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

肿瘤学知识快速警报：整合生物标志物驱动的临床决策支持，以在护理点选择治疗方法

• 批准号: 10526824
• 负责人: Christine M Micheel
• 金额: $ 45万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-19 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10526824
• 关键词: Academic Medical Centers; Adjuvant; Adopted; Adoption; Algorithms; Bioinformatics; Biological Markers; Cancer Patient; Clinical; Clinical Oncology; DNA; Data; Data Store; Decision Making; Diagnostic; Drug Targeting; Electronic Health Record; Electronic Mail; Environment; Evolution; Foundations; Funding; Future; Genomics; Goals; Health care facility; Histology; Human; Information Resources; Institution; Knowledge; Laboratories; Malignant Neoplasms; Medicine; Methodology; Methods; Molecular; Molecular Profiling; Non-Small-Cell Lung Carcinoma; Oncologist; Oncology; Output; Patients; Persons; Pharmaceutical Preparations; Positioning Attribute; Readability; Reporting; Research; Resource Development; Retrieval; Running; Scanning; Selection for Treatments; Structure; System; Test Result; Testing; Therapeutic; Time; Vendor; Visit; Work; application programming interface; base; biomarker-driven; cancer cell; cancer genome; cancer therapy; cancer type; clinical care; clinical decision support; clinical development; data integration; data standards; data structure; electronic structure; genomic data; improved; infancy; innovation; knowledgebase; natural language; open source; point of care; precision oncology; prognostic; research clinical testing; standard of care; support tools; targeted treatment; tool; tumor; web app; web services

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.

项目摘要/摘要 精密癌症医学使用患者分子测试结果来指导癌症治疗选择。越来越多 癌症疗法的数量现在针对特定的分子改变，肿瘤分子 测试现在是护理标准。推进精确癌症治疗的重要障碍是快速的速度 该领域的变化 - 肿瘤学家保持最新状态的挑战。这个长期目标 项目将直接将临床决策支持（CD）整合到精确的癌症治疗选择中 电子健康记录（EHR）。最终，我们将在EHR中创建一个集成的CD解决方案 包括对肿瘤学家的最佳实践警报以及将CD集成到分子测试结果中 报告。该R21的目的是开发CDS算法以匹配分子测试结果 从广泛使用的精确肿瘤学知识库中的治疗主张，提供开源网络服务 应用程序编程接口（API）可以支持任何使用 新的基因组数据标准，并在我们本地的EHR中提供了概念验证的CD 环境。我们将通过以下特定目标来实现这一目标： AIM 1。开发一种开源算法和API来计算和存储生物标志物驱动的CD 来自我的癌症基因组（MCG）知识基础。我们将开发一种将计算的算法 从存储在患者的肿瘤组织学和分子特征的目标治疗方案中存储的数据 MCG知识库。我们将构建一个接收分子测试数据的Web服务API，调用MCG 知识库，运行CD算法，并以可以存储的格式输出计算的CD 快速检索，并且使用新的基因组数据标准与EHR兼容。 目标2。开发将生物标志物驱动的CD集成到EHR中的方法 语句。在此目标中，我们将构建API函数，以接收EHR的通信 肿瘤学家认为肿瘤测试结果，检索缓存的CD并将CD发送到EHR。使用非小细胞肺 癌症作为概念证明，我们的初始用例将提供CD与分子结果的整合 向肿瘤学家报告和最佳实践警报。 该项目将提供与适当的靶向疗法相匹配的工具，以使患者分子测试结果与适当的靶向疗法相匹配。在 结论该项目，肿瘤科医生在访问患者分子时将能够查看CD EHR。这一点CD将喜欢在右边向合适的人提供正确的信息 是时候最大化对临床护理的影响。