Alternative Risk-Adjustment Approaches to Assessing the Quality of Home Health Care: Final Report. Other Empirical Research on Home Health Outcomes Using OASIS


Mathematica Policy Research (Cheh and Black, 2002), as part of Laguna Research Associates’ analysis of the impact of the Medicare home health interim payment system, also has analyzed home health outcomes using OASIS (or slightly modified OASIS) data. The investigators grouped OASIS items in their risk-adjusted models into the following broad categories:

  • Demographic measures;
  • Availability of informal care at home health admission;
  • Medical conditions, symptoms and needs at home health admission;
  • Prognosis at home health admission.

They also included in their models “Measures of Patient’s Prior Service Use Before Home Health Admission” derived from Medicare claims data.

Fortinsky and Madigan (1997) analyzed home health outcomes using standardized items from the “transition” and “full” OASIS data system. They used Andersen and Newman’s conceptual framework for organizing their explanatory variables although only bivariate analyses were conducted.

Prior work by the project team at the Center for Home Care Policy and Research at the Visiting Nurse Service of New York includes a study conducted by Peng, Navaie-Waliser and Feldman (2003) that examined physical functioning (activities of daily living (ADLs) and instrumental activities of daily living (IADLs)), psychological functioning (anxiety and depression) and discharge outcomes among home health care patients using OASIS data. They used a subset of OASIS items as case-mix adjusters, based on Andersen and Newman’s conceptual framework, with a focus on differences among patients across OASIS-derived race and ethnic categories.

Other prior work at the Center for Home Care Policy and Research has focused on the outcomes of heart failure patients. We relied on OASIS data for baseline patient measures and survey data at a uniform point in time after home health admission for outcome measures (Murtaugh et al., 2005; Feldman et al., 2005). In these studies, OASIS data at baseline were grouped into broad domains similar to those described above with key variables from each domain included as risk-adjusters in our models.

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