RXIQ Idea-a-thon: What if we could use big data to revolutionize community health?
Updated: Jun 3, 2019
The LKN Machine Learning Group was ready to tackle some of healthcare’s biggest challenges; the team works on big-data projects that are shaping the future, and they were considering a healthcare project about population health. The MLG recognized that we have unprecedented capabilities to learn from our data, but healthcare data privacy and issues with access hamper our ability to work with it. They enlisted us to ask their team, how might we use our healthcare data if consumers had more control? How might we safely share our healthcare data so that we can detect patterns and make strides in patient care? Working with their community, we knew that we could generate concepts that had the power to solve these tough challenges and create opportunities to shape the future.
We partnered with Launch LKN Machine Learning Group leaders to host the RXIQ Idea-a-thon, an experiential event where attendees learned innovation techniques to hack healthcare + reimagine the future of patient and community health. As the event would be attended by a group of tech-savvy ML experts and enthusiasts, we decided to tailor our content to facilitate a different type of thinking: divergent and collaborative. We focused on ways to think through ambiguity, on divergent thinking techniques, and on breakthrough idea generation with the 30+ data scientists and engineers, breaking participants into small groups to focus in on their ideas for presentation to the group.
We are consistently impressed by the results of idea-a-thons, but something truly impressive happens when a group of brilliant machine learning enthusiasts are equipped with the tools to collaboratively ideate.
Hundreds of ideas were generated around differential privacy, multi-party compute, and other privacy-preserving technologies. At the end of the event, groups pitched their ideas, which covered autonomy, physician decision support, neural nets to address pharmaceutical compounding, and the intersection of medical networks and multi-party compute.
Across the board, the quality of the new ideas sparked showed revolutionary potential. Participants created new connections, formed new teams, and laid the groundwork for new projects. A consensus among leaders showed that the session yielded more actionable ideas and was more enjoyable than any brainstorming methods they had attempted previously.
With renewed energy and a breadth of incredible ideas to choose from, the LKN Machine Learning Group is on the path to transforming healthcare from the inside out.