Lesson from a Turkey
Tim was born about three years ago. Curious from birth, Tim was particularly good at making astute observations and had a charmingly playful approach to the world. This was very adaptive for Tim. He was well liked by his peers and he was naturally very happy. He was also a great problem solver.
At two years old, Tim started to become a lot more self aware. He was intensely interested in his environment and his future. He was very proud about his mental and physical growth. He wondered when he would be as big as some of his older neighbors and what he might do in the future. Fireman, Policeman, Indian Chief? Typical stuff for some two-year olds.
Tim was careful about his observations. He learned to avoid things that got him into trouble. Part of his contentment came from these reassuring observations. For example, he observed that his living environment was almost always clean, warm, dry and well lit, and that meals were very reliably available at regular times every day. Everyone was pretty friendly.
Using this evidence-based experience and sound, empiric reasoning, Tim concluded that he had a great future. Tim passed away this week. Yes, he was a turkey and yes, he was "sacrificed" for Thanksgiving. Tim had concluded that three years of consistent experience, the availability of food, warmth and safety, with narrow variation portended more of the same. Tim was taken in by a number of fallacies, fallacies that can be shared with most applications of secondary data use, comparative effectiveness research, and observational studies in general. He was unaware of the impact the behaviors and plans of others had on the relevance of his observations.
As we look at retrospective data and fashion a story to explain it, it's extremely important to realize that most humans, including doctor-doctors (MD, PhDs, as well as executives with and without advanced degrees in every field) are highly prone to these fallacies. The narrative fallacy, the attempt to view data as linear (or Gaussian, with predictably low risks of falling outside of prior experience) (see " Fooled by Randomness"). Or the fallacy of failing to recognize the sampling and cognitive biases that financial incentives routinely cause for the best of us. Healthcare rarely follows relatively simple laws of physics like the law of gravity.
This holiday season, as we contemplate HCIT data collection (sometimes called documentation), assessments (sometimes called BI or quality and performance reporting), and interventions (sometimes called CPOE with EBM), it's important to stop now and then to think about the implications from Tim's life. We need to think about significant, rare events called black swans, and about including the "unknown, unknown" into our planning. We need to keep numerical risk management and prediction in its place. It's not an exhaustive framework that should be blindly trusted, as Tim learned too late.
Enjoy your holiday!