Open Access in Primary Care: Results of a North Carolina Pilot Project
Bibliographic record
Abstract
OBJECTIVE: Appointment delays impede access to primary health care. By reducing appointment delays, open access (OA) scheduling may improve access to and the quality of primary health care. The objective of this pilot study was to assess the potential impact of OA on practice and patient outcomes by using pilot-study data from 4 North Carolina primary care practices. METHODS: We conducted an interrupted time-series pilot study of 4 North Carolina primary care practices (2 family medicine and 2 pediatric practices) participating in a quality-improvement (QI) collaborative from May 2001 to May 2002. The year-long collaborative comprised 25 practices and consisted of three 2-day meetings led by expert faculty, monthly data feedback, and monthly conference calls. Our main outcome measures were appointment delays, appointment no-shows, patient satisfaction, continuity of care, and staff satisfaction during the 12-month study period. RESULTS: Providers in all 4 practices successfully implemented OA. On average, providers reduced their delay to the third available preventive care appointment from 36 to 4 days. No-show rates declined (first quarter [Q1] rate: 16%; fourth quarter [Q4] rate: 11%; no-show reduction: 5% [95% confidence interval: 1%, 10%]), and overall patient satisfaction improved (Q1: 45% rated overall visit quality as excellent; Q4: 61% rated overall visit quality as excellent; change in satisfaction: 16% [95% confidence interval: 0.2%, 30%]). Continuity of care followed a similar pattern of improvement, but the change was not statistically significant. Staff satisfaction neither improved nor declined. CONCLUSIONS: This pilot study suggests that primary care practices can implement OA successfully by using QI-collaborative methods. These results provide preliminary evidence that OA may improve practice and patient outcomes in primary care. These analyses should be repeated in larger groups of practices with longer follow-up.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".