Using large data sets in long-term care to measure and improve quality

The authors explore the evolution, potential uses, and limitations of four existing large data sets in long-term care: Minimum Data Set (MDS), Outcome Assessment and Information Set (OASIS), Online Survey and Certification Reporting System (OSCAR), and Consumer Surveys. They also describe the emerging Federal Nursing Home Quality Initiative and its potential for future research. All four existing large data sets have potential to be used to improve quality of care. Their utility is presently diminished because providers are not using the data for formal continuous quality improvement. However, the Center for Medicare and Medicaid Services (CMS) is currently engaged in a series of special studies designed to build and sustain a culture of continuous quality improvement in nursing homes and to make continuous, measurable improvement a growing part of the care of Medicare beneficiaries in those settings. These CMS studies, all of which will draw on the four existing large data sets in long-term care, offer the potential to develop, explicate and test theory about the assumed causal relationships between structure and process variables and related health care outcomes in long-term care.