Application of a Machine Learning Algorithm to Develop and Validate a Prediction Model for Ambulatory Non-Arrivals Prediction Model for Ambulatory Non-Arrivals

• Published in: Journal of general internal medicine : JGIM Vol. 38; no. 10; pp. 2298 - 2307
• Main Authors: Coppa, Kevin, Kim, Eun Ji, Oppenheim, Michael I., Bock, Kevin R., Zanos, Theodoros P., Hirsch, Jamie S.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2023; Springer Nature B.V
• Subjects: Adult; Algorithms; Appointments and Schedules; Arrivals; Calibration; Humans; Internal Medicine; Lead time; Learning algorithms; Machine Learning; Medicine; Medicine & Public Health; Minority & ethnic groups; Original Research; Patients; Prediction models; Primary care; Retrospective Studies; Time Factors
• ISSN: 0884-8734; 1525-1497; 1525-1497
• DOI: 10.1007/s11606-023-08065-y

Background Non-arrivals to scheduled ambulatory visits are common and lead to a discontinuity of care, poor health outcomes, and increased subsequent healthcare utilization. Reducing non-arrivals is important given their association with poorer health outcomes and cost to health systems. Objective To develop and validate a prediction model for ambulatory non-arrivals. Design Retrospective cohort study. Patients or Subjects Patients at an integrated health system who had an outpatient visit scheduled from January 1, 2020, to February 28, 2022. Main Measures Non-arrivals to scheduled appointments. Key Results There were over 4.3 million ambulatory appointments from 1.2 million adult patients. Patients with appointment non-arrivals were more likely to be single, racial/ethnic minorities, and not having an established primary care provider compared to those who arrived at their appointments. A prediction model using the XGBoost machine learning algorithm had the highest AUC value (0.768 [0.767–0.770]). Using SHAP values, the most impactful features in the model include rescheduled appointments, lead time (number of days from scheduled to appointment date), appointment provider, number of days since last appointment with the same department, and a patient’s prior appointment status within the same department. Scheduling visits close to an appointment date is predicted to be less likely to result in a non-arrival. Overall, the prediction model calibrated well for each department, especially over the operationally relevant probability range of 0 to 40%. Departments with fewer observations and lower non-arrival rates generally had a worse calibration. Conclusions Using a machine learning algorithm, we developed a prediction model for non-arrivals to scheduled ambulatory appointments usable for all medical specialties. The proposed prediction model can be deployed within an electronic health system or integrated into other dashboards to reduce non-arrivals. Future work will focus on the implementation and application of the model to reduce non-arrivals.