The confluence of Health Informatics and Data Science at ... · The confluence of Health Informatics and Data Science at the University of Melbourne Data, Methods, Governance, and

The confluence of Health Informatics and Data Science at the University of Melbourne

Data, Methods, Governance, and Use

Karin VerspoorThe University of MelbourneDeputy Director, ARC Training Centre in Cognitive Computing for Medical TechnologiesDeputy Director, Health and Biomedical Informatics [email protected] @karinv

(Degree courses)

• Grad Cert in Health Informatics and Digital Health

• Master of Information Systems (Health)

• Master of Applied Analytics (Health)

(Short course)

• Intro to Health Data Analytics for Clinicians (CME accredited)

Health Informatics at Uni Melb

Collaborations between Computing and Information Systems,Health and Biomedical Informatics Centre, School of Populationand Global Health, and Department of Mathematics & Statistics

The course is designed for hospital and primary care physicians, nurses, pharmacists,

allied health providers, and other health professionals with an interest in leveraging

data to address clinically-relevant questions. On completion of the course,

participants will have an understanding of the considerations involved in planning

and leading an analytic study and the tools available to support cutting edge analysis.

(Degree courses – Computing and Information Systems)

• Bachelor of Science, Computing and Software Systems Major

• Master of Information Technology

(Degree courses – Computing + Mathematics & Statistics)

• Bachelor of Science, Data Science Major

• Bachelor of Science, Computational Biosciences

• Master of Data Sciences

• Master of Bioinformatics

Computational and Data Sciences at Uni Melb

(M.Phil., Ph.D., Post-doctoral training)• (Health) Information Systems• (Health) Information Governance• Human/Computer Interaction• Computer Science• Artificial Intelligence• Machine Learning• Natural Language Processing• Bioinformatics / Genomics

Research Training

Aimed at creating a workforce that is expert in developing,applying and interrogating artificial intelligence (“AI”)applications in data-intensive medical contexts, tofacilitate the next generation of data-driven and machinelearning-based medical technologies.

https://aimedtech.org.au/

(Competencies for Health Informatics professionals)

• Health Knowledge Management

• Health Data Science

• Healthcare simulation

• Digital Health

(Competencies for Clinicians and Clinical Researchers)

• Planning a Study / Contextualizing Data / Ethics

• Mapping and Accessing Data

• Analytical approaches

two streams➤ confluence

Awareness of strategies for collection, organization, management,and analysis of health data. Emphasis on broad methodologies,resources, and tools.

(Health and Biomedical training for Technical and Informatics trainees)

• Opportunities to learn the domains of health, biomedicine, genomics

• Adaptation/application of methods to the biomedical context and data

(Training Centre for Cognitive Computing in Medical Technologies)

• Artificial Intelligence applications in Medicine

• Clinical Decision Making

• Advanced modeling

• Prognosis and Prediction

two streams➤ confluence

Awareness of complexities of health data, understanding ofcontext, and strategies for design and application of methods inthe biomedical context.

• Disciplines have distinct methods and motivators

• Depth of understanding of statistical and technical methods

• Real-world limitations on data collection and human data labelling and interpretation required to support development of automated methods

two streams ➤ muddy waters

Key common competencies (?)• Understanding of differences between data collected for a

specific (clinical) purpose and “real-world data”• Framework for designing clinically meaningful applied

technologies• Critical judgement skills for consideration of limitations of

automated methods• Framework for considering ethical use of data and applications

Thank you!@karinv @ARC_AIMedTech