The words “predict” and “forecast” sometimes get used interchangeably in healthcare, but they are not the same thing – and knowing when to use prediction versus forecasting (or vice-versa) can empower your success as a nurse executive.
Prediction involves using historical data to predict a future outcome. Let’s use a familiar healthcare example: Every year for the past ten years your emergency department sees an influx of intoxicated patients on New Year’s Eve. Thus, you can reliably predict that your ER will again see an uptick in intoxicated patients on the next New Year’s Eve. That’s a classic use of prediction.
But let’s say that after the most recent New Year’s Eve your city council banned alcohol from sale or consumption within the metropolis. This new variable greatly alters the reliability of your past decade of data.
If you rely on prediction to staff your ER in this scenario, you will be making decisions based on unreliable data. Instead you must move to forecasting, in which you factor new dynamics like the alcohol ban into your decision-making. Forecasting allows you to use data as well as intuition to model several possible future outcomes and then choose the best or most likely option.
Both prediction and forecasting have their place in healthcare. Savvy CNOs know when to employ each technique in order to simultaneously maintain high patient care standards and drive financial outcomes.
Use prediction to fine-tune staffing
The shift under the Affordable Care Act from acute, inpatient care to population health and outpatient care continues to affect census numbers across the board. But regardless of whether you’ve seen patient volumes fall on the inpatient side or rise on the outpatient side, you likely possess enough recent historical data to reliably predict your short-term staffing needs. In fact, your Chief Informatics Officer likely can provide you with highly detailed metrics that allow you to visualize census peaks and valleys over the course of time, even on a unit-by-unit basis. This type of rich data enables you to predict with some accuracy how to staff and schedule those units to enhance outcomes while conserving budget.
Use forecasting for nurse recruitment and retention strategy
For evaluating long-term strategies, prediction may not be adequate. When it comes to recruitment, for example, the historical data may fail to account for two significant new variables impacting the nursing population: the large deficit created by more nurses retiring than graduating and the tendency of millennial nurses to move more fluidly from position to position than nurses of previous generations did. If you rely solely on historical data to predict the future effectiveness of your recruitment and retention efforts, you might find the results miss the mark. Instead, you should use forecasting to factor in the new reality that open positions will continue to far outnumber the nurses available to fill them and that millennial nurses may require specialized retention efforts in order for you to maintain an adequate level of staffing.
Use forecasting when modeling budgets
Relying on past history alone to predict the financial future of the healthcare sector might be folly, as author and medical economist Jeffrey C. Bauer points out in an article on this subject. He rightly notes that different forces shape the healthcare landscape today, including changes in patient care models and the transferring of fiscal responsibility to the consumer, making it nearly impossible to predict tomorrow’s financial returns based on those of five years ago – or even last year.
A savvy CNO will use forecasting to anticipate three future financial scenarios: worse, better or the same. By gathering the best available recent data and considering the potential effects of new variables on your budget strategy, you can nimbly adjust to whatever financial challenges the future holds.
Prediction and forecasting will always be powerful tools for nurse executives. The key lies in knowing which tool to employ at the right time. By always evaluating whether or not novel forces may be affecting a scenario, you can easily determine whether prediction based on historical data will be reliable or whether you should turn to forecasting in order to weigh multiple hypothetical futures.