This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/77393
The final accepted version (not copyedited yet) is in this tab.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Unlocking the Full Potential of Health Care Teams: How Artificial Intelligence Can Help
ABSTRACT
Developing effective health care teams is critical to meet the rising complexity in patient care. However, optimizing team composition, interpersonal dynamics, and care processes in complex health care systems requires processing vast amounts of data that capture fluid interactions among professionals – a task that has been cumbersome, costly, and avoided by most organizations. Well-designed AI tools can meaningfully advance the frontier of health care teamwork, but the application of AI in this regard has been lagging. To support this development, we outline the potential for AI to help optimize team composition, strengthen norms and relationships among professionals, and standardize team-based clinical care processes. These applications can improve the integration of health care teams. Given the importance of relevant data for realizing such advances, we describe the potential types and sources of data that can support AI development. Furthermore, we suggest that data-sharing alliances may be crucial. Federated learning approaches, accountable care organization policy, and leadership support for AI-related team development may help. We end with a reminder to ensure that humans remain central to the interpretation and implementation of AI recommendations, since health care service delivery remains a human-driven activity.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.