Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

MD56

The traditional payment system between an insurer and hospitals does not incentivize hospitals to limit their prices and patient to choose less expensive providers, hence contributing to high insurer costs. Reference pricing (RP) has been proposed as a way to better align incentives and control rising costs. Under RP, the patient may be responsible for part of the cost if they select a high-price hospital. We propose a model to analyze the RP payment scheme that incorporates an insurer choosing the reference price, competing price-setting hospitals, and patients selecting a provider based on a multinomial logit choice model. Our goal is to understand how RP compares with the current payment system. 2 - The Role of Non-clinical Workforce on Patient Service: Evidence from NHS Helpline Bilal Gokpinar, UCL School of Management, 1 Canada Square (38th floor), Canary Wharf, London, E14 5AA, United Kingdom, Emmanouil Avgerinos Healthcare organizations rely on a mix of clinical and non-clinical personnel in delivering health services. Although non-clinical workers are vital in many healthcare delivery settings today, their impact on efficiency and quality of patient service has not been examined in the operations management literature. In this study, making use of a novel dataset based on National Health Service (NHS)’s new 111 non-emergency helpline in England, we quantify and demonstrate trade-offs associated with employing non-clinical personnel in delivering patient service. 3 - Pay-for-quality or Pay-for-selection? An Analysis of the Capitation Payment Models in Healthcare Zhaowei She, Georgia Institute of Technology, 755 Ferst Drive NW, Main 346 B, Atlanta, GA, 30332, United States, Turgay Ayer, Daniel Montanera Capitation payment models have been increasingly adopted by payers in U.S. healthcare markets during the past decade, as a remedy for fee-for-service (FFS) payment models. However, early empirical evidence found that Medicare Advantage (MA), the largest capitation program in the U.S., has been suffering from another kind of market failure: risk selection. While the existing literature attributes the observed risk selection in capitation programs primarily to imperfect estimation in risk adjustment, this paper discovers a new source of risk selection: Pay-for-Selection. We further conduct a difference-in-difference (DID) estimation to identify the pay-for-selection effect in MA market. 4 - Why Is Cost at a Modern Indian Hospital Lower than at Well-managed US Peers? Feryal Erhun, University of Cambridge, University of Cambridge, Trumpington Street, Cambridge, CB2 1AG, United Kingdom Modern Indian hospitals attain lower cost of care while meeting US-equivalent quality accreditation standards. This is unsurprising because they pay far lower market prices for inputs to care. Whether their cost advantage is also attributable to care delivery methods that could be adopted by hospitals in the US remains unresolved and is the focus of this study. n MD58 West Bldg 101C Joint Session HAS/Practice Curated: Analytics and Optimization in Health Systems Sponsored: Health Applications Sponsored Session Chair: Seyma Guven-Kocak, Georgia Institute of Technology, Atlanta, GA, 30340, United States Co-Chair: Pinar Keskinocak, Georgia Institute of Technology, Atlanta, GA, 30332, United States Co-Chair: Dave Goldsman, Georgia Institute of Technology,Atlanta, GA, 30332-0205, United States 1 - Pediatric Kidney Post-Transplant Survival Analysis and Risk Factor Identification Yao Xie, Georgia Institute of Technology, 4049 Wieuca Road NE, Atlanta, GA, 30342, United States, Xi He, Pinar Keskinocak, Joel Sokol We build statistical models that accurately predict the post-transplant survival functions for pediatric kidney transplant patients and identify the most important risk factors. The pediatric transplant recipients are less commonly studied in the existing literature, while models developed for the general transplant recipients are not applicable. We use a large-scale UNOS (United Network for Organ Sharing) dataset and apply statistical variable selection techniques, specifically the group lasso and the random forest variable importance, to identify the most important risk factors. We also successfully identify multiple subgroups where the survival characteristics are different.

n MD56 West Bldg 101A Medical Decision Analysis by Bonder Scholars Sponsored: Health Applications Sponsored Session Chair: Pooyan Kazemian, Harvard Medical School, Boston, MA, 02114, United States 1 - Ambulance Emergency Response Optimization in Developing Countries: Application Justin James Boutilier, University of Toronto, 532 Palmerston Boulevard, Apartment 6, Toronto, ON, M6G 2P5, Canada, Timothy Chan The lack of emergency medical transportation is viewed as the main barrier to the access and availability of emergency medical care in low and middle-income countries (LMICs). In this paper, we present a robust optimization approach to optimize both the location and routing of emergency response vehicles, accounting for uncertainty in travel times and spatial demand characteristic of LMICs. We then combine our robust optimization approach with two machine learning frameworks and real data from Dhaka, Bangladesh. The focus of this talk is to provide an in-depth investigation into three policy-related questions. 2 - Trial Termination and Drug Misclassification in Sequential Adaptive Clinical Trials Alba Rojas-Cordova, Southern Methodist University, Dallas, TX, 75219, United States, Ebru Korular Bish, Niyousha Hosseinichimeh We examine the nature and implications of wrongly terminating the development of an effective candidate drug, which may lead to unrecoverable expenses, and unfulfilled patients’ needs. To this end, we build a simulation model of a Phase 3 sequential adaptive trial, and focus on the continuation or termination decision at one of the planned interim analysis points, based on the feedback from the drug testing process. We examine the effects of imperfect information and the conditions that lead to drug misclassification by conducting an extensive Monte Carlo-style sensitivity analysis. Contrary to the literature’s focus on false positives, our results suggest that false negatives can be more likely. 3 - The Development and Validation of a New Microsimulation Model of Type 2 Diabetes Pooyan Kazemian, Harvard Medical School, 50 Staniford Street, We developed a novel microsimulation model of type 2 diabetes progression and outcomes using data from recent randomized clinical trials and cohort studies. We validated the model against outcomes from two clinical trials (ACCORD and VADT) using several statistical methods which demonstrate that our model can accurately predict clinical outcomes of patients with type 2 diabetes in the modern era. The model will be used to project long term clinical and economic outcomes of treatment interventions for type 2 diabetes. 4 - Data-driven Optimization Models for Concussion Management Decisions Gian-Gabriel P. Garcia, University of Michigan, Ann Arbor, MI, 48105, United States Concussion is an emerging public health issue. Recent research has begun to illuminate the relationship between concussion and long-term consequences including cognitive impairment, neurodegenerative disease, and early onset dementia. Concussion management plays a critical role in long and short-term health outcomes for those with concussion. In this talk, we discuss how we apply data-driven optimization to address major challenges in concussion diagnosis and post-injury management. n MD57 West Bldg 101B Financial Incentives in Healthcare Delivery Sponsored: Health Applications Sponsored Session Chair: Turgay Ayer, Georgia Institute of Technology, Atlanta, GA, 30332, United States Co-Chair: Zhaowei She, Georgia Institute of Technology, Atlanta, GA, 30332, United States 1 - Reference Pricing for Healthcare Services Shima Nassiri, University of Michigan, Ross School of Business, 701 Tappan Avenue, Ann Arbor, MI, 48109, United States, Elodie Adida, Hamed Mamani 9th Floor, Room 930B, Boston, MA, 02114, United States, Deborah Wexler, Naomi Fields, Robert Parker, Amy Zheng, Rochelle Walensky

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