Assessment of the impact on time to complete medical record using an electronic medical record versus a paper record on emergency department patients: a study
Bibliographic record
Abstract
BACKGROUND: Electronic medical records are becoming an integral part of healthcare delivery. OBJECTIVE: The goal of this study was to compare paper documentation versus electronic medical record for non-traumatic chest pain to determine differences in time for physicians to complete medical records using paper versus electronic mediums. We also assessed physician satisfaction with the electronic format. METHODS: We conducted this before-after study in a single large tertiary care academic emergency department. In the 'Before Period', stopwatches determined the time for paper medical recording. In the 'After Period', a template-based electronic medical record was introduced and the time for electronic recording was measured. The time to record in the before and after periods were compared using a two-sided t test. We surveyed physicians to assess satisfaction. RESULTS: We enrolled 100 non-traumatic patients with chest pain in the before period and 73 in the after period. The documentation time was longer using electronic charting, (9.6±5.9 min vs 6.1±2.5 min; p<0.001). 18 of 20 physicians participating in the after period completed surveys. Physicians were not satisfied with the electronic patient recording for non-traumatic chest pain. CONCLUSIONS: This is the first study that we are aware of which compared paper versus electronic medical records in the emergency department. Electronic recording took longer than paper records. Physicians were not satisfied using this electronic record. Given the time pressures on emergency physicians, a solution to minimise the charting time using electronic medical records must be found before widespread uptake of electronic charting will be possible.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".