Patient Interaction Phenotypes With an Automated SMS Text Message–Based Program and Use of Acute Health Care Resources After Hospital Discharge: Observational Study
Abstract BackgroundAutomated 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 program...
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| Format: | Article |
| Language: | English |
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JMIR Publications
2025-07-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e72875 |
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| _version_ | 1849713219581509632 |
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| author | Klea Profka Agnes Wang Emily Schriver Ashley Batugo Anna U Morgan Danielle Mowery Eric Bressman |
| author_facet | Klea Profka Agnes Wang Emily Schriver Ashley Batugo Anna U Morgan Danielle Mowery Eric Bressman |
| author_sort | Klea Profka |
| collection | DOAJ |
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Abstract
BackgroundAutomated 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.
ObjectiveOur primary aim was to identify and characterize distinct patient interaction phenotypes with a postdischarge automated SMS text messaging program.
MethodsThis 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.
ResultsA 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.
ConclusionsFor 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. |
| format | Article |
| id | doaj-art-58e609fc51ef43be94aefcb293edde6b |
| institution | DOAJ |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj-art-58e609fc51ef43be94aefcb293edde6b2025-08-20T03:14:01ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-07-0127e72875e7287510.2196/72875Patient Interaction Phenotypes With an Automated SMS Text Message–Based Program and Use of Acute Health Care Resources After Hospital Discharge: Observational StudyKlea Profkahttp://orcid.org/0009-0000-7222-3922Agnes Wanghttp://orcid.org/0009-0001-5232-3122Emily Schriverhttp://orcid.org/0000-0003-4522-1029Ashley Batugohttp://orcid.org/0000-0002-7463-4634Anna U Morganhttp://orcid.org/0000-0003-4641-1609Danielle Moweryhttp://orcid.org/0000-0003-3802-4457Eric Bressmanhttp://orcid.org/0000-0003-4688-0747 Abstract BackgroundAutomated 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. ObjectiveOur primary aim was to identify and characterize distinct patient interaction phenotypes with a postdischarge automated SMS text messaging program. MethodsThis 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. ResultsA 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. ConclusionsFor 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.https://www.jmir.org/2025/1/e72875 |
| spellingShingle | Klea Profka Agnes Wang Emily Schriver Ashley Batugo Anna U Morgan Danielle Mowery Eric Bressman Patient Interaction Phenotypes With an Automated SMS Text Message–Based Program and Use of Acute Health Care Resources After Hospital Discharge: Observational Study Journal of Medical Internet Research |
| title | Patient Interaction Phenotypes With an Automated SMS Text Message–Based Program and Use of Acute Health Care Resources After Hospital Discharge: Observational Study |
| title_full | Patient Interaction Phenotypes With an Automated SMS Text Message–Based Program and Use of Acute Health Care Resources After Hospital Discharge: Observational Study |
| title_fullStr | Patient Interaction Phenotypes With an Automated SMS Text Message–Based Program and Use of Acute Health Care Resources After Hospital Discharge: Observational Study |
| title_full_unstemmed | Patient Interaction Phenotypes With an Automated SMS Text Message–Based Program and Use of Acute Health Care Resources After Hospital Discharge: Observational Study |
| title_short | Patient Interaction Phenotypes With an Automated SMS Text Message–Based Program and Use of Acute Health Care Resources After Hospital Discharge: Observational Study |
| title_sort | patient interaction phenotypes with an automated sms text message based program and use of acute health care resources after hospital discharge observational study |
| url | https://www.jmir.org/2025/1/e72875 |
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