Informs Annual Meeting 2017

SB06

INFORMS Houston – 2017

4 - Truthful Mechanisms for Medical Surplus Product Allocation Can Zhang, Georgia Institute of Technology, 499 Northside Circle

5 - Non-Stationary Bandits with Habituation and Recovery Dynamics Yonatan Mintz, UC Berkeley, San Francisco, CA, 94103, United States, ymintz@berkeley.edu, Anil Aswani, Philip Kaminsky, Yoshimi Fukuoka, Elena Flowers Abstract Not Available.

NW, Apt 315, Atlanta, GA, 30309, United States, czhang2012@gatech.edu, Atalay Atasu, Turgay Ayer, Beril L. Toktay

We analyze resource allocation problems faced by medical surplus recovery organizations (MSROs) that recover reusable medical products to fulfill the healthcare needs of under-served regions/countries. We present strategies to improve MSROs’ value provision when recipient needs are private information, and validate our results using historical data from a partner MSRO.

SB06

320C Drug Shortages Sponsored: Health Applications Sponsored Session Chair: Emily Tucker, University of Michigan, Ann Arbor, MI, 48105, United States, eltuck@umich.edu 1 - Identifying Critical Components of a Multi Agent Drug Supply Chain Rozhin Doroudi, Northeastern University, 360 Huntington Ave, Boston, MA, 02115, United States, doroudi.r@husky.neu.edu, Ozlem Ergun, Jacqueline Griffin, Rana Azghandi The growing epidemic of drug shortages in the US indicates the fragility of drug supply chain in the face of disruptions. Identifying most critical components in the supply chain can assist policy makers in mitigating these shortages. In this study we propose a node criticality measure for underlying network of a drug supply chain in which nodes are agents and arcs are material flow between them. Unlike most node criticality measures in network science literature this measure does not abstract away domain-specific functions of network components. Therefore, it can be used in predicting the effect that disruption of one node can have on the overall supply chain performance. 2 - Evaluating Flexible Inventory Policies for Robust Pharmaceutical Supply Chains Rana Azghandi, Northeastern University, 360 Huntington Avenue, Boston, MA, 02115, United States, rana.azghandi@gmail.com, Jacqueline Griffin Despite efforts to increase robustness of pharmaceutical supply chains, they remain vulnerable to supply disruptions and, correspondingly, drug shortages continue to be common. Designing a system which is robust for these disruptions is complex and requires adaptive decision process that dynamically changes over time. We use a stochastic optimization model to characterize the effects of the inventory policy parameters (cycle service level) for varying supply disruption profiles. Additionally, we study the effects of flexibly inventory policies via a system dynamics simulation. 3 - Mitigating U.S. Drug Shortages through Supply Contracts Hui Zhao, The Pennsylvania State University, 419 Business Building, Smeal College of Business, University Park, PA, 16802, United States, huz10@psu.edu, Justin Jia Drug Shortage is a major challenge facing the US pharmaceutical industry and the government. We propose to mitigate shortage through Pareto-improving supply contracts that consider the manufacturer’s and GPO’s profits, the government spending, and the hospital’s cost. We show that price increases must be paired with strengthened failure-to-supply clauses (IPS) to achieve consistent and significant shortage reduction as well as Pareto improvement. With realistic data, a 30% price increase under IPS can lead to an average of 53% shortage reduction. We also show the impacts of IPS on different parties in the supply chain. 4 - Incentivizing Resilient Pharmaceutical Supply Chains to Prevent Drug Shortages Emily Tucker, PhD Candidate, University of Michigan, 1205 Beal Ave, Ann Arbor, MI, 48109, United States, eltuck@umich.edu, Mark S. Daskin, Burgunda V. Sweet, Wallace J. Hopp A substantial portion of drug shortages are caused by manufacturing issues. While these can be mitigated through redundancy and safety stock, many companies choose to maintain lean supply chains for drugs with low profit margins. We present a stochastic programming approach to solving the pharmaceutical supply chain design problem under disruption. We discuss potential policies to incentivize companies to maintain more resilient supply chains and reduce the impact of shortages.

SB05

320B Pierskalla Sponsored: Health Applications Sponsored Session Chair: Hamsa Sridhar Bastani, Stanford University, Stanford, CA, 94305, United States, hsridhar@stanford.edu Co-Chair: Mohsen Bayati, Stanford University, Stanford, CA, 94305, United States, bayati@stanford.edu 1 - An Examination of Early Transfers to the ICU Based on a Physiologic Risk Score Wenqi Hu, Columbia Business School, New York, NY, United States, wh2274@columbia.edu, Carri Chan, José R. Zubizarreta, Gabriel J. Escobar Unplanned transfers of patients from the ward to the Intensive Care Unit (ICU) can occur due to rapid deterioration and may increase the patients’ risk of death and lengths of stay in hospital. A new predictive model, the EDIP2, was developed with the intent to identify patients at risk of deterioration, which in some cases could trigger proactive transfers to the ICU. This work examines the potential costs and benefits of preventive ICU admissions based on this new dynamic warning system. We find that preventive ICU admissions have the potential to improve patient outcomes, and physicians’ fears of needlessly clogging the ICU may not be as dire as initially feared. 2 - Optimal Timing of Drug Sensitivity Testing for Patients on First-line Tuberculosis Treatment Sze-chuan Suen, University of Southern California, Tuberculosis (TB) patients who are not responsive to first-line treatment should be triaged to appropriate treatment alternatives. Treatment response can be monitored using inexpensive but noisy sputum-smear (SS) tests or expensive but accurate drug sensitivity tests (DST). We use a partially observed Markov decision process (POMDP) to determine whether and when DST should be administered. We apply our model to TB in India to identify patterns of SS test results that should prompt DST. We find an optimal DST policy that would save India approximately $1.9 billion. 3 - Outcomes-based Reimbursement Policies for Chronic Care Pathways Saša Zorc, INSEAD, Fontainebleau, France, sasa.zorc@insead.edu, Stephen Chick, Sameer Hasija We develop an outcomes-based model of contracting in care for chronic patients, using data from United Kingdom’s NHS. The government contracts with healthcare providers in effort to maximise population health minus the cost. We consider the decision of whether to contract with individual healthcare providers or groups of such providers, as well as which contract type to use. Individual contracts fail to provide the desired incentives if providers under such contracts cooperate (collusion), however so do group contracts if group members fail to coordinate (free-riding). We demonstrate that individual outcomes-adjusted capitation contracts are the most resistant to these adverse effects. 4 - Optimal Risk-based Group Testing in Public Health Screening 3715 McClintock Ave, GER 240, Los Angeles, CA, 90089, United States, ssuen@usc.edu, Margaret L. Brandeau, Jeremy D. Goldhaber-Fiebert Public health screening so as to classify subjects as positive or negative for a binary characteristic (e.g., an infection) is an essential tool that can save lives and reduce suffering. We study optimal group testing designs, in which multiple subjects are tested with a single test, considering subject-specific risk characteristics, and accuracy- and equity-based objectives, and characterize structural properties of optimal testing designs. These properties allow us to develop efficient algorithms and derive insights on equity versus accuracy trade- off. We demonstrate the value of risk-based testing through a case study on chlamydia screening in the United States. Hrayer Y. Aprahamian, Virginia Tech, 1145 Perry Street, Blacksburg, VA, 24060, United States, ahrayer@vt.edu, Douglas R. Bish, Ebru K. Bish

35