ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2023
Ochsner Emergency Department Overcrowding Scale (OEDOCS): predicting Emergency Department crowding among diverse academic hospital settings.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- [Adverse moral consequences of overcrowding among emergency department professionals: an explorative survey].Medizinische Klinik, Intensivmedizin und Notfallmedizin · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed the Ochsner Emergency Department Overcrowding Scale (OEDOCS) to help us measure and respond to crowding among diverse-sized Emergency Departments (ED) within our network. Not satisfied with our current Emergency Department (ED) crowding score, we first surveyed our ED staff to report perceived crowding and then developed models to predict perceived crowding from our Electronic Health Record (EHR) data. Staff at two ED locations, one large and one small, were asked to report a perceived crowding level between 0-200 every four hours for over 3 months. In addition, we collected Electronic Health Record (EHR) data during the same period. Next, we investigated models for predicting perceived crowding. Linear regression performed the best with an RMSE of 41.77 and 41.98% RMSE improvement over our previous crowding score. We have made OEDOCS publicly available.
Identifiers
37351799PMC10283135What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.