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 DisplayedMartinez DA, Levin SR, Klein EY
Early prediction of acute kidney injury in the emergency department with machine-learning methods applied to electronic health record data.
Researchers analyzed routinely collected emergency department (ED) data and developed prediction models with capacity for early identification of ED patients at high risk for acute kidney injury. They found that machine learning applied to routinely-collected ED data identified ED patients at high risk for acute kidney injury up to 72 hours before they met diagnostic criteria. They recommended further prospective evaluation.
AHRQ-funded; HS027793.
Citation: Martinez DA, Levin SR, Klein EY .
Early prediction of acute kidney injury in the emergency department with machine-learning methods applied to electronic health record data.
Ann Emerg Med 2020 Oct;76(4):501-14. doi: 10.1016/j.annemergmed.2020.05.026..
Keywords: Kidney Disease and Health, Emergency Department, Electronic Health Records (EHRs), Health Information Technology (HIT)
Danforth KN, Hahn EE, Slezak JM
Follow-up of abnormal estimated GFR results within a large integrated health care delivery system: a mixed-methods study.
This study examined the rates of follow-up with patients after abnormal estimated glomular filtration rate (eGFR) laboratory results, which may indicate chronic kidney disease. A large integrated health system was used with a total of 244,540 patients aged 21 or older with abnormal eGFRs were included from January 2010 through December 2015. Timely follow-up was defined as repeat eGFR testing within 60 to 150 days, follow-up testing before 60 days that indicated normal kidney function, or diagnosis before 60 days of chronic kidney disease or kidney cancer. Follow-up was found to be poor, with 58% of patients lacking timely follow-up. Fifteen physicians were also interviewed and it was found that both system-level and provider-level factors influenced follow-up rates.
AHRQ-funded; HS024437.
Citation: Danforth KN, Hahn EE, Slezak JM .
Follow-up of abnormal estimated GFR results within a large integrated health care delivery system: a mixed-methods study.
Am J Kidney Dis 2019 Nov;74(5):589-600. doi: 10.1053/j.ajkd.2019.05.003..
Keywords: Healthcare Delivery, Diagnostic Safety and Quality, Kidney Disease and Health, Electronic Health Records (EHRs), Health Information Technology (HIT), Chronic Conditions