Mobile Electronic Medical Records Promote Workflow: Physicians’ Perspective From a Survey
Bibliographic record
Abstract
BACKGROUND: As a result of demographic changes, physicians are required to deliver needed services with limited resources. Research suggests that tablet PCs with access to patient data may streamline clinical workflow. A recent study found tablets with mobile electronic medical records (EMRs) can facilitate data retrieval and produce time savings across the clinical routine within hospital settings. However, the reasons for these time savings, including details on how tablets were being used, remain unclear. The same applies to physicians' perceptions of this tool within an inpatient setting. OBJECTIVE: This study examined physicians' perception of tablets with EMRs in an inpatient setting. The rationale was to identify both subjective and objective factors that impacted the successful implementation and use of tablets running an EMR. METHODS: We developed a 57-item survey questionnaire designed to examine users' perception of and attitude toward tablets, which was administered to 14 participating physicians following 7 weeks of tablet use. Five participants volunteered to participate in a second study that investigated physicians' patterns of tablet use within the EMR environment by digitally tracking and storing usage behavior. Statistical analyses of questionnaire results included mean values with their bootstrapped 95% confidence intervals and multivariate analysis of variance to identify predictors of tablet use. RESULTS: Physicians reported high degrees of satisfaction with the tablets. There was a general consensus among physicians that tablet use streamlined clinical workflow through optimized data retrieval (rated 0.69, 0.23-1.15 points better than control) and improved communication with patients and other physicians (rated 0.85, 0.54-1.15 and 0.77, 0.38-1.15 points better than control, respectively). Age (F3,11=3.54, P=.04), occupational group (F1,11=7.17, P=.04), and attitude toward novel technologies (F1,11=10.54, P=.02) predicted physicians' satisfaction with the devices and their motivation regarding their further use. Tracking data yielded that only a few of the available functions were used frequently. CONCLUSIONS: Although tablet PCs were consistently perceived as beneficial, several factors contributed to the fact that their full potential was not fully exploited. Training in functionality and providing a reliable infrastructure might foster successful tablet implementation.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".