ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Exploring the Implementation Experience and Use of CONCERN Early Warning System in a Rural Community Hospital: A Mixed Method Convergent Approach.
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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10 authors.
Funding
Abstract
The Communicating Narrative Concerns Entered by RNs Early Warning System (CONCERN EWS) is a machine-learning predictive model that analyzes nursing documentation patterns to detect early signs of patient deterioration, with proven effectiveness in reducing the risk of in-hospital mortality and length of stay. This study extends the evaluation of CONCERN EWS beyond acute care settings to a rural community hospital, assessing user experience, system utilization, and accuracy in identifying patient deterioration. The study examined accuracy in a rural setting using a mixed-methods approach-qualitative interviews, quantitative data analysis, and clinical record reviews. Findingssuggestthat CONCERNEWS enhancesearly recognition of clinical deterioration andisusable in the context of busy acute care nursing workflows. These results support its adaptability to facilitate strengthening nursing surveillance, clinical decision-making, and patient safety in rural healthcare settings as well.
Indexed as
Identifiers
41726452PMC12919451What OpenQuestion holds
Registered trials
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