4 - Assessing the Overuse and Underuse of Diagnostic Testing

项目摘要/摘要 项目4--评估过度使用和使用不足的诊断测试 低价值的医疗保健--鉴于其成本和对患者的风险，几乎不能提供健康益处--是一种 这是政策制定者的核心关切。诊断测试是一个特别重要的例子：虽然使用高- 诊断测试的成本飙升，对许多人来说，测试识别的频率 新的诊断或触发有效的干预措施似乎很少，而一些可能受益较高的患者 未经测试。挑战是对每个患者在治疗期间可能受益的准确的前瞻性预测 做出测试决定的时间。 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：心肌梗死负荷测试；CT肺 血管造影术用于肺栓塞；脊柱核磁共振检查用于检测背部疼痛的可治疗原因。这些测试旨在 发现对健康有重要影响但涉及巨额费用的可治疗的医疗状况，以及 往往会给患者带来风险。基于早期证据，机器学习算法结合了海量数据 数据集可以做出非常准确的预测，我们将使用医生可以在以下位置获得的数据来预测测试值 订购测试的时间。然后我们可以评估医生在多大程度上(A)测试患者的 可预测的低福利，或(B)未能测试具有可预测的高福利的患者。我们也可以用这个 更好地了解跨地区和供应商的测试中记录良好的差异是否 提供者行为或潜在的患者因素的结果。然后，我们将利用此方法使用 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 可集成到个别医院电子健康记录中的决策支持干预 系统。 随着医疗技术和数据科学的进步，为更好地部署卫生保健提供了新的机会 保健资源，以满足老龄化人口的保健需求。通过开发广泛适用的 用于评估医疗保险索赔和电子健康记录数据中的风险的机器学习技术，这 该项目将产生所需的工具和见解，以帮助确保将医疗保健资源投入到 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.