Risk-based decision making framework for prioritizing patients' access to healthcare services by considering uncertainties
Bibliographic record
Abstract
Because of insufficient capacity of hospitals, all patients on waiting lists can't be treated immediately. Then, patients must be prioritized for treatment based on variety of factors. But, current decisions regarding patients' prioritization have been criticized as being highly subjective and inadequate to assess urgency and case-mix of patients. This study presents a new risk based prioritization framework using fuzzy soft sets, in an attempt to overcome the limitations of current approaches. The proposed framework can aid hospitals' decision makers to evaluate and select the high-risk patients in uncertain and complex environments. In order to show the effectiveness of the proposed framework, a numerical study for surgical patients' prioritization is considered. The numerical study suggests that the proposed framework not only considers various perspectives and risks in determining patients' priorities, but also remains noticeably robust as shown by sensitivity analysis. This framework can increases patients' safety, quality of care, and decrease uncertainties and total risks that threaten patients on waiting lists. Besides it can have significant impact on both the medical community and the public's faith in justice and equity.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".