IDEAS home Printed from https://ideas.repec.org/a/inm/orserv/v5y2013i1p17-28.html
   My bibliography  Save this article

A Process Flow-Based Framework for Nurse Demand Estimation

Author

Listed:
  • Jomon Aliyas Paul

    (Department of Economics, Finance and Quantitative Analysis, Coles College of Business, Kennesaw State University, Kennesaw, Georgia 30144)

  • Leo MacDonald

    (Department of Economics, Finance and Quantitative Analysis, Coles College of Business, Kennesaw State University, Kennesaw, Georgia 30144)

Abstract

The nursing shortage in the United States poses a serious problem to hospitals, given that nurses provide an indispensable service within the healthcare system. This issue is expected to worsen, especially given the aging population of baby-boomers, which includes those that are part of the nurse workforce. This has resulted in a wide variety of problems, including patient safety issues, inability to detect complications, and potential patient mortality rate increases. Nurse shortage implications go beyond healthcare quality, extending to health economics as well. Inaccurate estimates of the nursing resources required to satisfy patient demand in a hospital environment could make this already-challenging problem worse. In addition, mandatory nurse-to-patient ratios implemented in some states, though providing for simplification from a demand estimation perspective, create a risk of under- or overestimating required nurse resources. We develop a series of process flow-based models that take into account the inherent complexity in key hospital departments and hence become the basis of empirical models to estimate nurse demands and thereby the best use of the scarce available nurse resource pool. In addition, via an illustrative example of a simple intensive care unit system, we demonstrate the issues with mandatory nurse-to-patient ratios in addressing the nurse shortage crisis when subject to varying patient demand and hospital service quality goals.

Suggested Citation

  • Jomon Aliyas Paul & Leo MacDonald, 2013. "A Process Flow-Based Framework for Nurse Demand Estimation," Service Science, INFORMS, vol. 5(1), pages 17-28, March.
  • Handle: RePEc:inm:orserv:v:5:y:2013:i:1:p:17-28
    DOI: 10.1287/serv.1120.0032
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/serv.1120.0032
    Download Restriction: no

    File URL: https://libkey.io/10.1287/serv.1120.0032?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:orserv:v:5:y:2013:i:1:p:17-28. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.