Impact of a provincial quality-improvement program on primary health care in Ontario: a population-based controlled before-and-after study
Bibliographic record
Abstract
BACKGROUND: In Ontario, a province-wide quality-improvement program (Quality Improvement and Innovation Partnership [QIIP]) was implemented between 2008 and 2010 to support improved outcomes in Family Health Teams, a care model that includes many features of the patient-centred medical home. We assessed the impact of this program on diabetes management, colorectal and cervical cancer screening and access to health care. METHODS: We used comprehensive linked administrative data sets to conduct a population-based controlled before-and-after study. Outcome measures included diabetes process-of-care measures (test ordering, retinal examination, medication prescribing and completion of billing items specific to diabetes management), colorectal and cervical cancer screening measures and use of health care services (emergency department visits, hospital admission for ambulatory-care-sensitive conditions and rates of readmission to hospital). The control group consisted of Family Health Team physicians with at least 100 assigned patients during the study follow-up period (November 2009-February 2013). RESULTS: There were 53 physicians in the intervention group and 1178 physicians in the control group. Diabetes process-of-care measures improved more in the intervention group than in the control group: hemoglobin A1c testing 4.3% (95% confidence interval [CI] 1.2-7.5) more, retinal examination 2.5% (95% CI 0.8-4.4) more and preventive care visits 8.9% (95% CI 2.9-14.9) more. Medication prescribing also improved for use of statins (3.4% [95% CI 0.8-6.0] more) and angiotensin-converting-enzyme inhibitors or angiotensin receptor blockers (4.1% [95% CI 1.8-6.4] more). Colorectal cancer screening improved 5.4% (95% CI 3.1-7.8) more in the intervention group than in the control group, and cervical cancer screening improved 2.7% (95% CI 0.9-4.6) more. There were no significant differences in any of the measures of use of health care services. INTERPRETATION: This large controlled evaluation of a broadly implemented quality-improvement initiative showed improvement for diabetes process of care and cancer screening outcomes, but not for proxy measures of access related to use of health care services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".