Safety incidents in family medicine
Bibliographic record
Abstract
OBJECTIVE: To discuss the characteristics of incidents reported to the Medical Safety in Community Practice (MSCP) safety learning system. METHODS: Members of family physician offices in the Alberta Health Services--Calgary zone, confidentially reported patient safety incidents via web or fax from September 2007 to August 2010. The incident reporting form contained both open-ended and closed questions. Incidents were reviewed for their characteristics. RESULTS: A total of 19 family practices participated in MSCP. A total of 264 useable reports were collected. Reporting was higher when practices first joined and then decreased. There was an average of 1.4 reports per month. Physicians submitted the majority of reports. Physicians and nurses were more likely to report an incident than office staff. The vast majority of reported incidents were judged to have 'virtually certain evidence of preventability' (93%). Harm was associated with 50% of incidents. Only 1% of the incidents had a severe impact. The top four types of incidents reported were documentation (41.4%), medication (29.7%), clinical administration (18.7%) and clinical process (17.5%). CONCLUSION: MSCP has developed and implemented the first safety learning system in Canada for family practice. All clinic members were encouraged to submit reports, but most of the incidents were reported by physicians. The vast majority of incidents reported were preventable with limited severity. The most frequently reported types of incidents fell into the categories of documentation and medication. The low reporting rates suggest that for family practices incident reporting may not be the most effective method to determine the types and frequency of incidents in family medicine.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".