The Stanford Medicine data science ecosystem for clinical and translational research.

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.

Ten principles for data sharing and commercialization.

Digital medical records have enabled us to employ clinical data in many new and innovative ways. However, these advances have brought with them a complex set of demands for healthcare institutions regarding data sharing with topics such as data ownership, the loss of privacy, and the protection of the intellectual property. The lack of clear guidance from government entities often creates conflicting messages about data policy, leaving institutions to develop guidelines themselves. Through discussions with multiple stakeholders at various institutions, we have generated a set of guidelines with 10 key principles to guide the responsible and appropriate use and sharing of clinical data for the purposes of care and discovery. Industry, universities, and healthcare institutions can build upon these guidelines toward creating a responsible, ethical, and practical response to data sharing.

Direct2Experts: a pilot national network to demonstrate interoperability among research-networking platforms.

Research-networking tools use data-mining and social networking to enable expertise discovery, matchmaking and collaboration, which are important facets of team science and translational research. Several commercial and academic platforms have been built, and many institutions have deployed these products to help their investigators find local collaborators. Recent studies, though, have shown the growing importance of multiuniversity teams in science. Unfortunately, the lack of a standard data-exchange model and resistance of universities to share information about their faculty have presented barriers to forming an institutionally supported national network. This case report describes an initiative, which, in only 6 months, achieved interoperability among seven major research-networking products at 28 universities by taking an approach that focused on addressing institutional concerns and encouraging their participation. With this necessary groundwork in place, the second phase of this effort can begin, which will expand the network's functionality and focus on the end users.