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Data Science
A Phased-Implementation Feasibility and Proof-of-Concept Study to Assess Incorporating the NIDA CTN Common Data Elements into the Electronic Health Record in Large Primary Care Settings
This is a phased feasibility and proof-of-concept study seeking to incorporate addiction-specific screening and assessment CDEs into a widely used EHR, explore the logistics and time required to do this, and assess impacts on the frequency of identification, diagnosis and referral to treatment in large healthcare organizations.
0e472114eda111beae59fb39e136cd55 NCT02963948
COVID-19 and Substance Misuse Case Identification using Data Science: A Retrospective Cohort Study
This project will provide novel and critically important tools in artificial intelligence for the detection of substance misuse and COVID-19 from the electronic health record (EHR). Development and validation of a digital classifier would enable a standardized approach to perform screening on all patient encounters on a daily basis in health systems. We will rigorously develop and test the classifier retrospectively on an existing dataset of 60,000 patients who have been screened for COVID-19.
Maternal and Pediatric Precision in Therapeutics (MPRINT) Hub
Overview
The MPRINT Hub serves as a national resource to aggregate, present, and expand the available knowledge, tools, and expertise in maternal and pediatric therapeutics to the broader research, regulatory science, and drug development communities. It also conducts therapeutics-focused research in obstetrics, lactation, and pediatrics while enhancing inclusion of people with disabilities.
The MPRINT Hub comprises: