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
Topics
- Asthma (1)
- (-) Electronic Health Records (EHRs) (8)
- Emergency Department (1)
- Health Information Exchange (HIE) (1)
- (-) Health Information Technology (HIT) (8)
- Health Systems (1)
- Hospitalization (1)
- (-) Hospital Readmissions (8)
- Hospitals (3)
- Mortality (1)
- Pneumonia (1)
- Quality Indicators (QIs) (1)
- Quality of Care (1)
- Respiratory Conditions (1)
- Risk (2)
- Transitions of Care (1)
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 DisplayedLopez K, Li H, Lipkin-Moore Z
Deep learning prediction of hospital readmissions for asthma and COPD.
The purpose of this observational study was to identify Electronic Health Record (EHR) features of severe asthma and COPD exacerbations and assess the performance of four machine learning (ML) and one deep learning (DL) model in predicting readmissions using EHR data. The study included 31, 2017 patients hospitalized with asthma and COPD exacerbations. The study found that Black and Hispanic patients had a greater likelihood of readmission for asthma. Patients with COPD readmissions included a high percentage of Blacks and Hispanics. To identify patients at high risk of readmission, index hospitalization data of a subset of 2,682 patients, 777 with asthma and 1,905 with COPD, was analyzed with four ML models, and one DL model. The researchers discovered that multilayer perceptron, the DL method, had the best sensitivity and specificity compared to the four ML methods implemented in the same dataset.
AHRQ-funded; HS027626.
Citation: Lopez K, Li H, Lipkin-Moore Z .
Deep learning prediction of hospital readmissions for asthma and COPD.
Respir Res 2023 Dec 13; 24(1):311. doi: 10.1186/s12931-023-02628-7..
Keywords: Asthma, Respiratory Conditions, Hospital Readmissions, Electronic Health Records (EHRs), Health Information Technology (HIT)
Nguyen OK, Washington C, Clark CR
Man vs. machine: comparing physician vs. electronic health record-based model predictions for 30-day hospital readmissions.
Electronic health record (EHR)-based readmission risk prediction models can be automated in real-time but have modest discrimination and may be missing important readmission risk factors. Clinician predictions of readmissions may incorporate information unavailable in the EHR, but the comparative usefulness is unknown. In this study, the investigators sought to compare clinicians versus a validated EHR-based prediction model in predicting 30-day hospital readmissions.
AHRQ-funded; HS022418.
Citation: Nguyen OK, Washington C, Clark CR .
Man vs. machine: comparing physician vs. electronic health record-based model predictions for 30-day hospital readmissions.
J Gen Intern Med 2021 Sep;36(9):2555-62. doi: 10.1007/s11606-020-06355-3..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Hospital Readmissions
Elysee G, Yu H, Herrin J
Association between 30-day readmission rates and health information technology capabilities in US hospitals.
A study was conducted to determine if there is an association of health information technology (HIT) adoption and a decrease in 30-day hospital readmission rates. Data was used from the 2013 American Hospital Association IT survey which included non-federal U.S. acute care hospitals with self-reported capabilities. A 54-indicator 7-factor structure of hospital health IT capabilities was identified by exploratory factor analysis. A one-point increase in the hospital adoption of patient engagement capability latent scores generally leads to a 0.086% decrease in risk-standardized readmission rates (RSRRs). However, computerized hospital discharge and information exchange among clinicians did not seem as beneficial.
AHRQ-funded; HS022882.
Citation: Elysee G, Yu H, Herrin J .
Association between 30-day readmission rates and health information technology capabilities in US hospitals.
Medicine 2021 Feb 26;100(8):e24755. doi: 10.1097/md.0000000000024755..
Keywords: Electronic Health Records (EHRs), Health Information Technology (HIT), Hospital Readmissions, Hospitals, Quality Indicators (QIs), Quality of Care
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)
Vest JR, Unruh MA, Freedman S
Health systems' use of enterprise health information exchange vs single electronic health record vendor environments and unplanned readmissions.
Enterprise health information exchange (HIE) and a single electronic health record (EHR) vendor solution are 2 information exchange approaches to improve performance and increase the quality of care. This study sought to determine the association between adoption of enterprise HIE vs a single vendor environment and changes in unplanned readmissions. The investigators concluded that reductions in the probability of an unplanned readmission after a hospital adopts a single vendor environment suggested that HIE technologies can better support the aim of higher quality care.
AHRQ-funded; HS024717.
Citation: Vest JR, Unruh MA, Freedman S .
Health systems' use of enterprise health information exchange vs single electronic health record vendor environments and unplanned readmissions.
J Am Med Inform Assoc 2019 Oct;26(10):989-98. doi: 10.1093/jamia/ocz116..
Keywords: Health Systems, Health Information Exchange (HIE), Electronic Health Records (EHRs), Health Information Technology (HIT), Hospital Readmissions, Hospitals
Kimmel HJ, Brice YN, Trikalinos TA
Real-time emergency department electronic notifications regarding high-risk patients: a systematic review.
In this study, the authors systematically reviewed evidence on the feasibility and efficacy of real-time electronic notifications about patients at high risk of emergency department (ED) recidivism. They concluded that real-time electronic notifications of ED providers regarding patients at high risk of ED recidivism are feasible and may help reduce resource utilization and costs. The authors indicate that large knowledge gaps remain regarding patient- and provider-centered outcomes.
AHRQ-funded; HS022998.
Citation: Kimmel HJ, Brice YN, Trikalinos TA .
Real-time emergency department electronic notifications regarding high-risk patients: a systematic review.
Telemed J E Health 2019 Jul;25(7):604-18. doi: 10.1089/tmj.2018.0117..
Keywords: Emergency Department, Electronic Health Records (EHRs), Health Information Technology (HIT), Hospital Readmissions
Makam AN, Nguyen OK, Clark C
Predicting 30-day pneumonia readmissions using electronic health record data.
The objective of this study was to develop pneumonia-specific readmission risk-prediction models using EHR data from the first day and from the entire hospital stay ("full stay"). The investigators concluded that EHR data collected from the entire hospitalization can accurately predict readmission risk among patients hospitalized for pneumonia. They suggest that this approach outperforms a first-day pneumonia-specific model, the Centers for Medicare and Medicaid Services pneumonia model, and 2 commonly used pneumonia severity of illness scores.
AHRQ-funded; HS022418.
Citation: Makam AN, Nguyen OK, Clark C .
Predicting 30-day pneumonia readmissions using electronic health record data.
J Hosp Med 2017 Apr;12(4):209-16. doi: 10.12788/jhm.2711..
Keywords: Pneumonia, Hospital Readmissions, Hospitalization, Electronic Health Records (EHRs), Health Information Technology (HIT)
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