JMIR Publications

ABSTRACT

Background:

Automated bidirectional text messaging has emerged as a compelling strategy to facilitate communication between patients and the health system after hospital discharge. Understanding the unique ways in which patients interact with these messaging programs can inform future efforts to tailor their design to individual patient styles and needs.

Objective:

Our primary aim was to identify and characterize distinct patient interaction phenotypes with a post-discharge automated text messaging program.

Methods:

This was a secondary analysis of data from a randomized clinical trial which tested a 30-day post-discharge automated text messaging intervention. We analyzed text messages and patterns of engagement among patients who received the intervention and responded to messages. We engineered features to describe patient’s engagement with and conformity to the program, and used a k-means clustering approach to learn distinct interaction phenotypes among program participant subgroups. We also looked at the association between these interaction phenotypes and 1) patient demographics and clinical characteristics and 2) hospital revisit outcomes.

Results:

A total of 1731 patients engaged with the intervention, among which 1060 (61.2%) were female; the mean (SD) age was 65 (16.1); 782 (45.2%) and 828 (47.8%) identified as Black and White, respectively; and 970 (56.0%) and 317 (18.3%) were insured by Medicare and Medicaid, respectively. Using k-means clustering, we observed 4 distinct subgroups representing patient interaction phenotypes: 1) a high engagement, high conformity group (enthusiasts, n = 1029); 2) a low engagement, high conformity group (minimalists, n = 515); 3) a low engagement, low conformity group (non-adapters, n = 170); and 4) a high engagement with intense level of need group (high needs responders, n = 17). Differences were observed in demographic characteristics – including gender, race, and insurance type – and clinical outcomes across groups.

Conclusions:

For health systems looking to leverage a text messaging approach to engage patients after discharge, this work offers two main takeaways: 1) not all patients interact with text messaging equally, and some may require either additional guidance or a different medium altogether, and 2) the way in which patients interact with this type of program (in addition to the information they communicate through the program) may have added predictive signal toward adverse outcomes.