SynthesisHealth services research2022
Algorithms identifying low-acuity emergency department visits: A review and validation study.
Synthesis in Health services research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
What it found
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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.
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.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Pooled it
- Algorithms identifying low-acuity emergency department visits: A review and validation study.Health services research · 2022Pooled it
- Reducing Post-Fall Emergency Department Transfer From Residential Aged Care Homes: The Falls Outreach and Residential Mobile Assessment Team (FORMAT) Before-and-After Study.Emergency medicine Australasia : EMA · 2026Article
- Proposed ICD-10 framework for identifying emergency department visits that may be impacted by community pharmacy care clinics.International journal of clinical pharmacy · 2026Article
- Postpartum Primary Care Engagement and Acute Care Use: Secondary Analysis of a Randomized Clinical Trial.JAMA network open · 2026Observational
- Medicare Advantage Has Lower Resource Use and Better Quality of Care than Traditional Medicare.American journal of health economics · 2025Article
- Article
- Identifying low acuity Emergency Department visits with a machine learning approach: The low acuity visit algorithms (LAVA).Health services research · 2024Article
- Hospital ownership and admission rates from the emergency department, evidence from Florida.Health services research · 2024Article
- Hospitalization Risk Associated With Emergency Department Reasons for Visit and Patient Age: A Retrospective Evaluation of National Emergency Department Survey Data to Help Identify Potentially Avoidable Emergency Department Visits.Health services research and managerial epidemiologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
objectiveTo characterize and validate the landscape of algorithms that use International Classification of Disease (ICD) codes to identify low-acuity emergency department (ED) visits. DATA SOURCES: Publicly available ED data from the National Hospital Ambulatory Medical Care Survey (NHAMCS). STUDY
designWe systematically searched for studies that specify algorithms consisting of ICD codes that identify preventable or low-acuity ED visits. We classified ED visits in NHAMCS according to these algorithms and compared agreements using the Jaccard index. We then evaluated the performance of each algorithm using positive predictive value (PPV) and sensitivity, with the reference group specified using low-acuity composite (LAC) criteria consisting of both triage and clinical components. In sensitivity analyses, we repeated our primary analysis using only triage or only clinical criteria for reference. DATA COLLECTION: We used the 2011-2017 NHAMCS data, totaling 163,576 observations before survey weighting and after dropping observations missing a primary diagnosis. We translated ICD-9 codes (years 2011-2015) to ICD-10 using a standard crosswalk. PRINCIPAL
findingsWe identified 15 papers with an original list of ICD codes used to identify preventable or low-acuity ED presentations. These papers were published between 1992 and 2020, cited an average of 310 (SD 360) times, and included 968 (SD 1175) codes. Pairwise Jaccard similarity indices (0 = no overlap, 1 = perfect congruence) ranged from 0.01 to 0.82, with mean 0.20 (SD 0.13). When validated against the LAC reference group, the algorithms had an average PPV of 0.308 (95% CI [0.253, 0.364]) and sensitivity of 0.183 (95% CI [0.111, 0.256]). Overall, 2.1% of visits identified as low acuity by the algorithms died prehospital or in the ED, or needed surgery, critical care, or cardiac catheterization.
conclusionsExisting algorithms that identify low-acuity ED visits lack congruence and are imperfect predictors of visit acuity.
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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.