Managing patients in the emergency department with mental health and substance use disorders
Bibliographic record
Abstract
With approximately 3% of the Houston Methodist Hospital Emergency Department’s (ED) 2017 annual volume presenting with resource-intensive psychiatric conditions, a 6-prong approach was applied to address the growing area of concern on how to best manage this unique population safely and efficiently through the provision of high quality care. This approach included (1) the provision of dedicated care space, (2) placement of a trained team of providers and clinical staff, (3) contracting with a third-party, rapid-screen care team, (4) application of new technology, (5) instilling a partnership with the ancillary team, and (6) extending care after the hospital stay for better management of the longevity of the patients’ medical issues. Through these efforts, the overall time from ED arrival to ED departure for psychiatric patients who were discharged was reduced by 36%. In addition, the admit decision time to ED departure time for psychiatric patients was reduced by 30% from 2016 to the third quarter of 2017. Additionally, the number of violent patient incidents in the ED mental health unit was reduced to zero from 2016 to the third quarter of 2017, a number that is holding to date. Via the presence of heightened security measures, approximately 50% fewer security dispatches were requested in 2018 than in 2017. This is even more profound when considering the 9% growth in overall ED patient volume over the same time period. Thus, through the application of a multifaceted approach to the care of patients with mental health and substance use disorders presenting to the Houston Methodist Hospital ED there was an observed significant positive effect. Continued diligence to this topic in addition to further expanded resources are needed in both the community and clinical setting to mitigate the negative cycle of patients unnecessarily returning to the hospital or landing in jail that currently exists.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".