ArticleJAMA network open2026
Automated Health Care Messages and Unexpected Patient Responses.
Article in JAMA network open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Importance: Automated text messaging systems are crucial for health care communication, but text message software is often programmed to recognize only a limited range of responses, such as requests to stop messaging. However, patients may send replies that go beyond these expected responses. Systems may disregard or misclassify such replies, leading to accidental opt-outs or unaddressed needs. Identifying unexpected patient responses to automated messages might identify areas for improvement in communication systems. Objective: To develop a framework representing the range of patient responses to automated text messages and to identify considerations for improving health system communication design. Design, Setting, and Participants: This qualitative study examines all patient responses to automated text messages from January 1, 2022, to December 31, 2023, at Kaiser Permanente Colorado, an integrated health system serving more than 500 000 patients. Data were analyzed from January to November 2024. Main Outcomes and Measures: Patient response themes and domains were identified through inductive qualitative content analysis and interpretive system-level considerations informed by identified response patterns. The focus was on the diversity and implications of patient replies rather than their prevalence. Results: Among 28 456 patients who responded to 38 395 automated text messages (16 268 female [57.2%]; 11 692 [41.1%] aged 65 years or older), 743 unique responses were analyzed. Sixteen message types were identified that extended beyond the system's recognized commands, grouped into 8 overarching domains: opt-out or opt-in requests, appointment management, requests for help or information, communication preferences, corrections of inaccurate data, comments on usefulness and usability, expressions of frustration, and uninterpretable responses. Replies reflected expectations for bidirectional communication, highlighted errors in patient information, or pointed to usability barriers. These communications underscored limitations of exclusively automated processing and revealed opportunities to make communication systems more responsive. Conclusions and Relevance: In this qualitative study of patient responses to automated text messages, patients used these systems as an opportunity for bidirectional communication, revealing needs that extend beyond current system capabilities. Addressing these responses could reduce communication errors, improve appointment management, and strengthen trust in health care communication.
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