DECODE: Diagnostic Excellence Center on Diagnostic Error

DECODE：诊断错误诊断卓越中心

• 批准号: 10707234
• 负责人: Ramin Khorasani
• 金额: $ 99.41万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2026-09-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10707234
• 关键词: DECODE; Diagnostic; Excellence; Center; Error

Abstract We plan to establish a Diagnostic Center of Excellence that will focus on reducing diagnostic errors related to diagnostic imaging in two ways. We will: 1) implement a highly reliable and resilient system to enhance safety by reducing failures in timely performance of clinically necessary diagnostic imaging examinations and interpretative errors (Safety 2), and 2) improve diagnostic precision by building consensus using available evidence around four common causes of diagnostic errors – findings that may lead to a diagnosis of lung, prostate, pancreas and adrenal cancer. The DCE will build on strong pre-existing research and operational collaboration between a team of safety scientists, biomedical informaticists and health services researchers at the Massachusetts General Brigham (MGB) Health System. MGB is comprised of 2 tertiary academic hospitals, 7 community acute care hospitals, 3 specialty hospitals, multiple ambulatory care and outpatient imaging facilities serving patients in ambulatory, inpatient and ED settings, with a provider network of over 10,000 employed and affiliated primary care and specialty care physicians. Recent integration at MGB has prioritized clinical integration, creating a single Office of the Chief Operating Officer, a single Office of the Chief Medical Officer, and a single Enterprise Radiology governance including for quality and safety. We will enhance a set of pre-existing limited implementation information technology-enabled functions and workflows before MGB-wide expansion to address multiple types of diagnostic errors, including those leading to missed, incorrect, or delayed diagnoses. Enhancements will improve EHR-integration, monitoring and learning capabilities of our Clinical Dashboard to better address health disparities, to help advance an equity-informed resilient system and associated workflows. Another system for Peer Learning will also be implemented to target interpretive errors in diagnostic imaging. Finally, we will convene a multispecialty team of clinicians to build consensus on recommendations for diagnosis and management of findings that may lead to lung, prostate, pancreatic and adrenal cancer based on available evidence using a Modified Delphi process. Evidence that are agreed upon will be embedded in a clinical decision support system that will be integrated with the electronic health record. All specifications for systems and workflow processes, consensus results, and lessons learned will be disseminated broadly through national conferences and meetings, a public website, networks of clinical practice and institutions, and social media.

摘要 我们计划建立一个卓越的诊断中心，重点是减少与以下方面有关的诊断错误： 影像诊断有两种方法。我们会：1）实施高度可靠和具弹性的系统，以加强安全 通过减少在及时进行临床必要的诊断成像检查方面的失败， 解释错误（安全性2），和2）通过使用可用的数据建立共识来提高诊断精度 围绕诊断错误的四个常见原因的证据-可能导致肺部诊断的发现， 前列腺癌胰腺癌和肾上腺癌DCE将建立在强大的预先存在的研究和业务基础上， 安全科学家、生物医学信息学家和卫生服务研究人员在 马萨诸塞州布里格姆将军卫生系统。MGB由2名高等教育学者组成 医院、7家社区急症护理医院、3家专科医院、多个门诊护理和门诊 为门诊、住院和艾德环境中的患者提供服务的成像设施， 10，000名受雇和附属的初级保健和专科保健医生。最近在MGB的整合 优先考虑临床整合，创建一个单一的首席运营官办公室，一个单一的首席运营官办公室， 医疗官和单一的企业放射学管理，包括质量和安全。我们将 加强一套现有的有限实施信息技术功能和工作流程 在MG范围内扩展以解决多种类型的诊断错误之前，包括那些导致漏诊的错误， 错误或延迟诊断。增强功能将改进电子人力资源整合、监测和学习 我们的临床仪表板的功能，以更好地解决健康差距，以帮助推进公平知情 弹性系统和相关的工作流程。另一个同侪学习系统也将实施， 诊断成像中的目标解释错误。最后，我们将召集一个多专业的临床医生团队， 就诊断和管理可能导致肺部疾病的发现的建议达成共识， 前列腺癌、胰腺癌和肾上腺癌，基于使用改良德尔菲法获得的证据。 商定的证据将嵌入临床决策支持系统， 电子健康记录。系统和工作流程的所有规范，共识结果， 将通过各种国家会议、公众论坛和其他形式广泛传播所取得的经验教训， 网站、临床实践和机构网络以及社交媒体。

项目成果

Impact of an Automated Closed-Loop Communication and Tracking Tool on the Rate of Recommendations for Additional Imaging in Thoracic Radiology Reports.

自动闭环通信和跟踪工具对胸部放射学报告中附加成像建议率的影响。

• DOI: 10.1016/j.jacr.2023.05.004
• 发表时间: 2023
• 期刊: Journal of the American College of Radiology : JACR
• 作者: DeSimone,AriadneK;Kapoor,Neena;Lacson,Ronilda;Budiawan,Elvira;Hammer,MarkM;Desai,SonaliP;Eappen,Sunil;Khorasani,Ramin
• 通讯作者: Khorasani,Ramin

Development and External Validation of an Artificial Intelligence Model for Identifying Radiology Reports Containing Recommendations for Additional Imaging.

• DOI: 10.2214/ajr.23.29120
• 发表时间: 2023-04
• 期刊: AJR. American journal of roentgenology
• 作者: Nooshin Abbasi;Ronilda C. Lacson;Neena Kapoor;Andro Licaros;Jeffrey P. Guenette;Kristine S. Burk;M. Hammer;Sonali Desai;S. Eappen;Sanjay Saini;R. Khorasani