Bibliometric Analysis of Electronic Medical Records (EMR) Acceptance and Adoption: Trends, Insights, and Future Directions
Bibliographic record
Abstract
Background: The integration of Electronic Medical Records (EMRs) and Electronic Health Records (EHRs) has revolutionized healthcare by enabling digital storage, exchange, and management of patient information. This abstract explores the landscape of EMR acceptance and adoption through a bibliometric analysis of research literature indexed in Scopus from January 2014 to December 2023. The study identified 138 relevant articles focusing on EMR and EHR acceptance, employing tools like VOSviewer and Rstudio-Biblioshiny for data visualization and analysis. Method: This study is qualitative research with a literature study approach. The data collection technique in this study used Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and obtained 138 documents for analysis. This dataset is converted to CSV format for further processing in Mapchart, VosViewer, and Rstudio-Biblioshiny for thorough analysis. Result: Key findings reveal a predominant focus on factors influencing EMR adoption, including technological infrastructure, user training, and regulatory mandates. The United States and Canada emerged as leading contributors to EMR research, highlighting their advanced healthcare systems. Theoretical frameworks such as the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT) were frequently employed to assess adoption determinants. Conclusions: The study identifies gaps in research, particularly in areas such as cybersecurity and user satisfaction, suggesting future avenues for investigation. By addressing these gaps, researchers can enhance the usability and effectiveness of EMR and EHR systems, thereby improving healthcare delivery and patient outcomes globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.173 | 0.274 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".