Recommendations for the Safe, Effective Use of Adaptive CDS in the US Healthcare System

Figure 1. Policy recommendations for all stages of Adaptive CDS (ACDS)—design and development, implementation, evaluation, and ongoing monitoring—require further development to ensure safe and effective ACDS. A concerted multistakeholder effort to identify key transparency metrics for training datasets and communications standards for AI-driven applications in healthcare is needed to understand how bias can corrupt AI-driven decision support and identify ways to mitigate such bias. Additionally, policies that standardize in situ testing and evaluation, as well as ongoing maintenance, of ACDS should be established.

Recommendations for the Safe, Effective Use of Adaptive CDS…

The development and implementation of clinical decision support (CDS) that trains itself and adapts its algorithms based on new data—here referred to as Adaptive CDS—present unique challenges and considerations. Although Adaptive CDS represents an expected progression from earlier work, the activities needed to appropriately manage and support the establishment and evolution of Adaptive CDS require new, coordinated initiatives and oversight that do not currently exist.

Journal of the American Medical Informatics AssociationIn this AMIA position paper, the authors describe current and emerging challenges to the safe use of Adaptive CDS and lay out recommendations for the effective management and monitoring of Adaptive CDS.

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Petersen C, Smith J, Freimuth RR, Goodman KW, Jackson GP, Kannry J, Liu H, Madhavan S, Madhavan S, Sittig DF, Wright A. Recommendations for the safe, effective use of adaptive CDS in the US healthcare system: an AMIA position paper, Journal of the American Medical Informatics Association 2021; ocaa319, https://doi.org/10.1093/jamia/ocaa319