Massachusetts General Hospital Department of Radiology, Boston, Massachusetts; Massachusetts General Hospital Institute for Technology Assessment, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.
Massachusetts General Hospital Department of Radiology, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts.
J Am Coll Radiol. 2017 Oct;14(10):1303-1309. doi: 10.1016/j.jacr.2017.05.007. Epub 2017 Jun 30.
To test whether data elements available in the electronic medical record (EMR) can be effectively leveraged to predict failure to attend a scheduled radiology examination.
Using data from a large academic medical center, we identified all patients with a diagnostic imaging examination scheduled from January 1, 2016, to April 1, 2016, and determined whether the patient successfully attended the examination. Demographic, clinical, and health services utilization variables available in the EMR potentially relevant to examination attendance were recorded for each patient. We used descriptive statistics and logistic regression models to test whether these data elements could predict failure to attend a scheduled radiology examination. The predictive accuracy of the regression models were determined by calculating the area under the receiver operator curve.
Among the 54,652 patient appointments with radiology examinations scheduled during the study period, 6.5% were no-shows. No-show rates were highest for the modalities of mammography and CT and lowest for PET and MRI. Logistic regression indicated that 16 of the 27 demographic, clinical, and health services utilization factors were significantly associated with failure to attend a scheduled radiology examination (P ≤ .05). Stepwise logistic regression analysis demonstrated that previous no-shows, days between scheduling and appointments, modality type, and insurance type were most strongly predictive of no-show. A model considering all 16 data elements had good ability to predict radiology no-shows (area under the receiver operator curve = 0.753). The predictive ability was similar or improved when these models were analyzed by modality.
Patient and examination information readily available in the EMR can be successfully used to predict radiology no-shows. Moving forward, this information can be proactively leveraged to identify patients who might benefit from additional patient engagement through appointment reminders or other targeted interventions to avoid no-shows.
测试电子病历（EMR）中可用的数据元素是否可以有效地用于预测患者是否会错过预约的放射学检查。
利用来自一家大型学术医疗中心的数据，我们确定了所有在 2016 年 1 月 1 日至 2016 年 4 月 1 日期间安排诊断影像学检查的患者，并确定了患者是否成功进行了检查。记录了 EMR 中可能与检查就诊相关的每个患者的人口统计学、临床和医疗服务使用变量。我们使用描述性统计和逻辑回归模型来测试这些数据元素是否可以预测患者是否会错过预约的放射学检查。通过计算接收者操作特征曲线下的面积来确定回归模型的预测准确性。
在研究期间安排的 54652 次放射学检查预约中，有 6.5%的患者未出现。乳房 X 线摄影术和 CT 检查的未到诊率最高，而 PET 和 MRI 检查的未到诊率最低。逻辑回归表明，27 个人口统计学、临床和医疗服务使用因素中有 16 个与未出现预约放射学检查显著相关（P≤0.05）。逐步逻辑回归分析表明，之前的未到诊、预约时间和检查方式以及保险类型是预测未到诊的最重要因素。考虑到所有 16 个数据元素的模型对放射学未到诊具有良好的预测能力（接收者操作特征曲线下的面积=0.753）。当按检查方式分析这些模型时，其预测能力相似或有所提高。
EMR 中患者和检查信息可用于成功预测放射学未到诊。将来，可以主动利用这些信息来识别可能通过预约提醒或其他有针对性的干预措施受益于增加患者参与度的患者，以避免未到诊。
