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 DisplayedBucher BT, Yang M, Arndorfer J
Changes in the accuracy of administrative data for the detection of surgical site infections.
The authors performed a retrospective analysis of the changes in accuracy of International Classification of Diseases, Clinical Modification (ICD-CM) diagnosis codes for colectomy and hysterectomy surgical site infection surveillance. They found no significant change in the accuracy of these codes following the transition from ICD-CM ninth edition to tenth edition codes.
AHRQ-funded; HS025776.
Citation: Bucher BT, Yang M, Arndorfer J .
Changes in the accuracy of administrative data for the detection of surgical site infections.
Infect Control Hosp Epidemiol 2021 Sep;42(9):1128-30. doi: 10.1017/ice.2020.1346..
Keywords: Surgery, Healthcare-Associated Infections (HAIs), Diagnostic Safety and Quality
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