Application of Fuzzy Soft Set in Patients' Prioritization
Bibliographic record
Abstract
Based on studies, access to healthcare services and long waiting time is one of the main issues in many countries including Canada and United States. Healthcare organizations can't increase their limited resources nor treat all patients simultaneously. Then, patients' access to these services should be prioritized in a way that best uses the scarce resources and insures patients' safety. Prioritization is essential and inevitable not only because of resource shortage, which have not been improved during years, but also because it is a crucial issue that could contribute to the capability and stability of the healthcare systems, and most importantly to patients' safety. On the other hand, inappropriate prioritization of patients waiting for treatment, could affect directly on inefficiencies in healthcare delivery, quality of care, and most importantly on patients' safety and their quality of life and satisfaction. Inspired by these facts, in this chapter the importance of patients' prioritization and using fuzzy logic in this area will be discussed, and a novel hybrid framework using fuzzy soft sets for patients' prioritization will be proposed. The proposed framework may have a significant impact on patients' safety, and on both medical community and the public's faith in justice and equity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| 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 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".