Healthcare Forecasting saves Millions in Hospitals

When it comes to healthcare, the right information can prove vital to providing the proper care, products, and services to people in need.

Health forecasting is a novel area of forecasting, and a valuable tool for predicting future health events or situations such as demands for health services and healthcare needs. It facilitates preventive medicine and health care intervention strategies, by pre-informing health service providers to take appropriate mitigating actions to minimize risks and manage demand. Health forecasting requires reliable data, information and appropriate analytical tools for the prediction of specific health conditions or situations. There is no single approach to health forecasting, and so various methods have often been adopted to forecast aggregate or specific health conditions. Meanwhile, there are no defined health forecasting horizons (time frames) to match the choices of health forecasting methods/approaches that are often applied. The key principles of health forecasting have not also been adequately described to guide the process. This paper provides a brief introduction and theoretical analysis of health forecasting. It describes the key issues that are important for health forecasting, including: definitions, principles of health forecasting, and the properties of health data, which influence the choices of health forecasting methods. Other matters related to the value of health forecasting, and the general challenges associated with developing and using health forecasting services are discussed. 2 This overview is a stimulus for further discussions on standardizing health forecasting approaches and methods that will facilitate health care and health services delivery .

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” 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).

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 3 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.

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 4 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.

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” .

Assertion: Health forecasting is a dynamic process and requires frequent updates. This can be done with novel techniques and data, taking into consideration the principles of health forecasting. The methodologies currently used involve time series analyses with smoothing or moving average models, and less probabilistic forecasting models like QRM, which offers a useful alternative for predicting and forecasting extreme health events. The horizons of health forecasting are important but not classified in the literature, and so the approaches used to forecasting various horizons have no common benchmarks to guide new health forecasts. The patterns of health data can be exploited in health forecasting, using time series analysis or other probabilistic techniques. Health forecasting is a valuable resource for enhancing and promoting health services provision; but it also has a number of drawbacks, which are related either to the data source, methodology or technology. This overview is presented to stimulate further discussions on standardizing health forecasting approaches and methods, so that it can be used as a tool to facilitate health care and health services delivery.