The receipt of information by family physicians about their patient’s emergency department visits: a record linkage study of electronic medical records to health administrative data
Bibliographic record
Abstract
BACKGROUND: Canadians are known to be frequent users of emergency department (ED) care. However, the exchange of information from ED visits to family physicians (FPs) is not well known. Our objectives were to determine whether Canadian FPs received information about their patient's ED visit and the patient characteristics related to the receipt of ED information. METHODS: This study was a descriptive record linkage study of FP Electronic Medical Record (EMR) data linked to health administrative data. Our study cohort included patients who had at least one ED visit in 2010 or 2015 in Ontario, Canada. An ED visit could include a transfer to or from another ED. The receipt of information from an ED note was examined in relation to patient age, sex, neighbourhood income quintiles, rurality and comorbidity. RESULTS: There were 26,609 patients in 2010 and 50,541 patients in 2015 with at least one ED visit. In 2010, 53.3% of FPs received an ED note for patients having a single ED visit compared to 41.0% in 2015. For patients with multiple ED visits, 58.2% of FPs received an ED note in 2010 compared to 45.7% in 2015. FPs were more likely to receive an ED note for patients not living in low income neighbourhoods, older patients, patients living in small urban areas and for patients having moderate comorbidity. FPs were less likely to receive a note for patients living in rural areas. CONCLUSIONS: Community-based FPs are more likely to get information after an ED visit for their older and sicker patients. However, FPs do not receive any information from EDs for over half their patients. Electronic health record technologies and their adoption by ED providers need to improve the seamless transfer of information about the care provided in EDs to FPs in the community.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".