LibGuides: Big Data Sources: Health

Opioid Data

Drug Enforcement Administration’s Automation of Reports and Consolidated Orders System (ARCOS)
Drug Enforcement Administration’s Automation of Reports and Consolidated Orders System, (ARCOS) data mapped by the Washington Post and Charleston Gazette-Mail
WA DOH Opioid Prescription Dashboard
Maps patients with new and chronic opioid prescriptions
WA DOH Opioid Overdose Dashboard

DataMed | BioCADDIE
Data discovery tool from NIH searches across 75 repositories

National Vital Statistics System
Centers for Disease Control National Center for Health Statistics
County Health Rankings
University of Wisconsin Public Health Foundation and Robert Wood Johnson Foundation
CDC Wonder
Wide-ranging ONline Data for Epidemiologic Research. Disease, injury, mortality and public health data
CDC Web-based Injury Statistics Query and Reporting System (WISQARS)
Fatal, non-fatal and violent death injury statistics
HealthData.gov
Dept. Health and Human Service
World Health Organization Health Data and Statistics
NIH Data Sharing Repositories
Dartmouth Atlas Project
Understanding geographic variations in healthcare costs
NYU Data Catalog
Discovery tool makes researchers aware of datasets

Health Quality

Data Standards in Drug Life Cycle
National Library of Medicine Health Data Standards
Includes UMLS, SNOMED CT, RxNORM, LOINC
Observational Health Data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) Common Data Model
OHDSI is developing Open Source tools for observational study data quality and characterization, medical product safety surveillance, comparative effectiveness, quality of care, and patient-level predictive modeling.

Neuroscience Data in the Cloud: opportunities and challenges : proceedings of a workshop by National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Sciences Policy; Forum on Neuroscience and Nervous System Disorders; Clare Stroud (Editor); Amanda Wagner Gee (Editor); Lisa Bain (Editor) The cloud model of data sharing has led to a vast increase in the quantity and complexity of data and expanded access to these data, which has attracted many more researchers, enabled multi-national neuroscience collaborations, and facilitated the development of many new tools. Yet, the cloud model has also produced new challenges related to data storage, organization, and protection. Merely switching the technical infrastructure from local repositories to cloud repositories is not enough to optimize data use. To explore the burgeoning use of cloud computing in neuroscience, the National Academies Forum on Neuroscience and Nervous System Disorders hosted a workshop on September 24, 2019. A broad range of stakeholders involved in cloud-based neuroscience initiatives and research explored the use of cloud technology to advance neuroscience research and shared approaches to address current barriers. This publication summarizes the presentation and discussion of the workshop.
ISBN: 0309670551
Publication Date: 2020-06-06