Patient prioritization tools and their effectiveness in non-emergency healthcare services: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Waiting lists should be managed as fairly as possible to ensure that patients with greater or more urgent needs receive services first. Patient prioritization refers to the process of ranking referrals in a certain order based on various criteria with the aim of improving fairness and equity in the delivery of care. Despite the widespread use of patient prioritization tools (PPTs) in healthcare services, the existing literature on this subject has mainly focused on emergency settings. Evidence has not been synthesized with respect to all the non-emergency services. METHODS: This review aims to perform a systematic synthesis of published evidence concerning (1) prioritization tools' characteristics, (2) their metrological properties, and (3) their effect measures across non-emergency services. Five electronic databases will be searched (Cochrane Library, Ovid/MEDLINE, Embase, Web of Science, and CINAHL). Eligibility criteria guiding data selection will be (1) qualitative, quantitative, or mixed methods empirical studies; (2) patient prioritization in any non-emergency setting; and (3) discussing characteristic, metrological properties, or effect measures. Data will be sought to report tool's format, description, population, setting, purpose, criteria, developer, metrological properties, and outcome measures. Two reviewers will independently screen, select, and extract data. Data will be synthesized with sequential exploratory design method. We will use the Mixed Methods Appraisal Tool (MMAT) to assess the quality of articles included in the review. DISCUSSION: This systematic review will provide much-needed knowledge regarding patient prioritization tools. The results will benefit clinicians, decision-makers, and researchers by giving them a better understanding of the methods used to prioritize patients in clinical settings. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018107205.
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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 | 0.101 | 0.105 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.017 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.007 |
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".