Achieving the Promise:
Analytics and Machine Learning in Healthcare
The healthcare industry is increasingly under pressure to ensure a steady flow of high quality, actionable data. The consequences of overlooking this imperative are harmful: a bad data foundation results in meaningless and misleading outputs.
There has been a surge in data visualization to make data scientists out of citizens and get the general public to ‘see’ the bigger picture. This data driven healthcare culture, fueled by the pandemic, will without a doubt, outlive it.
The emphasis on unearthing facts and figures and making them universally digestible will be hugely consequential. Enacting this mandate though, hinges on putting the right infrastructure in place and this extends far beyond just investing in technology.
In this webinar, we will answers some key questions:
- What are the hurdles to getting high quality interoperable data that can be scaled and leveraged for analytics?
- How can visualization be used to improve data literacy globally?
- How conscious are we of the link between sources of data and bias? How does this impact our understanding of social determinants of health?
- How can we harness machine learning to make healthcare more efficient, and free up resources for care?