A Tailored SMS Text Message–Based Intervention to Facilitate Patient Access to Referred Community-Based Social Needs Resources: Protocol for a Pilot Feasibility and Acceptability Study
Bibliographic record
Abstract
BACKGROUND: Health care providers are increasingly screening patients for unmet social needs (eg, food, housing, transportation, and social isolation) and referring patients to relevant community-based resources and social services. Patients' connection to referred services is often low, however, suggesting the need for additional support to facilitate engagement with resources. SMS text messaging presents an opportunity to address barriers related to contacting resources in an accessible, scalable, and low-cost manner. OBJECTIVE: In this multi-methods pilot study, we aim to develop an automated SMS text message-based intervention to promote patient connection to referred social needs resources within 2 weeks of the initial referral and to evaluate its feasibility and patient acceptability. This protocol describes the intervention, conceptual underpinnings, study design, and evaluation plan to provide a detailed illustration of how SMS technology can complement current social needs screening and referral practice patterns without disrupting care. METHODS: For this pilot prospective cohort study, this SMS text message-based intervention augments an existing social needs screening, referral, and navigation program at a federally qualified health center. Patients who received at least one referral for any identified unmet social need are sent 2 rounds of SMS messages over 2 weeks. The first round consists of 5-10 messages that deliver descriptions of and contact information for the referred resources. The second round consists of 2 messages that offer a brief reminder to contact the resources. Participants will evaluate the intervention via a survey and a semistructured interview, informed by an adapted technology acceptance model. Rapid qualitative and thematic analysis will be used to extract themes from the responses. Primary outcomes are implementation feasibility and patient acceptability. Secondary outcomes relate to intervention effectiveness: self-reported attempt to connect and successful connection to referred resources 2 weeks after the initial referral encounter. RESULTS: The study received regulatory approval in May 2021, and we anticipate enrolling 15-20 participants for this initial pilot. CONCLUSIONS: This protocol presents detailed implementation methods about a novel automated SMS intervention for social care integration within primary care. By sharing the study protocol early, we intend to facilitate the development and adoption of similar tools across different clinical settings, as more health care providers seek to address the unmet social needs of patients. Study findings will provide practical insights into the design and implementation of SMS text message-based interventions to improve social and medical care coordination. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37316.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".