National Healthcare Quality and Disparities Report
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Search All Research Studies
Topics
- Blood Thinners (1)
- (-) Clinical Decision Support (CDS) (3)
- (-) COVID-19 (3)
- Decision Making (1)
- Diagnostic Safety and Quality (1)
- Electronic Health Records (EHRs) (1)
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AHRQ Research Studies
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 3 of 3 Research Studies DisplayedDjulbegovic B, Hozo I, Lizarraga D
Evaluation of a fast-and-frugal clinical decision algorithm ('pathways') on clinical outcomes in hospitalised patients with COVID-19 treated with anticoagulants.
The objective of this study was to assess if delivery of anticoagulant prophylaxis according to an algorithm improved clinical outcomes in patients hospitalized with COVID-19 in comparison with anticoagulant treatment given at individual practitioners' discretion. Findings indicated that the algorithm did not reduce death, venous thromboembolism, nor major bleeding, but helped avoid longer hospital stay and admission to an intensive-care unit.
AHRQ-funded; HS024917.
Citation: Djulbegovic B, Hozo I, Lizarraga D .
Evaluation of a fast-and-frugal clinical decision algorithm ('pathways') on clinical outcomes in hospitalised patients with COVID-19 treated with anticoagulants.
J Eval Clin Pract 2023 Feb; 29(1):3-12. doi: 10.1111/jep.13780..
Keywords: COVID-19, Clinical Decision Support (CDS), Blood Thinners, Medication, Evidence-Based Practice, Health Information Technology (HIT)
Hinson JS, Klein E, Smith A
Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions.
This study’s objective was to develop, implement, and evaluate an electronic health record (EHR) embedded clinical decision support (CDS) system that leveraged machine learning (ML) to estimate short-term risk for clinical deterioration in patients with or under investigation for COVID-19. The system translates model-generated risk for critical care needs within 24 hours and inpatient care needs within 72 hours into rapidly interpretable COVID-19 Deterioration Risk Levels made viewable within ED clinician workflow. A retrospective cohort of 21,452 ED patients who visited one of five ED study sites was used to derive ML models and were prospectively validated in 15,670 ED visits that occurred before (n = 4322) or after (n = 11,348) CDS implementation. Model performance and numerous patient-oriented outcomes including in-hospital mortality were measured across study periods. ML model performance was excellent under all conditions. AUC ranged from 0.85 to 0.91 for prediction of critical care needs and 0.80-0.90 for inpatient care needs. Total mortality was unchanged across study periods but was reduced among high-risk patients after the implementation.
AHRQ-funded; HS026640.
Citation: Hinson JS, Klein E, Smith A .
Multisite implementation of a workflow-integrated machine learning system to optimize COVID-19 hospital admission decisions.
NPJ Digit Med 2022 Jul 16;5(1):94. doi: 10.1038/s41746-022-00646-1..
Keywords: COVID-19, Clinical Decision Support (CDS), Health Information Technology (HIT), Implementation, Electronic Health Records (EHRs), Emergency Department, Decision Making
Joshi RP, Pejaver V, Hammarlund NE
A predictive tool for identification of SARS-CoV-2 PCR-negative emergency department patients using routine test results.
This retrospective case-control study investigated whether the use of a prediction tool based on complete blood count results and patient sex can better allocate testing for SARS-CoV-2 PCR testing in hospital emergency departments. Participants were emergency department patients who had concurrent complete blood counts and SARS-CoV-2 PCR testing in Northern California, Seattle, Washington, Chicago Illinois, and South Korea. A hypothetical scenario of 1000 patients requiring testing was developed, but in this scenario testing resources are limited to 60% of patients. This tool would allow a 33% increase in properly allocated resources.
AHRQ-funded; HS026385.
Citation: Joshi RP, Pejaver V, Hammarlund NE .
A predictive tool for identification of SARS-CoV-2 PCR-negative emergency department patients using routine test results.
J Clin Virol 2020 Aug;129:104502. doi: 10.1016/j.jcv.2020.104502..
Keywords: Emergency Department, COVID-19, Pneumonia, Respiratory Conditions, Diagnostic Safety and Quality, Clinical Decision Support (CDS)