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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 3 of 3 Research Studies DisplayedNguyen OK, Makam AN, Clark C
Predicting all-cause readmissions using electronic health record data from the entire hospitalization: model development and comparison.
The purpose of this study was to develop an all-cause readmissions risk-prediction model incorporating electronic health record (EHR) data from the full hospital stay, and to compare "full-stay" model performance to a "first day" and 2 other validated models. It found that incorporating clinically granular EHR data from the full hospital stay modestly improves prediction of 30-day readmissions.
AHRQ-funded; HS022418.
Citation: Nguyen OK, Makam AN, Clark C .
Predicting all-cause readmissions using electronic health record data from the entire hospitalization: model development and comparison.
J Hosp Med 2016 Jul;11(7):473-80. doi: 10.1002/jhm.2568.
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Keywords: Electronic Health Records (EHRs), Hospital Readmissions, Hospitalization, Risk
Taslimitehrani V, Dong G, Pereira NL
Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.
The authors proposed to apply a classification algorithm, Contrast Pattern Aided Logistic Regression (CPXR(Log)) with the probabilistic loss function, to develop and validate prognostic risk models to predict 1, 2, and 5 year survival in heart failure (HF). They found that the new loss function used in the algorithm outperforms other functions used in previous studies and that HF is a highly heterogeneous disease (different subgroups of patients require different types of considerations with their diagnosis and treatment). They concluded that logistic risk models often make systematic prediction errors and that it is prudent to use subgroup based prediction models such as those given by CPXR(Log) when investigating heterogeneous diseases.
AHRQ-funded; HS023077.
Citation: Taslimitehrani V, Dong G, Pereira NL .
Developing EHR-driven heart failure risk prediction models using CPXR(Log) with the probabilistic loss function.
J Biomed Inform 2016 Apr;60:260-9. doi: 10.1016/j.jbi.2016.01.009.
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Keywords: Electronic Health Records (EHRs), Heart Disease and Health, Health Information Technology (HIT), Risk
Marier A, Olsho LE, Rhodes W
AHRQ Author: Spector WD
Improving prediction of fall risk among nursing home residents using electronic medical records.
To identify individuals at highest risk for falls, the authors applied a repeated events survival model to analyze The Minimum Data Set ( MDS 3.0 and EMR data for 5129 residents in 13 nursing homes within a single large California chain. They found that incorporating EMR data improves ability to identify those at highest risk for falls relative to prediction using MDS data alone.
AHRQ-funded; AHRQ-authored; 290201000031I.
Citation: Marier A, Olsho LE, Rhodes W .
Improving prediction of fall risk among nursing home residents using electronic medical records.
J Am Med Inform Assoc 2016 Mar;23(2):276-82. doi: 10.1093/jamia/ocv061..
Keywords: Falls, Electronic Health Records (EHRs), Risk, Nursing Homes, Prevention