The problems worth solving are the ones where the answer isn’t known. They’re complex and ambiguous, and the solution has to work for users, customers and the business. For 20 years I’ve solved problems like these, mostly in health. Will clinicians trust a prediction enough to act on it? Can a record scattered across a dozen systems be made clear in seconds? Will people stick with a behaviour change they’ve given up on before?
I have done this across secondary, primary and preventative care and consumer health, in the US, the UK and Europe. That includes a 300,000-user enterprise portfolio, Google and DeepMind, and startups from zero to scale-up. I can work at the level of product strategy and business ownership and deeply embedded alongside empowered teams in day-to-day discovery, delivery and optimisation.
The method is continuous discovery. In today's modern AI native world - addressing the biggest risks first, focussing on value, usability, feasibility and viability is more crucial than ever before. I've kept I'm comfortable creating prototypes and small, safe experiments in days rather than months. In health, I know that safety is built into the experiment from the start and that utilising new technology is only important when it changes the outcome.
Work
Simple
Scale up and retention in consumer health treated as a question about user outcomes rather than the usual app mechanics.
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Kova
A 0-1 launch defined as much by what it left out as by what it included.
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Streams
Acute kidney injury prediction and detection in the hands of clinicians and care teams who could act on it.
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Care Studio
Search and summarisation over the patient record, several years before the rest of the industry arrived at the same shape.
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System C
Three electronic health record products — Medway, CareFlow and Graphnet — serving more than 300,000 users
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