Oncology Knowledge Rapid Alerts: Integrating biomarker-driven clinical decision support for therapy selection at point-of-care

项目总结/摘要 精准癌症医学使用患者分子检测结果来指导癌症治疗选择。越来越 许多癌症疗法现在靶向特定的分子改变，并且肿瘤分子 现在检测是标准治疗推进精确癌症治疗的一个重大障碍是快速发展 这一领域的变化，使肿瘤学家保持最新的挑战。长期目标是 该项目是将用于精确癌症治疗选择的临床决策支持（CDS）直接集成到 电子健康记录（EHR）。最终，我们将在EHR中创建一个集成的CDS解决方案， 包括向肿瘤学家发出最佳实践警报，并将CDS整合到分子检测结果中 次报告.本R21的目的是开发CDS算法，以将分子检测结果与目标 来自广泛使用的精确肿瘤学知识库的治疗断言， 应用程序编程接口（API），可支持与任何使用 新的基因组数据标准，并在我们当地的EHR中提供概念验证、即时护理CDS 环境我们将通过以下具体目标实现这一目标： 目标1.开发用于计算和存储生物标记驱动CDS的开源算法和API 我的癌症基因组（MCG）知识库我们将开发一种算法， 根据存储在微阵列中的数据， MCG知识库。我们将构建一个Web服务API，用于接收分子检测数据，调用MCG 知识库，运行CDS算法，并以可以存储的格式输出计算出的CDS， 快速检索，并与使用新的基因组数据标准的EHR兼容。 目标二。开发将生物标记驱动的CDS整合到EHR中的方法，使其成为人类可读的 报表在这个目标中，我们将构建API函数来接收来自EHR的通信， 肿瘤学家查看肿瘤测试结果，检索缓存的CDS，并将CDS发送到EHR。使用非小细胞肺 癌症作为概念验证，我们的初始用例将提供CDS与分子结果的集成 报告和最佳实践警报给肿瘤学家。 该项目将提供工具，将患者分子检测结果与适当的靶向治疗相匹配。在 该项目的结论，肿瘤学家将能够查看CDS时，他们访问患者的分子结果， 的EHR。这种即时护理CDS将有助于在正确的位置将正确的信息传递给正确的人。 最大限度地发挥对临床护理的影响。

PROJECT SUMMARY/ABSTRACT Precision cancer medicine uses patient molecular test results to guide cancer therapy selection. An increasing number of cancer therapies are now targeted to specific molecular alterations, and tumor molecular testing is now standard of care. A significant obstacle in advancing precision cancer therapy is the rapid pace of change in this field—making it challenging for oncologists to stay up to date. The long-term goal of this project is to integrate clinical decision support (CDS) for precision cancer therapy selection directly into the electronic health record (EHR). Ultimately, we will create an integrated CDS solution within the EHR that includes best practice alerts to oncologists and integration of CDS into the molecular testing result report. The objective of this R21 is to develop a CDS algorithm to match molecular test results to targeted therapy assertions from a widely used precision oncology knowledgebase, deliver an open-source web service application programming interface (API) that can support vendor-agnostic integration with any EHR that uses the new genomic data standards, and provide proof-of-concept, point-of-care CDS in our local EHR environment. We will accomplish this through the following specific aims: Aim 1. Develop an open-source algorithm and API for computing and storing biomarker-driven CDS from the My Cancer Genome (MCG) knowledgebase. We will develop an algorithm that will compute targeted therapy options for a patient’s tumor histology and molecular profile from the data stored in the MCG knowledgebase. We will build a web service API that receives molecular testing data, calls the MCG knowledgebase, runs the CDS algorithm, and outputs the computed CDS in a format that can be stored for rapid retrieval and that is compatible with EHRs using the new genomic data standards. Aim 2. Develop methods for integration of biomarker-driven CDS into the EHR as human-readable statements. In this aim, we will build API functions to receive communications from the EHR when an oncologist views tumor test results, retrieve cached CDS, and send CDS to the EHR. Using non-small cell lung cancer as a proof-of-concept, our initial use cases will provide integration of CDS with the molecular results report and best practice alerts to oncologists. This project will provide tools to match patient molecular test results to appropriate targeted therapies. At the conclusion of this project, an oncologist will be able to view CDS when they access patient molecular results in the EHR. This point-of-care CDS will facilitate delivery of the right information to the right person at the right time to maximize impact on clinical care.