How predictive analytics will change the future of the health economy

Random guesses or confidence in probability - which will you choose?

When healthcare executives use the phrase “directionally correct,” they are invariably, if unwittingly, using information about past events as a reference point for making decisions about the future. That information about past events is, in addition to being historical, often incomplete or otherwise flawed, wherein lies the “directionally correct” fallacy. Like the clinical concept of Original Antigenic Sin, in which the immune system learns to respond to a virus based upon the specific virus it first encountered, healthcare executives are inclined to make strategic decisions based upon their memory of a strategy that worked in the past. What’s past is not prologue in healthcare, and so history can never be used to predict the future with 100% accuracy, which is the reason that probability theory exists.

Probabilistic decisions are based on probability, that is “the extent to which an event is likely to occur, measured by the ratio of the favorable cases to the whole number of cases possible.”

The issue is not one of degree, but kind. “Directionally correct” decisions can occasionally be correct, or at least not fatally flawed, but they should never be viewed as “evidence-based.” Why? Because “directionally correct” decisions are fundamentally based on insufficient evidence, whether in terms of relevance or detail, and insufficient analytic rigor.

In contrast, probabilistic predictions developed from comprehensive, longitudinal data sets and advanced data science and engineering capabilities are truly “evidence-based.” These predictions provide a transparency that healthcare executives can, by definition, utilize with confidence to analyze potential outcomes from strategies and tactics.

Of course, evidence-based decisions are not infallible. As famed statistician George Box famously quipped, “All models are wrong, but some are useful.” Importantly, Box was not referencing a projection developed in Excel, but statistical modeling, which is defined this way:

“Statistical modeling is the use of mathematical models and statistical assumptions to generate sample data and make predictions about the real world. A statistical model is a collection of probability distributions on a set of all possible outcomes of an experiment.”10

In the new health economy, healthcare executives cannot afford to be satisfied with “directionally correct” – hope is not a strategy. Instead, healthcare executives should focus on two decision categories:

(1) decisions for which there is insufficient data to inform that decision, and

(2) decisions based on probabilistic predictions.

The former might involve future policies, technologies, or pandemics. The latter are the strategic and tactical decisions, whether strategic, operational, financial, or clinical, that stakeholders in the health economy expect executives and boards to get right.

The Risk of Being Left Behind

It is astonishing that the U.S. healthcare industry, the size of which exceeds the entire GDP of every country except China and Japan, is the least likely industry to use probability models to make predictions and recommendations. Every state and federal political candidate uses probability models in every election, just as the “FAANG” companies and every retailer with a customer loyalty program do in every consumer interaction. And Las Vegas sportsbooks make eerily accurate predictions 365 days a year. But not the healthcare industry.

To be clear, the healthcare industry does indeed make predictions every day, but the quality – and resulting accuracy – of those predictions is dreadful. And to make matters worse, the healthcare industry creates “models”, ironically focusing most broadly on what is least likely to be scalable, i.e., “personalized medicine.”

Truthfully, much of what the healthcare industry refers to as “analytics” is simply benchmarking. Even worse, much of that benchmarking is purely aspirational, calling to mind Jiminy Cricket, as if wishing that you were as good as Mayo Clinic or Optum or J&J is enough to make your dreams come true.

Sometime in the future some combination of legal and economic forces will force the health economy to change. Until 2015, failing to make evidence-based decisions was excusable. Even if a company in the healthcare industry understood the importance of making evidence-based decisions and could gather most or all the available data, analyzing information that vast was prohibitively expensive. In the last five years, cloud computing has eliminated those obstacles for the largest industry participants, and Moore’s Law suggests that the scale offered by cloud computing will soon make evidence-based decisions available to every market participant.

What is excused when something is impossible is not excusable when it becomes possible, which is an often-overlooked aspect of the legal responsibilities of officers and directors, particularly tax-exempt entities. In Wabash Railway Co. v. McDaniels, Justice Harlan defined the standard of “ordinary care”:

“Ordinary care, then…implies the exercise of reasonable diligence, and reasonable diligence implies, as between the employer and employee, such watchfulness, caution, and foresight as, under all the circumstances of the particular service, a corporation controlled by careful, prudent officers ought to exercise.”

It is a well-established principle of corporate law that officers and directors have the duty of ordinary care in the operation of a business, exercising the judgment of a reasonable person.

At some point in the future, it will become unreasonable, as a matter of law, to fail to incorporate probability-based predictions into operating healthcare businesses, especially considering the statutory duty of care required of tax-exempt healthcare organizations that, according to the IRS, operate “an implied public trust.”13,14

The stark reality facing the healthcare industry is that America, and Americans, cannot afford the cost of our healthcare system. In response, stakeholders in the health economy are playing the healthcare version of “hot potato,” devising myriad schemes to transfer financial risk through value-based care programs, capitation, narrow networks, and health insurance benefit design. Meanwhile, the healthcare consumer has an increasing amount of information, access, and choice, a decreasing amount of discretionary income for healthcare expenses, and little guidance from health economy stakeholders who fundamentally misunderstand consumer motives and preferences. The much-heralded advent of healthcare consumerism is more accurately described as a capitulation by health economy stakeholders that have failed to inaugurate necessary change, perhaps an inevitable outcome in such a highly regulated industry. It isn’t that American consumers are desperately seeking to wrest control of healthcare decision-making at a scale that even Jack Wennberg could not have envisioned, but rather that the health economy is increasingly forcing consumers to fend for themselves.

In today’s health economy, every stakeholder increasingly has a legal, financial, and, arguably, moral obligation to make evidence-based decisions in every aspect of their business. Instead, today’s healthcare industry is replete with point solutions delivering immaterial improvements to the innumerable ailments of the industry. In the words of the Allman Brothers, there is only “one way out” for the healthcare industry and America itself: the promise of predictive analytics to revolutionize the way that strategic, clinical, operational, and financial decisions are made.

This article is written by Trilliant Health's CEO Hal Andrews as part of an ongoing series,?The "Directionally Correct" Fallacy. For more articles like this one, visit?our blog.