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
Topics
- Adverse Drug Events (ADE) (1)
- Adverse Events (3)
- Cardiovascular Conditions (1)
- Comparative Effectiveness (1)
- (-) Data (4)
- Healthcare-Associated Infections (HAIs) (1)
- Health Information Technology (HIT) (1)
- Hospitals (1)
- Injuries and Wounds (1)
- Medical Devices (1)
- Medical Errors (2)
- Medication (1)
- Patient-Centered Outcomes Research (1)
- (-) Patient Safety (4)
- Quality Improvement (1)
- Quality Indicators (QIs) (1)
- Quality of Care (1)
- Research Methodologies (1)
- Surgery (2)
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 4 of 4 Research Studies DisplayedWang E, Kang H, Gong Y
Generating a health information technology event database from FDA MAUDE reports.
This study examined using a health information technology (HIT) event database to identify patient safety events (PSEs) or medical errors. The study used the FDA Manufacturer and User Facility Device Experience (MAUDE) database to extract HIT events. Classic and CNN models were utilized on a test set. The model was capable of identifying HIT event with about a 90% accuracy.
AHRQ-funded; HS022895.
Citation: Wang E, Kang H, Gong Y .
Generating a health information technology event database from FDA MAUDE reports.
Stud Health Technol Inform 2019 Aug 21;264:883-87. doi: 10.3233/shti190350..
Keywords: Health Information Technology (HIT), Medical Devices, Adverse Events, Data, Medical Errors, Patient Safety
Yao B, Kang H, Gong Y
Data quality assessment of narrative medication error reports.
This study examined the data quality of patient safety event (PSE) reports that are used to analyze the root causes of PSE. If the data quality is poor then the reporting and root cause analysis (RCA) will also be poor. Incomplete or missing data is the most prevalent problem in these reports. The researchers used an adapted taxonomy to assess the data quality of PSE reports, and extracted sample reports based on eight error types. The extracts were scored by experts. They found that most structured fields were ignored by reporters, but the narrative parts of the reports contained rich and valuable information. The results show that the adapted taxonomy could be a promising tool for report quality assessment and improvement.
AHRQ-funded; HS022895.
Citation: Yao B, Kang H, Gong Y .
Data quality assessment of narrative medication error reports.
Stud Health Technol Inform 2019 Aug 9;265:101-06. doi: 10.3233/shti190146..
Keywords: Adverse Drug Events (ADE), Medication, Medical Errors, Adverse Events, Data, Patient Safety
Hsu YJ, Kosinski AS, Wallace AS
Using a society database to evaluate a patient safety collaborative: the Cardiovascular Surgical Translational Study.
The authors assessed the utility of using external databases for quality improvement (QI) evaluations in the context of an innovative QI collaborative aimed to reduce three infections and improve patient safety across the cardiac surgery service line. They compared changes in each outcome between 15 intervention hospitals and 52 propensity score-matched hospitals, and found that improvement trends in several outcomes among the studied intervention hospitals were not statistically different from those in comparison hospitals. They conclude that using external databases may permit comparative effectiveness assessment by providing concurrent comparison groups, additional outcome measures, and longer follow-up.
AHRQ-funded; HS019934.
Citation: Hsu YJ, Kosinski AS, Wallace AS .
Using a society database to evaluate a patient safety collaborative: the Cardiovascular Surgical Translational Study.
J Comp Eff Res 2019 Jan;8(1):21-32. doi: 10.2217/cer-2018-0051..
Keywords: Patient Safety, Quality Improvement, Quality Indicators (QIs), Quality of Care, Surgery, Cardiovascular Conditions, Comparative Effectiveness, Data, Hospitals, Research Methodologies, Patient-Centered Outcomes Research
Warren DK, Nickel KB, Wallace AE
Can additional information be obtained from claims data to support surgical site infection diagnosis codes?
The authors sought to confirm a claims algorithm to identify surgical site infections (SSIs) by examining the presence of clinically expected SSI treatment. They found that over 94% of patients identified by their claims algorithm as having an SSI received clinically expected treatment for infection, including antibiotics, surgical treatment, and culture, suggesting that this algorithm has very good positive predictive value. They concluded that their method may facilitate retrospective SSI surveillance and comparison of SSI rates across facilities and providers.
AHRQ-funded; HS019713.
Citation: Warren DK, Nickel KB, Wallace AE .
Can additional information be obtained from claims data to support surgical site infection diagnosis codes?
Infect Control Hosp Epidemiol 2014 Oct;35 Suppl 3:S124-32. doi: 10.1086/677830.
.
.
Keywords: Data, Healthcare-Associated Infections (HAIs), Patient Safety, Surgery, Injuries and Wounds, Adverse Events