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Return on Investments in Healthcare IT infrastructure for healthcare projects

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Running Head: RETURN ON INVESTMENT 1
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Return on Investment
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RETURN ON INVESTMENT 2
QUESTION 1
Experts argue that its essentials to establish ROI parameters before embarking on new
public health projects especially those involve acquisition of new information technologies. This
means that before embarking on the projects, organizations should calculate the incremental gain
from such actions basing their parameters on the long term gain. Before undertaking healthcare
information systems and related projects, top decision makers should evaluate the investment
potential by comparing the timing and magnitude of the benefits to the investment costs. On this
reason, an effective determination of ROI of information systems on healthcare helps to provide
an excellent decision in consideration of alternative designs for their performance, stakeholder
expectations and costs. A recent study by The Standish Group International Inc., has found that
the U.S had wasted over $55 billion, which was an increase in unnecessary overspending for IT
related projects in healthcare. The determination of ROI on healthcare systems helps to avoid
unnecessary overspending on IT infrastructure.
Research has proven that unlike basic equipment for healthcare that require routine
improvements, in terms of ROI, clinical decision support applications are difficult to analyze.
For this reason, establishing effective parameters such as quantifying the real value of such
systems is essential. Quantifying the value of healthcare information systems helps decision
makers to understand interrelated factors such as clinical practices, patient care, organizational
structure and medical outcome. On this token, several healthcare organizations in the United
States have employed varied approaches to gain deep insights on their knowledge of business
processes before investing in healthcare systems. This has helped the organizations to avoid
costly and ineffective processes (Blachowicz, 2008).

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RETURN ON INVESTMENT 3
The establishment of ROI parameters on complex healthcare systems helps to determine
their long term impact on efficiency. Banner health, with the assistance of IBM healthcare, used
modeling and simulation to analyze the impact of technology on efficiency. The approach found
that the implementation of information systems to the organization helped to improve the
delivery of clinical services. This is because the information of the patients was effectively
shared among the physicians and nurses. This reduced errors in data entry as patient’s historical
information was safely guarded. The result of the ROI simulation and modeling proved that the
benefits of establishing information systems in healthcare outweighed their costs. The approach
of using ROI helps to prove intangible benefits such as improvement on efficiency and quality of
healthcare delivery. The establishment of returns of investment parameters helps healthcare
providers to access risk issues involved in the establishment of new information systems. The
pilot simulation model helps policy makers to discover the inconsistencies of such systems and
also the effectiveness of such systems. The simulation parameter helps one to discover the
ongoing and routine evaluations of the entire systems. This helps policy makers in analyzing
effective outcomes after the establishment of healthcare policies (Goldstein, 2007).
Establishment of ROI parameters such as simulation models helps to represent the future
outcomes after the establishment of information systems in the healthcare sector. The models
usually predict the outcome of an action being undertaken by decision makers. Parameter such
as agent-based modeling and simulation helps decision makers to have an experiment of the real
world situation of the healthcare outcome. The use of the experiment helps decision and policy
makers to use little investment that acts as an upfront to pose a variety of health delivery and
usage scenarios. This helps to examine the outcomes of their decisions in terms of long term
system dynamics and expected costs (Rodrigues, 2010).

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