Factors Associated with Family Physician Follow-up 30 Days Post-discharge from a Local Canadian Community Emergency Department
Bibliographic record
Abstract
Introduction Close outpatient follow-up with a specialist or family physician post-discharge from the emergency department (ED) has been shown to increase adherence to antihypertensive medications, decrease mortality in heart failure, and reduce the odds of myocardial infarction or death after ED presentation for chest pain. A Canadian study demonstrated that 21% of patients who left the ED with a new diagnosis of atrial fibrillation, heart failure, or hypertension were not seen by a physician within 30 days. There is a paucity of research investigating why this follow-up does not occur. This study aimed to elucidate factors that are associated with outpatient follow-up by a family physician clinic following discharge from a local Canadian community emergency department. Methods A retrospective chart review of patients rostered to a family physician who presented to the community ED in the past two years was conducted. The primary outcome examined was a documented follow-up visit with any physician at the clinic within 30 days of the index ED visit. Patients aged 18 or older at the time of the initial ED visit were eligible for inclusion in the study. Exclusion criteria were the following: patients aged 17 or younger at the time of the initial ED visit, those who were not fully assessed at ED visit (i.e., left against medical advice), those whose charts corresponding to the ED visit were unable to be found, patients who were admitted to any facility within 30 days of ED visit, and patients who died within 30 days of the ED visit. Variables of interest extracted from the ED chart and clinic electronic medical record were the following: Canadian Triage and Acuity Scale (CTAS) score, documented discharge instructions, age, sex, primary residence distance from the clinic, last documented clinic visit before ED visit, and the date of and presenting complaint of the next clinic visit after the ED visit. Data were collected as continuous and categorical variables. Descriptive statistics were used to show the number and percentages of patients who followed up in clinic. Binomial regression analysis was used to determine if a specific variable was associated with patient follow-up. Inter-rater reliability between data abstractors was calculated using Fleiss Κ. An alpha-value of 0.05 was chosen, and SPSS version 25.0 (IBM Corp., Armonk, NY) was used for all statistical analyses. Results A total of 234 patients out of 1292 patients met inclusion criteria. 53% of patients were female, and the mean age was 50. Seventy-two (31%) received discharge instructions from the ED physician to follow up with their family doctor. In total, 93 of the 234 patients proceeded to have a documented clinic visit within 30 days (40%). 52% (n = 48) of these were women. Receiving specific discharge instructions increased the adjusted odds of follow-up (OR 3.07, 95% CI: 1.64-5.76; P < 0.05). Patients who followed up also tended to have been seen in clinic in the last three months, but this was not statistically significant. Conclusion Receiving specific discharge instructions to follow-up increased the odds that patients followed up with their family physician after discharge from the ED. ED physicians may consider giving explicit instructions to patients to improve monitoring of ongoing clinical issues. More research needs to be conducted on how to improve transitions of care. Countries with different healthcare models may have other barriers to appropriate 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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".