BACKGROUND
Preventive screenings such as mammograms promote health and detect disease. However, mammogram attendance lags clinical guidelines, with roughly one quarter of women not completing their recommended mammograms. A scalable digital health intervention leveraging behavioral science and reinforcement learning and delivered via email was implemented in a US health system to promote uptake of recommended mammograms among patients overdue for the screening.
OBJECTIVE
The objective of this study was to establish the feasibility of a reinforcement learning-enabled mammography digital health intervention delivered via email. The research aims included understanding the intervention’s reach and ability to elicit behavioral outcomes of scheduling and attending mammograms.
METHODS
The digital health intervention was implemented in a large Catholic health system in the Midwestern US. From August 2020 to July 2022, 139,164 eligible women received behavioral science-based messages assembled and delivered by a reinforcement learning model to encourage follow-through on clinically recommended mammograms.
RESULTS
139,164 women received at least one intervention email during the study period, and 81.5% engaged with at least one email. Deliverability of emails exceeded 98%. Among message recipients, 25% scheduled mammograms and 22% attended mammograms (88% of scheduled). Results indicate no practical differences in the frequency with which people engage with the intervention or take action following a message based on their age, race, educational attainment, or household income, suggesting the intervention may equitably drive mammography across diverse populations.
CONCLUSIONS
Digital health interventions may be a valuable approach to prompt mammograms in a health system setting among patients who are overdue. In this feasibility study, the intervention showed proportionate reach across demographic sub-populations, and was associated with scheduling and attending mammograms.