Algorithmovigilance: Considerations for Systematic Monitoring and Continuous Learning for Equitable AI-Driven Healthcare
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This document discusses the need for algorithmvigilance, which is the systematic monitoring of algorithms used in healthcare to identify unintended impacts and ensure equitable outcomes. As the use of AI and machine learning grows in healthcare decision-making, there is evidence of data and algorithmic biases that can differentially impact populations. The author argues that just as pharmacovigilance monitors drug effects, algorithmvigilance is increasingly important to evaluate algorithms, understand adverse effects, and prevent inequities. An algorithm-driven healthcare learning cycle is proposed that continuously monitors effects, identifies needed changes, and improves algorithms based on real-world use.
Algorithmovigilance: Considerations for Systematic Monitoring and Continuous Learning for Equitable AI-Driven Healthcare
- 1. Algorithmovigilance: Considerations for Systematic Monitoring and Continuous Learning for Equitable AI-Driven Healthcare Peter J. Embi, MD, MS, FACP, FACMI, FIAHSI President & CEO, Regenstrief Institute Betley Professor of Medicine & Associate Dean, IU School of Medicine Associate Director, Indiana CTSI VP for Learning Health Systems, IU Health Incoming Chair, Biomedical Informatics, Vanderbilt University Medical Center Session: Translating Digital Equity CTSA Program Annual Meeting December 3, 2021
- 2. Algorithm-driven Healthcare • Growth in Algorithm-driven healthcare via decision support • Growing evidence of: • Data and algorithmic bias • Differential impacts across populations • Unintended impacts • Health equity concerns • Need for a “learning health system” approach to: • Monitor whether algorithm-driven healthcare has the intended effects • Mechanisms to identify and respond to unintended effects • An analogy from traditional clinical trials…
- 3. Clinical Trial Phases Phase I: • Testing for Safety • 20-80 participants Phase II: • Testing for Efficacy • 100-300 participants Phase III: • Testing for Effectiveness • 300-3000 FDA Review/ Approval • Approved for market use Phase IV: Post- marketing • Post- marketing testing • Thousands+
- 4. • Science relating to the collection, detection, assessment, monitoring, and prevention of adverse effects with pharmaceutical products. Pharmacovigilance: • Individual Case Safety Reports (standards) • Coding of adverse events • Seriousness determination • Expedited reporting • Spontaneous reporting • Aggregate reporting Adverse Effect Reporting Pharmacovigilance Processes for discovering adverse effects Systematic surveillance approaches growing Increasingly leveraging EHRs and related data for monitoring of safety and effectiveness
- 5. ”Algorithmovigilance” “The scientific methods and activities relating to the evaluation, monitoring, understanding, and prevention of adverse effects of algorithms in health care.” Akin to pharmacovigilance for monitoring drug effects Increasingly important as AI/ML- derived algorithms are used
- 6. Need to monitor healthcare Algorithms Biases in Data: Known and Unknown Caution about generalizability Unexpected results can be expected Must monitor to promote trust Need new systems and approaches Essential for safe, equitable, effective care
- 7. Algorithm-driven healthcare learning cycle Monitor to determine effects Change based upon findings Deploy and use in healthcare settings