Deep Learning for Pulmonary Embolism Imaging Decision Support: A Multi-institutional Collaboration

项目摘要 诊断成像每年花费1000亿美元。预计这些医疗费用在未来将会增加 随着全国人口老龄化和参保患者人数的增加，这一趋势将持续十年。这些公司的规模和增长 成本同样关系到政策制定者、支付者和社会。使用先进的成像技术治疗PE增加了27 近年来，这种急剧的升级有可能使患者面临不必要的手术， 测试，以及因偶然发现而产生的风险。尽管放射科医生不会安排大多数放射学检查，但这些 医生是批评的目标，因为不断上涨的成本和可能过度使用的放射服务。这个 医疗保健行业呼吁放射科医生管理先进成像的潜在过度使用，并 带头研究最佳使用高级成像的最佳实践。 影像应用指南的理想信息来源是随机、对照的影像临床 审判。然而，这些试验是成本和时间密集型的，非常难以进行，并且通常使用狭窄的 患者纳入标准，这使得将结果推广到更广泛的临床情况具有挑战性。备择 缺乏可靠的证据来源，如观察性或回溯性研究。广为流传 电子病历(EMR)的采用和计算方法的日益普及 处理海量的非结构化信息现在使直接从实践中学习成为可能 证据。我们建议“大数据”临床资料库，包括放射学报告，可以使自己成为 可用于创新且对成本敏感的医疗保健、相关、可操作的数据的宝库 评估适当使用医学成像的方法。我们的目标是创建一种预测模型， 利用来自顶级国家医疗中心的实时EMR临床数据来得出特定于患者的 影像结果预测。我们认识到，临床医生必须现场进行医学影像订购 这些决定通常不符合现有的临床决策支持规则。我们的研究旨在提供 临床医生拥有一种工具，可以利用聚合的患者数据进行即时医学成像决策 关爱。 这项研究的总体方法是利用可扩展的方法，可以广泛应用于 利用电子病历数据预测其他几项高成本、低收益成像测试的结果。这 提案有可能更好地为未来学习型医疗保健系统中的高级成像提供信息，并且 减少不必要的影像检查和医疗成本。

Project Summary Diagnostic imaging costs $100 billion annually. These healthcare costs are expected to increase in the coming decade as the national population ages and the pool of insured patients increases. The size and growth of these costs concern policy makers, payers, and society alike. The use of advanced imaging for PE has increased 27 fold in recent years, and this sharp escalation has the potential to expose patients to unnecessary procedures, tests, and risks due to incidental findings. Although radiologists do not order most radiology exams, these physicians are the target of criticism about the rising costs and possible overuse of radiology services. The healthcare industry has called upon radiologists to manage the potential overuse of advanced imaging and to take the lead on investigating best practices for the optimal use of advanced imaging. The ideal sources of information for imaging utilization guidelines are randomized, controlled imaging clinical trials. However, these trials are cost and time intensive, exceedingly difficult to conduct, and typically use narrow patient-inclusion criteria, making it challenging to generalize the results to broader clinical situations. Alternative sources of reliable evidence, such as observational or retrospective studies, have been lacking. The widespread adoption of electronic medical records (EMRs) and the increasing availability of computational methods to process vast amounts of unstructured information now make it possible to learn directly from practice-based evidence. We propose that “big data” clinical repositories, including radiology reports, can lend themselves to a treasure trove of point-of-care, relevant, actionable data that can be used in an innovative and cost-sensitive approach to evaluate the appropriate use of medical imaging. We aim to create a predictive model that leverages real-time EMR clinical data from top national medical centers to arrive at a patient-specific imaging outcome prediction. We recognize that clinicians have to make on-the-spot medical imaging-ordering decisions and they generally do not comply with existing clinical decision support rules. Our study aims to provide clinicians with a tool that can leverage aggregate patient data for medical imaging decision making at the point of care. The overarching approach of this study is to utilize scalable methodology that can be widely applied to leverage EMR data to predict the outcome of a several other high-cost, low-yield imaging tests. This proposal has the potential to better inform advanced imaging in the learning healthcare system of the future and reduce unnecessary imaging examinations and healthcare costs.