I ran across Bayesian Health’s | Essential Clinical AI Checklist – The 10 Components Every Predictive Tool Must Have
Per Bayesian Health – These are 10 consistent components every predictive tool needs to have in order to have an impact on patient outcomes
*Please note I had to trim the list to post it. The PDF is more detailed.
Looking at the list on the PDF I can agree on every point. However, at the same time, each point is up to interpretation. Furthermore, these are broad points and are written with #healthcare in mind. I think this list can help many who are looking at implementing #ai, and even #automation initiatives.
. – Health systems, clinicians, and frontline staff are overwhelmed and overburdened.
2. – – As health systems look to AI tools to solve multiple different complex problems, they need a platform upon which different solutions can be built as needed, as opposed to trying to win with single-point solutions.
. – Don’t choose between sensitivity, specificity, and lead time — you need all three.
4. – Clinical signals and recommendations must come with context, showing the fact pattern that suggests to physicians and nurses that a condition may be present, which they then validate (or not) with their professional judgment.
5. – – Each interaction between frontline staff and the machine learning platform provides a chance to capture crucial information that can be used to improve the tool.
6. – Different members of your care team have different needs when using the tool, and an intuitive interface is needed to prioritize the most relevant content for each set of stakeholders in specifically designed user views.
. – Measuring only health outcomes is not enough.
. – Datasets, populations, and practice patterns are always shifting.
. – Bias can enter a model in a variety of ways.
. – Developing effective models is challenging and mistakes are common.
Notice: The views expressed above are my own. The views within any of my posts, or articles are not those of my employer or the employers of any contributing experts. this post? for regular insights.
