National Healthcare Quality and Disparities Report
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AHRQ Research Studies Date
Topics
- Cancer (1)
- Cancer: Breast Cancer (1)
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- Children/Adolescents (1)
- Diabetes (1)
- Elderly (1)
- Electronic Health Records (EHRs) (3)
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- (-) Health Information Technology (HIT) (6)
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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 6 of 6 Research Studies DisplayedTopaz 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)
Saleh SN, Makam AN, Halm EA,
Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?
Despite focus on preventing 30-day readmissions, early readmissions (within 7 days of discharge) may be more preventable than later readmissions (8-30 days). In this study, the investigators assessed how well a previously validated 30-day EHR-based readmission model predicted 7-day readmissions and compared differences in strength of predictors. They suggested that improvements in predicting early 7-day readmissions will likely require new risk factors proximal to day of discharge.
AHRQ-funded; HS022418.
Citation: Saleh SN, Makam AN, Halm EA, .
Can we predict early 7-day readmissions using a standard 30-day hospital readmission risk prediction model?
BMC Med Inform Decis Mak 2020 Sep 15;20(1):227. doi: 10.1186/s12911-020-01248-1..
Keywords: Hospital Readmissions, Hospitals, Risk, Transitions of Care, Electronic Health Records (EHRs), Health Information Technology (HIT)
Tassone C, Keshavjee K, Paglialonga A
Evaluation of mobile apps for treatment of patients at risk of developing gestational diabetes.
This study evaluated mobile apps using a theory-based evaluation framework to discover their applicability for patients at risk of gestational diabetes. It assessed how well the existing mobile apps on the market met the information and tracking needs of patients with gestational diabetes and evaluated the feasibility of how to integrate these apps into patient care.
AHRQ-funded; HS021495; HS24869.
Citation: Tassone C, Keshavjee K, Paglialonga A .
Evaluation of mobile apps for treatment of patients at risk of developing gestational diabetes.
Health Informatics J 2020 Sep;26(3):1983-94. doi: 10.1177/1460458219896639..
Keywords: Diabetes, Risk, Health Information Technology (HIT), Women
Eden KB, Ivlev I, Bensching KL
Use of an online breast cancer risk assessment and patient decision aid in primary care practices.
A cross-sectional study evaluating a web-based breast cancer risk assessment and decision aid (MammoScreen) was conducted in an academic general internal medicine clinic. Breast cancer risk assessment and mammography screening decision support were efficiently implemented through a web-based tool for patients sent through an electronic patient portal. Findings indicated that integration of patient decision aids with risk algorithms in clinical practice may help support the implementation of USPSTF recommendations that include risk assessment and shared decision-making.
AHRQ-funded; HS026370.
Citation: Eden KB, Ivlev I, Bensching KL .
Use of an online breast cancer risk assessment and patient decision aid in primary care practices.
J Womens Health 2020 Jun;29(6):763-69. doi: 10.1089/jwh.2019.8143..
Keywords: U.S. Preventive Services Task Force (USPSTF), Cancer: Breast Cancer, Cancer, Screening, Shared Decision Making, Risk, Health Information Technology (HIT), Prevention, Women
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)
Angraal S, Mortazavi BJ, Gupta A
Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction.
This study developed models to predict the risk of death and hospitalization in patients with heart failure (HF) with preserved ejection fraction (HFpEF). Data was used from the TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist) clinical trial. Five methods: logistic regression with a forward selection of variables; logistic regression with a lasso regularization for variable selection; random forest (RF); gradient descent boosting; and support vector machine, were used to train models for assessing risks of mortality and HF hospitalization through 3 years of follow-up and were validated using 5-fold cross-validation. RF was found to be the best performing model for predicting mortality and HF hospitalization. Blood urea nitrogen levels, body mass index, and Kansas City Cardiomyopathy Questionnaire (KCCQ) subscale scores were strongly associated with mortality, while hemoglobin level, blood urea nitrogen, time since previous HF hospitalization, and KCCQ scores were the most significant predictors of HF hospitalization.
AHRQ-funded; HS023000.
Citation: Angraal S, Mortazavi BJ, Gupta A .
Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction.
JACC Heart Fail 2020 Jan;8(1):12-21. doi: 10.1016/j.jchf.2019.06.013..
Keywords: Heart Disease and Health, Cardiovascular Conditions, Mortality, Hospitalization, Risk, Health Status, Health Information Technology (HIT)