Systems integration is defined by the Society for Simulation in Healthcare (SSH) as “those simulation programs which demonstrate consistent, planned, collaborative, integrated and iterative application of simulation-based assessment and teaching activities with systems engineering and risk-­management principles to achieve excellent bedside clinical care, enhanced patient safety, and improved metrics across the healthcare system” [5].

Systems integration is a new concept to health care and therefore requires a new and thoughtful approach to meeting these standards. Adopting solutions from other disciplines and industries may offer an innovative “toolkit” that is applicable to the (IOM-driven) transformations essential for health care today.

Systems engineering tools have been used in a wide variety of applications to achieve major improvements in the quality, efficiency, safety, and/or customer centeredness of processes, products, and services in a wide range of manufacturing and services industries [3]. The addition of medical simulation to this evidence-based toolkit is not only complimentary but can enhance the associated transformative effects. Simulation techniques offer opportunities to enhance the accountability of health-care-providers’ skills acquisition through formative and summative training, which offer consistency, objectivity, and demonstrated proficiency in the management of clinically realistic and relevant problems.

Contemporary clinical medicine not only includes the obvious provider-patient interaction but also includes many complex “system” dimensions in the patient care setting. These include health-care provider performance characteristics; organizational factors including physician, nursing, allied health staff availability, environmental considerations, patient, and family member preferences; and the interactions between these components of the complex system [6]. Within organizations, each component is interconnected and interdependent with other system components. The impact of interactions between system components is difficult to predict since they are often remote in time and space. Inadequacy in such system components can negatively affect care delivery. System-related components (patient flow, work environment, information systems, human factors) contribute to delays and errors in care. The World Health Organization (WHO) has suggested that the (1) lack of communication, (2) inadequate coordination, and (3) presence of latent organization failures are important priorities for improving patient safety in developed and developing countries alike [7].

Outside the conceptual “walls” of an organization, complexity remains. Health care is often not delivered within a single “island-like” independent facility. Often, care delivery occurs within a large enterprise or system of systems of health-care delivery. Care across such system components is rarely seamless. Beyond the (larger) enterprise walls exist environmental forces (see Fig. 10.2) including external ­regulatory agencies, the press, public perception and other market forces, malpractice, and medicolegal concerns.

Health-care delivery systems continue to increase in complexity because of many factors including improvements in technology, advanced diagnostic technology, complex disease processes, increasing accountability and transparency of internal processes, an aging population, workforce shortages, generational differences, social networking, and mobile technology. These factors will transform both patient-provider and provider-provider interactions. Health-care’s increased complexity and sophistication comes at a price- increased risk of error and poor patient outcome. Sir Cyril Chantler stated, “Medicine used to be simple, ineffective and relatively safe. Now it is complex, effective and potentially dangerous” [8].

Although concepts of systems and engineering applications applied to health care may seem novel, large successful organizations have used systems engineering principles for many years. Dr. Henry Plummer, a Mayo Clinic internist from 1901 to 1936, creatively engineered many aspects of the world’s first integrated academic group practice of medicine and surgery and remains a recognized pioneer in conceptually embedding engineering philosophy into the practice of medicine [9]. Plummer’s innovations included an integrated inpatient and outpatient medical record, a “master sheet” clinical diagnoses list for research and practice improvement activities, a pneumatic tube system to distribute records and laboratory specimens across multiple geographic sites, and a color-coded lighting system to support central booking and the patient visitation process. All of these inventions remain in use today. In fact, Plummer’s novel applications served as the origins of the Division of Systems and Procedures at Mayo Clinic in 1947. Dick Cleeremans (Section Head 1966–1982) stated: “The decision by the Mayo Medical Center to hire industrial engineers in those early years was in keeping with the Mayo commitment to the patient, physician/patient relationship and Dr. Plummer’s expectation that the system and the organization of the clinical practice were important and a worthy management activity.”

More recently, Avedis Donabedian popularized the structure-process-outcome framework with which to assess quality-improvement efforts [10]. Donabedian also described the Systems Engineering Initiative to Patient Safety (SEIPS) framework, designed to improve health-care delivery in complex health-care systems (Fig. 10.3) [10, 11].

System approaches focus on the working environment rather than on the errors of individual providers, as the likelihood of specific errors increases with unfavorable ­environmental conditions. The effective system intervention is to design, test, and enhance the system’s components so it can prevent human error and identify a variety of vulnerabilities such as distraction, fatigue, and other latent factors [12].

Systems-based modeling and simulation (M&S) has several advantages for clinical decision support, systems analysis, and experimentation for multivariate system components that cannot be achieved by traditional quality-improvement processes. Computer modeling can simulate processes of health-care delivery by engineering techniques such as ­discrete-event simulation. In discrete-event simulation, the operation of a system is represented as a chronological sequence of events; events occur at a particular instant in time, thus defining a change of state in the system [13]. Because discrete-event simulation is time oriented (as clinical patient outcomes often are based on timeliness of delivered care), this form of computer-based simulation lends itself well to systems engineering analyses of contemporary complex health-care systems.

Coupling computer modeling techniques (such as discrete-event simulation) to realistic contemporary simulation programs can provide a synergy of process improvement design and analysis offering exceptionally strong opportunities in transformational process improvement. For instance, novel process optimization mechanisms through integration of engineering principles for health-care system analysis, simulation-based drills, and workflow redesigns for testing proposed interventions (virtual clinical trials or quality improvement) before clinical deployment may reduce the potential for preventable harm to patients.

Consider a patient entering an emergency department with acute abdominal pain, possibly due to a surgical ­emergency, as the beginning of a “process.” Computer models (e.g., a process flow diagram) can readily demonstrate patient time-dependent flow for a population of such patients, between the time of emergency room entry to the definitive surgery. Because many process components must occur (history and physical, laboratory analyses, radiographic imaging, consultations) in both series and in parallel, equations describing such patient “traffic” can be defined, which, if accurate enough, can be descriptors of both current and future performance. Such computer-based models are excellent adjuncts for process improvement via immersive experiential simulation such as drills and in situ simulations.

A stepwise M&S approach follows that illustrates the potential for improved health-care delivery process:

The National Academy of Engineering and Institute of Medicine of the National Academies directed attention to the issue of systems engineering and integration with their joint report in 2005, Building a Better Delivery System: A New Engineering/Health Care Partnership [3]. The proposed collaboration between clinicians, engineers, researchers, educators, and experts from medical informatics and management will provide a clinically relevant, systematic approach and a comprehensive solution to many of the challenging problems in clinical medicine.

The overall goal of systems engineering is to produce a system that meets the needs of all users or stakeholders within the constraints that govern the system’s operation. Systems engineering requires a variety of quantitative and qualitative tools for analyzing and interpreting system models (Table 10.2). Tools from psychology, computer science, operations research, management and economics, and mathematics are commonly utilized in systems engineering across a wide array of industries. Quantitative tools include ­optimization methods, control theory, stochastic modeling and simulation, statistics, utility theory, decision analysis, and economics. Mathematical techniques have the capability of solving large-scale, complex problems optimally using computerized algorithms [14].

Many of the systems engineering tools are found within different quality-improvement strategies, including Six Sigma, Toyota Production System, and Lean. But within these methodologies, examples of system engineering tools that have been utilized in health care include [3]:

Process engineering is a subset of systems engineering and represents a system process component, essentially a “building block” of a complex adaptive system (Table 10.3). A process is a set of interrelated tasks or steps that together transform inputs into a common output (i.e., workflow) toward a goal, (such as in the assessment steps involved within the “acute abdominal pain” patient group presenting to an emergency department described earlier). The tasks can be executed by people or technology utilizing available resources. Thus, within the processes of (complex adaptive) systems improvement, a focus within individual processes is a natural phenomenon, while recognizing the interrelated nature of system (process) component parts. The introduction of new processes, such as technology (and support) associated with the introduction of an electronic medical record (EMR), requires business process redesign before such processes are implemented. When, in an effort to meet project management deadlines, engineering processes are rushed or completely ignored, system havoc may ensue.

In terms of simulation, systems integration (SI) is the recognition of a variety of key principles as one better understands the role and capacity of simulation within the complex adaptive health-care system. This is intended to achieve a functional, highly efficient organization of dynamic teams and processes, interfacing to collectively and artfully optimize patient care. Therefore a simulation program’s impact on an organization’s intrinsic function includes the following principles as they apply to projects that facilitate seamless and optimal effective heath system process integration.

SI projects are:

Simulation can also be used strategically by the health-care organization to achieve business goals. When building new facilities, simulation can be used alone or within Six Sigma or Lean concepts [15, 16], systems engineering tools, to ensure that a facility develops efficient and effective workflows with input from the staff. The new facility can then be tested in simulation to identify and address any process or human factor issues before opening.

Organizational leaders, including clinicians, risk managers, and/or quality/safety process managers, may approach the simulation program with specific safety initiative. Below are a series of eight case studies highlighting the use of simulation for SI purposes.