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Implementing Change in Healthcare Systems

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If you set something up and it just goes into a black hole and no one ever sees it you have problems. Not only are clinicians not interested in it, but the data you get will be rubbish. You need that loop, you need that feedback, so that the people who are entering the data can see the value of it and they start acting on it, and they start making sure the data is of a high quality when it goes in

From “Leaders’ perspectives on learning health systems” BMC Health Services Research 2020

Changing practice is resource intensive and error prone and not to be taken lightly, even when it has potential to improve care. For more than half a century, AI systems have been developed to change clinical behaviour and failed to do so. Evidence is the pathway for changes to clinical practice and evidence that can motivate and guide worthwhile change must be reproducible and transparent.

Any rapidly learning health system undoubtedly uses multiple AI algorithms to measure, analyze and make recommendations for change. AI are good at finding complex mathematical relationships among elements in a dataset. They are operated by data scientist and rarely require, or even have room for, clinical input. The result is that AI recommendations are not trustworthy because they are hard to interpret because of their complexity, unpredictable behaviour when applied to data in a different format than what they were trained on. Systems that put the clinician at the centre of clinical care, are designed to support, not replace clinicians. They provide high quality evidence, in a language that doesn't require years of data science training so that clinical and administrative staff can undertsand and trust. Without trust there are no behaviour changes in behaviour.  

Evidentli’s analytics platform puts the clinician at the centre of the analysis: clinicians design analyses by choosing patient populations, outcomes and the clinical events to study; they may even choose to use AI as part of the analysis. The methodology is instantly available for peer review and the results are ready in minutes and can be interpreted, not by data scientists , but by clinicians.

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