This post was originally featured on HIStalk.
This blog post was inspired by a recent post by one of my favorite bloggers, Cadell Last. Last is a futurist who writes about biological and techno-cultural evolution. Like Steve Jobs, he looks backwards to connect the dots and tries to extrapolate.
Last only writes about human evolution at a macro scale, but meta-system transformations have been occurring within every industry vertical. It would be impossible to undergo meta-system change at the species level without analogous changes within the sub systems that humanity itself created.
Last identifies the following meta-system changes over the past few billion years of life on Earth. Below, I’ve listed analogous transformations in healthcare delivery since the inception of modern healthcare in the mid 1800s (modern = science based, not witchcraft based medicine).
I’m not going to delve into the first three meta-system changes up through federated regulation because they’re widely understood.
Healthcare in the US is just now entering the age of communication and self-awareness, and we’re struggling as we try to understand the new normal. We all know that robust interoperability is coming, albeit more slowly than many would have liked. Although some early ACOs aren’t doing so well, many of the pioneering ACOs are thriving. Why? Incentives matter.
Putting providers at risk creates meaningful changes. In order to make the right decisions, doctors need the right information at the right time. In time, every EHR will present cost information associated with every order on CPOE screens. At-risk models — which intrinsically foster cost awareness, coordination, and communication — are coming, no matter how loud certain individuals may scream.
Next up, we have the technological singularity, which will manifest in healthcare as IBM Watson-like technology (aka a recommendation engine). Lots of folks already love talking about IBM Watson for healthcare. It’s a novel, sexy, awesome concept and it will happen. Cleveland Clinic and others are already piloting Watson-derived technologies in a few avenues of care, primarily focused around primary care. Other even more powerful recommendation engines, such as Intelligent Artifacts, will expedite computerized automation of healthcare.
Recommendation engines will transform the role of the radiologist, then specialists, and then PCPs. Why that order? Radiology will indubitably be the first because recommendation engines can simply match patterns in large numbers of images (which provide large volumes of precise with little human variability) and patient medical history against ICD codes. The bulk of the data being is analyzed is generated by instruments, not humans, which means the data is standardized, which means that conclusions from it can be drawn more quickly. Specialists will be next because the scope of what they do is inherently less than that of PCPs. As such, the recommendation engines can tap into more focused, less error-prone data sets to provide more accurate diagnostics more quickly. PCPs will be last.
As recommendation engines power an increasingly larger percentage of diagnostics, that begs the question, what will doctors do? Radiologists don’t have many options except to turn to interventional radiology, but that’s obviously not a tenable solution for every radiologist. I don’t have a clue what thousands of laid off radiologists will do with themselves. Specialists and PCPs won’t go away, but their roles will change dramatically. Computer-based diagnostics will in time power at least 80 percent of diagnostics in the long run.
There will still be opportunities for physicians to discern complicated diagnoses, but most of the rest will devolve into clinical mechanics who perform large volumes in-office procedures at low margins. The line separating providers and mid-levels will blur. Physician assistants and nurse practitioners are diagnosing and treating an increasing number of diagnoses, and doctors are competing in a guaranteed-to-lose battle against computer-based diagnostics. And eventually with enough data, recommendation engines could even power robots to perform automated procedures.
And lastly, we have the transition to the global, connected brain. In healthcare terms, that will manifest as mass-scale quantified self and quantified civilization. When it’s cheap and easy enough to record one’s own data all the time, people will. Many will hesitate to quantify themselves at first, but the youth and the 133 million Americans with chronic conditions will lead the way.
As we collect more data about our bodies and our surroundings, we’ll develop far deeper understandings of causality, spread, and manifestation of every disease and symptom known to mankind. Once we have that data, we’ll crunch it every which way with superb analytics tools and tie the recommendations back into decision making processes.
Although I don’t find the global brain prediction to be particularly controversial (unlike the demise of the modern physician), I’m sure many might disagree with this vision based on the current state of quantified self and efficacy of most analytics technologies in healthcare. To them, I posit the Larry Page response: exponential growth. The human mind is inherently bounded by linear thought processes.
There’s an overarching theme across all of the transitions noted above: connectivity. Everything that I’ve outlined above is predicated on ubiquitous, cheap, fast Internet connectivity. Everyone must be connected and we must all share data freely. Your data will help diagnose and treat every other human, and the same is true of everyone else relative to you. Please support and participate in Fred Trotter’s Notice of Privacy Practices revision.
PS, Google, thank you for commercializing Google Fiber and Google Loon. Everyone needs faster Internet.
