New Models Address Problems Related to Timing, Scheduling of Surgeries, Capacity Planning, and Patients’ Stays in Recovery Units

Proposed Solutions Could Lower Delays, Congestion, Costs

In collaboration with a hospital, researchers examined problems related to the timing and scheduling of surgeries and patients’ stays in recovery units. They developed an integrated elective surgery assignment, sequencing, and scheduling problem (ESASSP) and devised new ways to solve it. Implementing solutions based on the study’s models could significantly reduce congestion in recovery units, delays in operating rooms, overtime, idle time, and costs, the authors conclude.

The study was conducted by researchers at Carnegie Mellon University, the University of Southern California (USC), Texas Tech University, and the Medical University of South Carolina. It is published in the European Journal of Operational Research.

“Our findings offer valuable insights into the ESASSP and demonstrate the practical impact of our integrated approaches,” says Rema Padman, professor of management science and healthcare informatics at Carnegie Mellon’s Heinz College, who coauthored the study.

The ESASSP is an important resource-constrained scheduling problem that presents a number of fundamental challenges, according to the study’s authors. Among them are:

• The problems associated with coordinating multiple resources (e.g., operating rooms, ICU beds, ward beds), most of which are expensive to operate and have tight capacities;
  
  
• The variation in duration of surgeries and lengths of stays in recovery units; and
  
  
• The decision making problems involved with assigning surgeries to operating rooms and downstream capacity, which often result in overtime, long waits for surgery, congestion in recovery units, and premature discharges from the ICU and wards, which can compromise health outcomes.

Based on these challenges, the authors suggest that hospitals could benefit greatly from computationally efficient, integrated approaches for solving the ESASSP.

In their study, which evaluated three sets of surgery data, the authors proposed and analyzed distributionally robust optimization (DRO) approaches for the ESASSP. Specifically, they formulated a DRO model, which identified ESASSP decisions that minimized the fixed costs associated with performing or postponing surgeries plus the maximum expectation of the operational costs. Then they evaluated the worst-case expectation over probability distributions within predefined ambiguity sets of possible probability distributions for surgery durations and lengths of stay. They also analyzed a stochastic programming model, finding ESASSP decisions that minimized the fixed and expected operational costs.

Through a comprehensive computational analysis of three sets of real-world surgical data, the study offers the following insights for researchers and practitioners:

• Hospitals offering elective surgeries that require recovery in the surgical ICU or ward should adopt an integrated operating room-to-downstream stochastic optimization model to schedule surgeries, which can reduce total costs from 24% to 60%.
  
  
• There is a tradeoff between access (measured by volume of surgery) and operational performance in the operating room and downstream units, with no clear winner among the proposed approaches to balancing this tradeoff.
  
  
• New data-driven optimization models for operating room-to-downstream elective surgery planning may be useful to hospitals where distributions of surgery duration and lengths of stay in recovery units are difficult to estimate or constantly changing.

The authors caution that while their proposed models could provide guidelines for practitioners to make ESASSP decisions, they are not directly implementable in practice without further work to develop and test tools that ensure successful adaptation—especially since most hospitals lack staff with expertise in optimization.

“Prior studies have tackled isolated components of the ESASSP,” explains Karmel S. Shehadeh, assistant professor of industrial and systems engineering at USC, who led the study. “Ours introduces the first models that account for uncertainties and ambiguities in surgery durations and post-operative lengths of stay in recovery units, while also addressing the challenges of optimizing surgery schedules under the limited capacities of the ICU and other wards.”

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Summarized from an article in the European Journal of Operational Research, "Operating Room-to-Downstream Elective Surgery Planning Under Uncertainty," by Shehadeh, KS (University of Southern California), Tsang, MY (Texas Tech University), Padman, R (Carnegie Mellon University), and Kilic, A (Medical University of South Carolina). Copyright 2025 Elsevier. All rights reserved.

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The Heinz College of Information Systems and Public Policy is home to two internationally recognized schools: the School of Information Systems and Management and the School of Public Policy and Management. Heinz College leads at the intersection of people, policy, and technology, with expertise in analytics, artificial intelligence, arts & entertainment, cybersecurity, health care, and public policy. The college offers top-ranked undergraduate, graduate, and executive education certificates in these areas. Our programs are ranked #1 in Information Systems, #1 in Information and Technology Management, #8 in Public Policy Analysis, and #1 in Cybersecurity by U.S. News & World Report. For more information, visit www.heinz.cmu.edu.