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
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 2 of 2 Research Studies DisplayedMcClellan C, Mitchell E, Anderson J
AHRQ Author: McClellan C, Mitchell E, Anderson J, Zuvekas S
Using machine-learning algorithms to improve imputation in the Medical Expenditure Panel Survey.
This AHRQ-authored study’s aim was to assess the feasibility of applying machine-learning (ML) methods to imputation in the Medical Expenditure Panel Survey (MEPS), using all data from the 2016-2017 survey. The authors examined five alternatives to linear regression: Gradient Boosting, Random Forests, Extreme Random Forests, Deep Neural Networks, and a Stacked Ensemble approach. Additionally, they introduced an alternative matching scheme which matches on a vector of predicted expenditures by sources of payment instead of a single total expenditure prediction to generate potentially superior matches. Their principal findings were that ML algorithms perform better at both prediction and matching imputation than Ordinary Least Squares (OLS), the most common prediction algorithm used in predictive mean matching (PMM). On average, the Stacked Ensemble approach that combines all the ML algorithms performs best, improving expenditure prediction R(2) by 108% and final imputation R(2) by 227%. There was also an improvement on alignment of sources of payments between donor and recipient events by matching on a prediction vector.
AHRQ-authored.
Citation: McClellan C, Mitchell E, Anderson J .
Using machine-learning algorithms to improve imputation in the Medical Expenditure Panel Survey.
Health Serv Res 2023 Apr;58(2):423-32. doi: 10.1111/1475-6773.14115.
Keywords: Medical Expenditure Panel Survey (MEPS), Health Information Technology (HIT)
Monestime JP, Biener AI, Wolford M
AHRQ Author: Wolford M
Characteristics of office-based providers associated with secure electronic messaging use: achieving meaningful use.
The purpose of this study was to identify characteristics of office-based provider used as a usual source of care (USC) associated with secure electronic messaging (SM) use. The investigators concluded that patients were more likely to have visited a USC that exchanged SMs if that practice also used other electronic health records functionalities. The authors indicated that findings suggested that while patients' USC practices were likely to exchange secure messages, there is a disparity in SM use between physician-owned practices, and hospital-owned practices.
AHRQ-authored.
Citation: Monestime JP, Biener AI, Wolford M .
Characteristics of office-based providers associated with secure electronic messaging use: achieving meaningful use.
Int J Med Inform 2019 Apr 4;129:43-48. doi: 10.1016/j.ijmedinf.2019.04.002..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Medical Expenditure Panel Survey (MEPS), Patient-Centered Healthcare, Provider