Though predicting has been the norm among healthcare industry observers for many decades, one industry expert says it’s time to stop predicting and begin forecasting in earnest. Why? In a dynamic and rapidly changing industry like healthcare, the limitations of prediction-making are becoming more and more obvious, says Jeffrey C. Bauer, Ph.D., an independent economist and health futurist who has been a medical school professor, consultant, speaker, and writer.
The Chicago-based Bauer has written several books and over 250 articles. His new book, Upgrading Leadership’s Crystal Ball: Five Reasons Why Forecasting Must Replace Predicting and How to Make the Strategic Change in Business and Public Policy (CRC Press/Taylor & Francis Group), has just been released this summer. As Bauer notes on the book’s jacket, “Although predicting and forecasting are usually used as synonyms for a single process, they are conceptually and methodologically quite different… Readers will learn the real-world value of distinguishing between predicting (extrapolating historical trends) and forecasting (estimating the probabilities of possibilities).
Jeffrey C. Bauer, Ph.D.
Bauer’s new book follows in the wake of a number of healthcare-specific books, including Statistical Analysis for Health Care Decision-Makers. (Back in 2007, he co-authored Paradox and Imperatives in Health Care: How Efficiency, effectiveness, and E-Transformation Can Conquer Waste and Optimize Quality, with Mark Hagland, who is now HCI Editor-in-Chief.) Hagland spoke recently with Bauer about his new book, and the implications for healthcare leaders of principles of forecasting. Below are excerpts from that interview.
You articulate it fully in the book, but for those who have not yet read the book, can you succinctly articulate the difference between prediction and forecasting, and why that difference matters?
Prediction is an estimate of a single value at a future time, such as, healthcare will account for this percentage of the GDP, or we’ll be short this many doctors, or there will be this much construction activity; whereas forecasting accepts the possibility of A, B, C, and D, as opposed to just A. And rather than any specific probabilities, it gives you values. In the book, I talk about the prediction of the percentage of GDP spent on healthcare; my current understanding is that healthcare is declining as a percentage of GDP. So I put about a 20-percent probability that overall healthcare spending as a percentage of GDP would rise from where it is today. I think there’s a 60-percent probability that healthcare will actually decline as a percentage of GDP. Wall Street believed that healthcare spending would reach 20 percent of GDP, and as recently as Monday of this week, the Wall Street Journal declared healthcare to be the great growth industry based on all the newly insured. But as a forecaster, I would say there’s a 60 percent change that healthcare will become a declining industry in the coming years. The current accepted prediction is that healthcare should be at 18 percent this year, but right now it’s at 17 or 17.2 percent, by many estimates.
Furthermore, predictions have never been right. And I note in the book that not a single prediction from the government has ever been right in the past 40 years.
You describe the “five fatal flaws of predicting”: discontinuities in system dynamics; violations of model assumptions; deficiencies of available data; failures of previous predictions; diversions from strategic innovation. What should our audience know about these flaws that are inherent in prediction activity?
First of all, they should know that predictions have never been right in healthcare. That’s the number-one reason not to pay attention to predictions. Now, why have they never been right? First of all, the data are rotten to the core. Our databases are terrible; so big data is laughable in healthcare, because we’ve got all these numbers that are neither reliable nor valid. And so predictions are never right, because the data are not at all representative of what’s happening. And then the models used have very strict requirements related to the data distribution, that almost never hold in healthcare. Healthcare data almost never come to look like the bell-shaped curve, which is a core assumption in predictive modeling. And even if you have good data and good models, if the underlying circumstances that explain the data and models, you’ll not be successful.
Essentially, your perspective is that healthcare is simply too dynamic right now, correct/
Yes, that’s right, things are simply changing too fast. The underlying concepts of how clinicians should be treating patients—the clinical knowledge is changing too fast now. There’s a change in the clinical paradigm taking place right now; and all of our data represents 20th-century acute-care-based medicine. But as 21st-century medicine moves forward, cures will eventually become largely irrelevant; it will be about forecasting what might happen, and managing the possible outcomes. We’re really at the point where the 20th-century clinical paradigm, involving waiting until a disease manifests, is quickly moving out the window. It’s now about managing disease, such as HIV, or musculoskeletal diseases, or preventing heart attacks.
Everything is moving towards a chronic care management platform.
Yes, though I prefer to use the term disease management.
With regard to forecasting, you talk in the book about the proper elements of forecasting, including identifying explanatory variables; specifying the model; measuring the explanatory variables; analyzing variables over time; assembling the forecast; and interpreting the forecast. What are the key things we should know about those elements, as they relate to their work?
Well, the fact that the underlying scientific and medical foundations of medical care have changed so much over the past decade, that’s number one. And number two is the advent of telemedicine and iHealth. Twentieth-century medicine didn’t happen unless you had, at a regular interval, face-to-face contact between a physician and a patient. But now, we’ve increasingly got new phenomena, such as monitors that can tell us when we’re heading towards a crisis. And the technology of telemedicine transforms the need for exam rooms, and so on.
And just today, the Wall Street Journal ran an article on social networks and their impact on medical research. My key point is that everything that determined how healthcare resources got allocated in the 20th century has changed; we can visit with patients over electronic networks, we can Skype, and so on. So suddenly we’ve got all these technologies that eliminate the need for face-to-face interaction. You’ve talked a lot on your website about changes in policy around this.
So the data we’ve got all rely on the idea of people coming together face-to-face. And the changes in payment systems are also affecting things. Early on, I was so disturbed by the Affordable Care Act because of how much out-of-pocket payment would be demanded of patients. And that dynamic is changing things; so CFOs are scared beyond belief. More patients are coming in with insurance, but who are also unable to pay their deductibles. And every medical economics book I read through the 20th century and most of the articles, were wrong, because they said that patients don’t have any information, and it’s not an information-driven industry. Well, obviously, now, consumers have an incredible amount of information. Consumers were never told in the 20th century that they had options. So consumer information is dramatically changing the landscape.
How will that affect forecasting?
Well, for one, it eliminates any value in predicting, because predicting was based on the assumption that consumers didn’t have access to information. In terms of forecasting, patients/consumers will more easily be able to make choices themselves. A 20th-century woman with breast cancer would be expected to do whatever the doctor said. But that’s all changing now, with consumer empowerment. They no longer see the doctor as the only person who can tell them what to do anymore.
As healthcare leaders set up forecasting for the future, what should they be doing now?
They need to look at all different directions. The book was not written specifically for healthcare. But it is the number-one case study that I look at. So your readers need to look at the fact that for any strategic question they have, something could go up, go down, stay the same, get better or get worse. So readers need to look carefully at the factors affecting their organizations. You have to look at the factors that could make things better or worse in healthcare. And what you do if you’re properly applying my technique, you begin working strategically with the elements that could become better; and also focus on the forces that could cause your organization’s demise. You look at both the positive and negative elements and look strategically at things you have to do to make sure things don’t get worse, and also at things that will help you reinforce the elements that will help your organization.
So my model today is multi-stakeholder partnerships. Trying to solve the problem with hospital or health system consolidation will never solve the problems. None of us can solve problems on our own. And if we simply become part of larger networks, we haven’t solved any of the problems already facing our organization, and haven’t worked with the employers to talk about how their practice of finding a new carrier every few years is counterproductive. Success will require working with all the stakeholder groups together. And even as everything is changing so fast in healthcare, it requires working with others collaboratively to go forward. I firmly believe that it takes several years to change any organization’s operations and strategy. And so progress requires working with purchasers, especially large employers. I’m trying to get people away from thinking that people should just get together and talk with other people in like enterprises; it means getting together with all the people who would be involved in your potential success, including employers and payers. I happen to think that all these mergers in healthcare are almost never a step in the right direction.
Do you have anything else to add?
Only that I hope the [healthcare] industry will change. I’m Harold Hill in The Music Man, trying to get people to focus on positive outcomes. Predictions won’t do that. Instead, we could be here, we could be there—and how do we get there? I’d rather have people focus on the different outcomes inherent in forecasting. Forecasting helps you rethink healthcare; predicting just helps you figure out how to get on the trendline—it doesn’t make you change. Forecasting involves your choosing which of the possible outcomes you most want to achieve, and is more focused on what you can do inside your organization.