Return on Investment
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).
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...
References: Blachowicz, D. (2008). How to Determine Future HER ROI. Agent-Based Modeling and Simulation Offers a New Alternative to traditional Techniques. The Electronic Health Record. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/19267006
Goldstein, D. E. (2007). Medical informatics 20/20: Quality and electronic health records through collaboration, open solutions, and innovation. Sudbury, Mass: Jones and Bartlett Publishers.
Rodrigues, J. (2010). Health information systems: Concepts, methodologies, tools and applications. Hershey PA: Medical Information Science Reference.
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