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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 DisplayedDutta S, McEvoy DS, Rubins DM
Clinical decision support improves blood culture collection before intravenous antibiotic administration in the emergency department.
This paper discusses the outcomes of using a clinical decision support (CDS) tool that was implemented in emergency departments (EDs) for sepsis patients to remind healthcare staff to take blood cultures before administration of intravenous (IV) antibiotics. The study compared timely blood culture collection outcomes prior to IV antibiotics for 54,538 adult ED patients 1 year before and after a CDS intervention implementation in the electronic health record. The baseline phase found that 46.1% had blood cultures prior to IV antibiotics, compared to 58.8% after the intervention. The CDS improved blood culture collection rates without increasing overutilization.
AHRQ-funded; HS02717.
Citation: Dutta S, McEvoy DS, Rubins DM .
Clinical decision support improves blood culture collection before intravenous antibiotic administration in the emergency department.
J Am Med Inform Assoc 2022 Sep 12;29(10):1705-14. doi: 10.1093/jamia/ocac115..
Keywords: Clinical Decision Support (CDS), Health Information Technology (HIT), Antibiotics, Emergency Department, Medication, Sepsis
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, Shared Decision Making
Jacobsohn GC, Leaf M, Liao F
Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments.
The authors used a collaborative and iterative approach to design and implement an automated clinical decision support system (CDS) for Emergency Department (ED) providers to identify and refer older adult ED patients at high risk of future falls. The system was developed using collaborative input from an interdisciplinary design team and integrated seamlessly into existing ED workflows. A key feature of development was the unique combination of patient experience strategies, human-centered design, and implementation science, which allowed for the CDS tool and intervention implementation strategies to be designed simultaneously. Challenges included: usability problems, data inaccessibility, time constraints, low appointment availability, high volume of patients, and others. The study concluded that using the collaborative, iterative approach was successful in achieving all project goals, and could be applied to other cases.
AHRQ-funded; HS024558.
Citation: Jacobsohn GC, Leaf M, Liao F .
Collaborative design and implementation of a clinical decision support system for automated fall-risk identification and referrals in emergency departments.
Healthc 2022 Mar;10(1):100598. doi: 10.1016/j.hjdsi.2021.100598..
Keywords: Elderly, Clinical Decision Support (CDS), Shared Decision Making, Falls, Risk, Emergency Department, Health Information Technology (HIT)