AMIA details policy framework for AI/ML-driven decision support

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AMIA details policy framework for AI/ML-driven decision support

Press releases may be edited for formatting or style | February 15, 2021 Artificial Intelligence Business Affairs
February 8, 2021, BETHESDA, MD — The American Medical Informatics Association (AMIA) released a Board-approved position paper in the Journal of the American Medical Informatics Association (JAMIA), calling for new and flexible oversight mechanisms to ensure the safe, effective use of artificial intelligence (AI) applications in healthcare. Specifically, AMIA focuses on AI-driven clinical decision support (CDS) systems, offering policy recommendations to the Food & Drug Administration (FDA) and articulating a new framework for the non-regulatory oversight of "Adaptive CDS."

Stemming from a policy meeting held in December 2019, the AMIA position paper uses the term "Adaptive CDS" to describe CDS that can learn and change its performance over time, incorporating new clinical evidence; new data types and data sources; and new methods for interpreting data. Adaptive CDS enables personalized decision support in a way that has not been possible previously because it has the capacity to learn from data and modify recommendations based on those data. Adaptive CDS stands in contrast to "static" CDS, which are those tools that provide the same output (recommendation/guidance) each time the same input is provided without change through use.

"The informatics community invented CDS, and AMIA members have championed the advancement of CDS for decades," said Patricia C. Dykes, PhD, RN, FAAN, FACMI, AMIA Board Chair and Program Director of Research at the Brigham and Women's Center for Patient Safety, Research, and Practice. "An exponential growth in health data, combined with growing capacities to store and analyze such data through cloud computing and machine learning, obligates the informatics community to lead a discussion on ways to ensure safe, effective CDS in such a dynamic landscape."

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"The use of AI in healthcare presents clinicians and patients with opportunities to improve care in unparalleled ways," said Carolyn Petersen, lead author and AMIA Public Policy Committee Member. "Equally unparalleled is the urgency to create safeguards and oversight mechanisms for the use of machine learning-driven applications for patient care."

AMIA focuses on the use case of Adaptive CDS because it represents a wide range of potential
tools and applications – some examples of which exist today, but many of which do not – and because Adaptive CDS represents a conceptual use case within a larger ecosystem of potential use cases of AI in healthcare. By framing discussion on Adaptive CDS, AMIA hopes to engender a practical discussion of policies needed to ensure safe and effective use of AI-driven CDS for patient care and facilitate a wider discussion of policies needed to build trust in the broader use of AI in healthcare.

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