Reducing Errors in the Diagnosis of Melanoma using an Intelligent Tutoring System

使用智能辅导系统减少黑色素瘤的诊断错误

• 批准号: 7291543
• 负责人: DANA MARIE GRZYBICKI
• 金额: $ 4.13万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-09-30 至 2007-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7291543
• 关键词: Reducing; Errors; Diagnosis; Melanoma; using

DESCRIPTION (PROVIDED BY APPLICANT): Our objective is to improve the diagnostic accuracy of community generalist pathologists for malignant melanoma through the use of a cognitive simulation system. Medical error research and clinical error reduction initiatives to date have primarily focused on therapeutic (e.g. medication) errors rather than on diagnostic errors. The diagnostic error rate in general surgical pathology is generally accepted to be approximately 1 %, although there is evidence that these errors are most likely underreported. Cancer of the skin is the most common of all cancers, and melanoma accounts for about 4% of skin cancer cases but causes a large majority of skin cancer deaths. The American Cancer Society estimates that the number of new melanomas diagnosed in the United States is increasing, and about 7,910 people are expected to die of melanomas during 2006. Since 1973, the mortality rate for melanoma has increased by 50%. False negative diagnostic errors for malignant melanoma are the most frequent diagnostic errors for which patients pursue litigation against pathologists and previous studies have estimated that approximately 10% of all pigmented lesions examined are associated with a clinically significant false negative error related to melanoma resulting in significant harm to patients. Previous studies have also shown that generalist pathologists in community private practices (the majority of pathologists examining pigmented lesions) tend to make more errors than pathologists who have received specialty dermatopathology training. This project represents a logical collaboration between two previously independent, successful, and productive investigators. The first is the proposed project PI; a significant portion of her work has been on a project showing how multi institutional voluntary sharing and root cause analysis of anatomic pathology diagnostic errors (including errors related to melanoma) may lead to successful process changes that reduce errors. The second is one of the proposed co-investigators (Crowley), whose major focus has been on the development, deployment, and evaluation of a highly novel cognitive simulation tool, an intelligent tutoring system, which has been shown to effectively improve diagnostic performance for melanomas and other melanocytic lesions. In order to achieve our objective, we aim to test the effectiveness of the intelligent tutoring system as a performance improvement and error reduction intervention in a prospective case control study, with volunteer community pathologists as study subjects. Pre- and post-interventional performance and error rates will be longitudinally measured and tracked. The result of this project will be a model diagnostic error reduction system for melanoma using cognitive simulation that we plan to use locally for proficiency testing and training and to enhance for use at national educational conferences and for national networking.

描述（申请人提供）：我们的目标是通过使用认知模拟系统提高社区通才病理学家对恶性黑色素瘤的诊断准确性。迄今为止，医疗差错研究和减少临床差错的举措主要集中在治疗（例如用药）差错，而不是诊断差错。一般外科病理学的诊断错误率通常被认为约为1%，尽管有证据表明这些错误很可能被低估。癌 皮肤癌是所有癌症中最常见的，黑色素瘤占皮肤癌病例的约4%，但导致绝大多数皮肤癌死亡。美国癌症协会估计，在美国诊断出的新的黑色素瘤的数量正在增加，并且在2006年期间预计约有7，910人死于黑色素瘤。自1973年以来，黑素瘤的死亡率增加了50%。恶性黑色素瘤的假阴性诊断错误是患者对病理学家提起诉讼的最常见的诊断错误，并且先前的研究估计，在检查的所有色素性病变中，约有10%与黑色素瘤相关的临床显著假阴性错误相关，导致对患者造成重大伤害。以前的研究也表明， 社区私人诊所（大多数病理学家检查色素性病变）往往比接受过专业皮肤病理学培训的病理学家犯更多的错误。这个项目代表了两个以前独立，成功和富有成效的调查员之间的逻辑合作。第一个是拟议的项目PI;她的工作的一个重要部分是在一个项目上，该项目显示了多机构自愿共享和解剖病理学诊断错误（包括与黑色素瘤相关的错误）的根本原因分析如何可能导致成功的流程变更，从而减少错误。第二个是拟议的共同研究者之一（克劳利），其主要重点是开发，部署， 以及评估一种高度新颖的认知模拟工具，即智能辅导系统，该系统已被证明可有效提高黑色素瘤和其他黑色素细胞病变的诊断性能。为了实现我们的目标，我们的目标是测试智能辅导系统的有效性，作为一个前瞻性的病例对照研究中的性能改善和减少错误的干预，志愿者社区病理学家作为研究对象。将纵向测量和跟踪介入前后的性能和错误率。该项目的结果将是一个使用认知模拟的黑色素瘤诊断错误减少系统模型，我们计划在当地使用该系统进行能力测试和培训，并加强在国家教育会议和国家网络中的使用。