Big data usability text mining of publicly available YouTube electronic health record (EHR) tutorials
Bibliographic record
Abstract
This study seeks to harness the power of big data through text mining the content of publicly available YouTube tutorials related to Electronic Health Record (EHR) systems. Most information about EHR systems is proprietary and hidden to the public. Nevertheless, understanding the capacity of EHRs from YouTube tutorials can offer insights to the public into health informatics and EHR functionality and usability for their patient care.We established a search strategy of the top three EHR vendors in North America (i.e., Oracle-Cerner, Meditech, and Epic Systems) from YouTube containing metadata and transcripts of EHR-related tutorials. Text mining techniques across a three-phased approach included establishing a search strategy to select the YouTube videos, auto-coding (from Voyant Tools) and manual coding (via standard usability heuristics), and analysis. The EHR components or applications in the tutorials were selected based on search strategy of total volume and quality of videos related to patient registration, scheduling, problem list, documentation, medication/order, and discharge/administration. The tutorials were analyzed for keyword frequencies, thematic patterns, and overall usability in the information presented via video transcriptions. Thematic patterns were established by heuristic categories of navigation, visibility, content, and complex interactions of learnability and memory.Preliminary findings suggest big data text mining led to analyzing 1,788,126 words via YouTube transcripts across 2280 videos. Oracle (Cerner) had a total of 799,653 words in the 1,066 videos. Meditech has 608 videos with 454,968 words. Epic has 466 videos with 436,598 words. There was a good range of content quality from medium to high, with some tutorials offering comprehensive step-by-step workflows like for medication/orders. The heuristics of content and navigation categories suggested that the tutorials covered enough material and word frequencies to be educational towards experiential learning of EHRs.The study underscores the significant role of using YouTube in disseminating knowledge about complex systems like EHRs to the public. Text mining of large, big data corpus can reveal insights into the quality and nature of online tutorials of more targeted and effective educational resources.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".