Monitoring patient pathways at a secondary healthcare services through process mining via Fuzzy Miner
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Bibliographic record
Abstract
BACKGROUND: This study explored workflow pathways followed by patients seeking secondary healthcare services at a local hospital in a rural part of Turkey using process mining to improve hospital resource management. METHODS: The study used process mining to discover process flows as patient pathways implied by hospital records for in-patient, out-patient, biochemical laboratory, and radiology services. Utilizing its flexibility, visualizations and robustness, authors implemented fuzzy-miner algorithm. First, we processed the relevant data from patient records. Then, this data was transformed into event and activity logs. Subsequently, all data components were collected into a data warehouse, and the process mining algorithm was applied. The process mining specified resource usage levels and workload, service waiting times, associated bottlenecks in hospital services, and related statistics/measures. RESULTS: The results from the proposed process mining analysis offer insights and decision support to improve hospital resource management. For example, the resulting statistics indicate the high waiting times (e.g., median of waiting times around 2 h within the selected time period) in the General Surgery and Cardiology services, whose resources were highly utilized (2,699 and 6,162 times). Overloads at laboratories and radiological imaging seem to be contributing to these long waiting times, and capacities for the associated services may need to be improved. Waiting times and resource workloads are higher on specific dates related to local commercial and social activities. CONCLUSIONS: Process mining successfully identified the real work flows, bottlenecks, and long waiting times at services within the considered local hospital and provided insights to the hospital management for improving their practices. Moreover, the analyses revealed unique challenges in providing care at a local hospital located far from the city center, emphasizing the potential of process mining to improve healthcare delivery tailored to the specific hospital environment. CLINICAL TRIAL NUMBER: Not applicable.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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