Usage Pattern Differences and Similarities of Mobile Electronic Medical Records Among Health Care Providers
Bibliographic record
Abstract
BACKGROUND: Recently, many hospitals have introduced mobile electronic medical records (mEMRs). Although numerous studies have been published on the usability or usage patterns of mEMRs through user surveys, investigations based on the real data usage are lacking. OBJECTIVE: Asan Medical Center, a tertiary hospital in Seoul, Korea, implemented an mEMR program in 2010. On the basis of the mEMR usage log data collected over a period of 4.5 years, we aimed to identify a usage pattern and trends in accordance with user occupation and to disseminate the factors that make the mEMR more effective and efficient. METHODS: The mEMR log data were collected from March 2012 to August 2016. Descriptive analyses were completed according to user occupation, access time, services, and wireless network type. Specifically, analyses targeted were as follows: (1) the status of the mEMR usage and distribution of users, (2) trends in the number of users and usage amount, (3) 24-hour usage patterns, and (4) trends in service usage based on user occupations. Linear regressions were performed to model the relationship between the time, access frequency, and the number of users. The differences between the user occupations were examined using Student t tests for categorical variables. RESULTS: Approximately two-thirds of the doctors and nurses used the mEMR. The number of logs studied was 7,144,459. Among 3859 users, 2333 (60.46%) users were nurses and 1102 (28.56%) users were doctors. On average, the mEMR was used 1044 times by 438 users per day. The number of users and amount of access logs have significantly increased since 2012 (P<.001). Nurses used the mEMR 3 times more often than doctors. The use of mEMR by nurses increased by an annual average of 51.5%, but use by doctors decreased by an annual average of 7.7%. For doctors, the peak usage periods were observed during 08:00 to 09:00 and 17:00 to 18:00, which were coincident with the beginning of ward rounds. Conversely, the peak usage periods for the nurses were observed during 05:00 to 06:00, 12:00 to 13:00, and 20:00 to 21:00, which effectively occurred 1 or 2 hours before handover. In more than 80% of all cases, the mEMR was accessed via a nonhospital wireless network. CONCLUSIONS: The usage patterns of the mEMR differed between doctors and nurses according to their different workflows. In both occupations, mEMR was highly used when personal computer access was limited and the need for patient information was high, such as during ward rounds or handover periods.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".