Artificial intelligence-based clinical decision support for liver transplant evaluation and considerations about fairness: A qualitative study.
Artificial intelligence-based clinical decision support for liver transplant evaluation and considerations about fairness: A qualitative study.
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DOI：
10.1097/hc9.0000000000000239
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发表时间：
2023-10-01
影响因子：
5.1
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中科院分区：
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使用大规模数据和人工智能（AI）来支持复杂的移植决策仍处于起步阶段。移植候选人决策严重依赖于主观评估（即高变异性），为基于AI的临床决策支持（CDS）提供了成熟的机会。然而，用于移植应用的AI-CDS必须考虑有关公平性（即健康公平性）的重要问题。本研究的目的是使用以人为本的设计方法，以引起供应商的感知AI CDS肝移植上市决策。在这项于2020年12月至2021年7月进行的多中心定性研究中，我们对来自2家移植中心的53名多学科肝移植提供者进行了半结构化访谈。我们使用归纳编码和访谈数据的常数比较分析。分析产生了6个对于设计公平的AI-CDS用于肝移植上市决策非常重要的主题：（1）AI-CDS背后的创建者及其动机的透明度;（2）了解AI-CDS如何使用数据来支持建议（即，可解释性）;（3）承认AI-CDS可以减轻情绪和偏见;（4）AI-CDS作为移植团队的一员，而不是替代品;（5）识别患者资源需求;以及（6）将患者的角色包括在AI-CDS中。总体而言，接受采访的供应商对AI-CDS改善患者临床和公平结局的潜力持谨慎乐观态度。这些发现可以指导多学科开发人员设计和实施故意考虑健康公平性的AI-CDS。
The use of large-scale data and artificial intelligence (AI) to support complex transplantation decisions is in its infancy. Transplant candidate decision-making, which relies heavily on subjective assessment (ie, high variability), provides a ripe opportunity for AI-based clinical decision support (CDS). However, AI-CDS for transplant applications must consider important concerns regarding fairness (ie, health equity). The objective of this study was to use human-centered design methods to elicit providers’ perceptions of AI-CDS for liver transplant listing decisions. In this multicenter qualitative study conducted from December 2020 to July 2021, we performed semistructured interviews with 53 multidisciplinary liver transplant providers from 2 transplant centers. We used inductive coding and constant comparison analysis of interview data. Analysis yielded 6 themes important for the design of fair AI-CDS for liver transplant listing decisions: (1) transparency in the creators behind the AI-CDS and their motivations; (2) understanding how the AI-CDS uses data to support recommendations (ie, interpretability); (3) acknowledgment that AI-CDS could mitigate emotions and biases; (4) AI-CDS as a member of the transplant team, not a replacement; (5) identifying patient resource needs; and (6) including the patient’s role in the AI-CDS. Overall, providers interviewed were cautiously optimistic about the potential for AI-CDS to improve clinical and equitable outcomes for patients. These findings can guide multidisciplinary developers in the design and implementation of AI-CDS that deliberately considers health equity.