Several potential barriers must be addressed as AI algorithms are integrated into diagnostic teams. For example, liability in the event of a missed, inaccurate, or delayed diagnosis is an active area of discussion.44 In addition, payer reimbursement strategies for the use of AI algorithms in the diagnostic process are evolving. Finally, attention must be given to the potential for historically marginalized individuals to be negatively impacted by biases perpetuated by algorithms and unequal access to effective algorithms. Addressing these challenges will require engagement from all members of the diagnostic ecosystem (Figure 3).
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Reimagining Healthcare Teams: Leveraging the Patient-Clinician-AI Triad To Improve Diagnostic Safety
Barriers
Table of Contents
- Reimagining Healthcare Teams: Leveraging the Patient-Clinician-AI Triad To Improve Diagnostic Safety
- Introduction
- The Patient-Clinician Dyad
- The Patient-Artificial Intelligence Dyad
- The Clinician-Artificial Intelligence Dyad
- The Patient-Clinician-Artificial Intelligence Triad
- Core Principles for the PCA Diagnostic Team
- Barriers
- Conclusion
- References
Page last reviewed July 2023
Page originally created July 2023
Internet Citation: Barriers. Content last reviewed July 2023. Agency for Healthcare Research and Quality, Rockville, MD.
https://www.ahrq.gov/diagnostic-safety/resources/issue-briefs/dxsafety-reimagining-healthcare-teams-7.html