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Data Science Governance in Healthcare
This document proposes a team structure for a multi-project health data science center. It recommends a two-layer structure with data science practitioners leading individual projects and reference groups providing various types of expertise. The reference groups would include healthcare, modeling, data, documentation/dissemination, administration, and each would have a defined role to support project teams. This is intended to provide clear accountability while enabling flexibility and maximum engagement of resources across projects.
Data Science Governance in Healthcare
- 1. Data Science GovernanceSeries How to organize a center for multi core - multi project Health Data Science Abbas Shojaee MD Associate Research Scientist - Healthcare Data Science Certified Project Manager / Software Architect Center for Outcomes Research and Evaluation Yale University July 2013
- 2. A few notes •This presentation proposes the team structure for a successful multi project data science practice • Please find other topics for data science project planning, life cycle management, risk management, activity chart, team interaction guidelines , result dissemination and post implementation in my other presentations • This is not meant to be inclusive • This design is based on my experiences in managing analytics and scientific computation Research and Development efforts in industry and in academia • I combined concepts from research management methodology, software project management methodology, enterprise analysis to devise this structure Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013
- 3. Multi core -Multi project Data Science Team Structure Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013
- 4. Overview • As inevery other project, in data science research a team structure is necessary • This team structure can scale up and down and get adapted to small startups or to big industries. Adapting to different environments is not covered in this presentation. • Team structure sets up clear accountability and shared responsibility • A two layer, flat, flexible structure for maximizing engagement of available human resources in a matrix project structure. Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013
- 5. Some presumptions • DataScience is a team work activity due to its interdisciplinary nature • The team structure is reversed. Team structure forms on top of the project lead. Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013
- 6. Team structure • Twolayer structure • Data Science Project layer • Data scientist or data science practitioner as the seed of project plus 1 or 2 assistants or trainees. • Reference layer • Provides administrative support, domain expertise, scientific computation expertise and data definition expertise. Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013
- 7. Research Center Reference Groups Data Healthcare Modeling Writing& publishing Disseminating & community building Administration & Project management Data science practitioners 6 1 2 3 4 5 n Modeling Healthcare Data Writing Administration Dissemination Team structure Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013
- 8. Team structure • Datascientist (DS) will work as project seed. • Data scientist would define the interesting clinical question, the required dataset that may be used and would propose the computational approach. • Data scientist will discuss his/her approach with different reference groups and will attract people from each reference group that are interested to help proposed project. • Data scientist will propose up to 3 projects to Center for Data Science, in formal proposals. • Data scientist is in charge to define the project plan and to submit timely progress reports • Center for Data Science will assign a project manager, who will monitor the progress, ensures the proper level of team work, monitors progress and informs Center for Data Science of direct and indirect costs Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013
- 9. Healthcare Data Scientist •Understanding of healthcare system or biomedical sciences • Expert in computational techniques • Hands on experience with data extraction, transforming and loading technologies • Self motivated, work in autonomy, ability to define the combined question in healthcare data science. • Each Data scientist initiates 1-3 parallel projects. • Data scientists will have trainees/ assistances on their projects : • To make research projects sustainable by making human capital • To boost productivity of project seeds to use the most of their abilities. Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013
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- 11. Reference groups • Centerfor Data Science forms reference groups by inviting, informing and organizing experts from other internal/ external departments or experts from other universities or industry • Each of the reference groups will have a defined role, task list, regular meetings and would support the rest of team by providing consultation and scientific lectures. • Members of reference group will be members of different DS teams. Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013
- 12. Clinical team: Biomedicalsubject matter team • Mission: • Enables other teams to comprehend and use meaning of medial data • Answers questions about translating healthcare domain knowledge to specific concepts that have an identity in some vocabulary • Resolves/ bridges vocabulary, ontology conflicts/ gaps • Maintains the set (and map ) of reference ontologies in team • Takes required steps to ensure best practice of ontology usage among the team. • knows about or required team expertise: • medicine • public health • health economy • healthcare delivery • outcome research • health informatics Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013
- 13. Modeling & Algorithmsubject matter team • Mission: • Algorithm coherency among the team • Oversees potentials of synergism among the teams based on algorithm connections • Takes required steps to ensure algorithm reuse. • Brainstorms on new and ground breaking algorithm research on major problems • Identifies and introduces promising or flourishing algorithms • knows about: • Different algorithms and their application • Algorithm time-space complexity • Available libraries • May provide general advice for, OR contribute to: • choosing or developing proper algorithm for specific clinical problem • customize or enhance existing algorithm • will help others as Computation designers Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013
- 14. Data subject matterteam • Mission: • Maintains information about available datasets • Traces new open & close datasets • Suggests potentials for integration • Maintains reusable code for data conversion preparation and reshaping • Practice efficient techniques for document retrieval & information extraction • Looks for incorporation of unusual data sources. • knows about • Computer science • Databases & Data formats • Database management systems • Efficient storage and data conversion • Information extraction • Healthcare interoperability standards • Structure • Consists of Center for Data Science statisticians. • Will have a subgroup of data practitioners that is: • consisted of a dynamic pool of 2-3 undergraduate or graduate students who are working in data extraction, transformation and loading. • Mission: to facilitate data reshaping and curation for the entire Center for Data Science and DS teams Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013
- 15. Documentary & Disseminationteam • Mission: • To keep track of expertise and people • To build online presence and build up community around the work as a matter of creating human and social capital, in order to keep projects sustainable and live. • To keep documentation, keep backups and manage updates of developed opensource work in a reusable manner. • About: • Knows about team building • Knows about science social networking and community building • Knows about online presence • Knows about software documentary Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013
- 16. Project management andadministrative reference group • Mission of reference group: • Orchestrates all teams toward over all mission. • Ensures a coherent and concordant implementation. • Keeps project master plan • Keeps track of fund sources and opportunities. • Mission of each member: • Works in DS teams as Center for Data Science advocate • Monitor the progress, • Keeps Center for Data Science informed of project progress and direct & indirect project costs • Tries to identify teamwork or other problems earlier than later proactively. • Works with Center for Data Science as DS team advocate • Knows about: • Matrix project management • Software project management • Resource planning • Readiness management • ensures the proper level of team work, monitors progress and informs Center for Data Science of direct and indirect costs Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013
- 17. My other relatedpresentations • Data science project planning • Life cycle management • Risk management and reasons for low productivity data science • Activity chart • Team interaction guidelines • Result dissemination • Post implementation phase Abbas Shojaee MD – Data Science Governance in Healthcare - part 1 - June 2013