Alternative Risk-Adjustment Approaches to Assessing the Quality of Home Health Care: Final Report. INTRODUCTION


One of the central goals of the U.S. Department of Health and Human Services is to improve the quality of health care received by all Americans. In the home health care area, the Department has two key initiatives developed and implemented by the Centers for Medicare and Medicaid Services (CMS) to assess, improve, and report quality. The Outcome-Based Quality Improvement (OBQI) program provides reports to all Medicare-certified home health agencies so that they can identify potential quality problems and devise appropriate strategies to address them. The Home Health Quality Initiative (HHQI) uses a subset of the OBQI quality measures for public reporting. The purpose of HHQI is to provide useful information for potential home health consumers to make informed decisions when choosing a home health agency, and to provide an incentive for home health providers to improve the quality of care they provide.

The source of the data used in OBQI and HHQI is the Outcome and Assessment Information Set (OASIS). Since July 1999, home health agencies participating in the Medicare or Medicaid programs have been required to collect OASIS on all patients age 18 or older admitted to Certified Home Health Agencies. The two exceptions are persons receiving pre or postpartum maternity services and those receiving only personal care, chore or housekeeping services. OASIS data subsequently are submitted to State Survey Agencies which in turn send the data to CMS where they become part of a National Repository. The Medicare Prescription Drug, Improvement and Modernization Act of 2003 suspended OASIS requirements, beginning December 2003, for patients who are not covered by Medicare or Medicaid.

There are 41 home health quality measures in the context of the OBQI framework. They include functional, physiologic, emotional/behavioral, cognitive, and health care utilization (e.g., hospitalization) outcomes (Table 1). Currently, 30 of the 41 OBQI quality indicators are risk-adjusted when comparing outcomes for patients from one agency with outcomes for patients from all agencies in OBQI reports.1 One of the OBQI patient outcome indicators (Improvement in Pain Interfering with Activity) is risk-adjusted for public reporting in HHQI but not in OBQI reports sent to agencies.

The quality indicators are risk-adjusted so that agencies serving different types of patients can be compared. The statistical modeling approach currently used to risk-adjust these measures is a data-driven “stepwise” approach with a separate set of risk factors used for each OBQI measure. One potential drawback of using a stepwise approach to risk-adjustment is finding a set of adjustors that are specific to the particular data set being modeled. Since the decision to retain a variable as a predictor in a given model is driven by the data being analyzed, there is a risk of an “overfit” of the data. The resulting model may predict the analytic data set well, but be a poor fit when applied to future data. To at least partially address this problem, the risk-adjustment models developed by the CMS contractor at the University of Colorado were estimated on a randomly selected subsample of the overall dataset, referred to as the “developmental sample.” The developmental sample models then were validated by applying them to data that were set aside for this purpose. In those cases where there was a substantial discrepancy in the explanatory power of the model between the developmental and validation samples, the model was re-estimated using the developmental sample.

The purpose of this project was to develop and test alternative risk-adjustment approaches to assessing the quality of home health care. A theory and evidence-based approach was used to develop risk-adjustment models for the OBQI quality indicators. Specifically, instead of using a separate set of risk-adjusters for each OBQI quality indicator where risk-adjusters are primarily determined based on their statistical fit to the model, this project used a core set of risk-adjusters in all models that theory and prior research suggest are important determinants of home health quality. Advantages of a theory and evidence-based approach include simplicity, understandability, stability of the risk-adjustment models over time, conceptual meaningfulness, and the potential for greater parsimony in data elements when a large number of outcome indicators are being risk-adjusted, as is the case in the OBQI program.

The alternative models were developed within the framework of the uniform data collection system (OASIS) at the time of the study. A project goal was to develop alternative models that could be implemented using existing data sources and project resources limited analyses to OASIS data elements. Within this framework, clinically relevant measures that may be included in future electronic record systems were distinguished from other measures in the model-building process. We identified the relative contribution of OASIS items supplementing the core set of risk-adjusters to inform efforts to determine whether OASIS items can be excluded from the instrument without jeopardizing the explanatory power of the risk-adjustment models.

Findings from this project will contribute to CMS’s future plans for continued refinement of risk-adjustment and outcome measures. They also will provide home health care providers with a better understanding of current and alternative modeling approaches for risk-adjustment of home health quality indicators. Finally, the results will support the Department’s efforts to reduce regulatory burden by streamlining OASIS.

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