A new study shows that AI can accurately predict no-shows for pediatric appointments using patients’ medical records and local weather information.
A deep-learning model can accurately predict pediatric no-shows using data from patient EHRs and local weather information, which can then be used to implement no-show prevention measures, according to a new study published in npj Digital Medicine.
Appointment no-shows can negatively affect health outcomes and health system resource utilization because check-ups, preventative care, and treatment cannot be provided if the appointment is missed. According to the study, patients with a prior history of no-shows, public health insurance, and lower socioeconomic status are more likely to miss their appointments. Patients who miss appointments often cite traffic, scheduling issues, time conflicts, and environmental factors as the main reasons.
The researchers developed their deep learning model by retrospectively collecting EHR data for 19,450 patients between Jan. 10, 2015, and Sept. 9, 2016, at Boston Children’s Hospital’s primary care pediatric clinic. These records included data from 161,822 medical appointments and information related to patient age, gender, previous no-show rate, and health insurance type. Of these appointments, 20.3 percent were no-shows, which means the patient neither showed up nor canceled.