2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
155
MB21
107A-MCC
Applications of Stochastic Models in Medical
Decision Making Problems
Sponsored: Health Applications
Sponsored Session
Chair: Mohammad Reza Skandari, University of British Columbia, #420
2053 Main Mall, Vancouver, BC, V6T-1Z2, Canada,
1 - Patient-centered HIV Viral Load Monitoring Strategies In
Resource-limited Settings
Diana Maria Negoescu, University of Minnesota,
, Heiner Bucher, Eran Bendavid
Viral load (VL) testing is the most critical monitoring tool for assessing the
effectiveness of treatment in HIV patients. The optimal frequency of VL
monitoring remains unknown, despite it being the costliest routine monitoring
tool for HIV in Sub-Saharan Africa. We formulate a model parameterized using
person-level longitudinal data to simulate adherence behavior and disease
dynamics over time, and to develop monitoring schedules that adapt to patient
characteristics. We then evaluate the total costs and quality-adjusted life years
achieved by monitoring VL at fixed intervals (status quo), as well as at variable
intervals based on an individualized risk assessment of virologic failure.
2 - Timing The Use Of Breast Cancer Risk Information In Biopsy
Decision Making
Mehmet Ayvaci, University of Texas at Dallas, Jindal School of
Management, Dallas, TX, United States, ,
Mehmet Eren Ahsen, Srinivasan Raghunathan, Zahra Gharibi
Available clinical evidence is inconclusive on whether radiologists should use the
patient risk profile information when interpreting mammograms. On the one
hand, risk profile information is informative and can improve radiologists’
performance, but on the other hand, it may impair their judgment by introducing
biases in mammography interpretation. Therefore, it is important to assess
whether and when profile information use translates into improved outcomes.
We model the use of profile information in mammography using a decision
theoretic approach and explore the value of profile information.
3 - Developing Near-optimal Biomarker-based Prostate Cancer
Screening Strategies
Christine Barnett, University of Michigan, Ann Arbor, MI, United
States,
Brian Denton
Recent advances in the development of new biomarker tests, which physicians
use for the early detection of cancer, have the potential to improve patient
survival by catching cancer at an early stage. We describe a partially observable
Markov decision process (POMDP) to compute near-optimal prostate cancer
screening strategies. We present results based on Monte Carlo simulation to
compare the policies developed using our approximated POMDP methods with
those recommended in the medical literature.
4 - Optimizing Breast Cancer Diagnostic Decisions While
Minimizing Overdiagnosis
Sait Tunc, University of Wisconsin-Madison, Madison, WI, United
States, , Oguzhan Alagoz, Elizabeth S Burnside
Although the early diagnosis of breast cancer saves millions of lives every year,
overdiagnosis of breast cancer may cause harm without benefit. We propose a
large-scale MDP that uses multi-dimensional cancer risk vectors to incorporate
cytologic grade to the breast cancer diagnostic decision problem and
concomitantly reduce the overdiagnosis. We present efficient algorithms to find
the exact solution to the given large-scale MDP, and introduce upper bounds to
further improve the computational performance.
MB22
107B-MCC
Policy Evaluation from Operations to Public Health
Invited: ORinformed Healthcare Policies
Invited Session
Chair: Diwakar Gupta, University of Minnesota and National Science
Foundation, Minneapolis, MN, United States,
1 - Facilitating Early Diagnosis Of Tuberculosis In India
Sarang Deo, Indian School of Business,
High incidence of TB in India is driven by long diagnostic delay resulting from
poor practices of unorganized private providers, who are often patients’ first point
of contact. We develop an operational model of patients’ diagnostic pathways and
calibrate it using data collected from household surveys. We use it to estimate the
impact of new technology and improved provider behavior on reduction of
diagnostic delay. We also develop a stylized economic model of private providers
and estimate the monetary incentive required to achieve reduction in diagnostic
delay. These models have informed the design of a large pilot program funded by
the Gates Foundation in two Indian cities of Mumbai and Patna.
2 - Casualty Distribution To Hospitals In The Aftermath Of
Mass-casualty Events
Nilay T Argon, University of North Carolina, Chapel Hill, NC,
27514, United States, Alex Mills, Serhan Ziya
Following a disaster, emergency responders must transport a large number of
casualties to hospitals by limited transportation resources. Based on a Markov
decision process formulation, we develop heuristic policies that use limited
information on travel times and congestion levels to determine how to allocate
ambulances to casualty locations and which hospitals should be the destination
for those ambulances. By means of a realistic simulation study, we show that the
proposed heuristics provide substantial improvement in the expected number of
survivors, even when only limited information about the system state is available.
3 - Hospital-physician Gainsharing Contract Design
Diwakar Gupta, University of Minnesota, Minneapolis, MN,
United States, , Mili Mehrotra, Xiaoxu Tang
Participation in the bundled payments for care improvement (BPCI) initiative has
provided hospitals the ability to gainshare with physicians. We formulate a model
to study the contracts that hospitals could offer physicians based on their
historical as well as ongoing performance improvement. Physicians have private
information about their costs of achieving different improvement targets.
Physicians may choose to either enter the gainsharing agreements with the
hospital or continue to operate under the fee-for-service schedule. We
characterize the optimal contracts and analyze the distribution of the gains within
a game-theoretic setting.
MB23
108-MCC
Healthcare Analytics: Collaborations
with Practitioners
Sponsored: Health Applications
Sponsored Session
Chair: Bruce L Golden, University of Maryland-College Park, 1,
Simpsonville, MD, 2, United States,
Co-Chair: Sean Barnes, Univ of Maryland-College Park, 4352 Van
Munching Hall, University of Maryland, College Park, MD, 20742,
United States,
1 - Understanding Emergency Department Jumper Behavior:
Actionable Insights From Claims Data Using Machine Learning
Xia (Summer) Hu, University of Maryland - College Park,
College Park, MD, 20740, United States,
Sean Barnes, Margret Bjarnadottir, Bruce L Golden
Emergency Department (ED) “jumpers” refers to patients whose ED consumption
levels have changed drastically over consecutive periods (e.g. frequent to non-
frequent, or vice versa). Based on yearly insurance claim records, we leverage
various learning algorithms to predict ED jumpers, whose behaviour are usually
difficult to capture using traditional methods. Further, we analyze the
characteristics of jumpers via clustering based on Bayesian Information Criteria.
Based on this analysis, we provide actionable insights about preventable ED usage
and risk management.
2 - Impact Of State And Federal Policy Changes By Socioeconomic
Status On Emergency Medicine Practice In Maryland
David Anderson, CUNY Baruch,
Edward Andrew Wasil, Bruce L Golden, Laura Pimentel,
Jon Mark Hirshon, Fermin Barrueto
We study the effect of the implementation of the Affordable Care Act (ACA) and
a Global Budgeting Revenue (GBR) structure for hospital reimbursement on the
operations of Maryland emergency departments. Using a 24-month longitudinal
dataset of monthly ED performance, we find that ACA/GBR implementation leads
to a decrease in admission rate, increased revenue capture by hospitals, a decrease
in the percent of uninsured patients, and a small increase in volume. Further, we
find that all of the changes are more pronounced at hospitals with patient
populations coming from lower socioeconomic status zip codes.
MB23