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dc.contributor.advisorRetsef Levi.en_US
dc.contributor.authorHu, Michael,Ph.D.Massachusetts Institute of Technology.en_US
dc.contributor.otherMassachusetts Institute of Technology. Operations Research Center.en_US
dc.date.accessioned2020-10-18T21:16:20Z
dc.date.available2020-10-18T21:16:20Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/128043
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2020en_US
dc.descriptionCataloged from student-submitted PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 147-158).en_US
dc.description.abstractHealthcare reform in the United States has received significant attention from the public, physicians, health administrators, and insurance payors. The important policy discussions surrounding healthcare reform have a wide-reaching impact, and often require quantitative data and analytics to support successful changes. This thesis studies a number of burning healthcare problems and offers actionable insights driven by data and analytics. In Chapter 2, we examine the effect of the EHR phenomenon and how it has fundamentally transformed physicians' work. While these computer systems are designed in part to streamline workflows and increase employee efficiency, physician experiences are often the exact opposite. Instead of having more face-to-face time seeing their patients, physicians are forced to spend the majority of their time completing EHR tasks.en_US
dc.description.abstractIn this chapter, we establish rigorous, quantitative methods for measuring and analyzing this problem to help health systems curb the exploding population of burned out physicians. In Chapter 3, we demonstrate how the methods established in Chapter 2 can also be used to predict physician workload, which may assist in the design of physician compensation models. While health systems are shifting away from fee-for-service payment schemes, alternative payment schemes often encounter significant implementation challenges. There are many open questions that need to be resolved before these new payment schemes can achieve widespread adoption. In this chapter, we address one such question, which involves how to properly risk adjust for different patient populations. We leverage the techniques from Chapter 2 to measure the workload imposed on physicians by individual patients.en_US
dc.description.abstractThis enables us to subsequently develop a risk adjustment model that substantially outperforms existing risk adjustment methods in determining the physician workload associated with managing different patient populations. In Chapter 4, we examine the problem of relaxing hospital capacity. Many hospitals frequently operate close to full-capacity which poses serious safety concerns. Most attempted solutions in this space focus on inpatient interventions such as optimizing patient flow and surgery schedules. In contrast to this, we propose an approach based on changes in the longitudinal care delivered by ambulatory services, specifically for the treatment of heart failure patients. Lastly, in Chapter 5, we develop a new modeling framework for real-time appointment scheduling.en_US
dc.description.abstractWhile others have applied existing algorithms from online binpacking to solve this problem, our modeling framework leverages unique aspects of appointment scheduling to further optimize scheduling decisions and reduce resource requirements. In doing so, we demonstrate that our modeling framework generalizes the classical bin-packing framework thereby enabling a potentially larger number of problems to be studied using similar techniques.en_US
dc.description.statementofresponsibilityby Michael Hu.en_US
dc.format.extent158 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectOperations Research Center.en_US
dc.titleLeveraging data analytics to improve outpatient healthcare operationsen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Centeren_US
dc.contributor.departmentSloan School of Management
dc.identifier.oclc1200117137en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Centeren_US
dspace.imported2020-10-18T21:16:19Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentSloanen_US
mit.thesis.departmentOperResen_US


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