ArticleBMC emergency medicine2018
Assessment of the Feasibility of automated, real-time clinical decision support in the emergency department using electronic health record data.
Article in BMC emergency medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Signs and Symptoms That Rule out Community-Acquired Pneumonia in Outpatient Adults: A Systematic Review and Meta-Analysis.Journal of the American Board of Family Medicine : JABFMPooled it
- Enhancing prehospital decision-making: exploring user needs and design considerations for clinical decision support systems.BMC medical informatics and decision making · 2025Article
- Automated classification of clinical diagnoses in electronic health records using transformer.PloS one · 2025Article
- Enhancing Prehospital Decision-Making: Exploring User Needs and Design Considerations for Clinical Decision Support Systems.Research square · 2024Article
- Machine learning-based delirium prediction in surgical in-patients: a prospective validation study.JAMIA open · 2024Article
- Limitations in the use of automated mental status detection for clinical decision support.International journal of medical informatics · 2023Article
- Integration of Risk Scores and Integration Capability in Electronic Patient Records.Applied clinical informatics · 2022Article
- Clinical risk calculators informing the decision to admit: A methodologic evaluation and assessment of applicability.PloS one · 2022Article
- Factors predicting failure of internal fixations of fractures of the lower limbs: a prospective cohort study.BMC musculoskeletal disorders · 2021Observational
- Discovering Composite Lifestyle Biomarkers With Artificial Intelligence From Clinical Studies to Enable Smart eHealth and Digital Therapeutic Services.Frontiers in digital health · 2021Article
- Risk prediction of delirium in hospitalized patients using machine learning: An implementation and prospective evaluation study.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Man vs machine in emergency medicine - a study on the effects of manual and automatic vital sign documentation on data quality and perceived workload, using observational paired sample data and questionnaires.BMC emergency medicine · 2018Observational
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe use of big data and machine learning within clinical decision support systems (CDSSs) has the potential to transform medicine through better prognosis, diagnosis and automation of tasks. Real-time application of machine learning algorithms, however, is dependent on data being present and entered prior to, or at the point of, CDSS deployment. Our aim was to determine the feasibility of automating CDSSs within electronic health records (EHRs) by investigating the timing, data categorization, and completeness of documentation of their individual components of two common Clinical Decision Rules (CDRs) in the Emergency Department.
methodsThe CURB-65 severity score and HEART score were randomly selected from a list of the top emergency medicine CDRs. Emergency department (ED) visits with ICD-9 codes applicable to our CDRs were eligible. The charts were reviewed to determine the categorization components of the CDRs as structured and/or unstructured, median times of documentation, portion of charts with all data components documented as structured data, portion of charts with all structured CDR components documented before ED departure. A kappa score was calculated for interrater reliability.
resultsThe components of the CDRs were mainly documented as structured data for the CURB-65 severity score and HEART score. In the CURB-65 group, 26.8% of charts had all components documented as structured data, and 67.8% in the HEART score. Documentation of some CDR components often occurred late for both CDRs. Only 21 and 11% of patients had all CDR components documented as structured data prior to ED departure for the CURB-65 and HEART score groups, respectively. The interrater reliability for the CURB-65 score review was 0.75 and 0.65 for the HEART score.
conclusionOur study found that EHRs may be unable to automatically calculate popular CDRs-such as the CURB-65 severity score and HEART score-due to missing components and late data entry.
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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.