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Automated Bed Assignments in a Complex and Dynamic Hospital Environment

Author

Listed:
  • Bex George Thomas

    (General Electric Global Research, Software Sciences and Analytics, Niskayuna, New York 12309)

  • Srinivas Bollapragada

    (General Electric Global Research, Software Sciences and Analytics, Niskayuna, New York 12309)

  • Kunter Akbay

    (General Electric Global Research, Software Sciences and Analytics, Niskayuna, New York 12309)

  • David Toledano

    (General Electric Global Research, Software Sciences and Analytics, Niskayuna, New York 12309)

  • Peter Katlic

    (General Electric Global Research, Software Sciences and Analytics, Niskayuna, New York 12309)

  • Onur Dulgeroglu

    (General Electric Global Research, Software Sciences and Analytics, Niskayuna, New York 12309)

  • Dan Yang

    (General Electric Global Research, Software Sciences and Analytics, Niskayuna, New York 12309)

Abstract

Bed management, an important function of any hospital, has a major impact on patient care, patient flow, patient and staff satisfaction, and ultimately on the hospital’s operating margin. A key challenge in bed management is optimizing the bed-assignment process in a complex and dynamic operating environment. Efficient bed assignment requires the merging of clinical information, hospital operations information, interdependencies between units, and real-time information on patients, resources, and workflows. We have developed analytical decision support tools with embedded mathematical models to periodically recommend bed-patient assignments. Using an innovative mixed-integer goal-programming modeling approach, we are able to accommodate the multiple goals and complex operating rules of different hospitals. We implemented and hosted our prototype bed-assignment solution as a cloud-based application for Mount Sinai Medical Center in New York.

Suggested Citation

  • Bex George Thomas & Srinivas Bollapragada & Kunter Akbay & David Toledano & Peter Katlic & Onur Dulgeroglu & Dan Yang, 2013. "Automated Bed Assignments in a Complex and Dynamic Hospital Environment," Interfaces, INFORMS, vol. 43(5), pages 435-448, October.
  • Handle: RePEc:inm:orinte:v:43:y:2013:i:5:p:435-448
    DOI: 10.1287/inte.2013.0701
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    Citations

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    Cited by:

    1. 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).
    2. Seung-Yup Lee & Ratna Babu Chinnam & Evrim Dalkiran & Seth Krupp & Michael Nauss, 2020. "Prediction of emergency department patient disposition decision for proactive resource allocation for admission," Health Care Management Science, Springer, vol. 23(3), pages 339-359, September.
    3. David Scheinker & Margaret L. Brandeau, 2020. "Implementing Analytics Projects in a Hospital: Successes, Failures, and Opportunities," Interfaces, INFORMS, vol. 50(3), pages 176-189, May.
    4. Guido, Rosita & Groccia, Maria Carmela & Conforti, Domenico, 2018. "An efficient matheuristic for offline patient-to-bed assignment problems," European Journal of Operational Research, Elsevier, vol. 268(2), pages 486-503.
    5. Aleida Braaksma & Martin S. Copenhaver & Ana C. Zenteno & Elizabeth Ugarph & Retsef Levi & Bethany J. Daily & Benjamin Orcutt & Kathryn M. Turcotte & Peter F. Dunn, 2023. "Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients," Health Care Management Science, Springer, vol. 26(3), pages 501-515, September.

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