Healthcare Finance: Analyzing Data for Strategic Budget Decisions

This report explores the critical aspects of financial management within healthcare organizations, focusing on the use of comparative data for planning, control, and decision-making. It addresses the challenges in obtaining accurate and comparable financial data due to data silos, large data volumes, and data volatility. The report highlights the importance of considering inflation factors, currency measures, and standardized methods for ensuring data comparability. It also discusses the role of operating and capital budgets in financial planning. The report suggests implementing an enterprise data warehouse (EDW) to overcome data access challenges and facilitate comprehensive analysis, enabling informed strategic decisions and improved financial management in healthcare.

Health Care Finance
1. Introduction
“An empowered organization is the one in which individuals have the knowledge, skill,
desire and opportunity to personally succeed in a way that leads to collective organizational
success” (Covey & Howard, 2011). For the success and stability of the organisation, financial
management within a healthcare organization is critical. Healthcare financial management
includes the following points:
a. To identify tools to review and measure comparative data within the same organization for
a different period as well as data across different healthcare units using common sizing, trend
analysis and forecasting of data. The same relative basis is used for common sizing of data.
Common size converts the numbers into percentages in order to carry out a comparative
analysis. Trend analysis is a time-limiting comparison of numbers. The horizontal analysis
examines amounts from financial statements over a long period of years. The trend analysis is
also known as horizontal analysis. The amounts from past financial statements will be
restated to be a percentage of the amounts from a base year.
b. Every amount in a financial statement using vertical analysis shows as a percentage of
another amount. The vertical analysis of the balance sheet displays each amount on the asset
side as a percentage of the total assets. Similarly, each item in a sales statement is expressed
as a percentage of net sales using the vertical analysis (Baker, Baker & Dworkin 2017).
c. Use of comparative data to compare expenditures of current position to that of the overall
budget and also compare actual current expenditure to that of the previous periods in the
same organization or different organizations having a similar setup. In the context of this
method, partial year expenses can be analyzed using the estimated data and prior year
expenditure. An inflation factor is used to calculate the inflation effect over time when
financial parameters are compared over different periods (Baker, Baker and Dworkin 2017).
d. Operating budgets:
The goals of the organization define specific activities, how they are put together and at what
levels of operation. The performance standards of an organization set levels of performance.
These activities are financially measured by a budget. In essence, there are two budget types,
i.e. operational and capital budgets. There are fundamental differences. Operating budgets
usually cover the coming year (12 months) and actually cover short and operating turnover.
Capital expenses budgets may cover the capital expenditure of the organization (no operating
income or expenditure) as well as future years and may cover a period of five or even ten
years (Baker, Baker & Dworkin 2017).
2. Getting Accurate Financial Data Comparable in Healthcare Report
a. The objective of the report
The hospital's management committee always wants its hospital to operate as a profit centre
without compromising its quality. The whole evaluation is presented on the basis of data.
And they are right to worry–after all, finance experts need accurate, timely and effective
financial data to decide their organizations in an informed way. However, for taking strategic
decisions it is difficult for the management to get appropriate information from a large

number of data sources which are incoherent in nature and the factors responsible for this
challenges are explained below:
b. Data is stored in many different silos.
Health data typically are saved throughout the organization in a number of independent
sources (also called transactional systems) ("Clinical Silos Overcoming" 2016). Source
systems include general leader, supply chain and human resources, costs systems, patient
satisfaction systems and databases for Excel and Access, for example. Although each source
system offers a certain functionality for the needs of this particular department, source
systems as a whole cannot share information. This lack of interconnectedness reduces the
capacity of the data analysts at the enterprise level to assess data to recommend
improvements.
c. Health systems need to store large amounts of data.
The data relating to each patient is stored in the data base and if we consider thousands of
patients, the storage size of the data will be very huge ( in terrabytes). To analyze and
generate a sinple report from such huge data, the analysts require time-intensive efforts who
normally have backlogs of requests in their queues.
d. Healthcare data is highly volatile.
Definitions of business are complex and the medical industry is constantly changing metrics
which produce high data volatility ("Healthcare data for five reasons is unique and difficult to
measure" 2014). Let us consider, inter alia, the length of stay (LOS), which the clinicians also
report, a key financial measure. But the problem lies in that there may be different definitions
of length of stay (LOS). Decisions will be different if the user does not know which
definition of LOS is to be applied for a particular query. With every problem in collecting
accurate information in a timely manner to make informed business decisions, decision-
makers can feel confident that they are seeing reliable information. And it is not easy to know
where the dollar is spent or where improvements can be made to offer the value-added care
that is so critical for the health environment today without a good idea of how your
operations go.
e. Inflation factors
While comparing financial data of the current period with the past and the future periods,
inflation factor is to be taken into account. Inflation can be defined as an increase in the
average price level of goods and services or a reduction in the purchasing power of the
currency unit. At the end of four years, an item of $1, with a current value of $1,464 will
have a 10% inflation rate. So while comparing both past value and future value, to make it
linear the same logic is applied to find the present value ("CHAPTER 3: FINANCIAL
ANALYSIS WITH INFLATION", 2019).
f. Currency measures
Similar like inflation, a factor is applied to the amounts due to fluctuation of currency rates
during the periods considered for comparison of financial data (Baker, Baker & Dworkin
2017).
g. Standardized methods

Finally, standardized measures aid comparability and especially assist in performance
measurement using longitudinal data comparison. This is done after applying time variance
variables like inflation, currency fluctuations etc. ("Standardizing The Data Of Different
Scales - Which Method To Use?”, 2017).
3.0 Conclusion
The problem of handling huge data and too many silo sources can be solved by designing a
system using the concept of enterprise data warehouse (EDW) with supporting analytics
applications. The applications can generate all possible reports which have been discussed.
An EDW overcomes the difficulties of accessing volumes by working as a layer above all
already existing transaction application databases. The EDW does not replace the systems of
individual sources; the systems continue to produce their departmental value. Instead, EDW
pulls raw data from all sources and stores them as a single source of truth in an easily
accessible central repository (Garcelon et al. 2018).
An EDW is a reservoir of data from where users get access and derive the required output for
his department. In other words, when a department has to analyze his department figures, he
can access the same data warehouse by running the built-in analytical queries.
The analysis made on the financial management of healthcare data enables to know the
fundamental concepts of accounting principles which are applicable in a medical setup.
References
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doi: 10.1523/jneurosci.5841-10.2011
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