Development and Testing of a Mobile App to Collect Social Determinants of Health Data in Cancer Settings: Interview Study
Bibliographic record
Abstract
BACKGROUND: Social determinants of health (SDOH) such as lack of basic resources, housing, transportation, and social isolation play an important role for patients on the cancer care continuum. Health systems' current technological solutions for identifying and managing patients' SDOH data largely focus on information recorded in the electronic health record by providers, which is often inaccessible to patients to contribute to or modify. OBJECTIVE: We developed and tested a patient-centric SDOH screening tool designed for use on patients' personal mobile phone that preserves patient privacy and confidentiality, collects information about the unmet social needs of patients with cancer, and communicates them to the provider. METHODS: We interviewed 22 patients with cancer, oncologists, and social workers associated with a US-based comprehensive cancer center to better understand how patients' SDOH information is collected and reported. After triangulating data obtained from thematic analysis of interviews, an environmental scan, and a literature search of validated tools to collect SDOH data, we developed an SDOH screening tool mobile app and conducted a pilot study of 16 dyadic pairs of patients and cancer care team members at the same cancer center. We collected patient SDOH data using 36 survey items covering 7 SDOH domains and used validated scales and follow-up interviews to assess the app's usability and acceptability among patients and cancer care team members. RESULTS: Formative interviews with patients and care team members revealed that transportation, financial challenges, food insecurity, and low health literacy were common SDOH challenges and that a mobile app that collected those data, shared those data with care team members, and offered supportive resources could be useful and valuable. In the pilot study, 25% (4/16) of app-using patients reported having at least one of the abovementioned social needs; the most common social need was social isolation (7/16, 44%). Patients rated the mobile app as easy to use, accurately capturing their SDOH, and preserving their privacy but suggested that the app could be more helpful by connecting patients to actual resources. Providers reported high acceptability and usability of the app. CONCLUSIONS: Use of a brief, patient-centric, mobile app-based SDOH screening tool can effectively capture SDOH of patients with cancer for care team members in a way that preserves patient privacy and that is acceptable and usable for patients and care team members. However, only collecting SDOH information is not sufficient; usefulness can be increased by connecting patients directly to resources to address their unmet social needs.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".