Predicting No-Shows in Radiology Using Regression Modeling of Data Available in the Electronic Medical Record

Abstract

Purpose: 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.

Materials and methods: 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.

Results: 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.

Conclusion: 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.

Keywords: No-show; missed appointment; missed care opportunity; modeling; regression.