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Abstract
Delay in inpatient discharge processes reduces patient satisfaction and increases hospital congestion and length of stay. Further, flow congestion manifests as patient boarding, where new patients awaiting admission are blocked by bed unavailability. Finally, length of stay is extended if the discharge delay incurs an extra overnight stay. These factors are often in conflict, thus, good hospital performance can only be achieved through careful balancing. We formulate the discharge planning problem as a two-stage stochastic program with uncertain discharge processing and bed request times. The model minimizes a combination of discharge lateness, patient boarding, and deviation from preferred discharge times. Patient boarding is integrated by aligning bed requests with bed releases. The model is solved for different instances generated using data from a large hospital in Texas. Stochastic decomposition is compared with the extensive form and the L-shaped algorithm. A shortest expected processing time heuristic is also investigated. Computational experiments indicate that stochastic decomposition outperforms the L-shaped algorithm and the heuristic, with a significantly shorter computational time and small deviation from optimal. The L-shaped method solves only small problems within the allotted time budget. Simulation experiments demonstrate that our model improves discharge lateness and patient boarding compared to current practice.
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Notes on contributors
Maryam Khatami
Dr. Maryam Khatami is a visiting Assistant Professor of Operations and Decision Technologies at the Indiana University Kelley School of Business. She received her PhD in industrial and systems engineering from Texas A&M University in 2020. Before joining Texas A&M University, she received the BS and MS degrees in industrial engineering from Amirkabir University of Technology, Iran, in 2010 and 2013. Her main research interest is in stochastic programming applied to health care systems engineering, supply chain management and energy. She has also conducted research on supply chain network design and location-routing problems. She develops rigorous mathematical models to formulate and solve these problems using real data.
Michelle Alvarado
Dr. Michelle Alvarado is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Florida. She received her PhD in industrial engineering from Texas A&M University. Dr. Alvarado's research interests lie in decision-making for complex systems uncertainty. She specializes in simulation modeling and stochastic programming applied to healthcare systems engineering, focusing on problems in healthcare operations, scheduling, and health policy. She is a member of IISE and INFORMS.
Nan Kong
Dr. Nan Kong is Professor of Biomedical Engineering at Purdue University. He received his BS from the Department of Automation, Tsinghua University, Beijing, China, and PhD from the Department of Industrial Engineering, University of Pittsburgh. He leads the Biomedical Analytics and Systems Optimization Research Lab at Purdue University. His primary research thrusts are (i) service operations management with focus on integrated care delivery system design and its joint optimization with operational planning decisions (primary focus); (ii) healthcare analytics with emphasis on EHR and claims data; and (iii) complex dynamics systems simulation and analysis for population health policy. He has published over 70 peer-reviewed journal articles.
Pratik J. Parikh
Dr. Pratik J. Parikh is a Professor and Chair of the Department of Industrial Engineering at the University of Louisville. Prior to that, he was a faculty at Wright State University in Dayton, OH. He received a B.S. degree in mechanical engineering from The M. S. University of Baroda (India) in 2001, MS degree in systems science from Binghamton University (NY, USA) in 2002, and a PhD. degree in industrial and systems engineering from the Virginia Polytechnic Institute and State University, Blacksburg (VA, USA) in 2006. His current research focuses on applying data mining, simulation, and optimization methodologies to address challenges in healthcare. He is a Senior Member of the Institute of Industrial and Systems Engineers.
Mark A. Lawley
Dr. Mark A. Lawley is Professor and former Head of the Department of Industrial and Systems Engineering at Texas A&M University. He is holder of the Andrew Rader Professorship and holds appointments in the department of biomedical engineering and the department of epidemiology and biostatistics. Dr. Lawley also serves as deputy director of the center for remote health technologies and systems, which focuses on developing breakthrough health care devices, technologies and systems for disease prevention, diagnosis and management in the global health setting. Before joining Texas A&M in 2014, Dr. Lawley served for 17 years on the faculty at Purdue University in the Schools of Industrial Engineering and Biomedical engineering. Dr. Lawley has also held engineering positions with Emerson Electric Company and Westinghouse Corporation and has done extensive consulting. He received a PhD in mechanical engineering from the University of Illinois at Urbana Champaign in 1995 and is a registered Professional Engineer in the state of Texas.