By Rishabh Srivastava, Founder of Loki.ai
This summary was largely done for my own note-taking, sharing it just in case it adds more value to other people.
I have no affiliation whatsoever with either the presenters or the organisers. This is a summary largely taken for my own reference, and may contain errors :)
Context
Source URL:
Why is it important: I need to figure out how governments and companies are thinking about data in the aftermath of Covid-19. panels like this are extremely useful
Keywords
Digital Transformation, Government
Summary
Organizations have had to pivot very rapidly to digital in the aftermath of Covid-19. This has led to both rapid adoption of new technologies, and rapid cultural change. Post Covid-19, many of these changes are likely to accelerate
Highlights
A. How Singapore's Healthcare Information Systems responded to Covid-19
By Chua Chee Yong of Integrated Health Information Systems (IHIS)
In "peace-time", IHIS uses data mostly to improve clinical outcomes and productivity
But during Covid-19, they shifted rapidly to tools for contact tracing and managing migrant workers instead
Now, as the situation is getting under control, they are trying to adapt to a "new normal" with more telemedicine capabilities. Patients beginning to expect better care at home
B. How is statistical monitoring by governments changing right now?
By Gemma Van Halderen of UN ESCAP
1) Governments are now comfortable with "big data" analysis in addition to traditional metrics
2) High frequency data is really important. Weekly data > annual data
3) Aggregating data about your country from trusted sources in other countries is now okay
C. How can sustainability/energy be impacted by data?
By Chang Sau Sheong of SP Digital
D. Panel Questions
IHIS moved very quickly in responding to Covid-19. What were the fundamentals that were important in that?
3 things:
- People: Ability to mobilize clinical resources, operational people, tech/IT people at short notice to respond
- Process: OODA (Observe, Orient, Decide, Act) loop --> incrementally observing on the ground, working on operational people, contextualizing the existing system, and then deciding very quickly what they need to do and act upon it. Remember that it's a loop and you must keep iterating
- Harness existing platforms: Lift, drop, evolve approach. Took existing systems, deployed them in new ways, trained people to use those systems, and then incrementally evolved them in Agile ways
New Zealand Experimental Approach in dealing with Covid-19. What can other countries learn from NZ?
- Seeing loads of experimentation in the region. Need to have trust and authority in "experimental statistics" to build on these experiments
- New statistics are very promising
How can organizations spot new patterns in the information and data they are gathering and adapt quickly to that?
- Tech is not the hardest problem. It's about figuring out new use cases
- You need to always have a lookout for what's changing. Then figure out what's the reason for those changes. Then figure out how you can use technological blocks
Big trends at a result of Covid-19
- Culture change in terms of thinking differently and using technology
How is UN ESCAP using data to target programs?
- Both quantitative and qualitative. A lot of requests for assistance (how I change my survey operations? how do I deal with the impact of not being able to go out into the field to collect my data)
New normal of the future--> how is IHIS preparing for this?
- It's difficult. Only way is to constantly look at how they can harness data for actionable insights. Always collecting data for sensing/surveillance. Both business and IT needs to speak the same language using a data-driven approach. We must industrialize the way we do things by relearning the OODA loop. Internalize interation. If if doesn't work, try again and iterate
How is the cloud changing how governments access and use data?
- Multi-cloud compatability is becoming much more important
- Can you do a combination of on-premise and cloud for your work?
What technologies are you excited about the future? Can AR/VR change public services?
- Yes. But personally, data + ML are the new drivers
- First order of AI: Computer Vision ==> use cases are handwriting recognition, face-recognication etc
- Second order: Self-driving/Robotics. Requires first order, but use-cases are totally different
- Right now, we are only at the first order. The second-order are the things that will literally change the world
"The future is already here, it's just unevenly distributed". What are some of the glimpses of the future that are already here?
- Chua Chee Yong: Convergence of AI into the clinical workflow. Leveraging IoT in patients homes and tracking their vitals, movements, and behavioral + combining it with their historical medical records and data to better predict the needs of individuals. With telehealth and robotics, we can hopefully care for these individuals at home.
- Henry Sowell: Pushing ML/AI to the edge
- Chang Sau Sheong: Tech that is required is already here today. AI/ML are here. Now we need to figure out the right use-cases for them. We haven't figured out these use cases yet. Incredibly excited about these new use cases and digitally transforming existing industries