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Proactive coordination of inpatient bed management to reduce emergency department patient boarding

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  • Lee, Seung-Yup
  • Chinnam, Ratna Babu
  • Dalkiran, Evrim
  • Krupp, Seth
  • Nauss, Michael

Abstract

Emergency departments (EDs) across the world are experiencing severe crowding and prolonged patient wait times for hospital admissions (a.k.a. patient “boarding”). Using data from a major healthcare system, we show that EDs suffer from severe boarding not only due to a high level of hospital inpatient bed occupancy but also due to reactive coordination of inpatient bed management activities. To reduce patient boarding, we explore early task initiation for the service network spanning the ED and inpatient units within a hospital. In particular, we investigate the value of predicting ED patient admissions (to be specific, disposition decisions) during the ED caregiving process to proactively initiate downstream tasks for reduced patient boarding. We show that the coordination mechanism can be modeled as a fork–join queueing system. The proposed modeling framework accounts for both imperfect patient disposition predictions and multiple hospital admission sources (in addition to the ED) for inpatient units. We maintain analytical tractability while preserving the complexities of real-world inpatient bed management operations by characterizing the state sets and transition sequences through the Markovian assumption. The proactive inpatient bed allocation scheme can lead to significant reductions in bed allocation delays for ED patients (nearly up to ∼50%) and does not increase delays for other admission sources. The insights from our model should guide hospital managers in embracing proactive coordination and adaptive workflow technologies enabled by modern health information technology systems and predictive analytics.

Suggested Citation

  • Lee, Seung-Yup & Chinnam, Ratna Babu & Dalkiran, Evrim & Krupp, Seth & Nauss, Michael, 2021. "Proactive coordination of inpatient bed management to reduce emergency department patient boarding," International Journal of Production Economics, Elsevier, vol. 231(C).
  • Handle: RePEc:eee:proeco:v:231:y:2021:i:c:s0925527320302085
    DOI: 10.1016/j.ijpe.2020.107842
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    References listed on IDEAS

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    Cited by:

    1. Tsai, Eline R. & Demirtas, Derya & Tintu, Andrei N. & de Jonge, Robert & de Rijke, Yolanda B. & Boucherie, Richard J., 2023. "Design of fork-join networks of First-In-First-Out and infinite-server queues applied to clinical chemistry laboratories," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1101-1117.
    2. Gupta, Shivam & Modgil, Sachin & Kumar, Ajay & Sivarajah, Uthayasankar & Irani, Zahir, 2022. "Artificial intelligence and cloud-based Collaborative Platforms for Managing Disaster, extreme weather and emergency operations," International Journal of Production Economics, Elsevier, vol. 254(C).
    3. Dinesh R. Pai & Fatma Pakdil & Nasibeh Azadeh-Fard, 2024. "Applications of data envelopment analysis in acute care hospitals: a systematic literature review, 1984–2022," Health Care Management Science, Springer, vol. 27(2), pages 284-312, June.
    4. Shivam Gupta & Sachin Modgil & Ajay Kumar & Uthayasankar Sivarajah & Zahir Irani, 2022. "Artificial intelligence and cloud-based Collaborative Platforms for Managing Disaster, extreme weather and emergency operations," Post-Print hal-04325638, HAL.
    5. Ma, Xin & Zhao, Xue & Guo, Pengfei, 2022. "Cope with the COVID-19 pandemic: Dynamic bed allocation and patient subsidization in a public healthcare system," International Journal of Production Economics, Elsevier, vol. 243(C).

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