JMIR Publications

ABSTRACT

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.