Stephanie S. Gervasi, Independence Blue Cross, Philadelphia, Pennsylvania.
Irene Y. Chen (
Health Aff (Millwood). 2022 Feb;41(2):212-218. doi: 10.1377/hlthaff.2021.01287.
As the use of machine learning algorithms in health care continues to expand, there are growing concerns about equity, fairness, and bias in the ways in which machine learning models are developed and used in clinical and business decisions. We present a guide to the data ecosystem used by health insurers to highlight where bias can arise along machine learning pipelines. We suggest mechanisms for identifying and dealing with bias and discuss challenges and opportunities to increase fairness through analytics in the health insurance industry.
随着机器学习算法在医疗保健领域的应用不断扩大，人们越来越关注机器学习模型在临床和商业决策中的开发和使用方式是否公平、公正和存在偏见。我们提供了一份使用健康保险公司数据生态系统的指南，以突出机器学习管道中可能出现偏见的地方。我们建议了一些识别和处理偏见的机制，并讨论了通过健康保险行业的分析来提高公平性的挑战和机遇。
