Impact of a Workflow-Integrated Web Tool on Resource Utilization and Information-Seeking Behavior in an Academic Anesthesiology Department: Longitudinal Cohort Survey Study
Bibliographic record
Abstract
BACKGROUND: Medical resident reading and information-seeking behavior is limited by time constraints as well as comfort in accessing and assessing evidence-based resources. Educational technology interventions, as the preferred method for millennial leaners, can reduce these barriers. We implemented an educational web tool, consisting of peer-reviewed articles as well as local and national protocols and policies, built into the daily workflow of a university-based anesthesiology department. We hypothesized that this web tool would increase resource utilization and overall perceptions of the educational environment. OBJECTIVE: The goal of this study was to demonstrate that an educational web tool designed and built into the daily workflow of an academic anesthesia department for trainees could significantly decrease barriers to resource utilization, improve faculty-trainee teaching interactions, and improve the perceptions of the educational environment. METHODS: Following Institutional Review Board approval, a longitudinal cohort survey study was conducted to assess trainee resource utilization, faculty evaluation of trainees' resource utilization, and trainee and faculty perceptions about the educational environment. The survey study was conducted in a pre-post fashion 3 months prior to web tool implementation and 3 months following implementation. Data were deidentified and analyzed unpaired using Student t tests for continuous data and chi-square tests for ordinal data. RESULTS: Survey response rates were greater than 50% in all groups: of the 43 trainees, we obtained 27 (63%) preimplementation surveys and 22 (51%) postimplementation surveys; of the 46 faculty members, we obtained 25 (54%) preimplementation surveys and 23 (50%) postimplementation surveys. Trainees showed a significant improvement in utilization of peer-reviewed articles (preimplementation mean 8.67, SD 6.45; postimplementation mean 18.27, SD 12.23; P=.02), national guidelines (preimplementation mean 2.3, SD 2.40; postimplementation mean 6.14, SD 5.01; P<.001), and local policies and protocols (preimplementation mean 2.23, SD 2.72; postimplementation mean 6.95, SD 6.09; P=.02). There was significant improvement in faculty-trainee educational interactions (preimplementation mean 1.67, SD 1.33; postimplementation mean 6.05, SD 8.74; P=.01). Faculty assessment of trainee resource utilization also demonstrated statistically significant improvements across all resource categories. Subgroups among trainees and faculty showed similar trends toward improvement. CONCLUSIONS: Learning technology interventions significantly decrease the barriers to resource utilization, particularly among millennial learners. Further investigation has been undertaken to assess how this may impact learning, knowledge retention, and patient outcomes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".