Interventions to reduce wait times for primary care appointments: a systematic review
Bibliographic record
Abstract
BACKGROUND: Accessibility and availability are important characteristics of efficient and effective primary healthcare systems. Currently, timely access to a family physician is a concern in Canada. Adverse outcomes are associated with longer wait times for primary care appointments and often leave individuals to rely on urgent care. When wait times for appointments are too long patients may experience worse health outcomes and are often left to use emergency department resources. The primary objective of our study was to systematically review the literature to identify interventions designed to reduce wait times for primary care appointments. Secondary objectives were to assess patient satisfaction and reduction of no-show rates. METHODS: We searched multiple databases, including: Medline via Ovid SP (1947 to present), Embase (from 1980 to present), PsychINFO (from 1806 to present), Cochrane Central Register of Controlled Trials (CENTRAL; all dates), Cumulative Index to Nursing and Allied Health (CINAHL; 1937 to present), and Pubmed (all dates) to identify studies that reported outcomes associated with interventions designed to reduce wait times for primary care appointments. Two independent reviewers assessed all identified studies for inclusion using pre-defined inclusion/exclusion criteria and a multi-level screening approach. Our study methods were guided by the Cochrane Handbook for Systematic Reviews of Interventions. RESULTS: Our search identified 3,960 articles that were eligible for inclusion, eleven of which satisfied all inclusion/exclusion criteria. Data abstraction of included studies revealed that open access scheduling is the most commonly used intervention to reduce wait times for primary care appointments. Additionally, included studies demonstrated that dedicated telephone calls for follow-up consultation, presence of nurse practitioners on staff, nurse and general practitioner triage, and email consultations were effective at reducing wait times. CONCLUSIONS: To our knowledge, this is the first study to systematically review and identify interventions designed to reduce wait times for primary care appointments. Our findings suggest that open access scheduling and other patient-centred interventions may reduce wait times for primary care appointments. Our review may inform policy makers and family healthcare providers about interventions that are effective in offering timely access to primary healthcare.
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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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".