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
- (-) Adverse Events (6)
- Blood Clots (1)
- Blood Pressure (1)
- Data (1)
- Diagnostic Safety and Quality (2)
- Electronic Health Records (EHRs) (5)
- Emergency Department (1)
- Healthcare-Associated Infections (HAIs) (2)
- (-) Health Information Technology (HIT) (6)
- Hospitalization (1)
- Hospitals (1)
- Injuries and Wounds (1)
- Medical Errors (2)
- Patient Safety (2)
- Prevention (1)
- Quality Improvement (5)
- Quality Indicators (QIs) (1)
- (-) Quality of Care (6)
- Registries (1)
- Surgery (3)
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 6 of 6 Research Studies DisplayedShi J, Hurdle JF, Johnson SA
Natural language processing for the surveillance of postoperative venous thromboembolism.
The objective of the study was to develop a portal natural language processing approach to aid in the identification of postoperative venous thromboembolism events from free-text clinical notes. The investigators concluded that accurate surveillance of postoperative venous thromboembolism may be achieved using natural language processing on clinical notes in 2 independent health care systems. They indicated that these findings suggest natural language processing may augment manual chart abstraction for large registries such as National Surgical Quality Improvement Program.
AHRQ-funded; HS025776.
Citation: Shi J, Hurdle JF, Johnson SA .
Natural language processing for the surveillance of postoperative venous thromboembolism.
Surgery 2021 Oct;170(4):1175-82. doi: 10.1016/j.surg.2021.04.027..
Keywords: Blood Clots, Health Information Technology (HIT), Quality Improvement, Quality of Care, Surgery, Adverse Events
Zhu Y, Simon GJ, Wick EC
Applying machine learning across sites: external validation of a surgical site infection detection algorithm.
Surgical complications have tremendous consequences and costs. Complication detection is important for quality improvement, but traditional manual chart review is burdensome. Automated mechanisms are needed to make this more efficient. The purpose of the study was to understand the generalizability of a machine learning algorithm between sites; automated surgical site infection (SSI) detection algorithms developed at one center were tested at another distinct center.
AHRQ-funded; HS024532.
Citation: Zhu Y, Simon GJ, Wick EC .
Applying machine learning across sites: external validation of a surgical site infection detection algorithm.
J Am Coll Surg 2021 Jun;232(6):963-71.e1. doi: 10.1016/j.jamcollsurg.2021.03.026..
Keywords: Healthcare-Associated Infections (HAIs), Surgery, Adverse Events, Diagnostic Safety and Quality, Electronic Health Records (EHRs), Health Information Technology (HIT), Quality Improvement, Quality of Care
Griffey RT, Schneider RM, Todorov AA
The emergency department trigger tool: validation and testing to optimize yield.
Researchers validated the emergency department trigger tool (EDTT) in an independent sample and compared record selection approaches to optimize yield for quality improvement. In this single-site study of the EDTT, they observed high levels of validity in trigger selection, yield, and representativeness of adverse events, with yields that are superior to estimates for traditional approaches to adverse event detection. Record selection using weighted triggers outperformed a trigger count threshold approach and far outperformed random sampling from records with at least one trigger. They concluded that the EDTT is a promising efficient and high-yield approach for detecting all-cause harm to guide quality improvement efforts in the emergency department.
AHRQ-funded; HS025052.
Citation: Griffey RT, Schneider RM, Todorov AA .
The emergency department trigger tool: validation and testing to optimize yield.
Acad Emerg Med 2020 Dec;27(12):1279-90. doi: 10.1111/acem.14101..
Keywords: Emergency Department, Electronic Health Records (EHRs), Health Information Technology (HIT), Medical Errors, Adverse Events, Patient Safety, Quality Improvement, Quality of Care
Muldoon MF, Kronish IM, Shimbo D
Of signal and noise: overcoming challenges in blood pressure measurement to optimize hypertension care.
This paper reviews the manifestations and consequences of BP mismeasurement and misinterpretation in clinical practice and draw on recent research to propose a set of solutions that leverage available technologies to optimize hypertension care.
AHRQ-funded; HS024262.
Citation: Muldoon MF, Kronish IM, Shimbo D .
Of signal and noise: overcoming challenges in blood pressure measurement to optimize hypertension care.
Circ Cardiovasc Qual Outcomes 2018 May;11(5):e004543. doi: 10.1161/circoutcomes.117.004543..
Keywords: Blood Pressure, Diagnostic Safety and Quality, Adverse Events, Medical Errors, Electronic Health Records (EHRs), Health Information Technology (HIT), Quality of Care
Bhise V, Sittig DF, Vaghani V
An electronic trigger based on care escalation to identify preventable adverse events in hospitalised patients.
Researchers refined the methods of the Institute of Healthcare Improvement's Global Trigger Tool application and leveraged electronic health record data to improve detection of preventable adverse events, including diagnostic errors. In the studied sample, preventable adverse events were identified, including adverse drug events, patient falls, procedure-related complications, and hospital-associated infections. The authors concluded that such e-triggers can help overcome limitations of currently available methods to detect preventable harm in hospitalized patients.
AHRQ-funded; HS022087; HS023602.
Citation: Bhise V, Sittig DF, Vaghani V .
An electronic trigger based on care escalation to identify preventable adverse events in hospitalised patients.
BMJ Qual Saf 2018 Mar;27(3):241-46. doi: 10.1136/bmjqs-2017-006975..
Keywords: Adverse Events, Electronic Health Records (EHRs), Health Information Technology (HIT), Hospitalization, Hospitals, Patient Safety, Prevention, Quality of Care, Quality Improvement, Quality Indicators (QIs)
Hu Z, Melton GB, Arsoniadis EG
Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.
Proper handling of missing data is important for many secondary uses of electronic health record (EHR) data. Data imputation methods can be used to handle missing data, but their use for postoperative complication detection is unclear. Overall, models with missing data imputation almost always outperformed reference models without imputation that included only cases with complete data for detection of SSI overall achieving very good average area under the curve values.
AHRQ-funded; HS024532.
Citation: Hu Z, Melton GB, Arsoniadis EG .
Strategies for handling missing clinical data for automated surgical site infection detection from the electronic health record.
J Biomed Inform 2017 Apr;68:112-20. doi: 10.1016/j.jbi.2017.03.009.
.
.
Keywords: Data, Electronic Health Records (EHRs), Healthcare-Associated Infections (HAIs), Registries, Surgery, Injuries and Wounds, Health Information Technology (HIT), Quality Improvement, Quality of Care, Adverse Events