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
Abstract Background Patient-generated health data (PGHD) can enhance patient-centered care by improving disease awareness and preparedness for clinical encounters. However, automated incorporation of PGHD into electronic medical records (EMRs), which is a prerequisite for broader clinical implementation, remains technically and administratively challenging. Objective This study describes the development of Miri-Alimi , a PGHD collection platform that delivers mobile social networking service–based previsit questionnaires with automated transfer of structured patient responses into the EMR, and evaluates patient participation, EMR documentation quality, and user satisfaction in a cardiology outpatient clinic. Methods This single-center observational study was conducted between August and November 2024 and included 751 consecutive cardiology outpatients, comprising 282 first-visit patients and 469 patients attending follow-up visits for heart failure. All eligible patients received a previsit electronic questionnaire link via KakaoTalk or multimedia messaging service prior to their scheduled visit. The primary outcomes were the overall survey response rate among all enrolled patients and EMR documentation completeness among follow-up patients with heart failure. Documentation quality was evaluated based on 3 prespecified parameters relevant to routine heart failure care—dyspnea, peripheral edema, and medication adherence status—and was quantified using an EMR completeness score ranging from 0 to 3. Secondary outcomes included patient and provider satisfaction assessed using postvisit 5-point Likert-scale surveys. Firth penalized logistic regression was used to evaluate the association between survey response status and EMR completeness, with adjustment for age and sex. Results The response rate was 38.5% (289/751), including 48.9% (138/282) of new patients and 32.2% (151/469) of follow-up patients with heart failure. Responders were younger than nonresponders (mean 62.0, SD 15.7 years vs mean 69.8, SD 12.5 years; P <.001). Among the follow-up patients with heart failure, EMR completeness was higher among responders (median score 3, IQR 3‐3) than among nonresponders (median score 0, IQR 0‐1; P <.001). Patient satisfaction was high: 82.9% (63/76) to 92.1% (70/76) agreed that the system was appropriate, easy to use, and helpful, and 78.9% (60/76) completed the survey in <10 minutes. Both cardiologists and 7 of the 8 participating nurses supported continued use of the system, citing workflow efficiency gains. Conclusions Miri-Alimi enabled patient-friendly PGHD collection without requiring log-ins or a dedicated app and demonstrated direct transfer of patient responses into the EMR. Its use was associated with effective transfer and structured integration of PGHD into the EMR, as well as high satisfaction among survey respondents and participating staff. Further studies should evaluate sustainability and associations with long-term clinical outcomes across diverse care settings.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".