Transforming Healthcare: Leveraging the Complementarities of Health Informatics and Systems Engineering

Transforming Healthcare: Leveraging the Complementarities of Health Informatics and Systems Engineering

Kalyan Sunder Pasupathy (University of Missouri, USA)
Copyright: © 2010 |Pages: 21
DOI: 10.4018/jhdri.2010040103
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Abstract

The healthcare system is facing several major quality challenges. In 2005, the Institute of Medicine published a report on how systems engineering and improvements in information technology can help address and solve some of these challenges. Systems engineering (SE) and health informatics (HI) have been undergoing advancements over the years. Health systems engineering is an interdisciplinary field that has grown to encompass the design, analysis, and management of complex health systems to improve quality and performance. HI is another interdisciplinary field around collection, storage, retrieval and analysis of data, reporting and enabling use of information, and (re)design and maintenance of systems to do all of these. SE and HI are complementary in their approach to identification of problems and solution procedure for (re)design and improvement. This combination has major implications for care delivery, research, and education to address the challenges.
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Introduction And Background

With advances in clinical sciences and medical technology, people are living longer and there is a greater demand for healthcare services. The Institute of Medicine’s report (Kohn et al., 2000) describes serious concerns within the health care industry owing to undesirable outcomes, including safety and quality issues. These issues have been attributed to breakdowns in processes embedded in the service delivery structure (Institute of Medicine, 2001). Healthcare delivery systems have two major goals – doing things right and doing the right things. These are known as efficiency and effectiveness, respectively, in systems theory. With rising costs, efficiency is an important goal for all systems including healthcare, and this can be ensured through proper allocation of resources and reducing costs. Effectiveness and obtaining the desired outcomes, on the other hand, is all the more important in health care, considering the dire consequences of errors and process breakdowns that may lead to harm and even death. Yet, healthcare organizations are struggling to provide safe and high quality care, while reducing costs. Health care expenditure has been on the rise and the United States, for example, spent nearly two trillion dollars in 2005. This amount accounted for 16% of its GDP, a proportion which is higher than any other country in the world (England, 2007; OECD, 2007). Hence, patients, healthcare providers, insurance companies, and health care policymakers are striving to find more cost-effective methods to deliver health care. Providers are increasingly looking at methods that would help them reduce costs without compromising on the quality of care. While costs are on the rise, there are also safety decrements and as noted, there may be as high as 98,000 deaths each year as a result of medical errors.

The Institute of Medicine (IOM) identified a four-level, patient-centered conceptual model as the unifying framework and guiding principle for redesigning and improving the healthcare system, to achieve better performance goals (Reid et al., 2005, p. 20). This IOM report proposes using information technology and systems-engineering tools to provide safe and high quality care that is efficient, effective, and patient-centered.

In today’s healthcare organization, data is collected every day and stored in large databases. Intuitively, when data are abundant and no other sources of expert knowledge exist, one could expect that knowledge could be gathered from the data that are available. Informatics is the science of collecting, storing, retrieving, analyzing, and reporting information acquired from health data (Coiera, 2003; Protti, 1995). Given the abundant information on clinical, financial, human resources, care delivery, quality, patient satisfaction and outcomes, data mining tools are needed for decision-making. These tools extract hidden information from large databases, so that organizations are able to identify important patterns, predict future behaviors, and make proactive, knowledge driven decisions (Medina-Borja & Pasupathy, 2007). Such decisions (both clinical and managerial) will not be effective, unless they are based on representations of processes that transcend any of the individual areas above, within a health system (e.g. clinical, financial, etc.) If no one within the organization has a holistic understanding of the system, finding an expert who will clarify the relationships or finding enough organizational documentation to point in the right direction is a challenging task. To be able to have a holistic representation of the processes within a hospital, an understanding of the broader systems, including the clinical system, hospital organizational system, etc. is necessary. Further, how can the relationships “mined” and the resulting patterns be validated against the real-world behavior of health systems? Several systems engineering tools classified under system-design, system-analysis, and system-control tools can come to the rescue. These are proposed by the Institute of Medicine report in 2005 under the umbrella of systems engineering (Reid et al., 2005).

The goal of this article is to accomplish the following,

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