Re-utilization outcomes and costs of minor acute illness treated at family physician offices, walk-in clinics, and emergency departments.
Bibliographic record
Abstract
OBJECTIVE: To examine factors associated with re-utilization of health services and to estimate and compare costs of treatment for minor acute illnesses in family physicians' offices (FPOs), walk-in clinics (WICs), and emergency departments (EDs). DESIGN: Prospective cohort study using questionnaires, telephone follow up, medical chart data, and costs according to Ontario Health Insurance Plan (OHIP) schedules. SETTING: 16 FPOs, 12 WICs, and 13 EDs in three Ontario cities. PARTICIPANTS: Consecutive patients with one of eight predefined minor acute illnesses found in all three settings (upper respiratory infection, pharyngitis, acute bronchitis, acute otitis media, serous otitis media, low back pain, gastroenteritis, and urinary tract infection). MAIN OUTCOME MEASURES: "Early" (< 3 days) versus "later" (3 days to 2 weeks) re-utilization of health services after initial encounter and direct cost to OHIP. RESULTS: The overall rate of re-utilization of health services for the same episode of illness was 11.3% for early and 20.6% for later re-utilization. Factors associated with early re-utilization were initial evaluation in ED setting (odds ratio [OR] = 6.5, confidence interval [CI] = 2.2-19.2) and, regardless of setting, less satisfaction with patient-centred care (OR = 1.7 for each one-point decrease on a four-point scale; CI = 1.1-2.7). Factors associated with later re-utilization were ED setting (OR = 4.9; CI = 2.4-9.9) and diagnosis of urinary tract infection (OR = 2.4; CI = 1.1-5.2). Factors tested and found not signifcantly associated with rate of re-utilization were patients' age, sex, responses to a variety of questions assessing psychosocial factors (stress, social support, independence), and opinions on health care. Cost of care was similar for FPOs and WICs and higher for EDs for all diagnoses. The initial visit was the largest component of cost in all settings, and this component (as well as total cost) was consistently higher in EDs. CONCLUSION: Both re-utilization rates and costs are higher for those seeking care in EDs for minor acute illness. Patient-centred care, an important feature of health care encounters regardless of setting, can reduce re-utilization rates.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".