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
AHRQ Research Studies Date
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 2 of 2 Research Studies DisplayedHekman DJ, Cochran AL, Maru AP
Effectiveness of an emergency department-based machine learning clinical decision support tool to prevent outpatient falls among older adults: protocol for a quasi-experimental study.
This article described a research protocol for evaluating the effectiveness of an automated screening and referral intervention tool for patients receiving falls risk intervention. The study will attempt to quantify the impact of a machine learning (ML) clinical decision support intervention on patient behavior and outcomes. The primary analysis will obtain referral completion rates from different emergency departments. The findings will inform ongoing discussion on the use of ML and artificial intelligence to augment medical decision-making.
AHRQ-funded; HS027735.
Citation: Hekman DJ, Cochran AL, Maru AP .
Effectiveness of an emergency department-based machine learning clinical decision support tool to prevent outpatient falls among older adults: protocol for a quasi-experimental study.
JMIR Res Protoc 2023 Aug 3; 12:e48128. doi: 10.2196/48128..
Keywords: Clinical Decision Support (CDS), Emergency Department, Health Information Technology (HIT), Elderly, Falls
Shear K, Rice H, Garabedian PM
Usability testing of an interoperable computerized clinical decision support tool for fall risk management in primary care.
The purpose of this study was to conduct usability testing of the ASPIRE fall risk management tool for use in divergent primary care clinics. Participants recruited from two sites with different electronic health records and clinical organizations used ASPIRE across two clinical scenarios; they rated ASPIRE usability as above average, based on usability benchmarks. Time spent on tasks decreased significantly between the first and second scenarios, indicating ease of learnability. The authors conclude that ASPIRE could be integrated into diverse organizations, since it allows a tailored implementation without the need to build a new system for each organization. ASPIRE is therefore well positioned to impact the challenge of falls at scale.
AHRQ-funded; HS027557.
Citation: Shear K, Rice H, Garabedian PM .
Usability testing of an interoperable computerized clinical decision support tool for fall risk management in primary care.
Appl Clin Inform 2023 Mar;14(2):212-26. doi: 10.1055/a-2006-4936.
Keywords: Clinical Decision Support (CDS), Shared Decision Making, Health Information Technology (HIT), Falls, Primary Care, Risk, Prevention