The Impact of University Provided Nurse Electronic Medical Record Training on Health Care Organizations: An Exploratory Simulation Approach
Bibliographic record
Abstract
Training providers appropriately, particularly early in their caregiving careers, is an important aspect of electronic medical record (EMR) implementation. Considerable time and resources are needed to bring the newly hired providers 'up to speed' with the actual use practices of the organization. Similarly, universities lose valuable clinical training hours when students are required to spend those hours learning organization-specific EMR systems in order to participate in care during clinical rotations. Although there are multiple real-world barriers to university/health care organization training partnerships, the investment these entities share in training care providers, specifically nurses, to use and understand EMR technology encourages a question: What would be the cumulative effect of integrating a mutually agreed upon EMR system training program in to nursing classroom training on downstream hospital costs in terms of hours of direct caregiving lost, and benefits in terms of number of overall EMR trained nurses hired? In order to inform the development of a large scale study, we employed a dynamic systems modeling approach to simulate the theoretical relationships between key model variables and determine the possible effect of integrating EMR training into nursing classrooms on hospital outcomes. The analysis indicated that integrating EMR training into the nursing classroom curriculum results in more available time for nurse bedside care. Also, the simulation suggests that efficiency of clinical training can be potentially improved by centralizing EMR training within the nursing curriculum.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".