ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Time-series Machine Learning Models to Support Emergency Department Operational Planning.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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Authors and funding
9 authors.
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Abstract
Predicting emergency department (ED) utilization can assist in resource planning like staff scheduling. Traditional time series methods and newer machine learning methods have been used to forecast ED metrics; however, they have seldom been implemented in operational settings. We leverage a user-centered design approach that engages nursing operations managers across multiple hospital sites in an integrated health system to identify the key metrics to predict, design and select the best models and time horizons, and design a production dashboard for ED operational planning. We tested various models in terms of mean absolute error and mean absolute percentage error and determined that Prophet (a non-linear open-source method) performed the best across multiple sites. We present the implementation and monitoring design for this model, generating daily, 14-day ahead predictions for ED arrival, admission, sitter needs, and ED holds, to be used by operational leaders to guide staffing decisions.
Indexed as
Identifiers
41726443PMC12919617What 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.