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$2025,5,707$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.
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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 | 0.024 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.153 | 0.294 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".