A Systematic Review of Patient Prioritization Tools in Non-Emergency Healthcare Services
Bibliographic record
Abstract
Abstract Background: Patient prioritization is a strategy used to manage access to healthcare services. Patient prioritization tools (PPT) contribute to supporting the prioritization decision process, and to its transparency and fairness. Patient prioritization tools can take various forms and are highly dependent on the particular context of application. Consequently, the sets of criteria change from one context to another, especially when used in non-emergency settings. This paper systematically synthesizes and analyzes the published evidence concerning the development and challenges related to the validation and implementation of PPTs in non-emergency settings. Methods: We conducted a systematic mixed studies review. We searched evidence in five databases to select articles based on eligibility criteria, and information of included articles was extracted using an extraction grid. The methodological quality of the studies was assessed by using the Mixed Methods Appraisal Tool. The article selection process, data extraction, and quality appraisal were performed by at least two reviewers independently. Results: We included 47 studies listing 33 different patient prioritization tools. Most of them are designed for managing access to elective surgeries in hospital settings. Two thirds of the tools were investigated based on reliability or validity. Inconclusive results were found regarding the impact of PPTs on patient waiting times. Advantages associated with PPT use were found mostly in relationship to acceptability of the tools by clinicians and increased transparency and equity for patients. Conclusions: This review describes the development and validation processes of PPTs used in non-urgent healthcare settings. Despite the large number of PPTs studied, implementation into clinical practice seems to be an open challenge. Based on the findings of this review, recommendations are proposed to develop, validate, and implement such tools in clinical settings. Systematic review registration: PROSPERO CRD42018107205
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".