It is often possible to facilitate desirable health-related behaviors with the use of reminders. There is strong evidence that even simple informative reminders are effective in increasing compliance to medical guidelines in different healthcare domains (such as medication adherence, vaccine uptake, appointment attendance, etc.). Nonetheless, there is no consensus as to the optimal message and content that should be used to increase the likelihood that patients act in accordance with the optimal health related behavior. The current dissertation tries to improve our understanding the impact of reminders is two domains: Attendance to a scheduled appointment and vaccine uptake.
study 1 builds of different principals from the field of behavioral science, and systematically compare the effects of several behaviorally informed reminders designed to reduce the “no show” rates to pre-scheduled medical appointments within outpatient clinics. It examines 9 type of reminders that were randomly assigned to Clalit’s members. The results show that behaviorally informed reminders can significantly reduce no-show rates by over 30 percent and increase advanced cancellation by over 17% (p<.001). study 2 compares two types of behaviorally informed reminders designed to increase COVID-19 vaccination. It was conducted in the spring of 2021when vaccines were available to the entire population aged 16 years and above. Results revealed that text reminders emphasizing medical benefit were 9% more effective than social benefit reminders (2.1% percentage points increase, p<.001). Study 3 assumes that the effect of the behaviorally informed reminders is limited to a local maximum, since they are designed in a mind of a one-size-fits-all approach instead of acknowledging individual differences. we combine the field of behavioral science and machine learning algorithms, to optimize the reminder system and tailor the content of appointment reminder to a specific patient.