S4-5.2 – How the Predictive Health System Works: Algorithms and Economic Pressures

S4-5.2 - How the Predictive Health System Works: Algorithms and Economic Pressures
Tim Jobson of Predictive Health Intelligence joins the podcast to introduce analyzing historic blood test results to identify those at risk of Fatty Liver disease. In this conversation, he fields questions from the co-hosts around how the system fundamentally works and its economic value proposition to the NHS.

Surfing NASH is joined by Tim Jobson, Co-founder of Predictive Health Intelligence, to discuss a system by which historic blood test results are combined and analyzed to flag patients in need of intervention. This conversation starts with Jörn Schattenberg asking questions around the underlying system and whether the algorithm is “plastic?”

This is based on the set of conditions that the algorithm has to exist with missing data and that what is missing may vary from patient to patient. In addition to these conditions, analysis has to be time-sequenced. In response, Tim dives into the details of how these things actually work within this Predictive Health system. Specifically, he comments on how simply looking at ALT and platelet levels longitudinally can improve overall patient assessment by exceptionally large margins.

Louise Campbell joins to commend the system and ask Tim whether there is pressure to utilize less often to save money. Tim explains that his team is running a full economic analysis to demonstrate massive potential savings the NHS can yield by adopting this system upfront rather than dealing with the financial burden of treating downstream disease progression. Interestingly, it seems health administrators in his region seem to feel the same way, at least intuitively. The rest of the session goes on to address questions around measuring impact and explore simple ideas around how to execute this technology and its processes.

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