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Health care has lagged behind other industries in achieving high quality and low cost at the same time. By employing the tenets of a 'lean' approach, non-health care sectors have yielded higher quality results while reducing waste.1 Within the health care industry, there is a belief that better quality must cost more, or that reducing costs will come at the expense of quality. However, in the Veterans Health Administration (VHA)we have found the opposite—high quality is associated with high efficiency or low cost across the medical centers.
Economists have long framed the theory of productivity and efficiency, and have developed methods of measuring them. Two common econometric approaches to assessing efficiency are stochastic frontier analysis (SFA) and data envelopment analysis (DEA). The merit of SFA lies in its ability to: (1) absorb all of the information yielded by the traditional regression; (2) enhance the traditional regression by separating random factors that are not within a manager's control from true inefficiency; and (3) benchmark each facility against a cost frontier (based on all facilities' data) rather than the national average. Since 2008, the VHA Office of Productivity, Efficiency and Staffing (OPES) has been routinely assessing VHA operational efficiency using SFA. The objectives of this effort are to determine whether and to what degree costs vary within the VA health care system, to unveil the factors correlated with greater or lesser efficiency, and to examine how quality of care relates to cost.
To measure operational efficiencies at the medical center level, we build two SFA models: one for clinical cost and one for administrative cost. In both models, the independent variables or the outputs consist of: (1) the number of patients and their characteristics (e.g., case-mix and demographics); (2) reliance (e.g., Medicare enrollment rate and covered by private insurance); (3) facility characteristics (e.g., teaching mission and infrastructure); and (4) quality measures (e.g., HEDIS and ORYX). Based on these variables, we estimate the VHA cost frontier and assign an efficiency score to each facility. An efficiency score of 1.0 represents the frontier, or best performance, and an efficiency score greater than 1.0 indicates inefficiency.
To assess how efficiency could affect quality of care or vice versa, we examine the correlation between the SFA efficiency score and the combined ORYX and HEDIS measures at the facility level. ORYX (also referred to as Hospital Quality Alliance [HQA] process measures) includes 31 inpatient measures, while HEDIS (Health Effectiveness Data and Information Set) is composed of 19 outpatient measures. Both ORYX and HEDIS are among the most recognized and accepted quality indicators in the industry. We find that better cost efficiency is associated with better quality of care, i.e., facilities that are more efficient also offer higher quality of care as measured by ORYX and HEDIS. Additionally, we do not find that avoidable hospitalization rates or waiting times are associated with high cost or low efficiency.
Moreover, we find that the variation in system efficiency remains low, with the most recent data (FY14) ranging from 2.6 percent to 19.4 percent inefficiency [Median 1.064 IQR: 1.049 - 1.086]. This association between efficiency and quality makes common sense—high quality of care improves health, prevents complications, and reduces costs. 2 Likewise, Dartmouth Atlas has consistently demonstrated that overuse of health services does not yield high quality. To reduce waste and improve efficiency, OPES has developed an Efficiency Opportunity Grid (EOG) that can help facilities identify areas to improve. The EOG contains more than a dozen statistical models such as the fee care expenditure model, administrative staffing model, and ambulatory care sensitive condition (ACSC) hospitalization model. All of these EOG models produce and report observed to expected ratios (O/E) for each facility after adjusting for risks and confounders. An O/E of 1.0 indicates utilization at the national average for a facility, less than 1.0 implies utilization below the national average, and greater than 1.0 means utilization above the national average. As intended, the EOG models provide tools for VISNs and facilities to understand where opportunities exist for efficiency improvement and to optimize resource deployment. It also can serve as a repository for 'best practices' by providing detail on high performing VISNs/facilities so that effective strategies can be shared for system wide improvements.
Health care is labor intensive. With over 300,000 employees and counting, VHA finds itself under increasing pressure to demonstrate its value—providing high quality services and being good stewards of taxpayer dollars. As such, VA must seek to improve employee productivity much in the same way as other industries.
In conclusion, VA faces unprecedented challenges relating to quality and efficiency. Its very future will vitally hinge on the value (quality and cost) of care it delivers; especially when compared with the private sector.
1. Staats, B. and Upton, D. "Lean Knowledge Work," Harvard Business Review, October 2011.
2. Gao, J. et al. "Variations In Efficiency and The Relationship To Quality Of Care In The Veterans Health System," Health Affairs April 2011; vol. 30 no.4:655-63.