Erich S. Huang, MD, PhD
Co-Director, Duke Forge
Departments of Biostatistics & Bioinformatics and Surgery
Duke University School of Medicine
In Dreams Begin Responsibilities: Data Science as a Service—Using AI to Risk Stratify a Medicare Population and Build a Culture
Data science; Data liquidity; Data standards; Machine learning; Duke Forge; Application programming interface; Artificial intelligence
- Duke Forge focuses on bringing the best methodological approaches to actionable data problems in health. It is motivated by a framework of value-based healthcare to address societal inequities in health.
- Essential components to building a data science culture include clinical subject matter expertise, quantitative and methodological expertise, and software architecture and engineering expertise, along with interoperable tools and applications.
- Like freight shipping containers, health-relevant data needs standardized containers that make any type of data easy to pack, grab, combine, and move around. The aim should be to build a “data liquidity ecosystem” equivalent to freighters, cranes, trains, and trucks that facilitate the logistics of health data transport.
If we’re trying to build an ecosystem, then the electronic health record (EHR) platform needs to be evaluated by whether it is truly participatory in this ecosystem. If not, then its deficiencies must be remediated.
The faster we can move to the cloud and use building blocks that “snap” together, the faster we can get answers. We want to be building applications instead of infrastructure.
Algorithms don’t have ethics; some have hidden biases. Algorithms need to be scrutinized and tested for such biases. They also must be secured so they cannot be manipulated.
#pctGR, @Collaboratory1, @DukeForge