Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers?
Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers?
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DOI：
10.1093/jamiaopen/ooz046
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发表时间：
2020-07-01
期刊：
影响因子：
2.1
通讯作者：
Poon, Eric G.
中科院分区：
文献类型：
--
作者：
Watson, Joshua;Hutyra, Carolyn A.;Poon, Eric G.
关于美国学术医疗中心（AMC）如何开发、实施和维护预测建模和机器学习（PM和ML）模型，人们知之甚少。我们对AMC的领导者进行了半结构化访谈，以评估他们在临床护理中使用PM和ML的情况，了解相关挑战，并确定推荐的最佳实践。每个转录的访谈由至少2名研究者迭代编码和协调，以确定在临床护理中采用和实施PM和ML的关键障碍和促进因素。采访了来自全国19个AMC的33名个人。AMC在临床护理中使用PM和ML的情况差异很大，从一些刚刚开始探索其效用到其他具有多种模式整合到临床护理中的AMC。知情者确定了在临床护理中采用和实施PM和ML的5个关键障碍：（1）文化和人员，（2）PM和ML工具的临床实用性，（3）融资，（4）技术和（5）数据。向信息学界提出的克服这些障碍的建议包括：（1）开发强大的评估方法，（2）与供应商建立合作伙伴关系，以及（3）开发和传播最佳实践。对于开发临床PM和ML应用程序的机构，建议他们：（1）制定适当的治理，（2）加强数据访问，完整性和来源，以及（3）坚持临床决策支持的5项权利。本文强调了在AMC临床护理中实施PM和ML的主要挑战，并提出了这些机构开发，实施和维护的最佳实践。
There is little known about how academic medical centers (AMCs) in the US develop, implement, and maintain predictive modeling and machine learning (PM and ML) models. We conducted semi-structured interviews with leaders from AMCs to assess their use of PM and ML in clinical care, understand associated challenges, and determine recommended best practices. Each transcribed interview was iteratively coded and reconciled by a minimum of 2 investigators to identify key barriers to and facilitators of PM and ML adoption and implementation in clinical care. Interviews were conducted with 33 individuals from 19 AMCs nationally. AMCs varied greatly in the use of PM and ML within clinical care, from some just beginning to explore their utility to others with multiple models integrated into clinical care. Informants identified 5 key barriers to the adoption and implementation of PM and ML in clinical care: (1) culture and personnel, (2) clinical utility of the PM and ML tool, (3) financing, (4) technology, and (5) data. Recommendation to the informatics community to overcome these barriers included: (1) development of robust evaluation methodologies, (2) partnership with vendors, and (3) development and dissemination of best practices. For institutions developing clinical PM and ML applications, they are advised to: (1) develop appropriate governance, (2) strengthen data access, integrity, and provenance, and (3) adhere to the 5 rights of clinical decision support. This article highlights key challenges of implementing PM and ML in clinical care at AMCs and suggests best practices for development, implementation, and maintenance at these institutions.