Respect and Trustworthiness in the Patient-Provider-Machine Relationship: Applying a Relational Lens to Machine Learning Healthcare Applications.
Respect and Trustworthiness in the Patient-Provider-Machine Relationship: Applying a Relational Lens to Machine Learning Healthcare Applications.
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DOI：
10.1080/15265161.2020.1820108
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发表时间：
2020-11
期刊：
影响因子：
--
通讯作者：
Kraft SA
中科院分区：
文献类型：
--
作者：
Kraft SA
医疗保健服务是一项人际交往的努力。在每一次临床互动中，提供者都有尊重患者的道德义务，理想情况下，随着时间的推移，这些互动会导致相互尊重和信任的关系。从关系的角度审视医疗保健，可以认识到患者和提供者是社会嵌入的存在，他们的互动和决定是由他们的个人观点、经验和环境以及决定这些互动发生的时间、地点和方式的更广泛的系统所决定的（Mackenzie和Stoljar 2000）。随着机器学习应用越来越多地与医疗保健交织在一起，它们有可能改变这些结构，并改变医患关系的轮廓。这些变化有时可能是微妙的，但它们对医疗保健关系的影响可能仍然是重大的，并且与道德相关。这篇评论将认为，有必要明确考虑机器学习在医疗保健中的关系影响，作为彻底的伦理分析的一部分。基于Char等人在其目标文章（Char et al. 2020）中提出的框架（该框架将关键的伦理考虑映射到机器学习医疗保健应用的开发、实施、评估和监督上），本评论将研究如何设计和实施这些应用，以维护和加强基本的关系价值，包括对人的尊重和可信度；特别注意对医患关系的任何影响，并确定要添加到Char等人的管道模型框架的每个阶段的关键问题。
Healthcare delivery is an interpersonal endeavor. In every clinical interaction, providers have an ethical obligation to show respect to their patients, and ideally over time these interactions lead to mutually respectful and trusting relationships. Examining healthcare through a relational lens recognizes patients and providers as socially embedded beings whose interactions and decisions are informed by their individual perspectives, experiences, and circumstances, as well as the broader systems that shape when, where, and how these interactions occur (Mackenzie and Stoljar 2000). As machine learning applications become increasingly intertwined in healthcare, they have the potential to alter these structures and change the contours of patient-provider relationships. These changes may sometimes be subtle, but their impact on healthcare relationships may nonetheless be significant and ethically relevant.This commentary will argue that it is necessary to consider explicitly the relational implications of machine learning in healthcare as part of a thorough ethical analysis. Building on the framework that Char et al. lay out in their Target Article (Char et al. 2020), which maps key ethical considerations onto the development, implementation, evaluation, and oversight of machine learning healthcare applications, this commentary will examine how these applications can be designed and implemented to uphold and reinforce essential relational values including respect for persons and trustworthiness, with particular attention to any impacts on patientprovider relationships, and identify key questions to add to each stage of Char et al.’s pipeline model framework.
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影响因子：
1.4
作者：
Dickert, Neal W.
通讯作者：
Dickert, Neal W.
影响因子：
1.5
作者：
Campelia, Georgina D.;Feinsinger, Ashley
通讯作者：
Feinsinger, Ashley
DOI：
10.1080/15265161.2020.1819469
发表时间：
2020-11
期刊：
The American journal of bioethics : AJOB
影响因子：
--
作者：
Char DS;Abràmoff MD;Feudtner C
通讯作者：
Feudtner C
影响因子：
4.8
作者：
Levesque JF;Harris MF;Russell G
通讯作者：
Russell G