care delivery systems or processes rather than improving existing systems or processes. Analysis tools can facilitate an understanding of how complex systems operate, how well they meet their overall goals (e.g., safety, efficiency, reliability, customer satisfaction), and how their performance can be improved with respect to these sometimes complementary, sometimes competing, goals. Controlling a complex system requires a clear understanding of performance expectations and the operating parameters for meeting those expectations; systems control tools, therefore, measure parameters and adjust them to achieve desired performance levels.

The reader will recognize that these categories are somewhat arbitrary—analysis is important to design, systems control is necessary for the effective operation of a system, and so on. Thus, the division is not prescriptive but is helpful for organizing the discussion.


Creating a mathematical representation that describes a feature of a system or a subsystem, although necessary, is seldom sufficient. A mathematical representation can only provide quantitative predictions of performance if it is based on good data. Therefore, sound data about the performance of the system or subsystem are also necessary.

The nature of these data depends on the problem being addressed, of course, but one important generalization can be made. In systems as complex as the health care system, processes are stochastic, that is, individual differences create significant variability over time. For example, the amount of time a physician spends with an individual patient varies greatly depending on the patient’s medical condition. To analyze the system, therefore, it is necessary to know both the mean and variance for relevant process times, such as the time involved in the delivery of each process, the fraction of patients who require each process, the number and required capabilities of individual providers, and the incidence of patients who do not keep appointments. Statistical distributions of times and usage for processes and providers also vary, not only among processes, but also among facilities. No norms have been established, however, so they must be determined. These issues are addressed in the discussion on queuing theory.

The variables to be measured depend on the particular analysis and, because data collection is often time consuming, determining which variables to measure is critical to the timely analysis of a system. However, understanding a complex system always entails time and effort to make measurements and observations.

The reader will note that the need for data is cited in many discussions of the applicability and uses of systems-engineering tools. Some of these needs can be met with a single sequence of measurements; others require massive databases. Good data are necessary to any systems analysis, but, because systems-engineering tools have not been routinely used in the health care delivery system, data for these analyses are often inadequate or missing altogether.


Systems-design tools are primarily used to create systems that meet the needs/desires of stakeholders (Table 3-1). In the health care system, stakeholders include patients seeking care, health care providers, organizations that must operate efficiently and provide a satisfying environment for caregivers and patients, and participants in the regulatory/financial environment that must provide mass access to good care. The system must meet the needs of all of these stakeholders.

Concurrent Engineering

In the last 20 years, manufacturers in a variety of industries have used a procedure called concurrent engineering to design, engineer, and manufacture products that meet the needs and aspirations of customers, are defect free, and can be produced cost effectively. Concurrent engineering can be thought of as a disciplined approach to overcoming silos of function and responsibility, enabling different functional units to understand how their individual capabilities and efforts can be optimized as a system. Using concurrent engineering, a team of specialists from all affected areas (departments) in an organization is established; this team is then collectively responsible for the design of a product or process. The team considers “from the outset…all elements of the product life-cycle, from conception through disposal, including quality, cost, schedule, and user requirement” (Winner et al., 1988). The process begins with the initial concept and continues until a successful product or process is delivered to the customer.

Organizations that use the concurrent-engineering process have realized substantial benefits: fewer design changes are

TABLE 3-1 Systems-Design Tools

Tool/Research Area





Concurrent engineering and quality function deployment





Human-factors engineering





Tools for failure analysis





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