencies, while zero (lowest possible reliability) means that all variation is a result of measurement error.

       

      To assess measure reliability, Abt used a split-half reliability test. First, we randomly divided each publicly reporting home health agency’s quality episodes into two separate equally sized groups. Then, we calculated risk-adjusted performance rates for each group. Then, using the paired performance rates, we calculated the absolute agreement intra-class correlation statistic or ICC(2,1) with a Spearman-Brown correction to address the artificial reduction in home health agency size by half. Additionally, we recalculate ICC(2,1) within each agency size decile, where size is measured as the number of quality episodes treated after denominator and public reporting exclusions.

       

      • Reliability of the Underlying Data Element: The measure is calculated by comparing patient functioning at the start and end of a home health quality episode, as reported by the home health OASIS data set. Patient ability to ambulate is based on response to OASIS item M1860 (Ambulation/Locomotion: Current ability to walk safely, once in a standing position, or use a wheelchair, once in a seated position, on a variety of surfaces): 
      1. Able to independently walk on even and uneven surfaces and negotiate stairs with or without railings (i.e., needs no human assistance or assistive device). 
      2. With the use of a one-handed device (e.g. cane, single crutch, hemi-walker), able to independently walk on even and uneven surfaces and negotiate stairs with or without railings. 
      3. Requires use of a two-handed device (e.g., walker or crutches) to walk alone on a level surface and/or requires human supervision or assistance to negotiate stairs or steps or uneven surfaces. 
      4. Able to walk only with the supervision or assistance of another person at all times. 
      5. Chairfast, unable to ambulate but is able to wheel self independently. 
      6. Chairfast, unable to ambulate and is unable to wheel self. 
      7. Bedfast, unable to ambulate or be up in a chair.

       

      In 2016 and 2017, Abt and partners conducted a field test of new and existing OASIS items on 12 home health agencies in four states for 213 home health patients.[1]Home health registered nurses and physical therapists, trained by the study team, collected data during home visits at start of care (SOC) or resumption of care (ROC), and/or at discharge. Follow-up visits were conducted within 24 hours of the initial field test visit, by a different registered nurse or physical therapist to test interrater reliability. M1860 was one of the existing OASIS items that was tested. Interrater reliability was assessed for SOC or ROC and at Discharge with a linear weighted kappa. The number patients for which inter-rater reliability could be tested was 105 at SOC/ROC and 83 at discharge.

       

      The kappa statistic is generally considered to be the “gold standard” statistic associated with item reliability as it factors in the possibility of chance agreement. Kappa values are reported as decimal values between 0.00 (poor) and 1.00 (perfect). These can be interpreted using the following seven categories:[2]

      • Poor < 0.10
      • Slight = 0.10 to 0.20
      • Fair = 0.21 to 0.40
      • Moderate = 0.41 to 0.60
      • Substantial = 0.61 to 0.80
      • Near perfect = 0.81 to 0.99
      • Perfect = 1.00

       

      [1] Abt Associates (2018). “OASIS Field Test Summary Report: Outcome and Assessment Information Set (OASIS) Quality Measure Development and Maintenance Project.”

      [2] Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics, 1977. 33(1):159-174.

      4.2.3 Reliability Testing Results

      Reliability of the Performance Measure Score: The table below summarizes the distribution of reliability scores for the 7,628 home health agencies exceeding the public reporting threshold of at least 20 eligible quality episodes of care.

       

       Reliability of the Underlying Data Element: The inter-rater reliability (weighted kappa) values for M1860 Ambulation/Locomotion was 0.43 at SOC/ROC and 0.67 at discharge. 

      Table 2. Accountable Entity-Level Reliability Testing Results
      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 0.933 0.853 0.903 0.929 0.951 0.955 0.961 0.970 0.968 0.983 0.988
      Mean Performance Score 0.798 0.000 0.678 0.729 0.753 0.779 0.796 0.824 0.839 0.858 0.863 0.861 1.000
      N of Entities 7,628 776 757 770 749 762 765 762 763 762 762
      N of Persons / Encounters / Episodes 4,407,460 21,568 37,470 59,605 87,796 133,995 199,740 299,379 464,732 778,829 2,324,346
      4.2.4 Interpretation of Reliability Results

      Reliability of the Performance Measure Score: The ICC(2,1) statistics exceed 0.800, even within the decile with the smallest home health agencies, suggesting strong reliability and acceptability for drawing inferences about home health agencies. 

       

      Reliability of the Underlying Data Element   Based on the weighted kappa statistics the inter-rater reliability indicated moderate agreement at SOC/ROC (0.43) and substantial agreement at discharge (0.67). Given the scale of the response to this item (seven possible responses), we conclude that the item achieves sufficient reliability. 

    • 4.3.1 Level(s) of Validity Testing Conducted
      Patient or Encounter-Level (e.g., sensitivity and specificity)
      Accountable Entity Level (e.g., criterion validity)
      4.3.2 Type of accountable entity-level validity testing conducted
      Empirical validity testing at the accountable entity-level (e.g., criterion validity, construct validity, known groups analysis)
      4.3.3 Method(s) of Validity Testing

      Below, we address validity at two levels: (1) the performance measure and (2) the underlying data element - OASIS item M1860 (Ambulation/Locomotion: Current ability to walk safely, once in a standing position, or use a wheelchair, once in a seated position, on a variety of surfaces). 

       

      • Validity of the Performance Measure Score:  Abt assessed the convergent validity of the measure. Convergent validity refers to the extent to which measures that are designed to assess the same construct are related to each other. To evaluate the convergent validity of the measure, Abt calculated the Spearman rank correlations of the Improvement in Ambulation/Locomotion (#0167) measure with other relevant OASIS-based measures and the fee-for-service (FFS) claims-based measure Discharge to Community (#3477) measure. 

      The Spearman rank correlation assesses the statistical dependence between the rankings of two variables. In our case, we rank home health agencies according to the Improvement in Ambulation/Locomotion (#0167) measure and other home health agency-level measures. High correlation or association between the Improvement in Ambulation/Locomotion (#0167) measure and other functional measures of improvement would be expected and desired. Low correlation would indicate that the measure may not be valid (is not measuring what we think it is measuring). We only expect a positive correlation with Discharge to Community (#3477), as the population differs by payer (FFS versus FFS, Medicare Advantage, and Medicaid) and the numerator criteria measure different outcomes (successful discharge to community versus improvement in function).

       

      • Validity of the Underlying Data Element: The OASIS item M1860: Ambulation/Locomotion has been used continuously as part of the OASIS since 2001. The behaviorally benchmarked responses were updated and improved based on input from clinicians and technical experts. The OASIS instrument has been published in the Federal Register for comment (both items and measures based off those items) and no objections or suggestions for revision have been noted regarding the response options.

      The original OASIS item was originally carefully designed for measuring and ultimately enhancing patient outcomes as part of the National OBQI Demonstration project (1995 – 2000). OASIS items were derived by first specifying a set of patient outcomes considered critical by home care experts (e.g., nurses, physicians, therapists, social workers, administrators) for evaluating the effectiveness of care. These outcomes were chosen from the most important domains of health status addressed by home care providers. OASIS data items were developed, tested in hundreds of agencies, and refined for measuring outcomes to evaluate and enhance the effectiveness of home care. OASIS data items and measurement methods were reviewed by multidisciplinary panels of research methodologists, clinicians, home care managers, and policy analysts. Several tests of validity were conducted for each OASIS item, including Ambulation/Locomotion. Validity testing included: 

      1) Consensus validity by expert researcher/clinical panels for outcome measurement and risk factor measurement

      2) Consensus validity by expert clinical panels for patient assessment and care planning

      3) Criterion or convergent/predictive validity for outcome measurement/risk factor measurement

      4) Convergent/predictive validity: case mix adjustment for payment

      5) Validation by patient assessment and care planning

       

      Descriptions for these validation assessments are taken from the “Volume 4: OASIS Chronicle and Recommendation” OASIS and Outcome-Based Quality Improvement in Home Health Care, November 2001, Center for Health Services Research, University of Colorado Health Sciences Center, Denver, CO.

      4.3.4 Validity Testing Results

      Validity of the Performance Measure Score: Table 7 (see supplemental attachment page 15) shows the Spearman rank correlations of the Improvement in Ambulation/Locomotion (#0167) measure with other publicly reported measures of home health quality derived from OASIS assessments and Medicare Fee-for-Service (FFS) claims.

       

      Validity of the Underlying Data Element: As noted above, 

      1. Consensus validity: The item was reviewed by panels of researchers and clinicians and was recommended for measuring patient outcomes relevant to home health care provision and quality measurement, or for risk adjustment of outcome analyses.
      2. Consensus validity by expert clinical panels for patient assessment and care planning: The item was reviewed by a panel of clinical experts and was recommended for inclusion in a core set of data items for patient assessment and care planning.
      3. Criterion or convergent/predictive validity for outcome measurement/risk factor measurement: The item was tested empirically for use in conjunction with outcome measures or risk factors predictive of patient outcomes. The item was found to be related to other indicators of health status and patient outcomes in a statistically significant and clinically meaningful way. 
      4. Convergent/predictive validity: Case-mix adjustment for payment: The item was tested and is used in the grouping algorithm that, in part, determines the per-episode payment to home health agencies for care provided under the Medicare home health benefit.
      5. Validation by patient assessment and care planning: The item has been used by clinicians for patient assessment and care planning in several hundred home health agencies and has been reported by practicing clinicians to be effective and useful for these purposes.

       

      Results of these validation assessments are taken from the “Volume 4: OASIS Chronicle and Recommendation” OASIS and Outcome-Based Quality Improvement in Home Health Care, November 2001, Center for Health Services Research, University of Colorado Health Sciences Center, Denver, CO.

      4.3.5 Interpretation of Validity Results

      Validity of the Performance Measure Score: As detailed in Table 7 (see supplemental attachment page 15), the Improvement in Ambulation/Locomotion (#0167) measure displays a statistically significant positive correlation with several publicly reported measures that similarly assess patient functioning and Discharge to Community (#3477), which lends evidence to the measure’s validity. It may be that strong performance on the other OASIS-based measures directly leads to an improvement in ambulation/locomotion. It may also be the case that high quality agencies perform well on both the Improvement in Ambulation/Locomotion (#0167) measure and other OASIS-based measures of patient functioning and communication due to cultural or organization-level factors.

       

      Validity of the Underlying Data Element: Item validity was established based on results of testing described above. In addition, the item was also reviewed as part of the OMB/PRA review process for the most recent OASIS data set revision which allowed for two national comment periods (60 days and 30 days) wherein the face validity of the item was supported by the comments received.

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

      A patient’s improvement in ambulation/locomotion is dependent on a variety of factors, including social risk factors, clinical and behavioral risk factors, and access to care. The conceptual model shown in Figure 2 (see supplemental attachment page 17) is inspired by a similar conceptual model proposed by the Committee on Accounting for Socioeconomic Status in Medicare Payment Programs. In this conceptual model, social risk factors influence access to care and clinical and behavioral risk factors, as well as the measure itself. In turn, clinical and behavioral risk factors influence health care and resource use, access to care, and the measure itself. Access to care only influences health care and resource use. Finally, the  affects the measure through interventions like skilled nursing, therapy, and care coordination. These interventions may be able to address some social, clinical, or behavioral risk factors, in part if not fully. 

       

      Improvement in Ambulation/Locomotion (#0167) attempts to measure a home health agency’s ability to improve patient ambulation/locomotion while the patient is in its care; however, because certain factors are outside of its control, we risk-adjust the measure. Risk adjustment is used to promote incentives for home health agencies to provide the same care to patients regardless of patient characteristics at SOC/ROC.

       

      The risk factors that can be fully addressed should not be included in the risk adjustment model because the home health agency is expected to be responsible for addressing that risk factor. For instance, if all other risk factors are identical, a home health agency is expected to provide two patients with identical quality care regardless of race or ethnicity. 

       

      By contrast, a patient who is living alone will have different needs than a patient who lives in a congregate setting. While a home health agency is expected to adapt its care to different living situations, it is not expected to address all the needs for a patient living alone, like having professionals on staff readily available during emergencies. Similarly, a home health agency is not expected to influence the patient’s clinical and behavioral status at SOC/ROC. As a result, relevant clinical and behavioral risk factors to ambulation/locomotion are included in the risk adjustment model.

       

      The risk adjustment methodology used is based on logistic regression analysis which results in a statistical prediction model for each outcome measure.  For each  patient who is included in the denominator of the outcome measure, the model is used to calculate the predicted probability that the patient will experience the outcome. The predicted probability for a patient is calculated using the following formula:

      P(x) = 1/(1 + e-(a+Σbixi))

      Where:

      P(x) = predicted probability of achieving outcome x 

      a = constant parameter listed in the model documentation 

      bi = coefficient for risk factor i in the model documentation 

      xi = value of risk factor i for this patient

      Predicted probabilities for all patients included in the measure denominator are then averaged to derive an expected outcome value for the agency.  This expected value is then used, together with the observed (unadjusted) outcome value and the expected value for the national population of  patients for the same data collection period, to calculate a risk-adjusted outcome value for the .  The formula for the adjusted value of the outcome measure is as follows:

      X(Ara) = X(Aobs) + X(Nexp) - X(Aexp)

      Where:

      X(Ara) = Agency risk-adjusted outcome measure value 

      X(Aobs) = Agency observed outcome measure value 

      X(Aexp) = Agency expected outcome measure value 

      X(Nexp) = National expected outcome measure value

      If the result of this calculation is a value greater than 100%, the adjusted value is set to 100%. Similarly, if the result is a negative number the adjusted value is set to zero.

       

      For a more detailed summary of risk adjustment specifications including definitions of the risk factors, please consult the Home Health Quality Reporting Program Risk Adjustment Technical Specifications 2024 (PDF).[1]


      [1] https://www.cms.gov/files/document/risk-adjustment-technicalspecifications2024.pdf

      4.4.3 Risk Factor Characteristics Across Measured Entities

      Table 8 (see supplemental attachment pages 17-20) shows the mean and standard deviation of the observed value for Improvement in Ambulation/Locomotion (#0167) by risk factor in CY 2022.

      4.4.4 Risk Adjustment Modeling and/or Stratification Results

      The risk adjustment model was developed using OASIS national repository data from assessments submitted between January 1, 2021, and December 31, 2021 (~6.2 million quality episodes). The population of 6.2 million quality episodes for calendar year 2021 was split in half such that 3.1 million quality episodes were used as a developmental sample and 3.1 million quality episodes were used as a validation sample. The following process was used to identify unique contributing risk factors to the prediction model: 

       

      1. Risk factors were identified based on OASIS items that will remain or will be added following the transition to OASIS-E. The statistical properties of the items were examined to specify risk factors (e.g., item responses were grouped when there was low prevalence of certain responses). Team clinicians then reviewed all risk factors for clinical relevance and redefined or updated risk factors as necessary. These risk factors were divided into 31 content focus groups (e.g., functional status, Hierarchical Condition Categories, etc.). Where possible, risk factors were defined such that they flagged mutually exclusive subgroups within each content focus group. When modelling these risk factors, the exclusion category was set to be either the risk factor flag for most independent or the most frequent within each content focus group. 
      2. A logistic regression specification was used to estimate coefficients among the full set of candidate risk factors. Those risk factors that are statistically significant at probability <0.0001 are flagged for further review in Step 3.
      3. Each risk factor flagged in Step 2 was reviewed to determine which one of the two groups its content focus group resided. Either its content focus group was explicitly tiered by increasing severity or it was not. This classification determined which risk factor covariates were kept and which were dropped from the final risk adjustment specification. For content focus groups that are explicitly tiered by increasing severity, either all risk factors are included within a content focus group or none of them. For example, if response option levels 1 and 2 for M1800 Grooming were statistically significant at a probability of <0.0001 for a particular outcome, then response option level 3 for M1800 Grooming was added to the list even if it was not statistically significant. If none of the risk factors within an explicitly tiered content focus group was statistically significant at <0.0001, the entire content focus group was removed from the model.
      4. A logistic regression was computed on the list of risk factors kept after Step 3 above. 
      5. Goodness of fit and reliability statistics (McFadden’s R2, C-statistic, and Intra-Class Correlation) were calculated to measure how well the predicted values generated by the prediction model were related to the actual outcomes. Separate bivariate correlations were constructed between the risk factors and the outcomes to confirm the sign and strength of the estimated coefficients in the logistic model. 
      6. The initial model was reviewed by a team of at least three experienced home health clinicians. Each risk factor was reviewed for its clinical plausibility. Clinicians were asked about the direction indicated by the coefficient in the risk adjustment model and how it compares to their perceived bivariate relationship given their experience treating patients in the home. Risk factors that were not clinically plausible were revised or eliminated if revisions were not possible. 
      7. The risk factors that were deemed not clinically plausible were revised or eliminated, and Steps 3, 4, and 5 in this process were repeated. The resulting logistic regression equation was designated as the risk adjustment model for the outcome. 
      8. The risk adjustment model was applied to the validation sample and goodness of fit statistics were computed. The statistics were similar to the goodness of fit statistics computed with the development sample. As additional testing, home health agencies were stratified across several observable characteristics, and the distributions of the risk-adjusted outcomes were checked to confirm that values remained similar across strata.

       

      Using CY 2021 data, the updated risk adjustment model specification yielded a McFadden’s R2 of 0.156 and a C-Statistic of 0.785 on the validation sample. Please refer to Appendix A (see supplemental attachment pages 27-32) for details on the risk factor coefficients, including standard deviations and p-values.

      4.4.5 Calibration and Discrimination

      We calibrated the most recent risk adjustment update by comparing changes in performance for home health agencies overall and by important subgroups (urbanicity/rurality, size, and share of quality episodes with non-white patients) to the prior risk adjustment specification. The results in Figure 3 (see supplemental attachment page 23) below indicate that most home health agencies overall and by subgroup perform equally well based on the updated risk adjustment model compared to the prior model, ranging between 86 percent among urban home health agencies and 88.6 percent among home health agencies with the highest percentage of non-white patients. 

      4.4.6 Interpretation of Risk Factor Findings

      A patient’s improvement in ambulation/locomotion is dependent on a variety of factors, including social risk factors, clinical and behavioral risk factors, and access to care. Risk adjustment is used to promote incentives for home health agencies to provide the same care to patients regardless of patient characteristics at SOC/ROC. All risk factors that are expected to impact the ability to improve in ambulation/locomotion between SOC/ROC and EOC and are based on OASIS items that will remain or will be added following the transition to OASIS-E are considered for model inclusion. The statistical properties of the OASIS items were examined to specify risk factors (e.g., item responses were grouped when there was low prevalence of certain responses). Team clinicians then reviewed all risk factors for clinical relevance and redefined or updated risk factors, as necessary. These risk factors were divided into 31 content focus groups (e.g., functional status, Hierarchical Condition Categories, etc.). The final risk adjustment model was selected by considering model fit statistics, statistical significance of risk factor coefficients, and clinical plausibility of magnitude and direction of coefficients. Please refer to other sections for details on the risk factor selection methodology. 

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

      Across home health agencies, we compared Improvement in Ambulation/Locomotion (#0167) CY 2022 performance by subgroups for urbanicity/rurality, size, and share of quality episodes with non-white patients (see Figure 4 on supplemental attachment page 24). 

       

      We define urbanicity as home health agencies located within a Core-Based Statistical Area (CBSA) as defined by the Office of Management and Budget (OMB). Urban home health agencies perform on average slightly worse at 0.797 than rural home health agencies at 0.810. 

       

      We define large home health agencies as home health agencies with quality episode counts in the top quartile for CY 2022 and small home health agencies as being in the bottom quartile. Large home health agencies perform much better at 0.862 than small home health agencies at 0.713.

       

      For “Highest Quartile Non-White” home health agencies, we use the M0140: Race/Ethnicity OASIS item to identify the patient’s race/ethnicity as non-white. Home health agencies in the lowest quartile share of quality episodes with non-white patients perform worse at 0.717 than home health agencies in the highest quartile at 0.817.

       

      The results, particularly for home health agency size and percentage of non-white patients, indicate a performance gap across home health agencies by subgroup. CMS is monitoring the persistence of these gaps and investigating next steps for addressing through reevaluated measure specifications or other policies (see https://www.cms.gov/medicare/quality/home-health-quality-reporting-program/home-health-qrp-health-equity for additional resources).

    • 6.1.1 Current Status
      Yes
      6.1.3 Current Use(s)
      Public Reporting
      Payment Program
      Quality Improvement with Benchmarking (external benchmarking to multiple organizations)
      Quality Improvement (Internal to the specific organization)
      • Name of the program and sponsor
        Public Reporting & Home Health Star Ratings; CMS
        URL of the program
        https://www.medicare.gov/care-compare/?providerType=HomeHealth%3B%20https%3A//w…
        Purpose of the program
        Quality improvement
        Geographic area and percentage of accountable entities and patients included
        Yes and see below
        Applicable level of analysis and care setting

        The Home Health Compare website is a federal government website managed by the Centers for Medicare & Medicaid Services (CMS). It provides information to consumers about the quality of care provided by Medicare-certified home health agencies throughout the nation. The measures reported on Home Health Compare include all Medicare-certified agencies with at least 20 home health quality episodes. In the period ending December 31, 2022, there were 7,747 such agencies (78.73 percent of the 9,840 agencies with at least one quality episode) that met the measure denominator criteria for reporting of Improvement in Ambulation/Locomotion. This included 4,443,810 episodes of care nationally.

         

         CMS’s Home Health Quality Initiative provides all Medicare-certified home health agencies with opportunities to use outcome measures for outcome-based quality improvement. The report allows agencies to benchmark their performance against other agencies across the state and nationally, as well as their own performance from prior time periods. 

    • 6.2.1 Actions of Measured Entities to Improve Performance

      All home health agencies with at least 20 qualifying quality episodes of care 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 system. There is an email box that home health agencies may submit questions to as well as a website on which the latest measure updates are posted. The OASIS Guidance Manual describes the OASIS-based reports that are available, report use(s), and provides guidance about OASIS and quality improvement. Home health agencies make use of these reports to monitor and improve the quality of care.  

      6.2.2 Feedback on Measure Performance

      Home health agencies receive quarterly measure reports on all their measures. There is an email box that home health agencies may submit questions to as well as a website on which the latest measure updates are posted. Because of the changes made to the OASIS in the OASIS-E version (effectively January 1, 2023), risk models for publicly reported outcome measures have been updated. 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

      The measure is important to report publicly. Although improvements in performance are small, home health agencies continue to improve overall, and for each subgroup measured over time. Performance gaps still exist, and potential future performance metrics are likely to reflect the expansion of the HHVBP Model. Publicly reported measure results illustrate variation in performance across home health agencies that may inform patient and family choice of a home health agency. 

       

      Figure 5 (see supplemental page 26) presents trends in risk-adjusted Improvement in Ambulation/Locomotion (#0167) by subgroup. In addition to overall improvement from CY 2019 to CY 2022, each subgroup improves. We expect improvement to be driven in part by the implementation of the Quality of Patient Care (QoPC) Star Rating beginning in July 2015 and the Home Health Value Based Purchasing (HHVBP) Model in 2016. Results prior to 2019 showed dramatic improvement (not shown), while improvement in 2019-2022 was smaller. Nonetheless, QoPC Star Rating and HHVBP still provide incentives for home health agencies to improve on this measure. We anticipate continued improvement as HHVBP expands nationwide in 2023. Data will not reflect this policy change, as we only report results through CY2022.

      6.2.5 Unexpected Findings

      We do not find any unexpected findings during implementation of this measure at this time.

    • Submitted by Olivia on Tue, 06/11/2024 - 14:37

      Permalink

      Hi! I'm Janice Tufte, patient partner. I didn't want to comment on all of them, but I think they all are important especially now that there's a little bit more investment and recognition about the issues that are going on in care facilities and nursing homes, post-acute care and long-term care facilities. I really feel that they're all something that should be addressed, and so unless I looked in more deeply into some of it, I would support all of these. Improvement in the next one or the 2 after this, I think, is extremely important. So I'm just going to comment on all right now.

      Organization
      Janice Tufte

      Submitted by Olivia on Tue, 06/11/2024 - 14:39

      Permalink

      I agree with Janice, 100%. All of these issues are very important. I have a close family friend, a childhood friend who's going through these things and she has MS. And they all hit these categories. So, thank you. I support it.

      Organization
      Shabina Khan

      Submitted by Olivia on Tue, 06/11/2024 - 14:40

      Permalink

      As a patient myself, I've had a lot of issues with these [activities captured in the measures], and I don't think people realize the significance and the impact that it can have on just our mental health and our care. So, I do want to just say that I appreciate all of these things, especially the bathing initiative. I've had sepsis in the past from a hospitalization. And so, I think, looking at these things proactively, is very important, but just wanted to lend my support and give a voice to this kind of a meeting where not too many people are commenting. So that's it. Thank you.

      Organization
      Christine Von Raesfeld

      Submitted by Olivia on Tue, 06/11/2024 - 14:43

      Permalink

      Hi Florence, patient partner. So, I'm just hoping that the data that's received from the measures will provide improvement for the patients. My experience being a caregiver, I don't know if this has been implemented or used, that the improvements for the ambulation, bathing, bed transferring, and management of their oral medications made an impact with the people that was providing caregiving for within my family. So, I'm hoping that the data that's being collected will improve their outcomes when it comes to these different measures especially, for what tools that are being used and the services that are being provided and the way they're being measured will actually achieve these outcomes. So that's what I'm hoping that these measures will actually achieve these outcomes. So, I don't think these are new, but we are just reassessing them? 

      • Correct. These four CBE (0167, 0174, 0175, and 0176) are all maintenance measures. 

       

      Maintenance measures. I'm hoping that there have been some additions added to the current maintenance measures that we're reviewing to make improvements from the last maintenance. That the last set of data that was collected, that we'll use that information to make improvements for these new set of measures. So, it's not just someone just providing these services and the patient never making any improvements. I'm hoping that these measurements have been improved so that we can actually see some improvements for the patients.

      • So, what measure changes have been made since the initial endorsement? And then, has there been any information on patient improvements over time? Did I capture that correctly?

       

      Yes.

      Organization
      Florence Thicklin (Committee member for Management of Acute Events and Chronic Conditions)

    • Importance

      Importance Rating
      Met
      Importance

      Strengths:

      • The measure assesses the extent to which patients’ ambulation/locomotion status improves from the start or resumption of care (SOC/ROC) to end of care (EOC).
      • The developer provides a logic model illustrating the process of improving patients’ ambulation/locomotion status from SOC/ROC to EOC. 
      • The logic model showcases the observed improvements that result from the combined support provided by the patient’s home health agency, caregivers, and the patient’s efforts in self-care.
      • The developer highlights the prevalence of difficulty with ambulation among patients receiving home health services. Decreased ability in ambulation heightens the risk of falls, hospitalizations, and functional decline.
      • The developer cites evidence on how ambulation directly affects the performance of activities of daily living (ADLs) and instrumental activities of daily living (IADLs), which are crucial to patient safety and quality of life.
      • The developer cites evidence on the impact of interventions such as physical and occupational therapy in helping patients maintain or enhance their ability to ambulate safely at home. 
        These therapies increase the likelihood of patients being discharged without hospital readmission.
      • The developer provides performance scores by decile: 
        The difference between the minimum (0.000) and maximum (1.000) scores, as well as the range of mean scores across deciles (0.678 to 0.863) suggests variations in performance among entities being measured.
      • The developer notes the alignment of the measure with the target population and the recognition of functional status as an important domain by a Technical Expert Panel (TEP), suggesting the measure’s utility and meaningfulness to Medicare-certified home health agencies.

      Limitations: 

      • None identified.

      Rationale: 

      • There is a business case for the measure along with supporting evidence for the importance of the measured outcomes with demonstrated gap in performance.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Strengths:

      • The developer notes this measure is part of the Home Health Quality Reporting Program and they did not identify any feasibility challenges.
      • The developer reports there were no issues regarding data availability, missing data, timing or frequency of data collection, patient confidentiality, or implementation.
      • The developer confirms that the information on ambulation status used to calculate this measure is part of the OASIS items embedded in the home health agency’s clinical assessment.
      • OASIS data are collected by the home health agency during the care episode and submitted electronically to CMS via the Internet Quality Improvement and Evaluation System (iQIES).
      • There are no proprietary components of this measure.

      Limitations:

      • None identified.

      Rationale:

      • There are no feasibility challenges, fees, or proprietary components of this measure.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Strengths:

      • The measure is clear and well defined. 
      • Data element reliability tested on OASIS item M1860 at start of care and at discharge. Kappa statistics are 0.43 and 0.67, respectively.
      • Entity-level reliability is calculated with data from 2022 across 7628 entities. The measure is risk-adjusted and a split-half reliability test is used, adjusted for agency size. Average entity-level reliability is >0.6 for all entity-size deciles. (The estimated reliability for the first decile is 0.853.)

      Limitations:

      • None identified.

      Rationale: 

      • The measure is well defined. Reliability was assessed at both the patient and entity level. Reliability statistics are above the established thresholds for most, if not all, entities.
      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Strengths: 

      • The developer assessed the convergent validity of the measure by calculating the Spearman Rank Correlations with 3 other OASIS-based measures assessing improvement in patient functioning, hypothesizing, and observing strong positive correlations (ρ = 0.75-0.85). 
      • The developer notes that this association does not distinguish if strong performance on one measure score leads to strong performance on another score, or if strong performance on multiple scores reflects quality of care.
      • Furthermore, the developer correlated the measure with the Discharge to Community measure that similarly assesses patient functioning, hypothesizing and observing a positive correlation (ρ = 0.2511).
      • In assessing item validity of the measure, the developer established consensus validity through review by panels of researchers and clinicians. This resulted in a recommendation of the data element for measuring patient outcomes relevant to home health care provision and quality measurement, or for risk adjustment of outcome analyses.
      • Expert clinical panels reviewed the data element and recommended its inclusion in a core set of data items for patient assessment and care planning.
      • The developer tested the data element for its criterion or convergent/predictive validity in measuring outcomes and risk factors. The results revealed statistically significant and clinically meaningful relationship with other indicators of health status and patient outcomes.
      • The developer notes the data element was tested and used in an algorithm that determines payment adjustments to home health agencies per episode of care under the Medicare home health benefit.
      • The developer notes that clinicians across numerous home health agencies report the data element’s efficacy in patient assessment and care planning.
      • The risk adjustment model was developed with 135 factors evaluated for statistical significance and clinical plausibility. C-statistic of 0.79 indicated good model performance and stratification of model scores by agency demographic subgroup resulted in similar performance.

      Limitations:

      • There is potential for biases or limitations in the composition of the expert panels. Additional details on the panel selection and deliberation methods would help asses the robustness of the consensus validity process.
      • The developer notes feedback from clinicians across numerous home health agencies on the efficacy of the data element in patient assessment and care planning but does not specific details of the feedback and real-world use.

      Rationale: 

      • The developer assessed measure validity using accountable entity-level empirical validity and data-element level validity. The interpretation of the empirical results supports an inference of validity.

      Equity

      Equity Rating
      Met
      Equity

      Strengths:

      • The developer compared performance for CY 2022 by subgroups for urbanicity/rurality, size, and share of quality episodes with non-white patients.
      • The developer reports that on average, urban home health agencies (0.797) performed slightly worse than rural home health agencies (0.810). 
      • Large home health agencies demonstrated better performance at 0.862 than small home health agencies at 0.713.
      • The developer used the M0140: Race/Ethnicity OASIS item to identify patients’ race/ethnicity as non-white. The developer reports that home health agencies with the lowest quartile share of quality episodes involving non-white patients performed worse at 0.717 than home health agencies in the highest quartile at 0.817.

      Limitations:

      • The share of quality episodes in non-white patients oversimplifies the complexity of racial and ethnic categorization. There may be important differences across various racial and ethnic groups that extend beyond the binary classification of white versus non-white.

      Rationale: 

      • The developer evaluated disparities in performance by subgroups for urbanicity/rurality, size, and share of quality episodes with non-white patients.

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Strengths: 

      • The developer indicates the measure is currently used in CMS’s Public Reporting and Home Health Star Ratings for outcome-based quality improvement.
      • The developer states that home health agencies, with a minimum of 20 qualifying quality episodes of care, receive quarterly measure reports on all their publicly reported measures. Home health agencies use these reports to compare their performance against other agencies both statewide and nationally, as well as their performance over different time periods. Additionally, providers can access confidential reports that detail individual measure results and national averages through CMS’s iQIES system.
      • The developer notes that home health agencies can submit questions via email in addition to accessing the latest measure updates online. Modifications to the OASIS-E version, effective from January 1, 2023, have resulted in updates to risk models for public outcome measures.
      • The developer highlights (risk-adjusted) overall improvement by patient subgroup from CY 2019 (0.760) to 2022 (0.798) with each subgroup demonstrating improvement.
      • The developer cites the implementation of the Quality of Patient Care (QoPC) Star Rating and the Home Health Value Based Purchasing (HHVBP) Model as potential drivers for the observed improvements.
      • The developer did not identify any unexpected findings during implementation of this measure.
         

      Limitations:

      • None identified.

      Rationale:

      • The developer provides data demonstrating overall improvement in the measure. The developer acknowledges the existence of performance gaps and anticipates further improvement with the nationwide expansion of HHVBP.

    • Submitted by Andrew on Tue, 06/25/2024 - 16:15

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      This and posture are an underrated and vital component for recovery and ADLs. Moreover, for morbidity and mortality. The mechanisms in place make this model a notion set up for success should the implementation be prepared to do so. I fully support this program.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance
      • Programs are in place. They even assessed based on the Home Health Quality Reporting Program and they did not identify any feasibility challenges.


       

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      The group broke down the reliability and validity nicely - far more extensively than the prior sections.

      Entity-level reliability is calculated with data from 2022 across 7628 entities. The measure is risk-adjusted and a split-half reliability test is used, adjusted for agency size. Average entity-level reliability is >0.6 for all entity-size deciles. (The estimated reliability for the first decile is 0.853.)

       

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Agree

      The developer assessed the convergent validity of the measure by calculating the Spearman Rank Correlations with 3 other OASIS-based measures assessing improvement in patient functioning, hypothesizing, and observing strong positive correlations (ρ = 0.75-0.85). 

      Equity

      Equity Rating
      Met
      Equity

      Addressed

      The developer compared performance for CY 2022 by subgroups for urbanicity/rurality, size, and share of quality episodes with non-white patients.

      The developer reports that on average, urban home health agencies (0.797) performed slightly worse than rural home health agencies (0.810). 

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Preliminary data noted, ongoing development and next steps addressed.

      Summary

      This is a vital function in our health care system, which needs further development and support.

      Submitted by Margherita C Labson on Wed, 06/26/2024 - 11:05

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      In addition to the information presented here, contemporary practice literature frequently cites the importance of improved ambulation as a factor for reducing ED visits and unplanned rehospitalizations leading to permanent declines in overall health.  Experientially, this reviewer knows this measure to be a frequent and reliable indicator used to determine improved patient outcomes.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Data source is a standard requirement in the industry and the methodology used reflects standard best practices

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Studies in the contemporary practice literature frequently point to the validity and reliability of this type of measure

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      see above 

      Equity

      Equity Rating
      Met
      Equity

      Avoid over-reliance on this as a mark of equity.  The  application of this indicator is limited to race and geography.  What is missing is indications of what professional services were provided i.e. were all patients, regardless of race, gender, locale offered the same services?  

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      No additional comments

      Summary

      No additional comments

      Submitted by Stephen Weed on Fri, 06/28/2024 - 15:30

      Permalink

      Importance

      Importance Rating
      Met
      Importance

      Documented well.

      Feasibility Acceptance

      Feasibility Rating
      Met
      Feasibility Acceptance

      Documented well.

      Scientific Acceptability

      Scientific Acceptability Reliability Rating
      Met
      Scientific Acceptability Reliability

      Documented well.

      Scientific Acceptability Validity Rating
      Met
      Scientific Acceptability Validity

      Documented well.

      Equity

      Equity Rating
      Met
      Equity

      In the summary measure on Table 1, the measure notes that larger organizations have better outcomes. What analysis has the sponsor or OASIS done to gain insights into this? What conversations have occurred on how to improve this?  Is this due to having better staffing coverage or staff training or something else?
      The full measure submission shows the differences between covariate assessments starting on page 27. This is indicative of the discrepancies and variabilities in increasing ambulatory ability. 

      Use and Usability

      Use and Usability Rating
      Met
      Use and Usability

      Documented well.

      Summary

      I had mobility issues one time myself and was visited in my home as part of my recovery. The therapist's assessment was perhaps the best anyone could do. While I had normal cognitive function, I didn't really understand my limitations. I appreciate the statements in the risk adjustment section. A home healthcare provider cannot mitigate some of the risk factors. However it is important to have this measure active because an outside set of eyes can make a world of difference.