. "Appendix F: The Expected Population Value of Quality Indicator Reporting (EPV-QIR): A Framework for Prioritizing Healthcare Performance Measurement." Future Directions for the National Healthcare Quality and Disparities Reports. Washington, DC: The National Academies Press, 2010.
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Future Directions for the National Healthcare Quality and Disparities Reports
quality and disparities, quality and disparities reporting is more often targeted at variability in the implementation of available information. Recently, value of research approaches have been adapted to address issues of imperfect implementation (Fenwick et al., 2008; Hoomans et al., 2009).
The expected population value of quality indicator reporting (EPV-QIR) we propose is intended to be a useful tool in selecting quality indicators that can produce the largest improvements in population health. Quality indicators can be ranked in terms of their EPV-QIR and a set of indicators can be identified that offer the highest expected returns to investing in quality improvement. The EPV-QIR depends on several factors:
The net health benefit of the appropriate implementation of the intervention, which is the magnitude of the potential health benefit of the intervention (measured in quality adjusted life years (QALYs)) net of the opportunity costs in health when the intervention is fully implemented to maximize its benefit net of costs,
The size of the population of persons who should receive the intervention given the standard of care, e.g., those with a positive net health benefit from the intervention,
The current state of implementation, which potentially includes both the rate of utilization among parts of the population with positive net health benefits and the rate of use among those parts of the population with negative net health benefits (for whom there are potential gains in net health benefits that can be obtained by eliminating inappropriate use in that population), and
The potential for quality improvement, especially as produced by reporting quality indicators. This depends on the probability that providers (or patients) will make choices likely to improve quality when given information on provider performance is provided, and the effectiveness of existing quality improvement interventions to improve outcomes. Because data on these effects may be especially incomplete, our approach also specifically highlights uncertainty in the extent to which quality reporting will stimulate quality improvement action, and quality improvement action will change implementation. This includes both estimating the expected (average) effects of reporting on quality, and bounding estimates of these effects when data on the effectiveness of reporting on quality is especially incomplete. For example, if an intervention is not currently used or at least not used in persons in whom it produces net harms, one such bound would the value of perfect implementation, which is the total benefit that can be achieved in a population if everyone who should receive an intervention receives it and everyone who should not receive an intervention does not receive it.
We explicate our framework in detail in the remainder of this paper, and demonstrate its application in calculating the expected value of quality improvement for selected NHQR measures. We develop our framework in Section II, progressively developing concepts that are critical to the EPV-QIR framework. In Section III, we demonstrate the EPV-QIR calculations for selected measures in the NHQR, while also paying close attention to opportunities to bound estimates of EPV-QIR with more limited data. In Section IV, we discuss the scope of potential application for the EPV-QIR method and its limitations and implementation issues. Section V concludes with a discussion of areas for future development.
Our framework begins with the assumption that all measures are based explicitly or implicitly on some standard of care, which we denote by S. We use O to denote all other alternatives, which could include some other standard of care, or “usual care” or “doing nothing.” Our model could easily be generalized to include multiple alternative standards of care (Oi) by indexing groups additionally according to the care they receive currently. For simplicity, however, we develop our theoretical framework in the case in which there is only a single alternative current pattern of care.
Given this single current pattern of care, the incremental benefit of S is the difference between the effectiveness of the standard of care (eS) and the effectiveness of the alternative (eO) current pattern. The incremental benefit of