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CC BY 4.0 · Appl Clin Inform 2025; 16(05): 1359-1367
DOI: 10.1055/a-2700-7036
Research Article

Nursing Performance Using Clinical Prediction Rules for Acute Respiratory Infection Management: A Case-Based Simulation

Authors

  • Victoria L. Tiase

    1   Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, Utah, United States

  • Patrice Hicks

    2   Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States

  • Haddy Bah

    3   Department of Population Health Sciences, School of Medicine, University of Utah, Salt Lake City, Utah, United States

  • Ainsley Snow

    3   Department of Population Health Sciences, School of Medicine, University of Utah, Salt Lake City, Utah, United States

  • Devin M. Mann

    4   Department of Population Health and MCIT Department of Health Informatics, NYU Grossman School of Medicine, New York, New York, United States

  • David A. Feldstein

    5   Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States

  • Wendy Halm

    6   University of Wisconsin-Madison School of Nursing, Madison, Wisconsin, United States

  • Paul D. Smith

    7   Department of Family Medicine and Community Health, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States

  • Rachel Hess

    3   Department of Population Health Sciences, School of Medicine, University of Utah, Salt Lake City, Utah, United States

Funding This study was supported by the U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Allergy and Infectious Diseases (grant no.: 5R01AI108680-10) and U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Nursing Research (grant no.: K01NR021256)
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Abstract

Background

Overuse and misuse of antibiotics is an urgent health care problem and one of the key factors in antibiotic resistance. Validated clinical prediction rules have shown effectiveness in guiding providers to an appropriate diagnosis and identifying when antibiotics are the recommended choice for treatment.

Objectives

We aimed to study the relative ability of registered nurses using clinical prediction rules to guide the management of acute respiratory infections in a simulated environment compared with practicing primary care physicians.

Methods

We evaluated a case-based simulation of the diagnosis and treatment for acute respiratory infections using clinical prediction rules. As a secondary outcome, we examined nursing self-efficacy by administering a survey before and after case evaluations. Participants included 40 registered nurses from three academic medical centers and five primary care physicians as comparators. Participants evaluated six simulated case studies, three for patients presenting with cough symptoms, and three for sore throat.

Results

Compared with physicians, nurses determined risk and treatment for simulated sore throat cases using clinical prediction rules with 100% accuracy in low-risk sore throat cases versus 80% for physicians. We found great variability in the accuracy of the risk level and appropriate treatment for cough cases. Nurses reported slight increases in self-efficacy from baseline to postcase evaluation suggesting further information is needed to understand correlation.

Conclusion

Clinical prediction rules used by nurses in sore throat management workflows can guide accurate diagnosis and treatment in simulated cases, while cough management requires further exploration. Our results support the future implementation of automated prediction rules in a clinical decision support tool and a thorough examination of their effect on clinical practice and patient outcomes.

Keywords

antimicrobial stewardship - clinical decision support - clinical practice guideline - clinical informatics - nurse

Protection of Human and Animal Subjects

This study was approved by the Institutional Review Board. Informed consent was obtained from all participants who were notified of data protection procedures and their right to refuse to participate.


Authors' Contributions

V.L.T. and R.H. have access to all the data in the study and take responsibility for the data and the accuracy of the data analysis. R.H., P.H., and V.L.T. are responsible for the concept and design of the study and the acquisition, analysis, and interpretation of data. P.H. and V.L.T. contributed to drafting the manuscript. All authors critically revised the manuscript and approved the final version. D.A.F., D.M.M., and R.H. obtained the funding.


Supplementary Material



Publication History

Received: 27 February 2025

Accepted: 09 September 2025

Accepted Manuscript online:
15 September 2025

Article published online:
17 October 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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