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AHRQ Research Studies Date
Topics
- Cardiovascular Conditions (1)
- Children/Adolescents (1)
- Data (1)
- Elderly (1)
- (-) Electronic Health Records (EHRs) (9)
- Emergency Department (2)
- Health Information Technology (HIT) (8)
- Heart Disease and Health (1)
- Home Healthcare (1)
- Hospitalization (1)
- Hospital Readmissions (2)
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- Injuries and Wounds (2)
- Mortality (2)
- Osteoporosis (2)
- (-) Risk (9)
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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 9 of 9 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)
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)
Wolfson J, Bandyopadhyay S, Elidrisi M
A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.
This paper proposed an adaptation of the well-known Naive Bayes machine learning approach to time-to-event outcomes subject to censoring. It compared the predictive performance of that method with the Cox proportional hazards model which is commonly used for risk prediction in healthcare populations, and illustrated its application to prediction of cardiovascular risk using an electronic health record dataset from a large Midwest integrated healthcare system.
AHRQ-funded; HS017622.
Citation: Wolfson J, Bandyopadhyay S, Elidrisi M .
A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.
Stat Med 2015 Sep 20;34(21):2941-57. doi: 10.1002/sim.6526..
Keywords: Risk, Electronic Health Records (EHRs), Health Information Technology (HIT), Cardiovascular Conditions
Amarasingham R, Velasco F, Xie B
Electronic medical record-based multicondition models to predict the risk of 30 day readmission or death among adult medicine patients: validation and comparison to existing models.
The purpose of this study was to evaluate the degree to which electronic medical record-based risk models for 30-day readmission or mortality accurately identify high risk patients and to compare these models with published claims-based models. The researchers found that a new electronic multicondition model based on information derived from the electronic medical record predicted mortality and readmission at 30 days, and was superior to previously published claims-based models
AHRQ-funded; HS022418.
Citation: Amarasingham R, Velasco F, Xie B .
Electronic medical record-based multicondition models to predict the risk of 30 day readmission or death among adult medicine patients: validation and comparison to existing models.
BMC Med Inform Decis Mak 2015 May 20;15:39. doi: 10.1186/s12911-015-0162-6.
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Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Mortality, Hospital Readmissions, Risk
LaFleur J, Steenhoek CL, Horne J
Comparing fracture absolute risk assessment (FARA) tools: an osteoporosis clinical informatics tool to improve identification and care of men at high risk of first fracture.
The researchers compared 2 fracture absolute risk assessment (FARA) tools for use with electronic health records (EHRs) to determine which would more accurately identify patients known to be high risk for fracture. They found that absolute fracture risk estimation with the VA-FARA is more predictive of a first fracture than the WHO’s eFRAX in male veterans when used in an EHR-based population screening tool.
AHRQ-funded; HS018582.
Citation: LaFleur J, Steenhoek CL, Horne J .
Comparing fracture absolute risk assessment (FARA) tools: an osteoporosis clinical informatics tool to improve identification and care of men at high risk of first fracture.
Ann Pharmacother 2015 May;49(5):506-14. doi: 10.1177/1060028015572819..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Injuries and Wounds, Osteoporosis, Risk
Unni S, Yao Y, Milne N
An evaluation of clinical risk factors for estimating fracture risk in postmenopausal osteoporosis using an electronic medical record database.
The researchers sought to identify variables in an EMR database for calculating fracture risk Assessment (FRAX) score in a cohort of postmenopausal women, to estimate absolute fracture risk. They found that mean 10-year risk for any major fracture was 11.1 percent when bone mineral density (BMD) was used and 11.2 percent when BMI was used.
AHRQ-funded; HS0018582.
Citation: Unni S, Yao Y, Milne N .
An evaluation of clinical risk factors for estimating fracture risk in postmenopausal osteoporosis using an electronic medical record database.
Osteoporos Int 2015 Feb;26(2):581-7. doi: 10.1007/s00198-014-2899-7..
Keywords: Electronic Health Records (EHRs), Injuries and Wounds, Risk, Osteoporosis, Health Information Technology (HIT)
Faerber AE, Horvath R, Stillman C
Development and pilot feasibility study of a health information technology tool to calculate mortality risk for patients with asymptomatic carotid stenosis: the Carotid Risk Assessment Tool (CARAT).
The researchers describe the development of the CArotid Risk Assessment Tool (CARAT) into a 2-year mortality risk calculator within the electronic medical record. They integrated the tool into the clinical workflow, trained the clinical team to use the tool, and assessed the feasibility and acceptability of the tool in one clinic setting.
AHRQ-funded; HS021581.
Citation: Faerber AE, Horvath R, Stillman C .
Development and pilot feasibility study of a health information technology tool to calculate mortality risk for patients with asymptomatic carotid stenosis: the Carotid Risk Assessment Tool (CARAT).
BMC Med Inform Decis Mak 2015;15:20. doi: 10.1186/s12911-015-0141-y..
Keywords: Health Information Technology (HIT), Electronic Health Records (EHRs), Mortality, Risk
Panahiazar M, Taslimitehrani V, Pereira N
Using EHRs and machine learning for heart failure survival analysis.
This study assessed the performance of the Seattle Heart Failure Model using EHRs at Mayo Clinic, and sought to develop a risk prediction model using machine learning techniques that applied routine clinical care data. Its results showed the models which were built using EHR data are more accurate (11 percent improvement in AUC) with the convenience of being more readily applicable in routine clinical care.
AHRQ-funded; HS023077.
Citation: Panahiazar M, Taslimitehrani V, Pereira N .
Using EHRs and machine learning for heart failure survival analysis.
Stud Health Technol Inform 2015;216:40-4..
Keywords: Electronic Health Records (EHRs), Heart Disease and Health, Risk, Data