Competing Interests

Joel  Goh

Chapter | Handbook of Healthcare Analytics | 2018

Competing Interests

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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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.

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.

Article | Operations Research | May–June 2018

Data Uncertainty in Markov Chains: Application to Cost-Effectiveness Analyses of Medical Innovations

Joel Goh, Mohsen Bayati, Stefanos A. Zenios, Sundeep Singh and David Moore

Cost-effectiveness studies of medical innovations often suffer from data inadequacy. When Markov chains are used as a modeling framework for such studies, this data inadequacy can manifest itself as imprecision in the elements of the transition matrix. In this paper, we study how to compute maximal and minimal values for the discounted value of the chain (with respect to a vector of state-wise costs or rewards) as these uncertain transition parameters jointly vary within a given uncertainty set. We show that these problems are computationally tractable if the uncertainty set has a row-wise structure. Conversely, we prove that the row-wise structure is necessary for tractability. Without it, the problems become computationally intractable (strongly NP-hard). We apply our model to assess the cost effectiveness of fecal immunochemical testing (FIT), a new screening method for colorectal cancer. Our results show that despite the large uncertainty in FIT's performance, it could be cost-effective relative to the prevailing screening method of colonoscopy.

Keywords: Markov Chains; cost effectiveness; medical innovations; Colorectal Cancer; Health Care and Treatment; Cost vs Benefits; Innovation and Invention; Mathematical Methods; Health Industry;

Citation:

Goh, Joel, Mohsen Bayati, Stefanos A. Zenios, Sundeep Singh, and David Moore. "Data Uncertainty in Markov Chains: Application to Cost-Effectiveness Analyses of Medical Innovations." Operations Research 66, no. 3 (May–June 2018): 697–715. (Winner, 2014 INFORMS Health Applications Society Pierskalla Award & Finalist, 2014 INFORMS George E. Nicholson student paper competition.) View Details

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In December 2014, Dr. Anthony Furlan, chair of the Department of Neurology at University Hospitals Cleveland Medical Center (UH), faced a mandate from the hospital’s executive leadership team. Specifically, all UH departments were directed to take steps within six months to reduce the waiting time for outpatient appointments—measured as the time to first available outpatient appointment—to no more than 15 days. For Furlan and his colleagues in neurology, achieving this target was a significant challenge, as the department’s current time to first available appointment was 93 days. The case considers several alternatives for reducing waiting time in outpatient neurology without increasing the total clinical staff. The case allows students to evaluate opportunities for expanding the effective capacity of a complex service operation and to understand the tradeoffs between customer service and labor utilization.

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