186: Trends in Emergency Department Utilization By Children with Mental Health Issues
Bibliographic record
Abstract
Pediatric mental health emergencies constitute a significant and growing proportion of overall visits to pediatric emergency departments (PEDs), as demonstrated by American studies. However, there is little published data on the utilization of PEDs by patients with mental health issues in Canada. To describe the trends in utilization of PED resources by mental health patients over the last 10 years at the British Columbia Children's Hospital (BCCH). We primarily reported the number and acuity of mental health related visits, their length of stay (LOS), admission rate and return visits, relative to all PED visits. We conducted a retrospective cohort study of PED visits at BCCH from 2003 to 2012. All visits with chief complaints or discharge diagnosis including terms related to mental health disorders were selected for evaluation. Descriptive statistics were used to summarize our findings. We observed a 38% increase in the number of pediatric mental health presentations compared to a 9% increase in the number of total visits to the PED over the study period. Repeat visits represented a significant proportion of all mental health related visits to the ED. Yearly, 31% to 36% of all mental health related visits were repeated visits, a third of those occurring within 30 days from the index visit. Moreover, while the proportion of visits for mental health concerns triaged to a high acuity level has steadily decreased, the proportion of visits triaged to the mid-acuity level has steadily increased. Mean LOS in the ED for mental health patients increased from 295 min in 2003 to 318 min in 2012. These LOS are significantly longer then for overall visits to the ED (171 min in 2003; 234 min in 2012). We also observed that the number of PED visits for a psychiatric concern resulting in an admission has increased by 50% between 2003 and 2012. Mental health related visits represent a significant and growing burden for the ED at a tertiary care PED. The largest proportion of psychiatric related visits are now triaged to mid-level acuity, possibly reflecting a shortage of community mental health services. These results highlight the need to reassess health resource allocation to ensure optimal care for children affected by mental health illnesses and to consider alternative ways to optimize risk assessment as well as improving the linkage to mental health services upon disposition from the PED.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 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".