Using mHealth to Increase the Reach of Local Guidance to Health Professionals as Part of an Institutional Response Plan to the COVID-19 Outbreak: Usage Analysis Study
Bibliographic record
Abstract
BACKGROUND: The ongoing coronavirus disease (COVID-19) pandemic forced health jurisdictions worldwide to significantly restructure and reorganize their medical activities. In response to the rapidly evolving body of evidence, a solid communication strategy is needed to increase the reach of and adherence to locally drafted and validated guidance to aide medical staff with COVID-19-related clinical decisions. OBJECTIVE: We present a usage analysis of a dedicated mobile health (mHealth) platform as part of an institutional knowledge dissemination strategy of COVID-19-related guidance to all health care workers (HCWs) in a large academic hospital. METHODS: A multidisciplinary team of experts drafted local guidance related to COVID-19. In total, 60 documents and 17 external links were made available through the platform. Documents were disseminated using a recently deployed mHealth platform for HCWs. Targeted dissemination of COVID-19-related content began on March 22, 2020. Using a third-party statistics tool, data concerning user activity and content use was anonymously collected. A quantitative analysis of user activity was performed over a 4-month period, separated into 3 periods: 2 months before (Period A), 2 weeks after (Period B), and 6 weeks following (Period C) targeted dissemination. Regional epidemiological data (daily new COVID-19 cases and total COVID-19-related hospitalizations) was extracted from an official registry. RESULTS: During the study period, the platform was downloaded by 1233 new users. Consequently, the total number of users increased from 1766 users before Period A to a total of 2999 users at the end of Period C. We observed 27,046 document views, of which 12,728 (47.1%) were COVID-19-related. The highest increase in activity occurred in Period B, rapidly following targeted dissemination, with 7740 COVID-19-related content views, representing 71.2% of total content views within the abovementioned period and 550 daily views of COVID-19-related documents. Total documents consulted per day increased from 117 (IQR 74-160) to 657 (IQR 481-1051), P<.001. This increase in activity followed the epidemiological curbing of newly diagnosed COVID-19 cases, which peaked during Period B. Total active devices doubled from 684 to 1400, daily user activity increased fourfold, and the number of active devices rose from 53 (IQR 40-70) to 210 (IQR 167-297), P<.001. In addition, the number of sessions per day rose from 166 (IQR 110-246) to 704 (IQR 517-1028), P<.001. A persistent but reduced increase in total documents consulted per day (172 [IQR 131-251] versus 117 [IQR 74-160], P<.001) and active devices (71 [IQR 64-89] versus 53 [IQR 40-70]) was observed in Period C compared to Period A, while only 29.8% of the content accessed was COVID-19-related. After targeted dissemination, an immediate increase in activity was observed after push notifications were sent to users. CONCLUSIONS: The use of an mHealth solution to disseminate time-sensitive medical knowledge seemed to be an effective solution to increase the reach of validated content to a targeted audience.
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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 | 0.016 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".