Operationalizing the emergent management of psychiatric patients during the COVID-19 pandemic at an academic public hospital
Bibliographic record
Abstract
Background: While the volume of Emergency Department (ED) visits has declined during the 2019 novel coronavirus disease or COVID-19, the opposite has been observed with mental-health related visits. The need to screen and manage potential COVID-19 symptoms in parallel with psychiatric complaints have imposed new challenges in the ED at an academic public hospital.Objective: The objective is to share operational modifications in addressing the challenges related to the influx of ED mentalhealth related complaints at the departmental, hospital-wide, and city-wide level within an academic, public hospital.Methods: At the departmental level, a triage algorithm for screening patients with concerning symptoms was developed. A dedicated Respiratory Decision Unit with psychiatrically safe rooms that adhered to infection prevention protocol was also created. All staff were trained to utilize personal protective equipment through lectures, asynchronous learning, and multidisciplinary simulations. The ED team worked with hospital leadership to increase inpatient medical psychiatric bed capacity and to develop testing protocols for patients being admitted to allow for cohorting of symptomatic patients. At the city level, leadership within the three main organizations that provide mental health services to the city met regularly to address operational issues.Conclusions: The COVID-19 pandemic has imposed new challenges in terms of increased psychosocial needs while limiting transmission risks. Based on the experiences shared, a multitier approach is necessary. At all levels, the goals were to screen appropriately, decrease transmission risk, and maintain throughput. The purpose of this descriptive manuscript is to encourage dialogue and to raise awareness about the unique needs of the mental health system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".