Established methods of environmental assessment and trend analysis can point in the wrong direction when they fail to reflect the way our business is changing. Predicting (extrapolating historical relationships to define a single future) is meaningless when different dynamics start determining future outcomes — today's situation. Fortunately, forecasting (estimating probabilities of more than one possible future) can identify options when scientific revolution, technological advances, economic transformation and political dysfunction are creating new opportunities for health providers.
Recent projections published by the Centers for Medicare & Medicaid Services and the Congressional Budget Office provide a good illustration of the differences between predicting and forecasting. These reports predict that total spending on health care will return to its past trend of consuming a larger share of gross domestic product every year, after an unprecedented decline over the past three years. Specifically, they say that health care will be 22.4 percent of GDP 10 years from now.
This prediction is surely seen as good news by health executives who have experienced lower revenues during the recent downturn and by investors who believe health care is a bulletproof industry. Alas, the prediction is almost certainly wrong and dangerously misleading. It was generated by an analytical methodology that should not be used in an economic sector being transformed by unprecedented forces. The future of health care is literally unpredictable under current circumstances. Why? Because key theoretical requirements of predictive modeling are not met in the medical marketplace.
Requirements for Making Predictions
The quantitative methods for making a prediction are completely appropriate under the right circumstances. (I should know, having taught predictive sciences to graduate students for 20 years and having authored two books on the subject.)
An essential step in making a prediction is building a mathematical model that expresses the relationship between a dependent variable, such as health spending as a percentage of GDP, and independent variables that explained previous variation over time. If the model's equation has produced accurate predictions in the past, it can be used appropriately to compute future values of the dependent variable as long as the equation that explained changes in the past will continue to explain changes in the future.
(For readers who might not care about this important theoretical issue, I have another compelling reason the CMS-CBO prediction should not be believed: Only four years ago, the same agencies said health spending would be 19.2 percent of GDP in 2014, but the actual figure is only 17.2 percent. The government's previous prediction was seriously flawed, and the new one was computed with the same methodology.)
Predictive science's essential assumption of continuity no longer applies in the medical marketplace. Different forces now define the share of national economic resources that will be spent on health care for the foreseeable future:
Shift in patient care paradigms. Medical science and clinical practice are shifting their orientation from acute care (treating patients after a disease has become a problem) to preventive management of chronic conditions, accelerating the shift from inpatient to outpatient care. Medical specialty organizations are simultaneously taking serious steps to eliminate unnecessary, unproductive care.
End of blank-check reimbursement. Purchasers and third-party payers have reached the limits of their willingness and ability to increase outlays for health care year after year, effectively transferring fiscal responsibility for any net increases to consumers. This transfer is occurring at a time when consumers are experiencing no growth in disposable income. Under these economic circumstances, I simply cannot identify the source of funds that would boost health spending from today's 17.2 percent of GDP to the predicted 22.4 percent.
Increasing demands from other sectors. Once the last area to be cut and the first to be restored in budget negotiations, health care is no longer the most favored sector of the American economy. Governments are under pressure to devote more resources to education, infrastructure, defense and the environment at a time when any spending increases are politically difficult, if not impossible.
The CMS-CBO prediction of a return to relative growth in health spending assumes that the medical marketplace is returning to business as usual, a proposition that does not mesh with today's experience. Health leaders must take the projected increase in spending with a big dose of skepticism because predictions are not meaningful when the predictive model's explanatory variables are changing [for a detailed discussion of these issues, see Chapter 2 of Paradox and Imperatives in Health Care: Redirecting Reform for Efficiency and Effectiveness by Jeffrey C. Bauer, CRC Press, 2014].
The Superiority of Forecasting
Fortunately, there's a good alternative to use when the future is uncertain and capable of evolving in more than one direction, as opposed to a prediction's single outcome. The alternative is forecasting, best known for its applications to weather, but increasingly used to look at the possibilities in other fields (e.g., geopolitics, elections) in which past performance is not an indicator of future results.
The difference between a prediction and a forecast is illustrated by meteorology. Like predicting, weather forecasting requires a model that represents the relationship between a dependent variable like rain and the independent atmospheric variables that cause rain (e.g., water, temperature, pressure and wind). However, instead of extrapolating from a best-fit mathematical equation, weather forecasting analyzes historical data to find what happened in the past when initial conditions were identical.
For example, if it rained the day after 40 of the last 100 days that started out just like today (atmospherically speaking), the forecast for tomorrow is a 40 percent chance of rain — which is also a 60 percent chance of something else [for details of the forecasting process and how to do it, including a discussion of the importance of climate change, see Chapters 4 and 5 of Upgrading Leadership's Crystal Ball: Five Reasons Why Forecasting Must Replace Predicting and How to Make the Change in Business and Public Policy by Jeffrey C. Bauer, CRC Press, 2014].
Having trained in both meteorology and economics, I intentionally use forecasting in my work as a health futurist because causal dynamics of the medical marketplace are changing. I also use forecasting because health care is likely to head in many different directions simultaneously — not a single direction as predicted by the government.
For example, my current five-year forecast for the future of medical spending is a 20 percent chance it will rise above the current level, a 35 percent chance it will remain at the current level and a 45 percent chance it will decline. What does this imply for health care providers and their business partners? I forecast that 35 percent of them will fail (cease to exist as currently organized), 40 percent will survive precariously and 25 percent will thrive.
Defining strategy as purposeful response to anticipated change, I believe that strategic planning compels us to focus on the new factors that determine how much will be spent on health care and who will get it.
On one hand, if you believe the CMS-CBO prediction, you don't need to make any changes in your business model because the old way of doing business is going to continue. Regulatory compliance is a sufficient strategy for survival because government analysts say revenue will grow every year for the next 10.
On the other hand, if you accept the uncertainty embedded in my forecasts, you must thoroughly evaluate actions that could lead your organization into worse, same or better futures. Responses should minimize the likelihood of decline and stagnation while maximizing the chances of growth under unprecedented scientific, technological, economic and political conditions. Successful strategies will reinvent health care — responding to changes with long-term, multistakeholder partnerships that provide care safely all the time, as inexpensively as possible.
Jeffrey C. Bauer, Ph.D., an independent health futurist and medical economist, has 45 years' experience as a medical school professor, health policy adviser and industry consultant. He is a member of Speakers Express.