The Stanford Medicine data science ecosystem for clinical and translational research
A Callahan, E Ashley, S Datta, P Desai, TA Ferris… - JAMIA …, 2023 - academic.oup.com
JAMIA open, 2023•academic.oup.com
Objective To describe the infrastructure, tools, and services developed at Stanford Medicine
to maintain its data science ecosystem and research patient data repository for clinical and
translational research. Materials and Methods The data science ecosystem, dubbed the
Stanford Data Science Resources (SDSR), includes infrastructure and tools to create,
search, retrieve, and analyze patient data, as well as services for data deidentification,
linkage, and processing to extract high-value information from healthcare IT systems. Data …
to maintain its data science ecosystem and research patient data repository for clinical and
translational research. Materials and Methods The data science ecosystem, dubbed the
Stanford Data Science Resources (SDSR), includes infrastructure and tools to create,
search, retrieve, and analyze patient data, as well as services for data deidentification,
linkage, and processing to extract high-value information from healthcare IT systems. Data …
Objective
To describe the infrastructure, tools, and services developed at Stanford Medicine to maintain its data science ecosystem and research patient data repository for clinical and translational research.
Materials and Methods
The data science ecosystem, dubbed the Stanford Data Science Resources (SDSR), includes infrastructure and tools to create, search, retrieve, and analyze patient data, as well as services for data deidentification, linkage, and processing to extract high-value information from healthcare IT systems. Data are made available via self-service and concierge access, on HIPAA compliant secure computing infrastructure supported by in-depth user training.
Results
The Stanford Medicine Research Data Repository (STARR) functions as the SDSR data integration point, and includes electronic medical records, clinical images, text, bedside monitoring data and HL7 messages. SDSR tools include tools for electronic phenotyping, cohort building, and a search engine for patient timelines. The SDSR supports patient data collection, reproducible research, and teaching using healthcare data, and facilitates industry collaborations and large-scale observational studies.
Discussion
Research patient data repositories and their underlying data science infrastructure are essential to realizing a learning health system and advancing the mission of academic medical centers. Challenges to maintaining the SDSR include ensuring sufficient financial support while providing researchers and clinicians with maximal access to data and digital infrastructure, balancing tool development with user training, and supporting the diverse needs of users.
Conclusion
Our experience maintaining the SDSR offers a case study for academic medical centers developing data science and research informatics infrastructure.
Oxford University Press以上显示的是最相近的搜索结果。 查看全部搜索结果