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ORIGINAL RESEARCH article

Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 7 - 2024 | doi: 10.3389/frai.2024.1422208

Graph Theoretic Visualization of Patient and Health worker Messaging in the EHR

Provisionally accepted
Muhammad Zia ul Haq Muhammad Zia ul Haq 1*Andrew Hornback Andrew Hornback 2*Arash Harzand Arash Harzand 3David A. Gutman David A. Gutman 4Bradley Gallaher Bradley Gallaher 4Evan D. Schoenberg Evan D. Schoenberg 4Yuanda Zhu Yuanda Zhu 4May Dongmei Wang May Dongmei Wang 2,3Blake Anderson Blake Anderson 4
  • 1 Aga Khan University, Karachi, Sindh, Pakistan
  • 2 Georgia Institute of Technology, Atlanta, United States
  • 3 Emory University, Atlanta, Georgia, United States
  • 4 Switchboard, MD., Atlanta, United States

The final, formatted version of the article will be published soon.

    The electronic health record (EHR) has greatly expanded healthcare communication between patients and health workers. However, the volume and complexity of EHR messages have increased health workers cognitive load, impeding effective care delivery and contributing to burnout. To understand these potential detriments resulting from EHR communication, we analyzed EHR messages sent between patients and health workers at Emory Healthcare, a large academic healthcare system in Atlanta, Georgia. We quantified the burden of messages interacted with by each health worker type and visualized the communication patterns using graph theory. Our analysis included 76,694 conversations comprised of 144,369 messages sent between 47,460 patients and 3,749 health workers across 85 healthcare specialties. On average, nurses/certified nursing assistants/medical assistants (nurses/CNA/MA) touched the most messages (350), followed by non-physician practitioners (NPP) (241), physicians (166), and support staff (155), with the average conversation involving 10.51 touches before resolution. Network analysis of the communication flow revealed that each health worker was connected to approximately two other health workers (average degree = 2.10). In message sending, support staff led in closeness centrality (0.44), followed by nurses/CNA/MA (0.41), highlighting their key role in fast information spread. For message reception, nurses/CNA/MA (0.51) and support staff (0.41) also had the highest values, highlighting their vital role in the communication network on the receiving end as well. Our analysis demonstrates the feasibility of applying graph theory to understanding communication dynamics between patients and health workers and highlights the burden of EHR-based messaging.

    Keywords: artificial intelligence, data visualization, Electronic Health Records, Electronic Medical Records, graph visualization, Network analysis

    Received: 23 Apr 2024; Accepted: 06 Nov 2024.

    Copyright: © 2024 Zia ul Haq, Hornback, Harzand, Gutman, Gallaher, Schoenberg, Zhu, Wang and Anderson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Muhammad Zia ul Haq, Aga Khan University, Karachi, 74800, Sindh, Pakistan
    Andrew Hornback, Georgia Institute of Technology, Atlanta, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.