National Healthcare Quality and Disparities Report
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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 4 of 4 Research Studies DisplayedWeiner SJ, Schwartz A, Weaver F
Effect of electronic health record clinical decision support on contextualization of care: a randomized clinical trial.
Researchers sought to determine whether contextualized clinical decision support (CDS) tools in the electronic health record (EHR) improve clinician contextual probing, attention to contextual factors in care planning, and the presentation of contextual red flags. In this randomized clinical trial, they found that contextualized CDS did not improve patients' outcomes but did increase contextualization of their care, suggesting that use of this technology could ultimately help to improve outcomes.
AHRQ-funded; HS025374.
Citation: Weiner SJ, Schwartz A, Weaver F .
Effect of electronic health record clinical decision support on contextualization of care: a randomized clinical trial.
JAMA Netw Open 2022 Oct;5(10):e2238231. doi: 10.1001/jamanetworkopen.2022.38231..
Keywords: Electronic Health Records (EHRs), Clinical Decision Support (CDS), Health Information Technology (HIT), Shared Decision Making
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
Bui LN, Marshall C, Miller-Rosales C
Hospital adoption of electronic decision support tools for preeclampsia management.
Maternal morbidity and mortality can be reduced by the utilization of evidence-based clinical guidelines for preeclampsia management. Electronic health record (EHR)-based clinical decision support tools can improve the use of those guidelines. The purpose of this study was to investigate the organizational capabilities and hospital adoption of HER-based decision tools for preeclampsia management. The researchers conducted a cross-sectional analysis of hospitals that provided obstetric care in 2017. A total of 739 hospitals that responded to the 2017-2018 National Survey of Healthcare Organizations and Systems (NSHOS) and their results were linked to the 2017 Area Health Resources File (AHRF) and the American Hospital Association (AHA) Annual Survey Database. A final total of 425 hospitals from 49 states were analyzed. The primary outcome of the analysis was whether a hospital adopted EHR-based clinical decision support tools for preeclampsia management. The study found that 68% of the hospitals utilized EHR-based decision support tools for preeclampsia, and that hospitals with a single EHR system were more likely to adopt EHR-based decision support tools for preeclampsia than hospitals with multiple systems, including a combination of EHR and paper-based systems. The researchers also determined that hospitals with more processes to disseminate best patient care practices were more likely to adopt EHR-based decision support tools for preeclampsia management. The study concluded that having standardized EHRs and policies to disseminate evidence can help hospitals advance the use of EHR-based decision support tools for preeclampsia management in those hospitals that have not yet adopted them.
AHRQ-funded; HS024075.
Citation: Bui LN, Marshall C, Miller-Rosales C .
Hospital adoption of electronic decision support tools for preeclampsia management.
Qual Manag Health Care 2022 Apr-Jun;31(2):59-67. doi: 10.1097/qmh.0000000000000328..
Keywords: Clinical Decision Support (CDS), Electronic Health Records (EHRs), Health Information Technology (HIT), Hospitals, Pregnancy, Women
Khan S, McCullagh L, Press A
Formative assessment and design of a complex clinical decision support tool for pulmonary embolism.
This study sought to determine the general attitude towards clinical decision support (CDS) tool integration and the ideal integration point into the clinical workflow. It highlighted: (1) formative assessment of EHR functionality and clinical environment workflow, (2) focus groups and key informative interviews to incorporate providers' perceptions of CDS and workflow integration and/or (3) the demonstration of proposed workflows through wireframes to help providers visualise design concepts.
AHRQ-funded; HS022061.
Citation: Khan S, McCullagh L, Press A .
Formative assessment and design of a complex clinical decision support tool for pulmonary embolism.
Evid Based Med 2016 Feb;21(1):7-13. doi: 10.1136/ebmed-2015-110214.
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Keywords: Clinical Decision Support (CDS), Electronic Health Records (EHRs), Health Information Technology (HIT)