Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
TA12 - FL5: Flash: Healthcare 1
Time:
Tuesday, 28/June/2022:
TA 8:30-10:00

Session Chair: Donghao Zhu
Location: Forum 16


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Presentations

Optimize and automate surgical block overbooking - sustained implementation

Christopher Thomas Borum Stromblad, Upasana Raval, Shok-Jean Yee, Kristy Zhou, Thomas Barber, Martin R Weiser

Memorial Sloan Kettering, United States of America

The Operating Rooms (ORs) are some of the most expensive and resource intensive areas of healthcare delivery. We developed and implemented a unique method to overbook surgeon blocks, i.e. assigning more OR block time than we have OR capacity to improve OR access. Using Mixed Integer Quadratic Programming, we spread the risk of overbooking equitably and enable block overbooking through an automated process for the front-line to manage and sustain easily.



On the use of partitioning in the inpatient surgical department: robust surgery scheduling

Lien Wang1, Erik Demeulemeester1, Nancy Vansteenkiste2, Frank E. Rademakers2

1KU Leuven, Faculty of Business and Economics, Department of Decision Sciences and Information Management, Research Centre for Operations Management; 2University Hospitals Leuven, Faculty of Medicine

To efficiently schedule operating rooms (ORs) in complex inpatient surgical departments, we consider separating the more predictable elective surgeries from the less predictable elective and non-elective surgeries. We solve this problem by heuristics and simulation. Using the data from a university hospital, we find that the partitioning can considerably reduce the cancellation rate and can fairly reduce the elective access times without much damaging the non-elective access times.



Joint admission and discharge control with readmissions

Zhiyuan Lou1, Jingui Xie1, Taozeng Zhu2

1Technical University of Munich, Germany; 2Dongbei University of Finance and Economics, China

Admission and discharge decisions play important roles in hospital intensive care unit (ICU) bed capacity management. In this model, we formulate the readmission of patients as an endogenous process that relies on previous discharge decisions. We develop a model to jointly consider early discharge decisions and admission control, including emergency diversion and elective scheduling. By applying the riskiness index, we can reformulate the problem and solve it efficiently.



Nudging patients towards cost-effective providers: analysis of an insurer’s effort-based and cash reward-based mechanisms

Fang Fang1, Mili, Mehrotra2, Hari Natarajan3

1California State University, Los Angeles; 2University of Illinois Urbana-Champaign; 3University of Miami

This work examines how health insurance companies (HICs) can exert effort and offer cash rewards to nudge patients towards cost-effective providers. We build a stylized model to analyze the HIC’s optimal effort and reward, individually and jointly, under different cost-share structures. We find that neither a reward-only nor an effort-only approach uniformly outperforms the other, and HIC strictly benefits most from the joint approach when the price difference is modest.



Optimal hearing loss screening for pediatric patients with cystic fibrosis disease

Narges Mohammadi, Mohammadreza Skandari

Imperial College Business School, United Kingdom

Patients with cystic fibrosis disease experience frequent pulmonary exacerbation and require antibacterial treatments. Intravenous aminoglycosides are the primary choice but they cause hearing loss. To detect possible hearing loss, there are several hearing assessment methods available. The overarching aim of this research is to design cost-effective strategies to monitor pediatric patients with CF disease to detect potential hearing loss and improve their quality of life using a hearing aid.



A two-timescale approach for incarceration diversion with community corrections programs

Xiaoquan Gao1, Pengyi Shi2, Nan Kong3

1School of Industrial Engineering, Purdue University, United States of America; 2Krannert School of Business, Purdue University, United States of America; 3Weldon School of Biomedical Engineering, Purdue University, United States of America

We study incarceration diversion decisions with community corrections as an alternative to jailing to alleviate the prominent issue of jail overcrowding. We formulate an MDP model to optimize incarceration diversions for individuals of different risks. To tackle the curse-of-dimensionality caused by non-memoryless, we develop a novel two-timescale approximation embedded in an actor-critic policy gradient algorithm. We provide structured insights for diversion decisions and service capacities.



Data-pooling reinforcement learning for personalized healthcare intervention

Xinyun Chen1, Pengyi Shi2, Xiuwen Wang1

1CUHK Shenzhen, China; 2Purdue University, United States of America

Personalized intervention management in healthcare has received a rapidly growing interest. A key challenge for personalization is data scarcity. In this research, we develop data-pooling technique in the reinforcement learning (RL) context to address the small sample issue. We develop a novel data-pooling estimator and establish theoretical performance guarantee. We demonstrate its empirical success on a real hospital dataset with an application to reduce 30-day hospital readmission rate.



 
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