P017: Chart audit of patients with no fixed address presenting to the emergency department to identify areas to improve care
Bibliographic record
Abstract
Background: Homelessness is a growing Canada-wide concern. Those with no fixed address have increased rates of emergency department (ED) utilization and increased healthcare spending compared to the general population, with higher rates of acute and chronic illnesses, as well as all-cause mortality. EDs are uniquely situated to act as an access point to the network of available community services, however referral rates from the ED is uncertain. To date, there has been no data collected on London, Ontario's homeless population, their health burden, and their utilization patterns of the ED. Aim Statement: The primary objective of this study is to describe ED visits for adult patients with no fixed address in London, Ontario to assess for potential areas to improve care. Measures & Design: This is a retrospective chart review, of patients with no fixed address visiting London, Ontario Emergency Departments in 2018. ED visits were identified and pulled using either a diagnosis of “homeless”, a lack of postal code, or a postal code for a known shelter. Cases included based on postal code were manually reviewed to determine whether the patient had a resident address with the same postal code. Evaluation/Results: From this search, 4,294 visits were identified for 1237 unique patients. The median visits per person was 1 (IQR 1-2), with 388 patients having 3 or more visits, and the max being 138 visits. The median age was 38 (IQR 28-52), with 73% male. Ground ambulance was used for 46% of visits. 28% of visits were CTAS 1&2 and 5% were CTAS 5. Police facilitated visits in 401 cases. Top 3 discharge diagnosis categories were mental health (19%), infection (18%), drug misuse (17%). Discussion/Impact: Several errors were identified with our search strategy suggesting the current system of capturing homelessness in the EPR is not accurate, leading to an underestimation of the problem and limiting our ability to describe this population. The Ministry of Health mandates homelessness be applied as a tertiary discharge diagnosis during coding of the patient visit if possible. However, use of this code is inconsistent leading to large-scale omission of visits and an underrepresentation of pediatric cases. Systemic steps should be taken to improve identification of these patients moving forward.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".