A Typology of Existing Machine Learning-Based Predictive Analytic Tools Focused on Reducing Costs and Improving Quality in Health Care: Systematic Search and Content Analysis.
A Typology of Existing Machine Learning-Based Predictive Analytic Tools Focused on Reducing Costs and Improving Quality in Health Care: Systematic Search and Content Analysis.
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DOI：
10.2196/26391
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发表时间：
2021-06-22
影响因子：
7.4
通讯作者：
Cho MK
中科院分区：
文献类型：
--
作者：
Nichol AA;Batten JN;Halley MC;Axelrod JK;Sankar PL;Cho MK
人工智能的开发已经投入了相当大的努力，包括用于医疗保健环境的基于机器学习的预测分析（MLPA）。MLPA的增长可以通过支付改革来推动，这些改革要求医疗保健组织负责提供高质量，具有成本效益的医疗服务。政策分析师，伦理学家和计算机科学家已经确定了在医疗保健中使用MLPA的独特伦理和监管挑战。然而，人们对当今市场上可用的MLPA保健产品类型或其既定目标知之甚少。本研究旨在更好地表征现有MLPA医疗保健产品，识别和表征最近或目前在美国医疗保健环境中使用的产品的声明，这些产品作为工具销售，通过提高护理质量同时降低成本来提高医疗保健效率。我们对相关商业新闻和学术研究进行了系统的数据库搜索，以确定MLPA产品的医疗保健效率符合我们的纳入和排除标准。我们使用内容分析来生成MLPA产品类别并描述营销产品的组织。我们确定了106种产品，并根据公开的信息对它们进行了描述，包括预测的类型以及营销它们的公司的规模，类型和临床培训。我们根据公开的产品营销材料确定了MLPA产品的5类预测：疾病发作和进展、治疗、成本和利用、入院和再入院以及失代偿和不良事件。我们的研究结果提供了一个基本的参考，为分析使用MLPA提高医疗保健效率所带来的具体伦理和监管挑战提供了信息。
Considerable effort has been devoted to the development of artificial intelligence, including machine learning–based predictive analytics (MLPA) for use in health care settings. The growth of MLPA could be fueled by payment reforms that hold health care organizations responsible for providing high-quality, cost-effective care. Policy analysts, ethicists, and computer scientists have identified unique ethical and regulatory challenges from the use of MLPA in health care. However, little is known about the types of MLPA health care products available on the market today or their stated goals. This study aims to better characterize available MLPA health care products, identifying and characterizing claims about products recently or currently in use in US health care settings that are marketed as tools to improve health care efficiency by improving quality of care while reducing costs. We conducted systematic database searches of relevant business news and academic research to identify MLPA products for health care efficiency meeting our inclusion and exclusion criteria. We used content analysis to generate MLPA product categories and characterize the organizations marketing the products. We identified 106 products and characterized them based on publicly available information in terms of the types of predictions made and the size, type, and clinical training of the leadership of the companies marketing them. We identified 5 categories of predictions made by MLPA products based on publicly available product marketing materials: disease onset and progression, treatment, cost and utilization, admissions and readmissions, and decompensation and adverse events. Our findings provide a foundational reference to inform the analysis of specific ethical and regulatory challenges arising from the use of MLPA to improve health care efficiency.
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