Cost-effectiveness analysis of phone reminders for outpatient magnetic resonance imaging (MRI) appointments in Singapore.

INTRODUCTION: Due to long wait times, rising demand and limited resources for Magnetic Resonance Imaging (MRI) services, phone call reminders were implemented as an intervention to increase scanner utilisation and improve non-attendance at the radiology department in Changi General Hospital, Singapore. AIM: This study aims to evaluate the impact of phone reminders on outpatient MRI non-attendance rate as well as the operational efficiency and savings of this intervention through cost-effectiveness analysis. METHODS: MRI outpatient records from January to December 2020 (pre-intervention period) and January to December 2021 (post-intervention period) were retrospectively obtained from the hospital systems. Non-attendance rates, costs and savings following the intervention were compared. RESULTS: Outpatient appointment non-attendance rates reduced from 12.85% to 8.93% after intervention. Following the phone reminders, 2,953 patients (21.69%) decided to cancel or reschedule their appointments. Based on the 91.07% attendance rate (100% - 8.93%), another 2689 slots were recovered from the cancellation of these appointments and were given to other patients. The reduction in non-attendance rates (3.92%) after the intervention translates to an increase in attendance of 533 patients while the net revenue generation with the phone reminder intervention was $387,179. CONCLUSION: Cost analysis indicates that phone reminders provide an inexpensive, easily implemented and personalised method to help increase adherence and improve appointment attendance. Reminding patients by phone calls two day before their appointments also leads to better optimization of appointment slots from cancelations and re-scheduling that can be used to allocate these appointments to other patients.

Artificial Intelligence Predictive Analytics in the Management of Outpatient MRI Appointment No-Shows.

OBJECTIVE. Outpatient appointment no-shows are a common problem. Artificial intelligence predictive analytics can potentially facilitate targeted interventions to improve efficiency. We describe a quality improvement project that uses machine learning techniques to predict and reduce outpatient MRI appointment no-shows. MATERIALS AND METHODS. Anonymized records from 32,957 outpatient MRI appointments between 2016 and 2018 were acquired for model training and validation along with a holdout test set of 1080 records from January 2019. The overall no-show rate was 17.4%. A predictive model developed with XGBoost, a decision tree-based ensemble machine learning algorithm that uses a gradient boosting framework, was deployed after various machine learning algorithms were evaluated. The simple intervention measure of using telephone call reminders for patients with the top 25% highest risk of an appointment no-show as predicted by the model was implemented over 6 months. RESULTS. The ROC AUC for the predictive model was 0.746 with an optimized F1 score of 0.708; at this threshold, the precision and recall were 0.606 and 0.852, respectively. The AUC for the holdout test set was 0.738 with an optimized F1 score of 0.721; at this threshold, the precision and recall were 0.605 and 0.893, respectively. The no-show rate 6 months after deployment of the predictive model was 15.9% compared with 19.3% in the preceding 12-month preintervention period, corresponding to a 17.2% improvement from the baseline no-show rate (p < 0.0001). The no-show rates of contactable and noncontactable patients in the group at high risk of appointment no-shows as predicted by the model were 17.5% and 40.3%, respectively (p < 0.0001). CONCLUSION. Machine learning predictive analytics perform moderately well in predicting complex problems involving human behavior using a modest amount of data with basic feature engineering, and they can be incorporated into routine workflow to improve health care delivery.