Data exchange in health analytics Steve Millward Chief Analytics Officer 22 October 2018 HISA
Contents 1. The case for greater liquidity in data 2. Examples of data exchange in the health sector 3. Components required for safe data exchange
The case for greater data liquidity
What can be seen Health outcomes Treatments 5% Diet Hobbies Spending patterns Retail Grocery Insurance Exercise Life and health insurance Online activity 95% Location What can’t be seen
The biggest, most useful datasets are still held in islands
Data has no liquidity Hard to move Hard to value Hard to control Hard to use
We need rails for data liquidity
Data Republic – Help the world decide wisely Founded 2014 Sydney headquarters 60 staff Offices in Los Angeles and Singapore Investors include Westpac, ANZ, NAB, Qantas
Examples of data exchange in the health sector
Simon Kyaga Creativity and mental illness
Google Google AI Deepmind AI
NSW Data Analytics Centre Grocery basket data
Inter-departmental data exchange
MBS datathon Second year NAB Qantas Medibank Victorian government Roy Morgan 250 data scientists
Mental health Exercise Social media
Assessing cohort behaviour after individual matching 8% Insurance portfolio A Data or Exchange Segment A 15% Insurance portfolio B Data or Exchange Segment B
Components for safe data exchange
Data exchange cannot breach privacy rules
1. A common legal framework “Can you imagine a world without lawyers?”
Govern the “permitted use” of data
2. Secure matching of PI
3. Senate governance platform
4. Secure analytics environment
5. Legislative traction
6. Giving back
7. Industry engagement Ethics Use cases Processes
Any questions?
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