Process mining in healthcare: a systematised literature review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it