Richard Jones
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Most NHS AI pilots end the same way. The model performs. The clinicians like it. The evaluation reads well. Then the pilot money runs out, the clinical lead rotates and it stops. Nobody did anything wrong, and nothing changed. -
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Traditional large language models (LLMs) are extraordinarily useful. They can summarise, draft, explain, search, translate, simplify and accelerate work that previously sat in queues, inboxes and clinical admin backlogs. But we need to be brutally clear about what they are. They are not truth machines. They are language machines. -
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The most important healthcare AI story recently is not another model launch. It is governance. -
Content ArticleA recent interesting study looking at AI tools to diagnose lung cancer highlights that AI does not change diagnosis speed. However, the care pathway was not changed and perhaps the most obvious finding is that care pathways must be optimised if AI is to highlight cases where specialists should take a second look.
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Content ArticleRichard Jones posted an article in Commissioning, service provision and innovation in health and careHere is a real example from the US of why embedding patient safety can be so difficult.
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hub topic lead Richard Jones highlights an incident where the sepsis warning AI system failed to highlight a patient's deterioration and led to an avoidable death. -
Content ArticleFascinating information in this graphic. What gets measured gets improved, but a 2024 Health Services Safety Investigations Body (HSSIB) investigation revealed that systematic underreporting of patient safety incidents involving general practitioner online consultation tools was occurring, and that the available data did not contain enough information to identify potential harm. From my own direct experience, unless you have risk-adjusted metrics for patient outcomes, the layer of incidents that are not flat out Never Events also remain hidden at scale. Patient safety work is still mainly at the tip of the iceberg!
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Content Article CommentHi Tejal There is a concern that at present, providers can't detect as much as 90% of avoidable harms. Where we report excess complications across different populations, we ignore the underlying comorbidities etc. Only by risk-adjusting for each patient can we detect that 90% and fix it. I know this works. I know the company went bust pushing the rock uphill to convince US healthcare that quality that improves costs as well is important. Thanks for sharing this information.
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Content Article CommentHi Mark This is a super interesting area. A concern is that regulation globally is failing to keep up and the new 'health' models from the big AI players are playing right on the edge of being medical devices. I hope that lobbying and interested parties do not lower the bar on appropriate regulatory oversight.
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Community PostThanks Theresa, Let me know what you think if there is anything you think if a bit off centre or really hits the mark. Regards Richard
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Community PostThe assurance part is very complex indeed. The difference between deterministic and non-deterministic AI is fascinating. The non-deterministic is the greater challenge for regulation. I don't envy those trying to come up with effective solutions. A simple search on Google Bard on me suggests my MBA is from three different places in three different drafts. None are correct.
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Community PostThe latest stat I heard is that each hospital generates more information than the Library of Congress. That is meant to store all media created (although I think that excludes Tik Tok videos and social media). I don't have a timescale for this but, if true, it's pretty impressive and also somewhat intimidating.
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Community PostI'm already seeing some of this come true with big payors in the US going off the idea of 'point solutions'. A lot of different concepts in here that will be unpacked in different ways in the next few months but what do you think? AI Hype versus Reality in Healthcare 20230803.pdf
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Community PostProjections indicate that there could be as much as 2,314 exabytes of new data generated in 2020. That’s 2,314 billion gigabytes of data. With a population of nearly 8 billion globally, that’s around 300 gigabytes of data per person per year. Is this realistic? How much of this data is being stored on phones and smartwatches, Fitbits etc.? So who has this data and how useful is it when it sits in a commercial company’s silo and does not complement health system’s own data? One simple truth - that volume of data requires collation, curation, contemplation (sorry - on an alliterative roll here).. but it really needs smart systems to convert it from data to wisdom. Are we on the right path or are we drowning in the data?
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Community PostIf ice cream and dalmations are ever in a hospital context.. I want to be there.
