Predicting costs of care using a pharmacy-based measure risk adjustment in a veteran population.

BACKGROUND: Although most widely used risk adjustment systems use diagnosis data to classify patients, there is growing interest in risk adjustment based on computerized pharmacy data. The Veterans Health Administration (VHA) is an ideal environment in which to test the efficacy of a pharmacy-based approach. OBJECTIVE: To examine the ability of RxRisk-V to predict concurrent and prospective costs of care in VHA and compare the performance of RxRisk-V to a simple age/gender model, the original RxRisk, and two leading diagnosis-based risk adjustment approaches: Adjusted Clinical Groups and Diagnostic Cost Groups/Hierarchical Condition Categories. METHODS: The study population consisted of 161,202 users of VHA services in Washington, Oregon, Idaho, and Alaska during fiscal years (FY) 1996 to 1998. We examined both concurrent and predictive model fit for two sequential 12-month periods (FY 98 and FY 99) with the patient-year as the unit of analysis, using split-half validation. RESULTS: Our results show that the Diagnostic Cost Group /Hierarchical Condition Categories model performs best (R2 = 0.45) among concurrent cost models, followed by ADG (0.31), RxRisk-V (0.20), and age/sex model (0.01). However, prospective cost models other than age/sex showed comparable R2: Diagnostic Cost Group /Hierarchical Condition Categories R2 = 0.15, followed by ADG (0.12), RxRisk-V (0.12), and age/sex (0.01). CONCLUSIONS: RxRisk-V is a clinically relevant, open source risk adjustment system that is easily tailored to fit specific questions, populations, or needs. Although it does not perform better than diagnosis-based measures available on the market, it may provide a reasonable alternative to proprietary systems where accurate computerized pharmacy data are available.

Some thoughts on outcomes research, quality improvement, and performance measurement.

Outcomes research, quality improvement, and performance measurement are important activities in the cancer research community. They are, indeed, closely related activities because indicators of quality and performance can logically be regarded as the outcomes of interest in outcomes research. Considerable progress has been made in the past decade in broadening the definition and strengthening the measurement of the important outcomes of health care in general, and cancer care specifically. The real value of studying outcomes, according to the Donabedian paradigm, lies in understanding their relation to the structure and processes (eg, the health care) that have produced them, because it is these latter factors which we can control. Therefore, the methods that we have available to us by which we can infer this relation of causality become very important. Because the systems that we study in health care research are usually complex, we will need to invest more of our resources in the future in the development of methods of inference beyond what we have available now if we are to realize the full potential of health outcomes research. This presents a unique opportunity for leadership by the cancer outcomes research program.

Assessing health status and quality-of-life instruments: attributes and review criteria.

The field of health status and quality of life (QoL) measurement - as a formal discipline with a cohesive theoretical framework, accepted methods, and diverse applications--has been evolving for the better part of 30 years. To identify health status and QoL instruments and review them against rigorous criteria as a precursor to creating an instrument library for later dissemination, the Medical Outcomes Trust in 1994 created an independently functioning Scientific Advisory Committee (SAC). In the mid-1990s, the SAC defined a set of attributes and criteria to carry out instrument assessments; 5 years later, it updated and revised these materials to take account of the expanding theories and technologies upon which such instruments were being developed. This paper offers the SAC's current conceptualization of eight key attributes of health status and QoL instruments (i.e., conceptual and measurement model; reliability; validity; responsiveness; interpretability; respondent and administrative burden; alternate forms; and cultural and language adaptations) and the criteria by which instruments would be reviewed on each of those attributes. These are suggested guidelines for the field to consider and debate; as measurement techniques become both more familiar and more sophisticated, we expect that experts will wish to update and refine these criteria accordingly.