Examining patient’s mobile phone access and planning a virtual care intervention using mHealth and conversation analytics
Bibliographic record
Abstract
Introduction: Unplanned hospital readmissions create stress for patients and their families while placing individuals at risk for negative outcomes and increasing healthcare system costs. Development of effective interventions to reduce readmissions involves timely discharge planning, transitional care, and stakeholder uptake. Mobile health (mHealth) and machine learning technology may help improve coordination of care, identify the underlying reasons for complications, and potentially reduce readmissions. Methods: To determine whether mHealth can help streamline and improve transitional care after discharge from the hospital, we will utilize a two-way text messaging virtual care platform to be piloted at the medical wards in the Vancouver General Hospital (VGH) Clinical Teaching Unit (CTU). Prior to launching the program, we conducted a survey of patients admitted to the CTU to determine mobile phone access, usage, and preferences to better understand the population we wish to serve. Using this information, we designed an mHealth intervention protocol that is patient-centered and collaborative. Results: We found that a two-way text messaging mHealth platform would likely be well-placed to facilitate better transitional care and to understand the underlying reasons for readmissions. Our survey results indicated that 86% of participants had access to a mobile phone, 63% of whom owned their own device and 23% of whom had access via a proxy (e.g., family or caregiver). These findings indicate that most patients can participate in mHealth interventions that rely on mobile phones and that engaging a proxy may further expand inclusivity. Lastly, we conducted training sessions and consulted with hospital staff to ensure the study protocol meets end-user needs and preferences. Using these findings, we developed a framework that utilizes natural language processing (NLP) and machine learning to analyze patient text message conversations with their health care provider (HCP). Conclusion: Our findings suggest that mHealth virtual care platforms are feasible and accessible in a hospital setting, which may help in reducing the burden of hospital readmission on patients, their families, and the health care system.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".