Process mining in healthcare: a systematised literature review
Bibliographic record
Abstract
Process mining is a promising approach that turns event logs into valuable insights about processes. One domain amenable to process mining is healthcare, where an enormous amount of data is generated by care processes, but where realistic care models are seldom available. In this paper, we perform a systematised literature review (SdLR) to assess the status of process mining, particularly in healthcare. We first provide an overview of process mining in general, and in healthcare in particular. On the basis of 2371 research publications related to process mining, obtained by querying six relevant search engines in May 2016, we found that the trend of publications in this domain has been growing over the past decade, especially in healthcare. Among the 11 existing literature reviews on process mining selected for further analysis, only two are systematised, and only three relate to healthcare. This paper contributes a systematised review in healthcare that is much needed to fill this void. Important challenges specific to healthcare are identified, and threats to the validity of the results are also discussed.
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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".