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Real-time Cohorting of Nursing Care into Bubbles
Conference proceeding

Real-time Cohorting of Nursing Care into Bubbles

Jeffrey Keithley, Tinh Tran, Lucas Zach-Ryan, D. M. Hasibul Hasan, Brodie McCuen, Sriram V. Pemmaraju and Bijaya Adhikari
Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems, pp.2500-2508
ACM Conferences
AAMAS 2026: Autonomous Agents and Multiagent Systems
05/25/2026
DOI: 10.65109/GLZA7190

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Abstract

Healthcare facilities, such as hospitals and long-term care facilities, which house vulnerable populations, are sites for pathogens (such as Antibiotic-Resistant Organisms, Clostridioides difficile, influenza viruses, and SARS-CoV-2, among others) to spread. The contacts between healthcare providers (HCPs) and the patients that naturally emerge during the course of care delivery also serve as pathways for the infections to flow. Prior work has identified that cohorting patients and HCPs into nearly isolated groups leads to reduction in infection. However, most of these works are either too simplistic (e.g. cohorting after observing infections) or have limited practical use (e.g. retrospective cohorting). In this paper, our goal is to minimize infection spread in real-time by creating cohorts of HCPs and patients on-the-fly as patients are being admitted, discharged or transferred. Specifically, we formulate the novel Online Bubble Clustering Problem, which asks to create and maintain cohorts of HCPs and patients that have limited external contacts, yet meet the care demands of the patients and care capacity of the HCPs. We also theoretically demonstrate that the problem is very challenging in both deterministic and stochastic settings, implying no algorithm could achieve results close to the optimal solution. Despite the hardness, we propose natural heuristics and design offline an integer linear programming (ILP) approach to contrast the heuristics against the optimal solution. We also conduct extensive agent-based modeling experiments on granular HCP mobility data collected using sensor systems we previously deployed in a medical intensive care unit over a 30-day period, overlaid with a COVID-19 agent-based disease model. Our simulation results show that real-time cohorting leads to significantly lower disease prevalence, cutting cases in half in some scenarios, while maintaining reasonably low levels of excess mobility and HCP workload.
 Applied computing -- Health care information systems Computing methodologies -- Simulation evaluation Mathematics of computing -- Combinatorial optimization Theory of computation -- Network optimization

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