Implementation and Evaluation of a Social Networking Service-based Mobile Patient-generated Health Data System with Direct Electronic Medical Record Integration: A Prospective Observational Study (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Patient-generated health data (PGHD) can make consultations more patient-centered and enhance patient disease awareness and visit preparedness. </sec> <sec> <title>OBJECTIVE</title> We developed “Miri-Alimi,” a mobile, Social Networking Service-delivered previsit questionnaire that automatically transfers results to the electronic medical record (EMR). This study assessed patient participation, documentation quality, and user satisfaction in a cardiology outpatient clinic. </sec> <sec> <title>METHODS</title> This single-center observational study (August–November 2024) included 751 outpatients consisting of 282 first-time cardiovascular patients and 469 heart failure follow-ups who received a previsit questionnaire link via KakaoTalk or a multimedia messaging service (MMS). The primary outcomes were the survey response rate among all patients (including both initial cardiovascular and follow-up heart failure patients) and the completeness of documentation for three heart failure parameters (dyspnea, edema, and medication adherence ≥80%) recorded in the EMRs of follow-up heart failure patients. Secondary outcomes were patient and provider satisfaction, measured using post-visit Likert -scale surveys. </sec> <sec> <title>RESULTS</title> The response rate was 38.5% (289/751); among these, 48.9% (138/282) were first-visit and 32.2% (151/469) were heart failure follow-up patients. Responders were younger than non-responders (mean 62.0, SD 15.7 vs mean 69.8, SD 12.5 years; P<.001). Among the heart failure follow-ups, EMR completeness was higher in responders (median 3, IQR 3–3) than in non-responders (median 0, IQR 0–1; P<.001). Patient satisfaction was high: 82.8% (63/76)–92% (70/76) agreed that the system was appropriate, easy to use, and helpful, and 78.9% (66/76) completed the survey in <10 minutes. Both cardiologists and eight of the nine nurses supported continued use of the system, citing workflow efficiency gains. </sec> <sec> <title>CONCLUSIONS</title> Miri-Alimi facilitates patient-friendly PGHD data collection and seamless EMR integration without requiring logins or apps. This significantly improved documentation completeness and resulted in high satisfaction scores. Future studies should assess long-term sustainability and clinical outcomes across diverse settings. </sec> <sec> <title>CLINICALTRIAL</title> Clinical Research Information Service of the Korea Disease Control and Prevention Agency number PRE20250218-002; https://cris.nih.go.kr. </sec>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".