Competing Interests

Joel  Goh

Chapter | Handbook of Healthcare Analytics | 2018

Competing Interests

by Joel Goh

Abstract

Book Abstract: The editors, aided by a team of internationally acclaimed experts, have curated this timely volume to help newcomers and seasoned researchers alike to rapidly comprehend a diverse set of thrusts and tools in this rapidly growing cross-disciplinary field. The Handbook covers a wide range of macro-, meso- and micro-level thrusts—such as market design, competing interests, global health, personalized medicine, residential care, and concierge medicine, among others—and structures what has been a highly fragmented research area into a coherent scientific discipline. The Handbook also provides an easy-to-comprehend introduction to five essential research tools—Markov decision process, game theory and information economics, queueing games, econometric methods, and data science—by illustrating their uses and applicability on examples from diverse healthcare settings, thus connecting tools with thrusts. The primary audience of the Handbook includes analytics scholars interested in healthcare and healthcare practitioners interested in analytics.

Keywords: healthcare; analytics; Health Care and Treatment; Research; Competition;

Format: Print

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Citation:

Goh, Joel. "Competing Interests." Chap. 4 in Handbook of Healthcare Analytics: Theoretical Minimum for Conducting 21st Century Research on Healthcare Operations, edited by Tinglong Dai and Sridhar Tayur, 51–78. John Wiley & Sons, 2018.

Article | Annals of Internal Medicine | 2010

Estimating the Attributable Cost of Physician Burnout in the United States

Shasha Han, Tait D. Shanafelt, Christine A. Sinsky, Karim M. Awad, Liselotte N. Dyrbye, Lynne C. Fiscus, Mickey Trockel and Joel Goh

Background: Although physician burnout is associated with negative clinical and organizational outcomes, its economic costs are poorly understood. As a result, leaders in health care cannot properly assess the financial benefits of initiatives to remediate physician burnout.
Objective: To estimate burnout-associated costs related to physician turnover and physicians reducing their clinical hours at national (U.S.) and organizational levels.
Design: Cost-consequence analysis using a mathematical model.
Setting: United States.
Participants: Simulated population of U.S. physicians.
Measurements: Model inputs were estimated by using the results of contemporary published research findings and industry reports.
Results: On a national scale, the conservative base-case model estimates that approximately $4.6 billion in costs related to physician turnover and reduced clinical hours is attributable to burnout each year in the United States. This estimate ranged from $2.6 billion to $6.3 billion in multivariate probabilistic sensitivity analyses. At an organizational level, the annual economic cost associated with burnout related to turnover and reduced clinical hours is approximately $7600 per employed physician each year.
Limitations: Possibility of nonresponse bias and incomplete control of confounders in source data. Some parameters were unavailable from data and had to be extrapolated.
Conclusion: Together with previous evidence that burnout can effectively be reduced with moderate levels of investment, these findings suggest substantial economic value for policy and organizational expenditures for burnout reduction programs for physicians.

Keywords: Physicians; burnout; Health; Health Care and Treatment; Employees; Cost; Programs; Policy; Health Industry;

Citation:

Han, Shasha, Tait D. Shanafelt, Christine A. Sinsky, Karim M. Awad, Liselotte N. Dyrbye, Lynne C. Fiscus, Mickey Trockel, and Joel Goh. "Estimating the Attributable Cost of Physician Burnout in the United States." Annals of Internal Medicine 170, no. 11 (June 4, 2019): 784–790. View Details

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Recent Medicare legislation seeks to improve patient care quality by financially penalizing providers for hospital-acquired infections (HAIs). However, Medicare cannot directly monitor HAI rates and instead relies on providers accurately self-reporting HAIs in claims to correctly assess penalties. Consequently, the incentives for providers to improve service quality may disappear if providers upcode, i.e., misreport HAIs (possibly unintentionally) in a manner that increases reimbursement or avoids financial penalties. Identifying upcoding in claims data is challenging due to unobservable confounders (e.g., patient risk). We leverage state-level variations in adverse event reporting regulations and instrumental variables to discover contradictions in HAI and present-on-admission (POA) infection reporting rates that are strongly suggestive of upcoding. We conservatively estimate that 10,000 out of 60,000 annual reimbursed claims for POA infections (18.5%) were upcoded HAIs, costing Medicare $200 million. Our findings suggest that self-reported quality metrics are unreliable and thus, recent legislation may result in unintended consequences.

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