2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
240
2 - Optimal Integration Of Kidney Exchange Programs With Antibody
Reduction Therapy To Increase Successful Transplant In Difficult
To Match Recipients
Naoru Koizumi, George Mason University, ,
Monica Gentili
Kidney paired donation (KPD) allows incompatible pairs to exchange kidneys
with other incompatible pairs. However, evidence suggests there stills exist
barriers to KPD utilization, especially among difficult-to-match transplant
candidates and positive actual or virtual crossmatches. We use optimization and
simulation analyses to optimally integrate antibody reduction therapy in KPD
matching runs to increase the overall number of transplants. The proposed
mathematical model matches incompatible pairs taking into consideration the
possibility that some of the recipients could undergo a desensitization protocol to
improve compatibility with the matched donor.
3 - Position-indexed Formulations For Kidney Exchange
Tuomas Sandholm, Carnegie Mellon University,
, John Dickerson, David Manlove,
Benjamin Plaut, James Trimble
We address the tractable clearing of kidney exchanges with short cycles and
practical (long, but not unbounded) chains. We introduce three compact integer
programming formulations with linear programming relaxations that are at least
as tight as the previous tightest formulation (which was not compact) for
instances in which each donor has a paired patient. Then, on real data from the
UNOS US-wide exchange and the NHS UK-wide exchange, as well as on
generated data, we show that our models dramatically outperform all prior
solvers. We also present models that are more scalable than the state-of-the-art
models for failure-aware kidney exchange.
TA23
108-MCC
Modelling Care and Treatment of Chronic Diseases
Sponsored: Health Applications
Sponsored Session
Chair: Michael W Carter, University of Toronto, 5 King’s College Rd.,
Toronto, ON, M5S 3G8, Canada,
1 - Chronic Care Disease Management Through Operations Research
& Analytics – Lessons From Ontario
Ali Vahit Esensoy, Manager, Strategic Analytics, CCO, Toronto, ON,
Canada, , Kiren Handa
CCO acts as a key Ontario government advisor on cancer care, renal, palliative
and access to care. Part of CCO’s mandate is to drive improvement through
developing multi-year system plans, setting standards and guidelines, developing
and deploying information systems, and measuring performance. Since 2010,
CCO has actively tested and deployed numerous operational research
methodologies as part of their advanced analytics work. This session will review
the evolution of CCO’s OR practice within the advanced analytics group and
discuss successes and challenges of applying OR for system planning and policy
decisions within Ontario’s healthcare system.
2 - An Analytics Approach To Dementia Capacity Planning
Tannaz Mahootchi, Cancer Care Ontario,
, Azadeh Mostaghel,
Ali Vahit Esensoy
We use a data-driven approach to identify and project capacity issues in Ontario
for the persons living with dementia (PLwD). Evidence suggests that while PLwD
prefer to stay at home for as long as possible. Lack of appropriate community-care
could lead to hospitalization and residential long-term care (LTC) placements.
Using the person-level care trajectory data and evidence from literature, we
develop a simulation model to predict the effect of augmented home and
community care options on the patient flow and LTC placements for the future
dementia incidence cases.
3 - Nurse Scheduling And Risk Analysis Of Hemodialysis Patients
Michael W Carter, University of Toronto, ,
Mahsa Shateri
Kidney failure patients require dialysis treatment three times a week until a
suitable donor is found. During dialysis, nurses monitor several patients at once,
but when complications occur, a nurse must be available quickly to attend to the
problem and restart dialysis. This paper provides a model to determine the
minimum staffing levels required in order to deliver safe, effective care.
4 - Two-stage Stochastic Programming For Adaptive Interdisciplinary
Pain Management With PIN Transition Models
Gazi Md Daud Iqbal, The University of Texas at Arlington,
Jay Michael Rosenberger,
Victoria C. P. Chen, Robert Gatchel
This research uses a two-stage stochastic programming approach to optimize
personal adaptive treatment strategies for pain management. Transition models
are represented by Piecewise Linear Networks. A multi-objective mixed integer
linear program is developed to optimize treatment strategies for patients based
upon on these transition models.
TA24
109-MCC
Practice-Based Research in Healthcare OM
Sponsored: Health Applications
Sponsored Session
Chair: Jónas Oddur Jónasson, London School of Business,
Regent’s Park, NW1 4SA, London, TX, 00000, United Kingdom,
1 - Staff Planning For Anesthesiologists
Sandeep Rath, University of California-Los Angeles,
, Kumar Rajaram
Staff planning for human resources like anesthesiologists at hospitals takes place
sequentially, where planning is done for regular staffing as well as reserve
capacity. The staff planners balance the expected overtime, under-utilization costs
as well as the cost of keeping staff on reserve. Some of these costs are not
explicitly known. We employ a structural estimation model to infer these implicit
costs and subsequently find heuristic solution for the medium term staff planning.
We apply this approach to staff planning for anesthesiologists.
2 - Separate & Concentrate: Accounting For Patient Complexity In
General Hospitals
Sandra Sülz, Assistant Professor, Erasmus University Rotterdam,
Rotterdam, Netherlands, , Ludwig M Kuntz,
Stefan Scholtes
We show that the positive association between patient volume, focus and service
quality is worse for complex patients, and that hospitals that route the majority
patients in a disease segment to the same department have fewer department
allocation errors and better outcomes, particularly for complex patients. These
findings suggest a redesign of general hospitals: Separate out routine patients and
route them away from general hospitals into high-volume and focused value-
adding process clinics and concentrate disease segments in the clinical
departments of solution shop hospitals rather than scattering patients across
several departments.
3 - Towards An Equitable Allocation Of Organs Among End-stage
Liver Disease Patients
Mustafa Akan, Associate Professor of Operations Management,
Carnegie Mellon University, Tepper School of Business,
Posner Hall 381C, Pittsburgh, PA, 15213, United States,
Ngai-Hang Z Leung, James F. Markmann,
Sridhar R Tayur, Heidi Yeh
Patients on the waiting list for liver transplants receive priority based on their
MELD scores, which reflect the severity of liver disease. Recent studies have
shown that Hepatocellular Carcinoma (HCC) patients have significantly higher
liver transplant rates than non-HCC patients due to non-individualized MELD
calculation of the former. By using clinical data from SRTR we calibrate Markov
Models and build a new simulator (MYATLAS). We then compute alternative
MELD scores for the HCC patients that are a function of the candidate’s tumor
biology and its progression.
4 - Ambulance Emergency Response Optimization In Developing
Urban Centers
Justin J. Boutilier, University of Toronto, Toronto, ON, Canada,
, Timothy Chan
Time sensitive medical emergencies are a major health concern comprising over
33% of all deaths in low and middle income countries (LMICs). Despite evidence
that ambulance services can save lives, poor access and availability of emergency
medical care in LMICs continues to be a widespread problem. In this work, we
develop a novel ambulance location-routing model, tailored to address the
challenges faced by urban areas in LMICs. We use extensive field data from
Dhaka, the capital of Bangladesh and one of the most densely populated cities on
earth, to develop and validate our modelling framework.
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