2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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2 - Incentive Programs For Reducing Readmissions When Patient
Care Is Co-produced
Dimitrios Andritsos, HEC Paris,
Christopher S Tang
To compare the effectiveness of three different hospital reimbursement schemes
(i.e., Fee-for-Service, Pay-for-Performance and Bundled Payment) in reducing
readmissions, we develop a “health co-production” model in which the patient’s
readmission is “jointly controlled” by the efforts exerted by both the hospital and
the patient.
3 - Reference Pricing For Healthcare Services
Shima Nassiri, University of Washington, ,
Hamed Mamani, Elodie Adida
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 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, competing hospitals, and patients with the goal of understanding how RP
compares with the current payment system.
4 - Role Of Payment Models In The Value And Adoption Of
Health-information Exchanges
Mehmet U Ayvaci, University of Texas-Dallas,
800 W Campbell Rd SM33, Richardson, TX, United States,
Huseyin Cavusoglu,
Srinivasan Raghunathan
We study the interrelationships among the payment model, the providers’
incentives to exchange health information (HIE), and the value of HIEs in terms
of improving quality or reducing costs. In the context of a stylized healthcare
setting, we examine the fee-for-service, performance-, and episode-based
payment contracts that induce socially optimal care levels and HIE adoption. Our
findings suggest that as payment models evolve over time, there is a real need to
reevaluate the value of HIE adoption and the government policies that induce
providers to adopt HIEs.
TC22
107B-MCC
Dealing with Uncertainty in Hospital Operations
Sponsored: Health Applications
Sponsored Session
Chair: Song-Hee Kim, USC Marshall School of Business, Bridge Hall
307A, 3670 Trousdale Pkwy, Los Angeles, CA, 90089, United States,
Co-Chair: Tinglong Dai, Johns Hopkins University, 100 International
Dr, Baltimore, MD, 21202, United States,
1 - Time-driven Activity Based Costing Of Coronary Artery
Bypass Grafting Across National Boundaries To Identify
Improvement Opportunities
Feryal Erhun, University of Cambridge,
Coronary artery bypass graft (CABG) surgery is a well-established, commonly
performed treatment for coronary artery disease—a disease that affects over 10%
of US adults and is a major cause of morbidity and mortality. In 2005, the mean
cost for a CABG procedure among Medicare beneficiaries in the USA was
$32,201±$23,059. The same operation reportedly costs less than $2,000 to
produce in India. The goals of this study are to (1) identify the difference in the
costs incurred to perform CABG surgery by three Joint Commission accredited
hospitals with reputations for high quality and efficiency and (2) characterize the
opportunity to reduce the cost of performing CABG surgery.
2 - Clinical Ambiguity And Conflicts Of Interest In Interventional
Cardiology Decision-making
Tinglong Dai, Assistant Professor, Johns Hopkins University,
100 International Drive, Baltimore, MD, 21202, United States,
, Xiaofang Wang, Chao-Wei Hwang, Chao-Wei Hwang
Cardiovascular disease is the leading cause of death in the United States, and
coronary artery disease (CAD) is the major underlying culprit. Percutaneous
coronary intervention (PCI) has proven to be beneficial to patients with acute
coronary syndrome, yet its benefit to stable CAD patients is more nuanced.
Indeed, unnecessary PCI procedures for stable CAD patients have contributed to
wasteful health spending and, in certain cases, patient harm. In this paper, we
model both clinical ambiguity and conflicts of interest in interventional cardiology
decision-making. Among other results, we show the PCI usage may be non-
monotonic in the conflict-of-interest level.
3 - The Value And Price Of Flexibility In Robust Assignment Of
Patients To Radiation Therapy Machines
Philip Allen Mar, Dept. of MIE, University of Toronto,
5 King’s College Road, Toronto, ON, M5S 3G8, Canada,
Timothy Chan
In a radiation cancer therapy program, radiation therapy machines are allocated
to treat particular types of cancers to form a fixed network that acts as a guideline
for the hospital when assigning patients to machines for treatment. We study the
operational efficiency of this system from a manufacturing process flexibility
viewpoint. Furthermore, we use robust optimization to prescribe new allocation
and assignment guidelines which are robust against deviations from the optimal
assignment, and against capacity uncertainty.
4 - Maximizing Intervention Effectiveness Through
Robust Optimization
Rong Qing Brian Han, Marshall School of Business,
University of Southern California, Los Angeles, CA, United States,
, Vishal Gupta,
Song-Hee Kim
In medicine and social science, practitioners often seek to implement
interventions that have previously been proven effective via randomized control
trials (RCT). Typically, practitioners cannot access the raw data of the RCT, but do
have summary statistics from published papers. We propose a novel robust
optimization framework to identify a small, targeted group of candidates for the
intervention to maximize effectiveness based on these summary statistics. Using
data from a large urban hospital, we show that our method often outperforms
conventional methods, especially when the target and RCT populations differ
substantially.
TC23
108-MCC
New Models in Health Care
Sponsored: Health Applications
Sponsored Session
Chair: Lawrence Wein, Stanford University, 655 Knight Way,
Stanford, CA, 94305, United States,
1 - Personalized Medicine
Dimitris Bertsimas, MIT,
We use a) Electronic Medical Records from 1.5 million patients over 15 years
from the Boston Medical Center and 200 thousand cancer patients from Dana
Farber and b) state of the art as well as new machine learning algorithms to
propose an algorithmic theory of personalized medicine for several human
diseases. We discuss the overall vision, results and possible impact.
2 - Data Uncertainty In Cost-effectiveness Analyses Of
Medical Innovations
Joel Goh, Harvard University, , Mohsen Bayati,
Stefanos Zenios, Sundeep Singh, David W 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. We study how to compute maximal and minimal values of
the chain 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 but generally intractable otherwise. We
apply our model to assess the cost-effectiveness of fecal immunochemical testing
(FIT), a new screening method for colorectal cancer.
3 - New Models For Fecal Microbiota Transplantations
Lawrence Wein, Stanford University, ,
Abbas Kazerouni
A nonprofit organization, OpenBiome, has created a public stool bank to facilitate
fecal microbiota transplantation, which is an effective treatment for Clostridium
difficile infection and is being investigated as a treatment for other microbiota-
associated diseases. We discuss two problems: optimizing OpenBiome’s
operations, and using pooled stools to improve the efficacy in clinical trials against
microbiota-associated chronic diseases such as ulcerative colitis.
4 - Designing Strategic National Stockpile – A Two-stage Robust
Optimization Approach
Peter Yun Zhang, Massachusetts Institute of Technology,
Cambridge, MA, United States, ,
Nikolaos Trichakis, David Simchi-Levi
We present a model that captures two sets of decisions a supply chain network
designer faces: placement of inventory in preparation for demand uncertainty,
and resource allocation after the uncertain events unfold. We show optimality
and tractability results for problem structure that arises from designing the
Strategic National Stockpile.
TC23