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Appl Clin Inform 2025; 16(01): 223-233
DOI: 10.1055/a-2451-9046
Research Article

Exploring Stakeholder Perceptions about Using Artificial Intelligence for the Diagnosis of Rare and Atypical Infections

Aysun Tekin
1   Division of Nephrology and Hypertension, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Svetlana Herasevich
2   Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Sarah A. Minteer
3   Department of Physical Medicine and Rehabilitation Research, Mayo Clinic, Rochester, Minnesota, United States
,
Ognjen Gajic
4   Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, United States
,
Amelia K. Barwise
4   Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, United States
› Author Affiliations
Funding This publication was supported by Grant Number UL1 TR002377 from the National Center for Advancing Translational Sciences (NCATS). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.This work is supported by benefactor funding via the Mayo Clinic Rochester Division of Pulmonary and Critical Care Medicine (O.G.) and by the Minnesota Partnership for Biotechnology and Medical Genomics (P008848012; O.G.).A.K.B. receives funding from the Agency for Healthcare Research and Quality (AHRQ; R21 HS028475).The funders had no influence on the study design, data collection, analysis, interpretation, and reporting of pooled data.
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Abstract

Objectives This study aimed to evaluate critical care provider perspectives about diagnostic practices for rare and atypical infections and the potential for using artificial intelligence (AI) as a decision support system (DSS).

Methods We conducted an anonymous web-based survey among critical care providers at Mayo Clinic Rochester between November 25, 2023, and January 15, 2024, to evaluate their experience with rare and atypical infection diagnostic processes and AI-based DSSs. We also assessed the perceived usefulness of AI-based DSSs, their potential impact on improving diagnostic practices for rare and atypical infections, and the perceived risks and benefits of their use.

Results A total of 47/143 providers completed the survey. Thirty-eight out of 47 agreed that there was a delay in diagnosing rare and atypical infections. Among those who agreed, limited assessment of specific patient factors and failure to consider them were the most frequently cited important contributing factors (33/38). Thirty-eight out of 47 reported familiarity with the AI-based DSS applications available to critical care providers. Less than half (18/38) thought AI-based DSSs often provided valuable insights into patient care, but almost three-quarters (34/47) thought AI-based DDSs often provided valuable insight when specifically asked about their ability to improve the diagnosis of rare and atypical infections. All respondents rated reliability as important in enhancing the perceived utility of AI-based DSSs (47/47) and almost all rated interpretability and integration into the workflow as important (45/47). The primary concern about implementing an AI-based DSS in this context was alert fatigue (44/47).

Conclusion Most critical care providers perceive that there are delays in diagnosing rare infections, indicating inadequate assessment and consideration of the diagnosis as the major contributors. Reliability, interpretability, workflow integration, and alert fatigue emerged as key factors impacting the usability of AI-based DSS. These findings will inform the development and implementation of an AI-based diagnostic algorithm to aid in identifying rare and atypical infections.

Keywords

artificial intelligence - atypical infections - critical care providers - decision support system - diagnostic delay

Protection of Human and Animal Subjects

The Mayo Clinic Institutional Review Board reviewed and approved the survey study as minimal risk (#22-009881). The survey instructions stated that continuing the survey would constitute implied consent.


Supplementary Material



Publication History

Received: 16 July 2024

Accepted: 24 October 2024

Accepted Manuscript online:
25 October 2024

Article published online:
05 March 2025

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