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Research Studies is a compilation of published research articles funded by AHRQ or authored by AHRQ researchers.
Results
1 to 4 of 4 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)
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