诊断物理错误:一个机器学习的方法,低价值的医疗保健
Sendhil Mullainathan1, Ziad Obermeyer2
1University of Chicago, Booth School of Business.
概括
医生在诊断心脏病发作时经常犯错误,既订购了太多的测试,也订购了太少的测试. 这导致患者的结果更差,并强调需要解决医生在医疗保健中的错误.
科学领域:
- 临床医学 临床医学
- 医疗信息学 医疗信息学
- 决策科学 决策科学 决策科学
背景情况:
- 医生对急性心肌梗塞 (心脏病发作) 的诊断准确性对于患者的结果至关重要.
- 当前的诊断实践可能与最佳风险评估策略不一致.
研究的目的:
- 通过比较他们的测试决定与机器学习风险预测模型来评估医生在诊断心脏病发作方面的有效性.
- 识别过度测试和不足测试的模式及其对患者健康结果的影响.
主要方法:
- 对比医生测试决策与机器学习算法的风险预测.
- 利用实际的健康结果数据来验证诊断决策.
- 采用了一项自然实验,检查轮班测试变化.
主要成果:
- 医生们表现出过多的测试 (执行可预测的无用的测试) 和不足的测试 (没有测试预测的高风险患者).
- 未经测试的高风险患者经历了显著更高的不良健康事件,包括死亡的发生率.
- 一个自然实验证实,增加的测试只有利于算法识别的高风险患者,改善健康和降低死亡率.
结论:
- 医生诊断错误,包括认知偏见,如重视突出或刻板印象信息,并使用过于简单的风险模型,有助于低于最佳的测试策略.
- 医疗保健模型和政策应该考虑医生的认知错误,而不仅仅是激励,以提高诊断准确性和对心脏病发作等疾病的患者护理.
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