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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.
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1 to 2 of 2 Research Studies DisplayedFritz BA, Cui Z, Zhang M
Deep-learning model for predicting 30-day postoperative mortality.
The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with intraoperative physiological perturbations. In this study the investigators sought to compare similar benchmarks to a deep-learning algorithm predicting postoperative 30-day mortality. They concluded that a deep-learning time-series model improved prediction compared with models with simple summaries of intraoperative data.
AHRQ-funded; HS024581.
Citation: Fritz BA, Cui Z, Zhang M .
Deep-learning model for predicting 30-day postoperative mortality.
Br J Anaesth 2019 Nov;123(5):688-95. doi: 10.1016/j.bja.2019.07.025..
Keywords: Adverse Events, Health Information Technology (HIT), Mortality, Risk, Surgery
Hannan EL, Barrett SC, Samadashvili Z
Retooling of paper-based outcome measures to electronic format: comparison of the NY State public risk model and EHR-derived risk models for CABG mortality.
This study assessed the feasibility of retooling the paper-based New York State coronary artery bypass graft (CABG) surgery statistical model for mortality and readmission into a model for electronic health records (EHRs). Researchers found that only 6 data elements could be extracted from the EHR, and outlier hospitals differed for readmission but was usable for mortality. They concluded that the EHR model was inferior to the NYS model, and that simplifying the EHR risk model couldn’t capture most of the risk factors in the NYS model.
AHRQ-funded; HS022647.
Citation: Hannan EL, Barrett SC, Samadashvili Z .
Retooling of paper-based outcome measures to electronic format: comparison of the NY State public risk model and EHR-derived risk models for CABG mortality.
Med Care 2019 May;57(5):377-84. doi: 10.1097/mlr.0000000000001104..
Keywords: Surgery, Electronic Health Records (EHRs), Health Information Technology (HIT), Mortality, Outcomes, Risk, Cardiovascular Conditions