National Healthcare Quality and Disparities Report
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Search All Research Studies
AHRQ Research Studies Date
Topics
- Adverse Events (1)
- Children/Adolescents (1)
- Elderly (2)
- (-) Electronic Health Records (EHRs) (5)
- (-) Emergency Department (5)
- Falls (1)
- Health Information Technology (HIT) (5)
- Home Healthcare (1)
- Hospitalization (1)
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- Quality of Care (1)
- Risk (2)
- Sepsis (1)
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 5 of 5 Research Studies DisplayedPatterson BW, Jacobsohn GC, Maru AP
Comparing strategies for identifying falls in older adult emergency department visits using EHR data.
This study compared seven different strategies for identifying falls in older adult emergency department (ED) visits using electronic health record (EHR) data. This retrospective cohort study used randomly selected data from 500 ED visits by patients 65 and older at an academic medical center from December 2016 to April 2017. The seven strategies tested were: Chief complaint (CC), ICD codes, Restrictive ICD codes, Broad ICD codes, Combined approaches, Natural language processing (NLP), and Manual abstraction (gold standard). When compared with manual chart review, NLP was found to be the most accurate fall identification strategy, followed by a combination of a restrictive ICD code-based definition with CC.
AHRQ-funded; HS024558.
Citation: Patterson BW, Jacobsohn GC, Maru AP .
Comparing strategies for identifying falls in older adult emergency department visits using EHR data.
J Am Geriatr Soc 2020 Dec;68(12):2965-67. doi: 10.1111/jgs.16831..
Keywords: Elderly, Falls, Emergency Department, Electronic Health Records (EHRs), Health Information Technology (HIT)
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
Topaz M, Woo K, Ryvicker M
Home healthcare clinical notes predict patient hospitalization and emergency department visits.
About 30% of home healthcare patients are hospitalized or visit an emergency department (ED) during a home healthcare (HHC) episode. Novel data science methods are increasingly used to improve identification of patients at risk for negative outcomes. The aim of the study was to identify patients at heightened risk hospitalization or ED visits using HHC narrative data (clinical notes).
AHRQ-funded; HS027742.
Citation: Topaz M, Woo K, Ryvicker M .
Home healthcare clinical notes predict patient hospitalization and emergency department visits.
Nurs Res 2020 Nov/Dec;69(6):448-54. doi: 10.1097/nnr.0000000000000470..
Keywords: Elderly, Home Healthcare, Emergency Department, Hospitalization, Risk, Electronic Health Records (EHRs), Health Information Technology (HIT)
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.
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)
Scott HF, Colborn KL, Sevick CJ
Development and validation of a predictive model of the risk of pediatric septic shock using data known at the time of hospital arrival.
The purpose of this observational cohort study was to derive and validate a model of risk of septic shock among children with suspected sepsis, using data known in the electronic health record at hospital arrival. The investigators concluded that their model estimated the risk of septic shock in children at hospital arrival earlier than existing models. They indicate it leveraged the predictive value of routine electronic health record data through a modern predictive algorithm and suggest it has the potential to enhance clinical risk stratification in the critical moments before deterioration.
AHRQ-funded; HS025696.
Citation: Scott HF, Colborn KL, Sevick CJ .
Development and validation of a predictive model of the risk of pediatric septic shock using data known at the time of hospital arrival.
J Pediatr 2020 Feb;217:145-51.e6. doi: 10.1016/j.jpeds.2019.09.079..
Keywords: Children/Adolescents, Sepsis, Emergency Department, Hospitals, Risk, Electronic Health Records (EHRs), Health Information Technology (HIT)