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A dynamic nonlinear flow algorithm to model patient flow.

Arsineh Boodaghian Asl1, Jayanth Raghothama2, Adam S Darwich2

  • 1Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Huddinge, 14157, Sweden. arsineh@kth.se.

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This study introduces a modified flow algorithm to analyze patient flow in hospitals. The algorithm identifies bottlenecks by considering factors like bed capacity and staff availability, improving hospital performance insights.

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Area of Science:

  • Healthcare Systems Engineering
  • Operations Research
  • Network Flow Analysis

Background:

  • Hospitals are complex systems with dynamic and nonlinear patient flow.
  • Existing flow algorithms primarily focus on transportation and telecommunication, with limitations in healthcare applications.
  • Hospital bottlenecks arise from ward behavior (capacity, staff, service time) and care pathway distribution.

Purpose of the Study:

  • To develop a modified flow algorithm for modeling dynamic patient flow in hospitals.
  • To identify and quantify bottlenecks within hospital networks.
  • To provide a holistic view of hospital performance and analyze dynamic ward behavior.

Main Methods:

  • A modified network flow algorithm is proposed.
  • The algorithm iterates over patient arrival rates, considering vertices' capacities, servers, service time variability, and edge distribution probability.
  • A dynamic residual graph is generated to measure bottleneck persistency and severity.

Main Results:

  • The algorithm effectively measures patient flow dynamics within a hospital network.
  • It identifies bottlenecks, their root causes, and the nonlinear behavior of hospital wards.
  • The dynamic residual graph provides insights into bottleneck severity and persistence over time.

Conclusions:

  • The modified flow algorithm offers a novel approach to analyzing hospital patient flow.
  • It enables a comprehensive understanding of hospital operational performance and bottleneck dynamics.
  • This method aids in optimizing hospital resource allocation and improving patient care pathways.