data science governance in healthcare

Data Science Governance Series

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 EvaluationYale University

July 2013

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

Multi core - Multi project Data Science

Team Structure


Overview

• As in every 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.


Some presumptions

• Data Science is a team work activity due to its interdisciplinary nature

• The team structure is reversed. Team structure forms on top of the project lead.


Team structure

• Two layer 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.


Research Center

Reference Groups

Data

Healthcare

Modeling

Writing & publishing

Disseminating & community building

Administration &Project management

Data science practitioners

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2

34

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Modeling

Healthcare

Data

Writing

Administration

Dissemination

Team structure

Abbas Shojaee MD – Data Science Governance in Healthcare -part 1 - June 2013

Team structure

• Data scientist (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


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.


Reference Groups

Reference groups

• Center for 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.


Clinical team: Biomedical subject 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


Modeling & Algorithm subject 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


Data subject matter team• 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


Documentary & Dissemination team

• 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


Project management and administrative 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


My other related presentations

• 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


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