National Healthcare Quality and Disparities Report
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Search All Research Studies
Topics
- (-) Adverse Events (3)
- Blood Clots (1)
- Data (1)
- Diagnostic Safety and Quality (1)
- Electronic Health Records (EHRs) (2)
- Healthcare-Associated Infections (HAIs) (2)
- (-) Health Information Technology (HIT) (3)
- Injuries and Wounds (1)
- Quality Improvement (3)
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- Registries (1)
- (-) Surgery (3)
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 3 of 3 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
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.
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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