Division of Gastroenterology and Hepatology.
Data Management Office, University of Vermont Health Network, Burlington VT.
J Clin Gastroenterol. 2024 Aug 1;58(7):656-660. doi: 10.1097/MCG.0000000000001915.
To develop an automated method for Adenoma Detection Rate (ADR) calculation and report card generation using common electronic health records (EHRs).
ADR is the most widely accepted colonoscopy quality indicator and is inversely associated with interval colorectal cancer incidence and mortality. However, ADR is difficult to efficiently measure and disseminate, due to need for data integration from distinct electronic databases.
We migrated data from an endoscopy reporting software (Endosoft) to Epic Reporting Servers where it was combined with anatomic pathology data (Beaker Lab Information System, EPIC Systems). A natural language processing expression was developed to search Beaker pathology reports for accurate identification of adenomatous polyps. A blinded physician manually validated a final cohort of 200 random procedures. ADR report cards were automatically generated utilizing the Crystal Reports feature within EPIC.
Validation of the natural language processing algorithm for ADR showed a sensitivity, specificity, and accuracy of 100%. ADR was automatically calculated for 12 endoscopists over a calendar year. Two thousand two hundred seventy-six screening colonoscopies were performed with 775 procedures having a least one adenoma detected, for a total ADR of 34%. Report cards were successfully generated within the EPIC EHR and distributed to endoscopists by secure e-mail.
We describe an accurate, automated and scalable process for ADR calculation and reporting utilizing commonly adopted EHRs and data integration methods. By integrating the process of ADR collection and streamlining dissemination of reports, this methodology is poised to enhance colonoscopy quality care across health care networks that use it.
开发一种使用常见电子健康记录（EHR）自动计算腺瘤检出率（ADR）并生成报告卡的方法。
ADR 是最广泛接受的结肠镜检查质量指标，与结直肠癌的间隔发病率和死亡率呈负相关。然而，由于需要从不同的电子数据库集成数据，因此 ADR 难以有效地进行测量和传播。
我们将数据从内镜报告软件（Endosoft）迁移到 Epic 报告服务器，在那里与解剖病理学数据（Beaker Lab Information System，EPIC Systems）相结合。开发了一种自然语言处理表达式，用于在 Beaker 病理报告中搜索准确识别腺瘤性息肉。一位盲法医生手动验证了 200 例随机手术的最终队列。使用 EPIC 中的 Crystal Reports 功能自动生成 ADR 报告卡。
ADR 的自然语言处理算法验证的灵敏度、特异性和准确性均为 100%。在一个日历年内，自动计算了 12 名内镜医生的 ADR。共进行了 2276 例筛查性结肠镜检查，其中 775 例至少检出 1 个腺瘤，总 ADR 为 34%。报告卡成功地在 EPIC EHR 中生成，并通过安全电子邮件分发给内镜医生。
我们描述了一种利用常见的 EHR 和数据集成方法准确、自动和可扩展的 ADR 计算和报告方法。通过整合 ADR 收集过程并简化报告的传播，这种方法有望在使用该方法的医疗保健网络中提高结肠镜检查质量护理。
