Effective Design, Development, and Evaluation of Video Tutorials for Electronic Medical Record Training
Bibliographic record
Abstract
Abstract Background Electronic medical record (EMR) use by primary care physicians (PCP) in the United States and Canada is suboptimal, especially for supporting chronic diseases like diabetes. PCPs need postimplementation training to achieve value-adding EMR use. Video tutorials demonstrate how to accomplish tasks using software. However, there is a dearth of research on the use of video tutorials for EMR training. Objective The purpose of the study was to design, develop, and evaluate video tutorials for training PCPs in using EMR advanced features for diabetes care. This study addressed three research questions related to PCP's views of video tutorials as an EMR training method/approach, barriers, and facilitators to applying the EMR video tutorials to PCPs' practice, and how the design of EMR video tutorials can be improved. Methods The overall research study employed a QUAN (qual) mixed methods approach with an embedded design. This article focuses on the qualitative phase of the mixed methods study. A series of four theory-informed and evidence-based video tutorials for diabetes care was developed with a physician champion. Qualitative data were collected at four time points: 1 month before (O1), immediately before (O2), 3 months after (O3), and 6 months (O4) after the intervention. Semistructured interviews with participants were held at O3 and O4. Qualitative data were analyzed using thematic analysis. Results In total, 14 PCPs from the overall study participated in interviews (78%). The thematic analysis of the qualitative data revealed seven themes, which fall into two main categories: (1) design and development of EMR video tutorials, and (2) adoption and use of EMR video tutorials. Conclusion PCPs liked the EMR video tutorials for diabetes care, and would like more EMR video tutorials on various topics and EMR use levels. The study offers a roadmap for health informatics professionals everywhere to develop EMR training videos that meet evidence-based design criteria. It also help to identify opportunities to improve the design, delivery, and adoption of EMR video tutorials for future training interventions.
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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 | 0.040 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".