Enhancing Advanced Access in Primary Healthcare: Key Change Strategies from a Quality Improvement Initiative
Bibliographic record
Abstract
Context: Timely access is crucial for high-quality primary healthcare delivery, yet remains a pervasive challenge globally, including in Canada. The Advanced Access (AA) model, designed to support timely access, has faced implementation and sustainability hurdles. In Quebec, efforts to implement AA in Family Medicine Groups (FMGs) yielded partial success, necessitating comprehensive change strategies to ensure a tangible impact of the model. Objective: To delineate key change strategies from a 3.5-year Quality Improvement initiative aimed at enhancing AA in multidisciplinary primary healthcare. Study design and analysis: Retrospective descriptive qualitative study. Data from field notes, QI action plans, and semi-structured interviews were triangulated to identify and describe impactful change strategies. Setting: 8 multidisciplinary FMGs in Quebec, Canada Population: All healthcare providers and administrative staff. FMGs included physicians, nurses, social workers, pharmacists. Outcomes measures: Change strategies that demonstrated the capacity to improve timely access such as 3rd next available appointment, care continuity and team collaboration. Results: Seven key change strategies emerged, which could be grouped under 4 categories. These are related to shaping healthcare supply to patients’ demand by 1) providing an individual assessment of caseload size to all physicians and nurse practitioners (professionals with whom patients are affiliated; and 2) reinforcing communication in the appointment scheduling process. A second category consists of tailoring care to patients’ needs by 3) streamlining appointment scheduling through referral algorithms and 3) diversifying care modalities (face-to-face or telehealth). Optimizing roles through 4) interprofessional collaboration and optimal care trajectories by 5) using individual and collective orders to enhance care efficiency are also necessary strategies. Additionally, promoting care continuity via 7) dedicated urgent care slots and 8) optimizing trainee supervision in teaching FMGs bolstered patient-provider relationships while ensuring consistent and relevant care. Conclusion: The findings suggest that the implementation of these change strategies has the potential to significantly improve AA in primary healthcare settings. By addressing barriers to timely access and enhancing coordination among healthcare team members, these strategies can contribute to better healthcare outcomes for patients.
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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 | 0.035 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".