National Healthcare Quality and Disparities Report
Latest available findings on quality of and access to health care
Data
- Data Infographics
- Data Visualizations
- Data Tools
- Data Innovations
- All-Payer Claims Database
- Healthcare Cost and Utilization Project (HCUP)
- Medical Expenditure Panel Survey (MEPS)
- AHRQ Quality Indicator Tools for Data Analytics
- State Snapshots
- United States Health Information Knowledgebase (USHIK)
- Data Sources Available from AHRQ
Search All Research Studies
AHRQ Research Studies Date
Topics
AHRQ Research Studies
Sign up: AHRQ Research Studies Email updates
Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 8 of 8 Research Studies DisplayedHobensack M, Ojo M, Barrón Y
Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians.
The objectives of this study were to identify risk factors that home healthcare clinicians associate with patient deterioration and to understand clinicians’ response to and documentation of these risk factors. The authors interviewed multidisciplinary home healthcare clinicians and used directed content analysis to identify risk factors for deterioration. A total of 79 risk factors were identified by the clinicians, who responded most often by communicating with the prescribing provider or following up with patients and caregivers. Clinicians also acknowledged that social factors played a role in deterioration risk. The authors noted that, since most risk factors were documented in clinical notes, methods such as natural language processing are needed to extract them. They concluded that by providing a comprehensive list of risk factors grounded in clinician expertise and mapped to standardized terminologies, the results of their study supported the development of an early warning system for patient deterioration.
AHRQ-funded; HS027742.
Citation: Hobensack M, Ojo M, Barrón Y .
Documentation of hospitalization risk factors in electronic health records (EHRs): a qualitative study with home healthcare clinicians.
J Am Med Inform Assoc 2022 Apr 13;29(5):805-12. doi: 10.1093/jamia/ocac023..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Home Healthcare, Risk, Hospitalization
Kamran F, Tang S, Otles E
Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study.
The authors sought to create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with COVID-19 across institutions, through use of a novel paradigm for model development and code sharing. They determined that a model to predict clinical deterioration was developed rapidly in response to the COVID-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.
AHRQ-funded; HS028038.
Citation: Kamran F, Tang S, Otles E .
Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study.
BMJ 2022 Feb 17;376:e068576. doi: 10.1136/bmj-2021-068576..
Keywords: COVID-19, Hospitalization, 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.
.
.
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