Do financial incentives linked to ownership of specialty hospitals affect physicians' practice patterns?
ABSTRACT Although physician-owned specialty hospitals have become increasingly prevalent in recent years, little research has examined whether the financial incentives linked to ownership influence physicians' referral rates for services performed at the specialty hospital.
We compared the practice patterns of physician owners of specialty hospitals in Oklahoma, before and after ownership, to the practice patterns of physician nonowners who treated similar cases over the same time period in Oklahoma markets without physician-owned specialty hospitals.
We constructed episodes of care for injured workers with a primary diagnosis of back/spine disorders. We used pre-post comparisons and difference-in-differences analysis to evaluate changes in practice patterns for physician owners and nonowners over the time period spanned by the entry of the specialty hospital.
Findings suggest the introduction of financial incentives linked to ownership coincided with a significant change in the practice patterns of physician owners, whereas such changes were not evident among physician nonowners. After physicians established ownership interests in a specialty hospital, the frequency of use of surgery, diagnostic, and ancillary services used in the treatment of injured workers with back/spine disorders increased significantly.
Physician ownership of specialty hospitals altered the frequency of use for an array of procedures rendered to patients treated at these hospitals. Given the growth in physician-owned specialty hospitals, these findings suggest that health care expenditures will be substantially greater for patients treated at these institutions relative to persons who obtain care from nonself-referral providers.
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ABSTRACT: Background: Multiple studies have investigated physician-owned specialized facilities (specialized hospitals and ambulatory surgery centres). However, the evidence is fragmented and the literature lacks cohesion. Objectives: To provide a comprehensive overview of the effects of physician-owned specialized facilities by synthesizing the findings of published empirical studies. Methods: Two reviewers independently researched relevant studies using a standardized search strategy. The Institute of Medicine's quality framework (safe, effective, equitable, efficient, patient-centred, and accessible care) was applied in order to evaluate the performance of such facilities. In addition, the impact on the performance of full-service general hospitals was assessed. Results: Forty-six studies were included in the systematic review. Overall, the quality of the included studies was satisfactory. Our results show that little evidence exists to confirm the advantages attributed to physician-owned specialized facilities, and their impact on full-service general hospitals remains limited. Conclusion: Although data is available on a wide variety of effects, the evidence base is surprisingly thin. There is no compelling evidence available demonstrating the added value of physician-owned specialized facilities in terms of quality or cost of the delivered care. More research is necessary on the relative merits of physician-owned specialized facilities. In addition, their corresponding impact on full-service general hospitals remains unclear. The development of physician-owned specialized facilities should thus be monitored carefully.Health Policy 09/2014; 118(3). DOI:10.1016/j.healthpol.2014.09.012 · 1.73 Impact Factor
BMJ (online) 09/2013; 347:f5535. DOI:10.1136/bmj.f5535 · 16.38 Impact Factor
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ABSTRACT: Objective To evaluate the effects of specification choices on the accuracy of estimates in difference-in-differences (DID) models.Data SourcesProcess-of-care quality data from Hospital Compare between 2003 and 2009.Study DesignWe performed a Monte Carlo simulation experiment to estimate the effect of an imaginary policy on quality. The experiment was performed for three different scenarios in which the probability of treatment was (1) unrelated to pre-intervention performance; (2) positively correlated with pre-intervention levels of performance; and (3) positively correlated with pre-intervention trends in performance. We estimated alternative DID models that varied with respect to the choice of data intervals, the comparison group, and the method of obtaining inference. We assessed estimator bias as the mean absolute deviation between estimated program effects and their true value. We evaluated the accuracy of inferences through statistical power and rates of false rejection of the null hypothesis.Principal FindingsPerformance of alternative specifications varied dramatically when the probability of treatment was correlated with pre-intervention levels or trends. In these cases, propensity score matching resulted in much more accurate point estimates. The use of permutation tests resulted in lower false rejection rates for the highly biased estimators, but the use of clustered standard errors resulted in slightly lower false rejection rates for the matching estimators.Conclusions When treatment and comparison groups differed on pre-intervention levels or trends, our results supported specifications for DID models that include matching for more accurate point estimates and models using clustered standard errors or permutation tests for better inference. Based on our findings, we propose a checklist for DID analysis.Health Services Research 12/2014; DOI:10.1111/1475-6773.12270 · 2.49 Impact Factor