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. 2024 May 9;3(5):e0000390.
doi: 10.1371/journal.pdig.0000390. eCollection 2024 May.

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

Jee Young Kim  1 , Alifia Hasan  1 , Katherine C Kellogg  2 , William Ratliff  1 , Sara G Murray  3 , Harini Suresh  4 , Alexandra Valladares  5 , Keo Shaw  6 , Danny Tobey  7 , David E Vidal  8 , Mark A Lifson  8 , Manesh Patel  9 , Inioluwa Deborah Raji  10 , Michael Gao  1 , William Knechtle  1 , Linda Tang  11 , Suresh Balu  1 , Mark P Sendak  1
Affiliations

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

Jee Young Kim et al. PLOS Digit Health. .

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 was developed to evaluate the potential impact of implementing an AI solution on health equity. 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 eight 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.

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Conflict of interest statement

WR, MG, SB, and MPS have declared co-inventing software at Duke University licensed by Duke University to external commercial entities Clinetic, Cohere Med, Kela Health, and Fullsteam Health. MG, SB, and MPS also own equity in Clinetic. No other competing interests were declared.

Figures

Fig 1
Fig 1. Participants and their roles and responsibilities in co-designing HEAAL.
Fig 2
Fig 2. Four phases of co-design processes and participants engaged in each phase.
Fig 3
Fig 3. Prototype development during the Develop phase.
Responses to guiding questions were gathered and synthesized to create the initial prototype. It contained procedures for evaluating six health equity assessment domains. After the initial testing by a case study team, this prototype evolved into the second prototype. The second prototype was structured around eight key decision points of AI adoption and tested by the case study team. It was then shared with the framework developers and the HAIP leadership team for feedback and evaluation.
Fig 4
Fig 4. Overview of HEAAL.
HEAAL delineates health equity assessment domains, active stakeholders, data sources, and testing highlights across eight key decision points.
See this image and copyright information in PMC

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References

    1. He J, Baxter SL, Xu J, Xu J, Zhou X, Zhang K. The practical implementation of artificial intelligence technologies in medicine. Nat Med. 2019;25(1):30–36. doi: 10.1038/s41591-018-0307-0 - DOI - PMC - PubMed
    1. Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019. Apr 4;380(14):1347–1358. doi: 10.1056/NEJMra1814259 - DOI - PubMed
    1. Sendak MP, D’Arcy J, Kashyap S, Gao M, Nichols M, Corey K, et al.. A path for translation of machine learning products into healthcare delivery. EMJ Innov. 2020. Jan 27;10:19–00172.
    1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. doi: 10.1038/s41591-018-0300-7 - DOI - PubMed
    1. Adams R, Henry KE, Sridharan A, Soleimani H, Zhan A, Rawat N, et al.. Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis. Nat Med. 2022;28(7):1455–1460. doi: 10.1038/s41591-022-01894-0 - DOI - PubMed

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