PhD Defense by Jan Vlachy

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Dear Faculty Members and Fellow Students,


You are warmly invited to attend my thesis defense.


Title: Topics in Healthcare Analytics


Advisor: Dr. Turgay Ayer


Committee members (ordered alphabetically):

Dr. Mehmet Ayvaci (University of Texas at Dallas)

Dr. David Goldsman

Dr. Pinar Keskinocak

Dr. Karthik Ramachandran (Scheller College of Business)



Date and Time: March 2nd (Friday), 2018, at 10:00 AM


Location: Groseclose 226




The overarching objective of the research described in this dissertation is to analyze some pressing healthcare problems in various areas, ranging from health IT to payment models and healthcare operations, using mathematical and economic models. Analytical models have been at the forefront of the recent transformational efforts in healthcare. The dissertation comprises three chapters corresponding to three research projects: 1) econometric evaluation of Health Information Exchanges, 2) game-theoretical modeling of the Bundled Payments financing model, and 3) queueing algorithm design for Bed Management with overflows in hospitals. This dissertation has been a result of many collaborations, reflecting the wider trends in both healthcare and research. Next, we provide a more detailed overview of each of these chapters.


In Chapter 1, we study health information exchanges (HIEs), and especially their role in emergency departments (EDs). HIEs are expected to improve poor information coordination in EDs; however, whether and when HIEs are associated with better operational outcomes remains poorly understood. In this work, we study HIE and length of stay (LOS) relationship using a large dataset from the Healthcare Cost and Utilization Project consisting of about 7.4 million treat-and-release visits made to 63 EDs in Massachusetts. Overall, we find that HIE adoption is associated with a 10.2% reduction in LOS and the percentage reduction increases to 14.8% when the hospital is part of an integrated health system or to 21.0% when a patient has a previous visit to an HIE-carrying hospital. We further find that 1) teaching hospitals benefit more from HIE adoption compared with non-teaching hospitals, 2) patients with severe or multiple comorbid conditions spend less time in the ED under HIE presence. Together, these results imply that 1) HIE adoption reduces overall ED LOS, 2) wider HIE adoption would scale up the benefits for individual hospitals, 3) magnitude of the association between HIE and LOS is higher when financial incentives for HIE adoption are stronger (e.g., integrated health systems), and 4) the size of the reduction depends on certain contextual moderating factors. Given that HIEs are a key component of healthcare delivery and ongoing reforms, we believe that our findings have important implications and may inform policymakers regarding the nationwide HIE adoption.


Chapter 2 is concerned with one of the emerging new payment models in healthcare, the bundled payments. Under the prevailing fee-for-service payments (FFS), hospitals receive a fixed payment, while physicians receive separate fees for each treatment or procedure performed for a given diagnosis. Under FFS, incentives of hospitals and physicians are misaligned, leading to large inefficiencies. Bundled payments, an alternative to FFS unifying payments to the hospital and physicians, are expected to encourage care coordination and reduce ever increasing healthcare costs. However, as hospitals differ in their relationships with physicians in influencing care (level of physician integration), it remains unclear what spectrum of physician integration will facilitate bundling. There is a lack of both academic and practical understanding of hospitals’ and physicians’ bundling incentives. Our study builds on and expands the recent Operations Management literature on alternative payment models. We formulate game-theoretic models to study (1) the impact of the level of integration between the hospital and physicians in the uptake of bundled payments, (2) cost and quality characteristics of a care context that facilitate bundling, and (3) when feasible, the consequences of bundling with respect to overall care quality and costs/savings across the spectrum of integration levels. We find that (1) hospitals with high physician integration or with low physician integration are less likely to gain from bundled payments, while the hospitals that lie in between these two cases will likely benefit the most; (2) although bundled payments are likely to decrease costs, quality may also decrease; (3) initiatives that promote quality awareness in hospitals may dampen the incentives for bundling in hospitals with independent physicians, whereas they are likely to enhance incentives for bundling in hospitals with salaried physicians. Our findings have important managerial implications for both hospitals and payers: (1) in deciding whether to enroll in bundled payments, hospitals should consider their level of physician integration, and (2) payers should be aware of and account for potential negative effects of bundling, including a possible quality reduction, or even a cost increase. Based on our findings, we expect that a widespread use of bundled payments may trigger further market concentration via hospital mergers or service-line closures.


Chapter 3 focuses on the problem of patient “boarding” in emergency departments before they are accepted to hospital internal wards. In particular, we are interested in hospital patient-bed matching and bed pooling when ED patients who cannot be accommodated by the most appropriate internal ward can be redirected to other internal ward that can care for them. We derive structural properties of the patient-bed matching Markov Decision Process and then exploit these properties to propose threshold policies and reserve-k-beds policies. We demonstrate by simulation using real-world hospital data that these policies outperform policies without pooling and allow for less mistreatment than naive pooling policies. The results suggest substantial potential improvements in operational metrics and patient outcomes once we are able to deploy the policies in hospital practice.



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