National Healthcare Quality and Disparities Report
Latest available findings on quality of and access to health care
Data
- Data Infographics
- Data Visualizations
- Data Tools
- Data Innovations
- All-Payer Claims Database
- Healthcare Cost and Utilization Project (HCUP)
- Medical Expenditure Panel Survey (MEPS)
- AHRQ Quality Indicator Tools for Data Analytics
- State Snapshots
- United States Health Information Knowledgebase (USHIK)
- Data Sources Available from AHRQ
Search All Research Studies
Topics
AHRQ Research Studies
Sign up: AHRQ Research Studies Email updates
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 DisplayedSoares 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)
Enayati M, Sir M, Zhang X
Monitoring diagnostic safety risks in emergency departments: protocol for a machine learning study.
This study’s objective will be to identify variables associated with diagnostic errors in emergency departments using large-scale EHR data and machine learning techniques. It will use trigger algorithms with electronic health record (EHR) data repositories to generate a large data set of records that are labeled trigger-positive or trigger-negative, depending on if they meet certain criteria. This study will be conducted by 2 academic medical centers with affiliated community hospitals.
AHRQ-funded; HS027363; HS026622.
Citation: Enayati M, Sir M, Zhang X .
Monitoring diagnostic safety risks in emergency departments: protocol for a machine learning study.
JMIR Res Protoc 2021 Jun 14;10(6):e24642. doi: 10.2196/24642..
Keywords: Emergency Department, Diagnostic Safety and Quality, Patient Safety, Risk, Electronic Health Records (EHRs), Health Information Technology (HIT)
Carayon P, Hoonakker P, Hundt AS
Application of human factors to improve usability of clinical decision support for diagnostic decision-making: a scenario-based simulation study.
This study used a scenario-based simulation to compare a human factor (HF)-based clinician decision support (CDS) with a web-based CDS (MDCalc) for clinicians to diagnose pulmonary embolism (PE) in the emergency department. A total of 32 emergency physicians participated using both CDS types. Emergency physicians made more appropriate diagnoses decisions with the PE-Dx CDS (94%) than with the web-based CDS (84%). Experimental tasks were also performed faster (average 96 seconds per scenario versus 117 seconds). They also reported lower workload and higher satisfaction with the HF-based CDS.
AHRQ-funded; HS024342; HS024558; HS022086.
Citation: Carayon P, Hoonakker P, Hundt AS .
Application of human factors to improve usability of clinical decision support for diagnostic decision-making: a scenario-based simulation study.
BMJ Qual Saf 2020 Apr;29(4):329-40. doi: 10.1136/bmjqs-2019-009857..
Keywords: Clinical Decision Support (CDS), Shared Decision Making, Health Information Technology (HIT), Diagnostic Safety and Quality, Emergency Department