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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.
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1 to 4 of 4 Research Studies DisplayedMisra-Hebert AD, Milinovich A, Zajichek A
Natural language processing improves detection of nonsevere hypoglycemia in medical records versus coding alone in patients with type 2 diabetes but does not improve prediction of severe hypoglycemia events: an analysis using the electronic medical record
The purpose of this study was to determine if natural language processing (NLP) improves detection of non-severe hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes and to measure if NLP detection improves the prediction of future severe hypoglycemia (SH). The authors identified NSH events by diagnosis codes and NLP 2005 to 2017 and built an SH prediction model. Their findings showed that detection of NSH improved with NLP in patients with type 2 diabetes without improving SH prediction.
AHRQ-funded; HS024128.
Citation: Misra-Hebert AD, Milinovich A, Zajichek A .
Natural language processing improves detection of nonsevere hypoglycemia in medical records versus coding alone in patients with type 2 diabetes but does not improve prediction of severe hypoglycemia events: an analysis using the electronic medical record
Diabetes Care 2020 Aug;43(8):1937-40. doi: 10.2337/dc19-1791..
Keywords: Diabetes, Electronic Health Records (EHRs), Health Information Technology (HIT), Diagnostic Safety and Quality
Bowen ME, Merchant Z, Abdullah K
Patient, provider, and system factors associated with failure to follow-up elevated glucose results in patients without diagnosed diabetes.
Patient, provider, and system factors associated with failure to follow-up elevated glucose values in electronic medical records (EMRs) are not well described. The researchers conducted a chart review in a comprehensive EMR with a patient portal and results management features but found no associations between patient characteristics, diabetes risk factors, or provider characteristics and follow-up failures.
AHRQ-funded; HS022418.
Citation: Bowen ME, Merchant Z, Abdullah K .
Patient, provider, and system factors associated with failure to follow-up elevated glucose results in patients without diagnosed diabetes.
Health Serv Res Manag Epidemiol 2017 Aug 29;4:2333392817721647. doi: 10.1177/2333392817721647.
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Keywords: Diabetes, Electronic Health Records (EHRs), Web-Based, Patient-Centered Healthcare, Diagnostic Safety and Quality
Flory JH, Roy J, Gagne JJ
Missing laboratory results data in electronic health databases: implications for monitoring diabetes risk.
Researchers assessed the value of lab results added to diagnosis codes and dispensing claims to identify incident diabetes. Inclusion of lab results increased the number of diabetes outcomes identified by 21 percent. In settings where capture of lab results was relatively complete, the absence of lab results was associated with implausibly low rates of the outcome.
AHRQ-funded; HS023898.
Citation: Flory JH, Roy J, Gagne JJ .
Missing laboratory results data in electronic health databases: implications for monitoring diabetes risk.
J Comp Eff Res 2017 Jan;6(1):25-32. doi: 10.2217/cer-2016-0033.
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Keywords: Diabetes, Diagnostic Safety and Quality, Electronic Health Records (EHRs)
Lawrence JM, Black MH, Zhang JL
Validation of pediatric diabetes case identification approaches for diagnosed cases by using information in the electronic health records of a large integrated managed health care organization.
The researchers explored the utility of different algorithms for diabetes case identification by using electronic health records. They found that case identification accuracy was highest in 75% of bootstrapped samples for those who had 1 or more outpatient diabetes diagnoses or 1 or more insulin prescriptions and in 25% of samples for those who had 2 or more outpatient diabetes diagnoses and 1 or more antidiabetic medications.
AHRQ-funded; HS019859.
Citation: Lawrence JM, Black MH, Zhang JL .
Validation of pediatric diabetes case identification approaches for diagnosed cases by using information in the electronic health records of a large integrated managed health care organization.
Am J Epidemiol 2014 Jan;179(1):27-38. doi: 10.1093/aje/kwt230..
Keywords: Children/Adolescents, Diabetes, Chronic Conditions, Electronic Health Records (EHRs), Health Information Technology (HIT), Diagnostic Safety and Quality