A few years ago, when I lived in Canada, I had an interesting healthcare experience. My right eye was bloodshot and irritated. It was quite uncomfortable, so after a couple of days I decided to go see my physician. He took a look, prescribed an antibiotic ointment, and sent me home with the comment: "Come back and see me if it hasn't cleared in 24 hours." So, a day later, I'm back on his doorstep. He takes another look, and sends me to the ER of the local hospital. At the ER, another doc peers thoughtfully into my eye with some contraption. She says: "Well, I guess he sent you down here because he is worried that you may have iritis, but I really don't see it." Iritis is an inflammation of the iris that can lead to numerous vision problems. She continued: "You're lucky, our ophthalmologist happens to be in today (it was a Saturday), I'm going to send you up to see him, just in case." So I wander through the deserted hospital corridors and find the eye-specialist. He's packing up for the day, but is happy to take a look, so I sit in his chair. He takes one look and pronounces the diagnosis: "Rip-roaring iritis." In fact, it is apparently so severe, that he is considering the ultimate treatment, which is a steroid injection straight into the eye. Just to be sure, he wants this to be confirmed with another ophthalmologist in Toronto, who is the preeminent eye-guy in the land. So, I go see the uber-eye-guy in Toronto. His recommendation: "Here's some topical steroid ointment, we'll watch it for a while to see what happens."
This experience was quite the "eye-opener" for me regarding decisioning in healthcare. Four doctors, arguably all considered authorities in some fashion, with four different opinions.
Following some discussions with esteemed colleagues around the use of analytics in healthcare, I decided to read the book "How doctors think" by Jerome Groopman. This is a very enlightening book describing how errors in diagnosis occur. Apparently some 10% to 15% of diagnoses are incorrect, which is rather frightening. Interestingly though, the very large majority of mistakes do not come from lack of knowledge, but are rather ascribable to cognitive errors by the physicians. So, the question becomes: how can we help doctors make fewer mistakes?
In banking, which is my background, predictive analytics are of course used far and wide, and have been shown to be extremely effective at "diagnosing" risk. Naturally you expect that you'll make some mistakes, but the nice thing is that you can adjust the price for everyone, so that the goods make up for the bads. This is called risk-based pricing. So, this has been used for decades in the financial industry, why isn't it used in healthcare? Surely there is enough data around to build fantastic models. Automation of decisioning could be a huge cost saver and it could free up physicians from mundane ailments, right?
As I read the book mentioned above it dawned on me that the analogy with the financial industry is flawed. The problem is this: in banking the cost of an error is relatively small, in healthcare that cost is catastrophic. In banking, the 999 good decisions make up for the one bad decision. In healthcare, the 999 right diagnoses do not pay for the one that you missed. This, I think, is why there is so much resistance to analytics based decisioning in healthcare.
Obviously this does not discount the benefits that analytics in medicine diagnostics can have. What's required is to tailor analytics for use in healthcare. In this sense, it seems to me that the easiest thing that could be done is some sort of system that helps physicians by keeping them honest. Most of the mistakes in diagnosis occur due to all the familiar cognitive traps that causes the human brain to focus on one answer while discounting other possibilities. Many great physicians force themselves to religiously ask themselves "What else could it be?" every time, with every patient.
So it seems to me that a good place to start would be to create a system that, given certain symptoms, would produce a list of possible causes. Critically, it should also list the statistical confidence in each of the hypothesized causes. This would ensure that doctors keep an open mind, and it would ensure that they keep questioning their hypothesis, in particular if the data shows low confidence in their hypothesis.
