Startseite Optimizing use of illness script builder templates in medical education
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Optimizing use of illness script builder templates in medical education

  • Erin H. Yang , Joseph Rencic ORCID logo , Alec B. Rezigh und Prathit A. Kulkarni EMAIL logo
Veröffentlicht/Copyright: 29. Mai 2025
Diagnosis
Aus der Zeitschrift Diagnosis

Abstract

Illness scripts are mental networks of information about medical diseases and are used by clinicians to efficiently make diagnostic and treatment decisions. The traditional components of an illness script include the epidemiologic risk factors, pathophysiology, and clinical findings of a disease [Feltovich PJ, Barrows HS. Issues of generality in medical problem solving. In: Schmidt HG, De Volder ML, editors. Tutorials in problem-based learning. Assen/Maastricht: Van Gorcum; 1984:128–42 pp]. Here, we coin the term illness script builder templates (ISBTs), which are educational tools to help learners develop their illness scripts. While the actual concept of ISBTs is not new, we aim to explicitly distinguish these discrete learning tools from illness scripts themselves, which are mental frameworks. ISBTs have significant pedagogic value. They are being more widely adopted in clinical curricula in medical school, as they have been well-received by students and educationally effective. ISBTs allow the integration of new knowledge to be intuitive, as they take advantage of our predilection toward story-based learning. Limitations to ISBTs also exist, especially when considering their optimal length and structure as well as complexity for different levels of learners. We additionally explore the specific strengths and limitations of ISBTs and propose strategies to maximize their use in education and clinical practice.


Corresponding author: Prathit A. Kulkarni, MD, Medical Care Line, Michael E. DeBakey Veterans Affairs Medical Center, 2002 Holcombe Blvd., Mailstop: MCL-111, Houston, 77030, TX, USA; and Infectious Diseases Section, Department of Medicine, Baylor College of Medicine, Houston, TX, USA, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-01-09
Accepted: 2025-05-06
Published Online: 2025-05-29

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Heruntergeladen am 8.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/dx-2025-0005/html
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