Patient Interaction Phenotypes With an Automated SMS Text Message–Based Program and Use of Acute Health Care Resources After Hospital Discharge: Observational Study

TL;DR

This study identifies different ways patients interact with automated SMS messaging after hospital discharge and how these patterns relate to healthcare use and patient characteristics.

Contribution

The study introduces a novel approach to classify patient interaction patterns with SMS messaging post-discharge and links these patterns to clinical and demographic factors.

Findings

Four distinct patient interaction phenotypes were identified using k-means clustering.

Interaction patterns correlated with demographic factors like gender, race, and insurance type.

Certain interaction styles predicted higher risk of hospital revisits.

Abstract

Automated bidirectional SMS 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.

Our primary aim was to identify and characterize distinct patient interaction phenotypes with a postdischarge automated SMS text messaging program.

This was a secondary analysis of data from a randomized controlled trial that tested a 30-day postdischarge automated SMS text messaging intervention. We analyzed SMS text messages and patterns of engagement among patients who received the intervention and responded to messages. We engineered features to describe patients’ 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.

A total of 1731 patients engaged with the intervention, among which 1060 (61.2%) were female; the mean age was 65 (SD 16.1) years; 782 (45.2%) and 828 (47.8%) patients identified as Black and White, respectively; and 970 (56%) and 317 (18.3%) patients were insured by Medicare and Medicaid, respectively. Using k-means clustering, we observed four 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 (nonadapters, n=170); and (4) a high engagement with an 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.

For health systems looking to leverage an SMS text messaging approach to engage patients after discharge, this work offers two main takeaways: (1) not all patients interact with SMS 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.

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