Evaluating the Bias in Hospital Data: Automatic Preprocessing of Patient Pathways Algorithm Development and Validation Study
Bibliographic record
Abstract
BACKGROUND: The optimization of patient care pathways is crucial for hospital managers in the context of a scarcity of medical resources. Assuming unlimited capacities, the pathway of a patient would only be governed by pure medical logic to meet at best the patient's needs. However, logistical limitations (eg, resources such as inpatient beds) are often associated with delayed treatments and may ultimately affect patient pathways. This is especially true for unscheduled patients-when a patient in the emergency department needs to be admitted to another medical unit without disturbing the flow of planned hospitalizations. OBJECTIVE: In this study, we proposed a new framework to automatically detect activities in patient pathways that may be unrelated to patients' needs but rather induced by logistical limitations. METHODS: The scientific contribution lies in a method that transforms a database of historical pathways with bias into 2 databases: a labeled pathway database where each activity is labeled as relevant (related to a patient's needs) or irrelevant (induced by logistical limitations) and a corrected pathway database where each activity corresponds to the activity that would occur assuming unlimited resources. The labeling algorithm was assessed through medical expertise. In total, 2 case studies quantified the impact of our method of preprocessing health care data using process mining and discrete event simulation. RESULTS: Focusing on unscheduled patient pathways, we collected data covering 12 months of activity at the Groupe Hospitalier Bretagne Sud in France. Our algorithm had 87% accuracy and demonstrated its usefulness for preprocessing traces and obtaining a clean database. The 2 case studies showed the importance of our preprocessing step before any analysis. The process graphs of the processed data had, on average, 40% (SD 10%) fewer variants than the raw data. The simulation revealed that 30% of the medical units had >1 bed difference in capacity between the processed and raw data. CONCLUSIONS: Patient pathway data reflect the actual activity of hospitals that is governed by medical requirements and logistical limitations. Before using these data, these limitations should be identified and corrected. We anticipate that our approach can be generalized to obtain unbiased analyses of patient pathways for other hospitals.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".