Validation of a Patient Prioritization Tool: Addressing Decision-Support Tools’ Development in Complex Systems
Bibliographic record
Abstract
As times to access health services have significantly increased worldwide in recent years, strategies and tools to better manage patients’ waiting lists have gained research interest. Computer-Based Patient Prioritization Tools (PPT) aim to manage access to care by ranking patients on waiting lists equitably and rigorously so that higher-priority patients are treated ahead of those with lower priority, regardless of when they were added to the list. However, healthcare systems are inherently complex, involving multiple stakeholders, dynamic interactions, and contextual constraints that make the implementation of such tools challenging. The development of decision-support tools in such environments follows an iterative life cycle that includes design, implementation, verification, validation, and deployment. Among these stages, validation is critical to ensure that the tool not only meets its intended specifications but also produces improved outcomes without unintended consequences when integrated into real-world workflows. Although the literature devoted to PPT is rich, works describing the transition of research prototypes to real-world applications within these complex systems are relatively scarce. This paper presents and discusses the validation process of a PPT, illustrating how this step contributes to improving the tool, building future users’ confidence, and providing insights into the challenges and difficulties related to expert evaluation in complex healthcare environments.
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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 | 0.081 | 0.258 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".