[Application Status of Evaluation Methodology of Electronic Medical Record: Evaluation of Bibliometric Analysis].
Bibliographic record
Abstract
In order to provide a reference and theoretical guidance of the evaluation of electronic medical record (EMR) and establishment of evaluation system in China, we applied a bibliometric analysis to assess the application of methodologies used at home and abroad, as well as to summarize the advantages and disadvantages of them. We systematically searched international medical databases of Ovid-MEDLINE, EBSCOhost, EI, EMBASE, PubMed, IEEE, and China's medical databases of CBM and CNKI between Jan. 1997 and Dec. 2012. We also reviewed the reference lists of articles for relevant articles. We selected some qualified papers according to the pre-established inclusion and exclusion criteria, and did information extraction and analysis to the papers. Eventually, 1 736 papers were obtained from online database and other 16 articles from manual retrieval. Thirty-five articles met the inclusion and exclusion criteria and were retrieved and assessed. In the evaluation of EMR, US counted for 54.28% in the leading place, and Canada and Japan stood side by side and ranked second with 8.58%, respectively. For the application of evaluation methodology, Information System Success Model, Technology Acceptance Model (TAM), Innovation Diffusion Model and Cost-Benefit Access Model were widely applied with 25%, 20%, 12.5% and 10%, respectively. In this paper, we summarize our study on the application of methodologies of EMR evaluation, which can provide a reference to EMR evaluation in China.
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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 | 0.159 | 0.318 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.068 | 0.102 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".