Nonurgent Emergency Department Patient Characteristics and Barriers to Primary Care
Bibliographic record
Abstract
OBJECTIVE: Nonurgent (NU) emergency department (ED) use is at the forefront of medico-political agendas, and diversion of NU patients has been entertained as a management strategy. Before policy changes are implemented, this population should be better understood with respect to their characteristics and reasons for not presenting to primary care providers (PCPs) instead of EDs. This study compares NU with urgent and semiurgent (USU) patients and describes the NU patients' reasons for not seeking care with a PCP before presenting to the ED. METHODS: This was a secondary analysis from a cross-sectional study with sequential sampling in the EDs of five Quebec tertiary care hospitals (October 19, 1999, to May 26, 2000). Data on medical history, social support, awareness and utilization of health care, ED visits, referrals, activities of daily living, and sociodemographics were obtained. The NU group included patients with triage code 5 and the USU group included patients with triage codes 2, 3, and 4 using the Canadian Triage and Acuity Scale. Patient characteristics were structured into the Andersen behavioral model for health care utilization. RESULTS: Of 2,348 patients approached, 1,783 patients (77%) were eligible and agreed to participate. NU patients (n = 454) were younger than USU patients (n = 1,329) (mean age, 43 [SD +/- 18.1] vs. 49 [SD +/- 20.1] years). Patients in the NU group had better health (number of prior conditions, 3.1 vs. 3.9), were less likely to arrive by ambulance (5% vs. 22%), and were less often admitted from the ED (4% vs. 24%). While 70% of NU compared with 75% of USU patients were followed up by a PCP, only 22% of NU and 27% of USU patients sought PCP care before presenting to the ED. The reasons given by NU patients for not seeking PCP care were accessibility (32%), perception of need (22%), referral/follow-up to the ED (20%), familiarity with the ED (11%), trust of the ED (7%), and no reason (7%). CONCLUSIONS: NU ED patients are different from USU patients and have multiple reasons for not seeking primary care before going to the ED. This may help explain why various diversion strategies have been unsuccessful and indicate that a multifaceted approach may be better suited to this group of patients. The design of new interventions, however, will benefit from further research that clarifies the impact of NU patients on the health care system.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".