Effects of a single-entry intake system on access to outpatient visits to specialist physicians and allied health professionals: a systematic review
Bibliographic record
Abstract
Background: Canada lags behind other countries with respect to wait times for specialist physician and allied health professional consultations. We conducted a systematic review to assess the effects of a single-entry model on waiting time, referral volume and the satisfaction of patients and health care providers. Methods: We searched MEDLINE, Embase, Cochrane CENTRAL and CINAHL databases from inception to December 2019. We included studies from countries in the Organisation for Economic Co-operation and Development that reported on the effects of a single-entry model on the time between referral to first assessment by a specialist physician or allied health professional, termed wait time 1 (WT1). Patient volume and the satisfaction of providers and patients were secondary outcomes. We conducted a narrative synthesis using descriptive statistics. Results: Of the 4637 citations identified, 17 met the eligibility criteria, and we included 10 of these in the final analysis. All of the included studies reported an absolute reduction in WT1 after implementation of the single-entry model. The average percent reduction in WT1 across specialties was greatest for surgical referrals (57%) and urgent internal medicine referrals (40%). Higher initial WT1 was associated with a greater absolute reduction in WT1 after implementation of the single-entry model (p = 0.002). Patient and provider satisfaction with the single-entry model was high in all studies. The effect estimates from all included studies were at high risk of bias. Interpretation: Single-entry models were associated with an absolute reduction in time from referral from primary care to consultation. These models represent a promising option to improve access to a range of health services, but there is a need for rigorous prospective evaluations to inform policy. PROSPERO Registration: CRD42018100395
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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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".