National Healthcare Quality and Disparities Report
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Search All Research Studies
Topics
- Chronic Conditions (2)
- Clinical Decision Support (CDS) (1)
- Diagnostic Safety and Quality (2)
- (-) Electronic Health Records (EHRs) (4)
- Emergency Department (1)
- Healthcare Delivery (1)
- Health Information Technology (HIT) (2)
- (-) Kidney Disease and Health (4)
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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 4 of 4 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
Sequist TD, Holliday AM, Orav EJ
Physician and patient tools to improve chronic kidney disease care.
This study sought to determine if electronic health record (EHR) tools and patient engagement can improve the quality of chronic kidney disease (CKD) care. It found that, among high-risk patients, those in the intervention arm were significantly more likely to have an office visit with a nephrologist compared with those in the control arm.
AHRQ-funded; HS018226.
Citation: Sequist TD, Holliday AM, Orav EJ .
Physician and patient tools to improve chronic kidney disease care.
Am J Manag Care 2018 Apr;24(4):e107-e14.
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Keywords: Chronic Conditions, Electronic Health Records (EHRs), Kidney Disease and Health, Patient and Family Engagement, Quality Improvement
Goldstein SL
Automated/integrated real-time clinical decision support in acute kidney injury.
The author argues that early, real-time identification and notification to healthcare providers of patients at risk for, or with, acute or chronic kidney disease can drive simple interventions to reduce harm. Similarly, he believes that screening patients at risk for acute kidney injury with these platforms to alert research personnel will lead to improve study subject recruitment.
AHRQ-funded; HS023763; HS021114.
Citation: Goldstein SL .
Automated/integrated real-time clinical decision support in acute kidney injury.
Curr Opin Crit Care 2015 Dec;21(6):485-9. doi: 10.1097/mcc.0000000000000250.
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Keywords: Clinical Decision Support (CDS), Kidney Disease and Health, Electronic Health Records (EHRs), Patient-Centered Outcomes Research, Diagnostic Safety and Quality