Process Management in Healthcare: Bibliometric Study
Bibliographic record
Abstract
Introduction: Process mapping (PM) is used in the healthcare sector to visualize workflows, identify inefficiencies, bottlenecks and opportunities for improvement. This research aims to analyze the profile of scientific production on process mapping in the healthcare sector. Methods: A bibliometric review was conducted using data collected in May 2025 from Scopus and Web of Science. Data was processed with the Bibliometrix package in RStudio, while Microsoft Excel was employed to enhance visualization when necessary. Results and Discussion: From 2015 to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2025,5,707$</tex> unique articles were retrieved, of which 2,793 addressed different applications of PM, reflecting the thematic diversity of healthcare. Scientific output is concentrated in the United States (31%), with the University of Toronto as the most productive institution, whereas the most impactful authors are affiliated with Chinese institutions. Trend topic analysis revealed strong associations between terms such as health, processes, and process management, underscoring the connection between digital process analysis and healthcare delivery. Thematic mapping identified a motor cluster—including process modeling, process design, and procedures-indicating the influence of emerging technologies that require updated procedures and process reconfiguration. Conclusion: The bibliometric analysis highlights gaps such as the absence of standardized guidelines and the need for clearer standards for PM application. International collaboration remains limited and dispersed, presenting opportunities for stronger global scientific interaction in the future.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.155 | 0.387 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".