4 - Assessing the Overuse and Underuse of Diagnostic Testing

项目总结/摘要 项目4 -评估诊断测试的过度使用和使用不足 低价值的医疗保健-鉴于其成本和对病人的风险，提供的健康益处很少-是一种 决策者的核心关切。诊断测试是一个特别重要的例子：虽然使用高- 成本诊断测试已经飙升，对于许多执行的测试识别的频率 新的诊断或触发有效的干预措施似乎很低，而一些可能获益很高的患者则会 未经测试挑战在于对每个患者在治疗期间的可能获益做出准确的前瞻性预测。 测试决定的时间。 为此，我们利用机器学习算法的最新进展来预测哪些医疗保险患者 可能受益于特定的诊断测试：心肌梗死的压力测试; CT肺 血管造影检查肺栓塞;脊柱MRI检查发现背痛的可治疗原因。这些测试寻求 检测具有重要健康影响但涉及大量费用的可治疗的医学状况，以及 往往给患者带来风险。基于早期的证据，机器学习算法结合了大量的 数据集可以做出高度准确的预测，我们将使用医生可用的数据预测测试值， 测试的时间是有序的。然后，我们可以评估医生在多大程度上（a）测试患者， 可预测的低益处，或（B）未能测试具有可预测的高益处的患者。我们也可以用这个 更好地了解各地区和提供商之间的测试中是否存在记录良好的差异的方法 提供者行为或潜在患者因素的结果。然后，我们将利用这种方法， 电子健康记录数据。这也将使我们能够评估将我们的工作转化为 决策支持干预可以集成到各个医院的电子健康记录中 系统. 随着医疗技术和数据科学的进步，有新的机会可以更好地部署健康 护理资源，以满足人口老龄化的医疗保健需求。通过开发广泛适用的 机器学习技术用于估计Medicare索赔和电子健康记录数据中的风险， 该项目将产生所需的工具和见解，以帮助确保卫生保健资源用于 患者可能从中受益最多。

PROJECT SUMMARY/ABSTRACT Project 4 – Assessing the Overuse and Underuse of Diagnostic Testing Low-value health care – care that provides little health benefit in light of its costs and risks to patients – is a central concern for policy-makers. Diagnostic testing is a particularly important example: while the use of high- cost diagnostic tests has skyrocketed, for many the frequency with which the tests that are performed identify new diagnoses or trigger effective interventions appears low, while some patients with likely high benefit go untested. The challenge is to make an accurate prospective prediction of each patient’s likely benefit at the time the testing decision is made. To this end, we draw on recent advances in machine learning algorithms to predict which Medicare patients are likely to benefit from specific diagnostic tests: stress testing for myocardial infarction; CT pulmonary angiography for pulmonary embolism; and spine MRI to detect treatable causes of back pain. These tests seek to detect treatable medical conditions with important health implications, but involve substantial costs, and often risks to patients. Building on early evidence that machine learning algorithms combined with massive datasets can make highly accurate predictions, we will predict test value using data available to the doctor at the time the test is ordered. We can then assess the extent to which doctors are (a) testing patients with predictably low benefit, or (b) failing to test patients with predictably high benefit. We can also use this approach to better understand whether well-documented variations in testing across regions and providers are the result of provider behavior or underlying patient factors. We will then leverage this approach using electronic health record data. This will also allow us to assess the feasibility of translating our work into a decision support intervention that could be integrated into individual hospitals’ electronic health record systems. With advances in medical technology and data science, there are new opportunities to better deploy health care resources to meet the health care needs of an aging population. By developing broadly applicable machine learning techniques for estimating risk in both Medicare claims and electronic health record data, this project will generate the tools and insights needed to help ensure that health care resources are devoted to patients likely to benefit the most from them.