When it comes to healthcare, the right information can prove vital to providing the proper care, products, and services to people in need.
Garnering useful data could help improve the quality of care patients receive. As Bauer suggests, leaders must do extensive research to move beyond mere prediction to forecasting (2015). Armstrong (2001) argues healthcare “forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations.” As a critical element of the planning and implementation process forecasting, per Soyiri and Reidpath (2013) identify “future events based on foreknowledge acquired through a systematic process or intuition” (p. 1). Studies suggest the ability to create short, and long-term plans for meeting customer’s demands and optimizing operations, depend greatly on the timeliness and quality of the information leaders retrieve and analyzes. “The simplest forecasts occur in stable environments” where there is plenty of data available. This data typically consists of “historical data,” recent occurrences, and trending information which can be used for “projecting” future impacts and results (Kasapoglu, 2016).
In the decision-making process, forecasts could be considered the life source, in which data gathered can either enhance or thwart the organization’s survival. In healthcare, using the right information is crucial. Since there are multiple forecasting methods, the healthcare leader must seriously consider which approach will provide them with the best information, and the right guidance towards implementing that information successfully (Gilliland 2011). This article provides general information about what forecasting means and why it is vital in healthcare. Additionally, it references Tetlock and Gardner (2015) “Ten Commandments, to further assist healthcare leaders in making the right decisions for their organization.
Why is forecasting important?
The more people consider future possibilities, the better able they become to create and implement plans of action (Conway & Voros, 2002). Conceivably every person will make one plan or another regarding their future aspirations, and the decision made will be based on what they learned from the information garnered. Likewise, “virtually every decision leaders make today” is based on some “kind of forecast” (Chambers, Mullick & Smith,1971). Conducting forecasts prove essential to the leader’s ability to predict trends which impacts the organization’s successful operations in the future. Considering all management decisions depends on the data gathered, it is essential to use the right forecasting methods to help them make their determinations.
Every decision made depends heavily on the information collected; which means leaders must carefully consider which forecasting technique will provide them with the information they need. The quality of forecasting tools impacts the leader’s ability to gather adequate assumptions about the organization’s future demands and trends (Stark, Mould, & Schweikert, 2008, p. 100). The closer the future information resembles the past, “the more accurate the forecast” argues Sekhri et al. (2006). Depending upon the accuracy of the data collected, forecasting leaders might more effectively formulate strategies to overcome most challenges and move the organization closer toward realizing goal successes in the future. In their first commandment, Tetlock and Gardner (2015) recommend seeking answers for questions that matters; real ones “where effort pays off the most” without failing to predict the “potentially predictable” rather than the “unpredictable.” The more discovered, the more accurate the predictions might be.
Healthcare forecasting techniques
The healthcare field is an ever-evolving entity, and thanks to technology it is alarmingly transforming every day (Thimbleby, 2013). Growing and expanding healthcare services to keep up with the demands present both opportunities and threats. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. The right forecast tools prove valuable at predicting “future health events or situations such as demands for health services and healthcare needs” (Soyiri & Reidpath, 2013, p. 1). Stark, Mould, and Schweikert describe forecasting techniques as the “algorithm that determines projections based on identified business drivers, influencing factors, and business constraints” (2008, p. 102). Stark et al. (2008) add decisions are made associated with categories, including “cause-and-effect for long-range forecasts such as “revenue and patient volume;” time series for short-range forecasts such as “reimbursement rates,” and judgment or best choice” (p. 102).
Healthcare forecasting tools permit decision-makers to analyze trending “health status at the population-level; determine the impacts of different interventions; and make informed program and policy implementation decisions” (Masum, Ranck, & Singer, 2010). As a healthcare executive, one must learn to use forecasting tools effectively. Forecasting tools could reveal information which permits “providers to take appropriate mitigating actions to minimize risks and manage demand” (Soyiri & Reidpath 2013, p. 1). Stark, Mould, and Schweikert (2008) categorize this as a cause-and-effect since it pertains to long-range forecasts and goal setting. Healthcare executives make critical decisions every day mostly “based on subjective experience and judgment,” (Armstrong, 2001); including scheduling demands, organizational realignments, and distributions for funding and “resources to fulfill the demand for service. These decisions align with a combination of Stark, Mould and Schweikert categories, which are used to help leaders make the best decision possible. Tetlock and Gardner (2015) seventh commandment recommend not to become overly confident in data which may include errors, but rather to strike the right balance “between prudence and decisiveness” to avoid forecasting errors and mistakes.
Forecasting for accuracy
Forecasting should be considered an art, and not science for predicting future events. Since prediction needs vary, there is seldom one superior method that works best for every organization in every given situation. What works for one agency, might prove disastrous in another, (Kasapoglu, 2016). Forecasts are seldom perfect. The reality is that the “outside factors that cannot be predicted or controlled often impact the forecast” (Kasapoglu, 2016). For example, statistical methods often fail because historical data not fully utilized could lead to subjectivity, selectivity, biases, and inconsistencies in the way the information is used (Makridakis, 1987). Tetlock and Garner (2015) recommend that leaders “strive to distinguish as many degrees of doubts as the problem permits.” In this commandment, leaders grow in understanding some information may not be all that “informative” or even useful.
Makridakis argues missing data “from which to draw deductions,” create challenges with correlating “various interrelationships between initiatives: such as lifestyle changes to stop smoking, improvement of diet, reduced alcohol intake and increased exercise, all at the same time.” Per Wharam and Weiner (2012) there are benefits and pitfalls to forecasting. Though there are “innovative patient care improvement strategies,” the pitfalls such as exacerbated “health disparities,” ineffective propagated inventions, “lack of transparency,” and short-sightedness also exists (Wharam & Weiner, 2012). Wharam and Weiner (2012) conclude combatting pitfalls and “maximizing benefits of healthcare forecasting” require dedicated involvement from healthcare planners, “method developers, and policymakers.” All members should become actively engaged in gathering the data needed to make the most accurate decision. Tetlock and Garner (2015) suggest the more degrees of uncertainty addressed, the “better a forecaster you are likely to be.”
Conclusion
Healthcare forecasting plays an essential role in the organization’s ability to plan and implement strategies for keeping up with the demands of a rapidly changing health environment. Every decision the leader makes, virtually hinges on the accuracy of the information gathered (Chambers, Mullick, & Smith, 1971). The quality of that information could help leaders foresee and prepare to tackle future challenges to move efficiently toward achieving successes. The use of the right forecasting tools can help leaders combat “future health events or situations such as demands for health services and healthcare needs” and facilitate preventative health strategies (Soyiri, & Reidpath, 2013).
Leaders are cautioned to realize forecasting is not an exact science, and results are rarely perfect. Leaders must develop the ability to “blend experience and good judgment with technical expertise” for accuracy (Chambers, Mullick, & Smith, 1971). The primary goal is to forecast accurately enough that it brings out the best in patients and you as a leader (Tetlock & Gardner, 2015). Healthcare leaders should familiarize themselves with Tetlock and Gardner (2015) commandments to further assist them in creating the forecasted future their organizations need to survive a rapidly-changing market in healthcare.
Call to action
Healthcare leaders must seek out and utilize tools that will help them more efficiently create assessments, plans, and strategies. They must continually seek and analyze information which allows them to devise the best approach that meets the needs of all stakeholders. Remember to acknowledge no one technique works for every situation. Therefore, leaders must regularly consider all forecasting techniques and methodologies to remain successful in the future.
References:
Armstrong, J. S. (2001). Standards and Practices for Forecasting. Principles of Forecasting: A Handbook for Researchers and Practitioners, 1-46. Retrieved from http://forecastingprinciples.com/files/standardshort.pdf
Bauer, J. C. (2015). As Health Care Changes, Don’t Predict – Forecast. Hospitals & Health Networks Magazine. Retrieved from https://www.hhnmag.com/articles/3720-as-health-care-changes-dont-predict-forecast
Conway, M., & Voros, J. (2002). Implementing Organizational Foresight: A Case Study in Learning from the Future, pp. 1-15. Retrieved from https://static1.squarespace.com/static/580c492820099e7e75b9c3b4/t/59b12e709f8dce2f6a64d76c
Forecasting methods in healthcare planning. (2010). HSJ.Co.Uk, Retrieved from http://eres.regent.edu:2048/login?url=https://search-proquest-com.ezproxy.regent.edu/docview/807440289?accountid=13479
Ganguly, A., & Nandi, S. (2016). Using Statistical Forecasting to Optimize Staff Scheduling in Healthcare Organizations. Journal of Health Management 18(1) 172–181. SAGE Retrieved from http://journals.sagepub.com/doi/pdf/10.1177/0972063415625575
Gilliland, M. (2011). Value Added Analysis: Business forecasting effectiveness. Should accuracy be an organization’s biggest concern? Analytics-Magazine. Retrieved from http://analytics-magazine.org/value-added-analysis-business-forecasting-effectiveness/
Kasapoglu, O.A. (2016). Selection of the Forecasting Model in Health Care. Retrieved from http://hospital-medical-management.imedpub.com/selection-of-the-forecasting-model-in-health-care.php?aid=17612
Makridakis, S. (1987). Metaforecasting: Ways of Improving Forecasting Accuracy and Usefulness.
Masum, H., Ranck, J., & Singer, P.A. (2010). Five promising methods for health foresight. Foresight. VOL. 12, NO. 1, pp. 54-66. Emerald Group.
Sekhri, N., Chisholm, R., Longhi, A., Evans, P., Rilling, M., Wilson, E. & Madrid, Y. (2006). Principles for Forecasting Demand for Global Health Products. Retrieved from https://www.cgdev.org/doc/ghprn/Demand_Forecasting_Principles%2CSept-06.pdf
Soyiri, I. N., & Reidpath, D. D. (2013). An Overview of Health Forecasting. Environmental Health and Preventive Medicine. 18(1): 1-9. doi: 10.1007/s1299-012-0294-6
Stark, D., Mould, D., & Schweikert, A. (2008). 5 steps to creating a forecast. Healthcare Financial Management; Apr 2008; 62, 4; ProQuest Central. pp. 100-105
Tetlock, P. and Gardner, D. (2015). Ten Commandments for Aspiring Superforecasters, an excerpt from their book, Superforecasting: The art and science of predicting. New York, NY: Crown Publishers
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