National Healthcare Quality and Disparities Report
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Search All Research Studies
Topics
- Adverse Events (1)
- (-) Cardiovascular Conditions (8)
- Clinical Decision Support (CDS) (1)
- Cultural Competence (1)
- (-) Diagnostic Safety and Quality (8)
- Emergency Department (3)
- Emergency Medical Services (EMS) (1)
- Health Information Technology (HIT) (3)
- Heart Disease and Health (4)
- Medical Errors (1)
- Neurological Disorders (1)
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- Stroke (4)
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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 8 of 8 Research Studies DisplayedVaghani V, Wei L, U
Validation of an electronic trigger to measure missed diagnosis of stroke in emergency departments.
Diagnostic errors are major contributors to preventable patient harm. In this study, the investigators validated the use of an electronic health record (EHR)-based trigger (e-trigger) to measure missed opportunities in stroke diagnosis in emergency departments (EDs). The investigators concluded that a symptom-disease pair-based e-trigger identified missed diagnoses of stroke with a modest positive predictive value, underscoring the need for chart review validation procedures to identify diagnostic errors in large data sets.
AHRQ-funded; HS017820; HS024459.
Citation: Vaghani V, Wei L, U .
Validation of an electronic trigger to measure missed diagnosis of stroke in emergency departments.
J Am Med Inform Assoc 2021 Sep 18;28(10):2202-11. doi: 10.1093/jamia/ocab121..
Keywords: Stroke, Cardiovascular Conditions, Emergency Department, Diagnostic Safety and Quality, Medical Errors, Adverse Events
Mayampurath A, Parnianpour Z, Richards CT
Improving prehospital stroke diagnosis using natural language processing of paramedic reports.
Accurate prehospital diagnosis of stroke by emergency medical services (EMS) can increase treatments rates, mitigate disability, and reduce stroke deaths. IN this study, the investigators aimed to develop a model that utilized natural language processing of EMS reports and machine learning to improve prehospital stroke identification. The investigators conducted a retrospective study of patients transported by the Chicago EMS to 17 regional primary and comprehensive stroke centers.
AHRQ-funded; HS025359; HS027264.
Citation: Mayampurath A, Parnianpour Z, Richards CT .
Improving prehospital stroke diagnosis using natural language processing of paramedic reports.
Stroke 2021 Aug;52(8):2676-79. doi: 10.1161/strokeaha.120.033580..
Keywords: Stroke, Cardiovascular Conditions, Diagnostic Safety and Quality, Health Information Technology (HIT), Emergency Medical Services (EMS)
Soares WE, Knee A, Gemme SR
SC, et al. A prospective evaluation of Clinical HEART score agreement, accuracy, and adherence in emergency department chest pain patients.
The HEART score is a risk stratification aid that may safely reduce chest pain admissions for emergency department patients. However, differences in interpretation of subjective components potentially alters the performance of the score. In this study, the investigators compared agreement between HEART scores determined during clinical practice with research-generated scores and estimated their accuracy in predicting 30-day major adverse cardiac events.
AHRQ-funded; HS024815.
Citation: Soares WE, Knee A, Gemme SR .
SC, et al. A prospective evaluation of Clinical HEART score agreement, accuracy, and adherence in emergency department chest pain patients.
Ann Emerg Med 2021 Aug;78(2):231-41. doi: 10.1016/j.annemergmed.2021.03.024..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Emergency Department, Diagnostic Safety and Quality, Clinical Decision Support (CDS), Health Information Technology (HIT)
Ganguli I, Cui J, Thakore N
Downstream cascades of care following high-sensitivity troponin test implementation.
This study sought to determine the association of high-sensitivity cardiac troponin (hs-cTn) assay implementation with cascade events. The investigators found that hs-cTn assay implementation was associated with more net upfront tests yet fewer net stress tests, percutaneous coronary interventions, cardiology evaluations, and hospital admissions in patients with chest pain relative to patients with other symptoms.
AHRQ-funded; toHS023812.
Citation: Ganguli I, Cui J, Thakore N .
Downstream cascades of care following high-sensitivity troponin test implementation.
J Am Coll Cardiol 2021 Jun 29;77(25):3171-79. doi: 10.1016/j.jacc.2021.04.049..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Diagnostic Safety and Quality
Zachrison KS, Li S, Reeves MJ
Strategy for reliable identification of ischaemic stroke, thrombolytics and thrombectomy in large administrative databases.
Administrative data are frequently used in stroke research. Ensuring accurate identification of patients who had an ischaemic stroke, and those receiving thrombolysis and endovascular thrombectomy (EVT) is critical to ensure representativeness and generalisability. In this study, the investigators examined differences in patient samples based on mode of identification, and proposed a strategy for future patient and procedure identification in large administrative databases.
AHRQ-funded; HS024561.
Citation: Zachrison KS, Li S, Reeves MJ .
Strategy for reliable identification of ischaemic stroke, thrombolytics and thrombectomy in large administrative databases.
Stroke Vasc Neurol 2021 Jun;6(2):194-200. doi: 10.1136/svn-2020-000533..
Keywords: Stroke, Cardiovascular Conditions, Diagnostic Safety and Quality
Huda A, Castaño A, Niyogi A
A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy.
Transthyretin amyloid cardiomyopathy, an often-unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. In this study, the investigators showed that a random forest machine learning model could identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data.
AHRQ-funded; HS026385.
Citation: Huda A, Castaño A, Niyogi A .
A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy.
Nat Commun 2021 May 11;12(1):2725. doi: 10.1038/s41467-021-22876-9..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Neurological Disorders, Diagnostic Safety and Quality, Risk
Shah NR, Eisman AS, Winchester DE
E-consult protocoling to improve the quality of cardiac stress tests.
Rarely appropriate cardiac stress tests remain prevalent in the range of 10% to 20% and unnecessarily prolong wait times. To address this ongoing problem, the investigators designed the EPIQ-Stress workflow, which included a structured electronic consult (“econsult”) with all outpatient stress test orders. In this study, the investigators assessed whether EPIQ-Stress implementation was associated with a reduction in rarely appropriate testing and in order-to-report wait times.
AHRQ-funded; HS022998.
Citation: Shah NR, Eisman AS, Winchester DE .
E-consult protocoling to improve the quality of cardiac stress tests.
JACC Cardiovasc Imaging 2021 Feb;14(2):512-14. doi: 10.1016/j.jcmg.2020.08.009..
Keywords: Telehealth, Health Information Technology (HIT), Heart Disease and Health, Cardiovascular Conditions, Diagnostic Safety and Quality
Zachrison KS, Natsui S, Luan Erfe BM
Language preference does not influence stroke patients' symptom recognition or emergency care time metrics.
The objective of this study was to determine whether acute ischemic stroke (AIS) patients' language preference was associated with differences in time from symptom discovery to hospital arrival, activation of emergency medical services, door-to-imaging time (DTI), and door-to-needle (DTN) time. The investigators concluded that consistent with prior reports examining disparities in care, a systems-based approach to acute stroke prevents differences in hospital-based metrics.
AHRQ-funded; HS024561.
Citation: Zachrison KS, Natsui S, Luan Erfe BM .
Language preference does not influence stroke patients' symptom recognition or emergency care time metrics.
Am J Emerg Med 2021 Feb;40:177-80. doi: 10.1016/j.ajem.2020.10.064..
Keywords: Stroke, Cardiovascular Conditions, Emergency Department, Cultural Competence, Diagnostic Safety and Quality