Health forecasting is a valuable tool that can help predict future healthcare needs and demands. It allows healthcare providers to better plan and implement preventative measures. The article discusses how health forecasting requires reliable data and analytical tools to predict specific health conditions. It also notes there is no single approach to health forecasting and various methods have been used. The article aims to provide an overview of health forecasting and stimulate further discussion around standardizing approaches and methods to help facilitate healthcare delivery.

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Healthcare Forecasting saves Millions in Hospitals
By.Dr.Mahboob Khan
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.

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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

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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

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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.

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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” .

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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.