The impact of electronic medical record implementation on operating room efficiency
Bibliographic record
Abstract
Background: First start delays in the operating room have a downstream effect on operating room efficiency and patient satisfaction. In accordance with the American Recovery and Reinvestment Act, in February 2014, our institution adopted EPICTM as our electronic health record (EHR). The impact of the transition from paper to electronic documentation on operating room efficiency is not known. This study analyzed first start data as a measure of overall operative suite efficiency, looking at the initial impact and the learning curve to return to baseline parameters.Methods: A retrospective review of on time start data was reviewed for three months prior and 4 months after implementation of the EHR. A start was considered delayed if the patient arrived to the room after the 7:30 start time. Patients transported from the intensive care unit were excluded from analysis. Data was analyzed using control charts for the percentages and comparison of means using Dunnet’s methods. Confidence intervals were calculated at .05 and .01 for significance.Results: After EPIC implementation, there was an initial drop in on time starts from over 60% to 41% followed by gradual return to pre-implementation level within 4 months (p < .01).Conclusions: Implementation of an EHR produced decreased efficiency in on time first starts in the operative suite, but the learning curve was brief, returning to baseline values in 4 months. These findings can serve as a guide for other institutions that are undergoing transition from a paper to an electronic medical record.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".