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Bringing team science to the ambulatory diagnostic process: how do patients and clinicians develop shared mental models?

  • Aubrey Samost-Williams ORCID logo EMAIL logo , Eric J. Thomas , Olivia Lounsbury , Scott I. Tannenbaum , Eduardo Salas and Sigall K. Bell
Published/Copyright: October 21, 2024

Abstract

The ambulatory diagnostic process is potentially complex, resulting in faulty communication, lost information, and a lack of team coordination. Patients and families have a unique position in the ambulatory diagnostic team, holding privileged information about their clinical conditions and serving as the connecting thread across multiple healthcare encounters. While experts advocate for engaging patients as diagnostic team members, operationalizing patient engagement has been challenging. The team science literature links improved team performance with shared mental models, a concept reflecting the team’s commonly held knowledge about the tasks to be done and the expertise of each team member. Despite their proven potential to improve team performance and outcomes in other settings, shared mental models remain underexplored in healthcare. In this manuscript, we review the literature on shared mental models, applying that knowledge to the ambulatory diagnostic process. We consider the role of patients in the diagnostic team and adapt the five-factor model of shared mental models to develop a framework for patient-clinician diagnostic shared mental models. We conclude with research priorities. Development, maintenance, and use of shared mental models of the diagnostic process amongst patients, families, and clinicians may increase patient/family engagement, improve diagnostic team performance, and promote diagnostic safety.


Corresponding Author: Aubrey Samost-Williams, MD, MS, Assistant Professor, Anesthesiology, Critical Care, and Pain Medicine, The University of Texas Health Science Center at Houston, 6431 Fannin St, MSB 5.020, Houston, TX, 77030, USA, E-mail:

Award Identifier / Grant number: 5T32GM135118-02

Award Identifier / Grant number: 5R18HS029362-02

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: The 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: Aubrey Samost-Williams was funded by the National Institutes of Health grant #5T32GM135118-02. Eric Thomas and Sigall Bell were funded by the Agency for Health Care Research and Quality grant #5R18HS029362-02.

  7. Data availability: Not applicable.

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Received: 2024-07-03
Accepted: 2024-09-23
Published Online: 2024-10-21

© 2024 Walter de Gruyter GmbH, Berlin/Boston

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