How responsible AI can boost patient outcomes

How responsible AI can boost patient outcomes

Lisa Jarrett, an artificial intelligence expert at PointClickCare, previews her HIMSS24 session that will dive into AI topics including transparency, fairness, explainability and transparency.

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The article emphasizes the need for hospitals and health systems to strike a balance between leveraging the opportunities presented by artificial intelligence (AI) to improve patient outcomes and ensuring responsible AI practices. Lisa Jarrett, Senior Director at PointClickCare, discusses ethical considerations, regulatory issues, and the principles of responsible AI in an educational session at the HIMSS24 Global Conference & Exhibition.

Jarrett highlights the importance of transparency and fairness in AI applications, urging the incorporation of ethical considerations from the early planning stages through customer use and ongoing management. She emphasizes the need for active partnerships with clinicians and users to understand their perspectives on how AI impacts daily activities.

Legislation and regulations for AI in healthcare are evolving, with industry groups developing principles for responsible AI in clinical decision support. Jarrett mentions the HHS ONC HT1 provision, which outlines the FAVES framework (Fairness, Appropriateness, Validity, Effectiveness, and Safety) for algorithm transparency in healthcare.

Diverse perspectives exist on what responsible AI practices should be, and Jarrett stresses the importance of engaging with clinicians to integrate their questions and concerns into AI product development. The article also underscores the need for data security, privacy, and explainability in algorithm development to build user trust.

Jarrett recommends that IT leaders evaluate responsible AI on AI-driven or enabled systems as thoroughly as they assess the quality of the system itself. She emphasizes the importance of understanding the AI supply chain, ensuring user acceptance, and incorporating clinical collaboration throughout AI product development to achieve both transformational opportunities and responsible AI levels in clinical decision support.




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