Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities

Abstract

The use of data-driven technologies such as Artificial Intelligence (AI) and Machine Learning (ML) is growing in healthcare. However, the proliferation of healthcare AI tools has outpaced regulatory frameworks, accountability measures, and governance standards to ensure safe, effective, and equitable use. To address these gaps and tackle a common challenge faced by healthcare delivery organizations, a case-based workshop was organized and a framework to assess the potential impact of a new AI solution on health equity was developed. The Health Equity Across the AI Lifecycle (HEAAL) is co-designed with extensive engagement of clinical, operational, technical, and regulatory leaders across healthcare delivery organizations and ecosystem partners in the US. It assesses 5 equity assessment domains–accountability, fairness, fitness for purpose, reliability and validity, and transparency–across the span of 8 key decision points in the AI adoption lifecycle. It is a process-oriented framework containing 37 step-by-step procedures for evaluating an existing AI solution and 34 procedures for evaluating a new AI solution in total. Within each procedure, it identifies relevant key stakeholders and data sources used to conduct the procedure. HEAAL guides how healthcare delivery organizations may mitigate the potential risk of AI solutions worsening health inequities. It also informs how much resources and support are required to assess the potential impact of AI solutions on health inequities.

Proactive Algorithm Monitoring to Ensure Health Equity

“…Developing procedures for health care organizations to assess potential impacts of algorithms on health inequities similarly found important activities across the entire product life cycle. 5 The implication of these findings is that an algorithm cannot be certified as equitable. How an algorithm is put into practice determines the impacts on equity of the solution and continuous monitoring of the overall solution is required to prevent harm.…”

Healthcare reform: let science, not politics, lead the way

“…The report's other limitation lies solely in considering factors decreasing workforce productivity, neglecting the positive impacts of recent medical technology advancements. For instance, a US government document [ 4 ] suggests that artificial intelligence will boost productivity. Hazarika [ 5 ] also shows that artificial intelligence technologies could reduce physician work hours by 17%, a trend that is gaining traction in the healthcare industry.…”