Improving Health Care Management Through the Use of Dynamic Simulation Modeling and Health Information Systems

Improving Health Care Management Through the Use of Dynamic Simulation Modeling and Health Information Systems

Daniel Goldsmith (MIT Sloan School of Management, USA) and Michael Siegel (MIT Sloan School of Management, USA)
DOI: 10.4018/jitsa.2012010102
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Abstract

To better understand the performance of hospital operations in response to IT-enabled improvement, this paper reports the results of a system dynamics model designed to improve core medical processes. Utilizing system dynamics modeling and emerging Health Information Systems (HIS) data, the authors demonstrate how current behavior within the hospital leads to a ‘stove-pipe’ effect, in which each functional group employs policies that are rational at the group level, but that lead to inefficiencies at the hospital level. The authors recommend management improvements in both materials and staff utilization to address the stove-pipe effect, estimate the resultant cost-saving, and report the results of an experiment conducted in the hospital to validate the approach. Results indicate that the major gains in health information systems use will accompany new information gathering capabilities, as these capabilities result in collections of data that can be used to greatly improve patient safety, hospital operations, and medical decision support.
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Introduction

This paper discusses the strategies required to develop system dynamics capabilities in hospital environments and to use simulation analysis to help hospital organizations address important operational problems. The system dynamics perspective has the ability to create improvements in strategic management, both in overcoming single-issue challenges and in spurring continuous process improvement (Sterman, 2000). Prior system dynamics work has often addressed systematic health care challenges from a disease perspective, such as oral health (Hirsch et al., 1975); cardiovascular disease (Hirsch & Myers,1975; Luginbuhl et al., 1981); diabetes (Homer et al., 2004; Jones et al., 2006); obesity (Homer et al., 2006); smoking (Tengs et al., 2001); and chronic illnesses more generally (Hirsch & Immediato, 1999; Homer et al., 2007) This work, however, contributes to a growing body of literature that focuses on how structures and decisions embedded within hospital organizations subvert efforts to change and improve the performance of health care delivery, such as ward management (Akiyama et al., 2009); patient flow (Wolstenholme, 1999); and safe design capacity (Wolstenholme et al., 2007).

Of particular importance are the dynamics relating to the emergence of new Health Information Systems (HIS) that have the potential to revolutionize hospital practice and management, improve patient safety, and create vast new rich new datasets. Many excellent HIS systems, however, go unused or under-utilized because HIS implementation is met with resistance by staff and managers. For example, Dr. Steven Cantrill, a practicing emergency medical doctor, describes the challenge as thus: “health-care providers (especially physicians) have little tolerance for systems that serve as impediments to getting their work done, often regardless of what positives might accrue from using such a system.” (Cantrill, 2010) Further, if HIS are implemented, unanticipated behavioral decisions resulting from HIS implementation can create counterintuitive outcomes that actually subvert overall hospital efficiency. Implementations resulting in unintended negative “side-effects” include computerized prescriber order entry (Zhan et al., 2006), electronic health records (Sidorov, 2006), bar code technology (Poon, 2006), and overall HIT systems (Ash et al., 2003; Wears & Berg, 2005; Kohn, 2000). Finally, once developed, there are often significant barriers to utilize HIS data-sets to help hospitals implement changes and manage operations (Goodman et al., 2011).

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