Houston VA Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX; Department of Medicine, Baylor College of Medicine, Houston, TX.
Houston VA Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX; Department of Medicine, Baylor College of Medicine, Houston, TX.
Chest. 2016 Sep;150(3):613-20. doi: 10.1016/j.chest.2016.05.001. Epub 2016 May 10.
A "trigger" algorithm was used to identify delays in follow-up of abnormal chest imaging results in a large national clinical data warehouse of electronic health record (EHR) data.
We applied a trigger in a repository hosting EHR data from all Department of Veterans Affairs health-care facilities and analyzed data from seven facilities. Using literature reviews and expert input, we refined previously developed trigger criteria designed to identify patients potentially experiencing delays in diagnostic evaluation of chest imaging flagged as "suspicious for malignancy." The trigger then excluded patients in whom further evaluation was unnecessary (eg, those with terminal illnesses or with already completed biopsies). The criteria were programmed into a computerized algorithm. Reviewers examined a random sample of trigger-positive (ie, patients with trigger-identified delay) and trigger-negative (ie, patients with an abnormal imaging result but no delay) records and confirmed the presence or absence of delay or need for additional tracking (eg, repeat imaging in 6 months). Analysis included calculating the trigger's diagnostic performance (ie, positive predictive value, negative predictive value, sensitivity, specificity).
On application to 208,633 patients seen between January 1, 2012, and December 31, 2012, a total of 40,218 chest imaging tests were performed; 1,847 of the results were suspicious for malignancy, and 655 (35%) were trigger-positive. Review of 400 randomly selected trigger-positive patients found 158 (40%) with confirmed delays and 84 (21%) requiring additional tracking (positive predictive value, 61% [95% CI, 55.5-65.3]). Review of 100 trigger-negative patients identified 97 without delay (negative predictive value, 97%; [95% CI, 90.8-99.2]). Sensitivity and specificity were 99% (95% CI, 96.2-99.7) and 38% (95% CI, 32.1-44.3), respectively.
Application of triggers on "big" EHR data may aid in identifying patients experiencing delays in diagnostic evaluation of chest imaging results suspicious for malignancy.
在一个大型电子健康记录（EHR）数据临床数据仓库中，使用了一种“触发”算法来识别异常胸部影像学结果随访中的延迟。
我们在一个托管 EHR 数据的存储库中应用了一种触发机制，该存储库来自所有退伍军人事务部医疗设施，并分析了来自七个设施的数据。通过文献回顾和专家意见，我们改进了以前开发的触发标准，旨在识别胸部影像学标记为“疑似恶性肿瘤”的患者中可能存在诊断评估延迟的患者。然后，该触发排除了进一步评估不必要的患者（例如，患有绝症或已经完成活检的患者）。标准被编程到一个计算机算法中。审查员检查了随机选择的触发阳性（即，有触发识别延迟的患者）和触发阴性（即，有异常影像学结果但无延迟的患者）记录，并确认了延迟或需要额外跟踪（例如，6 个月内重复影像学检查）的存在或不存在。分析包括计算触发的诊断性能（即，阳性预测值、阴性预测值、敏感性、特异性）。
在 2012 年 1 月 1 日至 2012 年 12 月 31 日期间应用于 208633 名患者，共进行了 40218 次胸部影像学检查；结果中有 1847 例可疑恶性肿瘤，655 例（35%）为触发阳性。对随机选择的 400 例触发阳性患者进行审查，发现 158 例（40%）有确诊的延迟，84 例（21%）需要额外的跟踪（阳性预测值，61%[95%置信区间，55.5-65.3]）。对 100 例触发阴性患者的审查发现 97 例没有延迟（阴性预测值，97%[95%置信区间，90.8-99.2]）。敏感性和特异性分别为 99%（95%置信区间，96.2-99.7）和 38%（95%置信区间，32.1-44.3）。
在“大数据”EHR 数据上应用触发器可能有助于识别胸部影像学结果可疑恶性肿瘤的诊断评估中存在延迟的患者。
