Clutching for the handrail of science...

We can't really judge scientific claims for ourselves in most cases. And indeed this is actually true for most scientists as well outside of their own specialties. So if you think about it, a geologist can't tell you whether a vaccine is safe. 

​Most chemists are not experts in evolutionary theory. A physicist cannot tell you, despite the claims of some of them, whether or not tobacco causes cancer. So, if even scientists themselves have to make a leap of faith outside their own fields, then why do they accept the claims of other scientists? Why do they believe each other's claims? And should we believe those claims?--Naomi Oreskes

My inbox "dings" with articles from Retraction Watch and a variety of statistical challenges to widespread beliefs including scientific claims with surprisingly non-existent or weak evidence propped up behind them. If, like me, you prefer to be on the science side of debates--what gives? What can we do as we navigate our data heavy eminence-based evidentiary framework? 

My favorite part of Naomi's brief talk below is a distinction she makes between the logical fallacy--Appeal to Authority and what we should actually glean from scientific claims. The goal needs to be consensus. We have a moral obligation to be curious, to be honest, and to persevere. Not only pay attention during eureka moments but also "what is that" queries.

I was walking across the vendor floor at HIMSS and noticed a compelling visual, A Day in the Life of Dr. Jones. Arcadia Healthcare Solutions shares a common goal. Collectively, we need to liberate the information within complex EHR systems--prioritized for billing and scheduling--and look for insights to help providers at the point of care. This visualization highlights where the healthcare provider is spending time within the EHR system.

The Cost & Frequency of Disease is a shining example of how to invigorate a 360 degree look at a potentially tabular response into something interactive, informative, and dynamic. Creating interactive data displays allows deeper exploration and improved granularity.

The more we search and challenge evidence from multiple queries or visualize the task at hand--best practices rise to the surface for consensus building. It is easy to dismiss data from one source--analyzed one way, by one team.

What can we find if we link data sources, reimagine the data embedded in our own systems, and look to the larger network of connectivity? We can move across multiple nodes engaging Genuine Intelligence--or GI. We can allow Artificial Intelligence to augment our behavior but let's rely on Genuine Intelligence to formulate meaningful human level solutions.