Physician Follow-up after Hospital Discharge in Alberta and Saskatchewan
Bibliographic record
Abstract
The period immediately after discharge from hospital can potentially be high risk and a vulnerable transition point for patients.This analysis from the Canadian Institute for Health Information assessed adherence to best practices for patient follow-up in the community after hospitalization in Alberta and Saskatchewan.For three selected conditions -acute myocardial infarction, heart failure and chronic obstructive pulmonary disease -the majority of patients (77-92%) saw a physician within a month of their discharge.However, fewer patients saw a physician within the first week (35-56%).C ontinuity of care is critical during the patient's transition from the hospital to the community.Continuity of care has many benefits, such as fewer medical errors, improved patient satisfaction with care and better ongoing management of the patient's condition.Canadian and international guidelines suggest that follow-up for certain conditions should occur from one week to one month after discharge (American Lung Association of the Upper Midwest 2013; Howlett 2010; Tran 2003).Despite these guidelines, most previous studies reporting on 7-or 30-day physician follow-up rates in Canada suggest room for improvement (Health Quality Ontario 2014; McAlister 2013), though results vary by patient population, follow-up time and geographic location.This study examined physician follow-up for patients hospitalized for acute myocardial infarction (AMI), heart failure (HF) or chronic obstructive pulmonary disease (COPD).These three conditions were chosen owing to the role of follow-up in reducing potential post-discharge complications: timely followup (usually within 1 or 2 weeks) has often been recommended for these specific conditions by a number of researchers and medical associations (Abramson et al. 2014; American Lung Association of the Upper Midwest 2013; Barber 2014; Canadian Heart Failure Network 2015; Howlett 2010).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".