3
The Tools of Systems Engineering

An understanding of the performance of large-scale systems must be based on an understanding of the performance of each element in the system and interactions among these elements. Thus, understanding a large, disaggregated system such as the health care delivery system with its multitude of individual parts, including patients with various medical conditions, physicians, clinics, hospitals, pharmacies, rehabilitation services, home nurses, and many more, can be daunting. To add to the complexity of improving this system, different stakeholders have different performance measures. Patients expect safe, effective treatment to be available as needed at an affordable cost. Health care provider organizations want the most efficient use of personnel and physical resources at the lowest cost. Health care providers want to serve patients effectively and minimize, or at least reduce, the time devoted to other tasks and obligations. Advancing all six of the IOM quality aims for the twenty-first century health care system—safety, effectiveness, timeliness, patient-centeredness, efficiency, and equity—will require understanding the needs and performance measures of all stakeholders and making necessary trade-offs among them (Hollnagel et al., 2005).

Understanding interactions and making trade-offs in such a complex system is difficult, sometimes even impossible, without mathematical tools, many of them based on operations research, a discipline that evolved during World War II when mathematicians, physicists, and statisticians were asked to solve complex operational problems. Since then, these tools have been used to create highly reliable, safe, efficient, customer-focused systems in transportation, manufacturing, telecommunications, and finance. Based on these and other experiences, the committee believes that they can also be used to improve the health care sector (McDonough et al., 2004). Indeed, improvements in health care quality and productivity have already been demonstrated on a limited scale in isolated elements at all four levels of the health care system (patient, care team, organization, and environment). These limited, but encouraging, first steps led the committee to conclude that the effective, widespread use of these tools could lead to significant improvements in the quality of care and increases in productivity throughout the health care system.

This chapter provides detailed descriptions of several families of systems-engineering tools and related research that have demonstrated significant potential for addressing systemic quality and cost challenges in U.S. health care. Although the descriptions do not include all of the tools or all of the challenges to the health care system, they illustrate potential contributions at all four levels of the health care system in all six characteristics identified by IOM.

The first part of this chapter is focused on three major functional areas of application for mathematical tools, namely the design, analysis, and control of large, complex systems; discussions include examples of current or potential uses in health care delivery. In the second part of the chapter, mathematical tools are considered from the perspective of the four levels of the health care system; the tools most relevant to the challenges and opportunities at each level are highlighted. Many of the tools described in this chapter are applicable to more than one level but generally address different questions or issues at each level. It will become obvious to the reader that each level of the system has different data requirements and a different reliance on information/communications technology systems.

The systems tools discussed below have been shown to provide valuable assistance in understanding the operation and management of complex systems. Some of these have been used sparingly, but successfully, in various circumstances in health care. Others will require further development and adaptation for use in the health care environment. To assist the reader in classifying these tools, they are divided into three sections: (1) tools for systems design; (2) tools for systems analysis; and (3) tools for systems control. Design tools are primarily used for creating new health



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Building a Better Delivery System: A New Engineering/Health Care Partnership 3 The Tools of Systems Engineering An understanding of the performance of large-scale systems must be based on an understanding of the performance of each element in the system and interactions among these elements. Thus, understanding a large, disaggregated system such as the health care delivery system with its multitude of individual parts, including patients with various medical conditions, physicians, clinics, hospitals, pharmacies, rehabilitation services, home nurses, and many more, can be daunting. To add to the complexity of improving this system, different stakeholders have different performance measures. Patients expect safe, effective treatment to be available as needed at an affordable cost. Health care provider organizations want the most efficient use of personnel and physical resources at the lowest cost. Health care providers want to serve patients effectively and minimize, or at least reduce, the time devoted to other tasks and obligations. Advancing all six of the IOM quality aims for the twenty-first century health care system—safety, effectiveness, timeliness, patient-centeredness, efficiency, and equity—will require understanding the needs and performance measures of all stakeholders and making necessary trade-offs among them (Hollnagel et al., 2005). Understanding interactions and making trade-offs in such a complex system is difficult, sometimes even impossible, without mathematical tools, many of them based on operations research, a discipline that evolved during World War II when mathematicians, physicists, and statisticians were asked to solve complex operational problems. Since then, these tools have been used to create highly reliable, safe, efficient, customer-focused systems in transportation, manufacturing, telecommunications, and finance. Based on these and other experiences, the committee believes that they can also be used to improve the health care sector (McDonough et al., 2004). Indeed, improvements in health care quality and productivity have already been demonstrated on a limited scale in isolated elements at all four levels of the health care system (patient, care team, organization, and environment). These limited, but encouraging, first steps led the committee to conclude that the effective, widespread use of these tools could lead to significant improvements in the quality of care and increases in productivity throughout the health care system. This chapter provides detailed descriptions of several families of systems-engineering tools and related research that have demonstrated significant potential for addressing systemic quality and cost challenges in U.S. health care. Although the descriptions do not include all of the tools or all of the challenges to the health care system, they illustrate potential contributions at all four levels of the health care system in all six characteristics identified by IOM. The first part of this chapter is focused on three major functional areas of application for mathematical tools, namely the design, analysis, and control of large, complex systems; discussions include examples of current or potential uses in health care delivery. In the second part of the chapter, mathematical tools are considered from the perspective of the four levels of the health care system; the tools most relevant to the challenges and opportunities at each level are highlighted. Many of the tools described in this chapter are applicable to more than one level but generally address different questions or issues at each level. It will become obvious to the reader that each level of the system has different data requirements and a different reliance on information/communications technology systems. The systems tools discussed below have been shown to provide valuable assistance in understanding the operation and management of complex systems. Some of these have been used sparingly, but successfully, in various circumstances in health care. Others will require further development and adaptation for use in the health care environment. To assist the reader in classifying these tools, they are divided into three sections: (1) tools for systems design; (2) tools for systems analysis; and (3) tools for systems control. Design tools are primarily used for creating new health

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Building a Better Delivery System: A New Engineering/Health Care Partnership 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. THE NEED FOR GOOD DATA 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 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 Patient Team Organization Environment Concurrent engineering and quality function deployment   X X   Human-factors engineering X X X X Tools for failure analysis   X X  

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Building a Better Delivery System: A New Engineering/Health Care Partnership required once the production or process has been introduced; the time from design to full production is significantly shortened; the number of defects in the product is greatly reduced; and the process (or production) costs less. In addition to these direct, readily measurable benefits, the concurrent engineering process can also yield indirect, or “spill over,” benefits to an organization. These include improved cross-disciplinary/ cross-unit learning, improved teamwork, improved quantitative and qualitative characterizations of processes and systems, and improved understanding and appreciation of the overall system (i.e., how the decisions and actions of individual units affect the performance of the organization as a whole.) Concurrent engineering has been used mostly in the manufacturing arena, but the idea can be applied to the health care delivery system to develop a process for delivering care rather than manufacturing a product. Concurrent engineering teams have different compositions for different organizations (or “processes”). A concurrent engineering team for an operating room (OR), for example, would include surgeons, nurses, laboratory technicians, and others, depending on the goal. For other units of a hospital (e.g., an intensive care unit [ICU], a neonatal care unit, the business office, etc.), teams would include the individuals and members of groups relevant to that unit. For the hospital as a whole, teams would be established at many levels. Each unit team would provide input to a more comprehensive team with members from all parts of the hospital, including the admissions staff, laboratory technicians, nurses, pharmacists, physicians, physical therapists, representatives of the OR, ICU, and so on. Each unit team would receive feedback from the comprehensive team, which would provide a basis for modifying the original conclusions and moving closer to optimizing overall performance. For the extended enterprise, the team would include members of other caregiver groups (e.g., pharmacists, rehabilitation technicians, home nurses, etc.). Simply defined, concurrent engineering is an attempt to break down silos in an enterprise through effective teamwork. Many tools have been developed to assist in this process for manufacturing operations, but for our purposes we will highlight only one—quality functional deployment (QFD). Quality Functional Deployment1 QFD can be very useful for designing processes and procedures that meet the level of service a customer/patient wants. Although QFD is not a mathematical construct, it provides a structure to help the concurrent engineering team identify (1) factors that determine the quality of performance and (2) actions that ensure the desired performance is achieved. The QFD procedure might be applicable to a team in an emergency room, the operation of an ambulatory clinic, or the operation of an entire hospital. QFD is a procedure by which a stakeholder’s wants/needs are spread throughout the elements of an organization to ensure that the final product/service satisfies those wants/ needs. The concept of QFD, which was introduced in Japan by Katsukichi Ishihari in 1969, was later developed for U.S. manufacturers by L.P. Sullivan (1986) and Hauser and Clausing (1988). Sullivan describes QFD as “a system to assure that customer needs drive the product design and production process” by translating them into the technical requirements of the product and then into a process for delivering a product/service that meets those requirements. QFD has been used to design a wide range of products and processes, including a new automobile (Sullivan, 1988) and wave-solder processes used in manufacturing integrated circuits (Shina, 1991). The QFD procedure is also applicable to the development of a service function, such as the design of a library system, the provision of fast food, the creation of a traffic-control system, or the delivery of health care (Chaplin et al., 1999). The QFD process begins with the identification of team members who represent all activities involved in the creation of the final product/process/service. Team members are chosen for their expertise and not just to represent their organizational units, and the team strives to make the best decisions for the organization as a whole. The QFD team begins by listing stakeholders’ wants. The number of stakeholders can vary greatly, depending on the unit being studied. Stakeholders in the health care system could include inpatients, outpatients, ambulatory patients, physicians, nurses, payers, health care system managers, even communities, or they could include only a few of these. Once the stakeholders have been identified, the team compiles a list of their needs. Depending on who the stakeholders are, these might include ready access to physicians, low costs, absence of paperwork, prompt payment of claims, high-quality treatment, rewarding careers, keeping of appointments, financial system stability, and so forth. Obviously, some of these needs may conflict with each other. For example, physicians and nurses may not have compatible career objectives, and community expectations may differ from payers’ expectations. In the initial identification step, no attempt is made to resolve these conflicts. In step one, the team prepares a list of “what” is wanted. In step two, they prepare a list of “how” these wants can be satisfied. The second step involves translating needs (or wants) into requirements that must be met to satisfy them. An example of “whats” and “hows” for a component of an ambulatory clinic is provided in Table 3-2. Of course, many more steps are involved in implementing QFD for a manufactured product, and similar steps are required for a QFD for the health care system. In complex 1   For the purpose of illustration, the description of quality function deployment has been simplified to two steps. For complicated sub-elements of the system or for a much larger system, the process would be expanded.

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Building a Better Delivery System: A New Engineering/Health Care Partnership TABLE 3-2 “Whats” and “Hows” for Stakeholders in an Element of an Ambulatory Care Clinic Stakeholder Wants (“Whats”) System Attributes (“Hows”) Ready access to the physician of choice for the patient. Reduce caseload for physicians. Better management of physician caseload. No waiting for patients between steps during in-house procedures. Ensure seamless handoffs between departments. Add staff and facilities. Streamline processes. Fewer repeat procedures during an examination. Create electronic health records (EHRs) with decision support. Absence of errors in diagnosis. Use EHR system. Practice evidence-based medicine. Better understanding by the patient of his/her role in ensuring his/her health. Provide more counseling for patients. Improve patient access to information and knowledge. More time for nurses to spend with patients. Reduce paperwork. More responsibility for nurses. Solicit agreement from physicians that nurses should have more responsibility. More time for physicians to develop professional expertise. Reduce caseload for physicians. More cooperation by physicians in independent practitioners’ associations (IPAs) in eliminating errors. Provide incentives to encourage physicians in IPAs to participate. Improved operating efficiency. Reduce costs. systems in which several “hows” may be important to several “whats,” the material is presented in matrices. In this simplified example, the material is presented in tabular form. Once the “hows” have been identified, they must be translated into detailed instructions. In the QFD procedure, the right-hand column in Table 3-2 becomes the left-hand column in Table 3-3. The right-hand column in Table 3-3 then becomes the “hows” for satisfying the stakeholder needs that were identified initially. Note that even in this simple example, many of the “hows” in Table 3-3 will require a third step, and some may require more. At this stage, some of the “whats” appear to conflict (e.g., the need for both more and less staff and facilities). In addition, the “hows” in both tables sometimes conflict. It is best TABLE 3-3 The “Whats” and “Hows” for Meeting System Objectives System Attributes (“Whats”) Actions (“Hows”) Smaller caseload for physicians. Redesign processes with input from physicians. Add staff and facilities. Creation of electronic health records (EHRs). Involve physicians and staff in planning the EHR system. Identify responsibilities, available expertise, and consultants. Use of the EHR system with decision support. Practice evidence-based medicine. Ensure seamless handoffs between departments. Less paperwork. Make full use of EHRs. Use clinical physician order entry. Write all prescriptions electronically. Agreement by physicians that nurses take more responsibility. Establish multidisciplinary teams. Implement training in teamwork. Identify physician with responsibility. More counseling for patients. Make follow-on contact by provider. Use Internet. Provide sources of medical information. Incentives to encourage physicians in independent practitioners’ associations to participate. Document improvements in quality of care. Document improvements in safety. Reduce wasted physician time. Provide appropriate compensation for direct care and case management. Additional staff and facilities. Increase number of counselors. Increase number of staff capable of using engineering tools. Lower cost. Make optimal use of facilities. Improve scheduling of facilities. Improve facility maintenance. Decrease staff.

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Building a Better Delivery System: A New Engineering/Health Care Partnership to allow conflicts to arise naturally and not to suppress them when they first occur but to resolve them in subsequent steps. Teams have a tendency to jump to conclusions in the second step instead of pursuing a careful examination of trade-offs and conflicts. Redesigning processes with input from physicians and nurses, providing training in teamwork, and documenting improvements in quality of care and safety will have immediate benefits, even though further efforts will be needed before the design of major organizational changes (the next major step) can be undertaken. Throughout the QFD process, the team must work within certain constraints established by the organization, such as cost objectives for the final service and the time available to implement the QFD procedure. For example, the team might conclude that achieving zero errors in the writing of prescriptions by all physicians, including those associated with independent practitioners’ associations, is not possible in the time frame for the project. If this is the case, the QFD steps must be repeated with modifications, which may result in changing some previously agreed upon decisions. It is essential that all members of the QFD team continue to participate in this sometimes painful process. In the unusual event that the objectives cannot be accomplished within the constraints, the team must meet with senior management and determine if the constraints can be relaxed or if the processes must be changed. These decisions must be made in conjunction with management. The QFD process can be both time consuming and difficult, and success requires the availability of the resources of the organization. Accomplishing a QFD analysis for a complicated project requires considering a vast array of details, and QFD team members may find it necessary to consult with many people in their organizational areas and ask for detailed studies and analyses at various stages. Thus, team members will need the support of many people to accomplish their tasks, especially the support and encouragement of upper management. Nevertheless, experience in other industries has shown that if QFD is done properly, that is, if all relevant stakeholders are involved and objectives and constraints have been well defined, the direct and indirect benefits generally far outweigh the costs and risks of the QFD process. The committee is confident that QFD applications to the design of health care delivery processes, particularly at the careteam and organization levels, will yield significant, measurable performance gains in quality and efficiency. In addition, QFD will have significant indirect or spill-over benefits in health care delivery, where disciplinary and functional silos of responsibility are deeply entrenched. Indirect benefits include improvements in the quantitative and qualitative characterization of processes and systems, improvements in cross-disciplinary/cross-unit learning, improvements in teamwork, and a better understanding and appreciation of how the actions/decisions of individual units affect the performance of the system as a whole. Human-Factors Research In general, complexity is the enemy of very high levels of human-systems performance. In nuclear power and aviation, this lesson was learned at great cost. Simplifying the operation of a system can greatly increase productivity and reliability by making it easier for the humans in the system to operate effectively. Adding complexity to an already complex system rarely helps and often makes things worse. In health care, however, simplicity of operation may be severely limited because health care delivery, by its very nature, includes, creates, or exacerbates many forms of complexity. Therefore, in the health care arena, success will depend on monitoring, managing, taming, and coping with changing complexities (Woods, 2000). Human-factors engineering and related areas, such as cognitive-systems engineering, computer-supported cooperative work, and resilience engineering, focus on integrating the human element into systems analysis, modeling, and design. In health care, for example, the human-technology system of interest may be organizing an intensive care area to support cognitive and cooperative demands in various anticipated situations, such as weaning a patient off a respirator. Human-factors engineering could also provide a workload analysis to determine if a new computer interface would create bottlenecks for users, especially in situations that differ from the “textbook” scenario. At the patient level, the focus might be on the provider-patient relationship, such as making sure instructions are meaningful to the patient or encouraging the patient’s active participation in care processes (Klein and Isaacson, 2003; Klein and Meininger, 2004). At the team level, human-systems analysis might be used to assess the effectiveness of cross-checks among care groups (e.g., Patterson et al., 2004a). At the organizational level, the human-systems issue might be ensuring that new software-intensive systems promote continuity of care (e.g., avoid fragmentation and complexity). At the broadest level, human-systems engineering may focus on how accident investigations can promote learning and system improvements (Cook et al., 1989). Patterns of human-systems interactions that have been analyzed in studies in aviation, industrial-process control, and space operations also appear in many health care settings. A single health care issue (e.g., mistakes in administering medications) is likely to involve many human-performance issues, depending on the context (e.g., Internet pharmacies, patient self-managed treatment, administration of medication through computerized infusion devices, computer-based communication in a computerized physician order entry system). For example, a human-factors analysis of the effects of nurses being interrupted while attempting to administer medication could lead to changes in work procedures. Once the processes in human performance that play out in the health care setting are understood, the human-factors knowledge base can be used to guide the development

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Building a Better Delivery System: A New Engineering/Health Care Partnership and testing of ways to improve human performance on all four levels of the health care system (Box 3-1). Modeling, supporting, and predicting human performance in health care, as in any complex setting, requires language appropriate to different aspects of human performance. Patterns in human judgment, for example, are described in concepts such as bounded rationality, knowledge calibration, heuristics, and oversimplification fallacies (Feltovich et al., 1997). Patterns in communication and cooperative work include the concepts of supervisory control, common ground, and open versus closed work spaces (Clark and Brennan, 1991; Patterson et al., 2004b). Concepts relevant to patterns in human/computer cooperation include mental models, data overload, and mode error (Norman, 1988, 1993). Generic patterns in human-systems performance are apparent in many health care settings, and identifying them can greatly accelerate the development of changes to improve health care. This will require integrating a medical or health care frame of reference and a human-systems frame of reference based on cognitive sciences and research on cooperative work and organizational safety. Numerous partnerships between human-factors engineers and the medical profession have already led to improvements in patient safety (Bogner, 1994; Cook et al., 1989; Hendee, 1999; Howard et al., 1992, 1997; Johnson, 2002; Nemeth et al., 2004; Nyssen and De Keyser, 1998; Xiao and Mackenzie, 2004). Thus, results already in the human-factors research base can provide a basis for rapid improvements in health care. A recent example is the improvement in handoffs and shift changes in health care based on a number of promising results in other industries that were directly applicable to this health care setting (Patterson et al., 2004b). Another example is in the cognitive processes involved in diagnosis. Faced with a difficult diagnosis, a provider may focus on a single point of view and exclude other possibilities (e.g., Gaba et al., 1987). Human-performance techniques (critical-incident studies and crisis simulation) have been used in other settings to study these kinds of situations and recommend ways that computer prompts and displays can be used to avoid this problem (Cook et al., 1989; Howard et al., 1992). Another success story is the application of a human-systems perspective to improve medication-administration systems based on bar codes. The analysis of the problem involved identifying complexities and other side effects, such as workload bottlenecks and new error modes that arose when new computerized systems were introduced (e.g, Ash et al., 2004; Patterson et al., 2002). As advances in technology lead to improvements in telemedicine and the continuity BOX 3-1 Improving Medical Instructions Prescription medicines are generally accompanied by information sheets (e.g., take with food; do not use when certain other medications are being used; avoid alcohol; or store in an appropriate location). A study was undertaken to see if incorporating the principles of cognitive psychology could make medication information sheets more user friendly. Human-factors/ergonomics (HF/E) research related to interface design, information processing, and perception suggest that the physical features (e.g., size and consistency of fonts, line spacing, etc.) of the information sheet and the language used in the text (e.g., simple, explicit, unambiguous phrases in brief sentences) can significantly affect the usability of the information. The organization of the material also influences understanding. For example, a list format is easier to understand than a prose format. These features can be especially important for patients with special limitations (e.g., elderly patients, people with short attention spans, patients under severe stress, etc.). In the study, the readability and understandability of commercial information sheets and HF/E-modified sheets were evaluated by two groups. Sixty-two college-age students and 41 elderly subjects (ages 58 to 87) were asked to read and complete a multiple-choice test on a commercial or HF/E-modified sheet for two drugs. Subjects who read the commercial sheet for drug A were given the modified sheet for drug B. Subjects could take as much time as they needed to review each sheet and complete the test. The review times and test times for the college-age group were 20 to 30 percent shorter than for the older group. This was statistically significant. Eighty-seven percent of the subjects overall expressed a preference for the HF/E-modified sheets. For older subjects, the reading time for the redesigned sheets was approximately 30 percent shorter than for the commercial sheets. Even with improved physical features, simple, clear language, and clear organization, the participants in the study continued to make errors, showing that improvements were still necessary. The authors of the study concluded that “[a]dvances in medication self-management information will depend on knowledge of how users understand information and how they select a course of action…. Medications information sheets must accommodate the characteristics and limitations of users to be effective.” Improving information sheets will require the participation of health care professionals, insurance providers, and users. Source: Klein and Isaacson, 2003.

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Building a Better Delivery System: A New Engineering/Health Care Partnership of care, similar applications will no doubt be useful in the future. Trade-offs will involve economic constraints and the development of new medical capabilities (e.g., Xiao et al., 2000). As these and other examples show, human-factors research can contribute to the development of highly reliable processes, systems, and organizations in health care that would advance the goals of safety, effectiveness, efficiency, and patient-centeredness. Simplification and standardization can increase reliability in many complex systems, including complex health care systems. However, simplification and standardization alone will not be enough to manage many areas of changing complexity in health care delivery. Human-factors research and applications will also be useful for monitoring, managing, taming, and coping with these dynamic complexities. Tools for Failure Analysis The purpose of failure-mode effects analysis (FMEA) is to identify the ways a given procedure can fail to provide desired performance. The analysis may include disparate elements, such as the late arrival of information and laboratory errors because of a lack of information about the interactions of certain drugs. In FMEA, a mathematical model is usually created and used in the analysis. Prior to releasing a new product design, manufacturers analyze how the product might fail under a variety of conditions. FMEA is a methodical approach to analyzing potential problems, errors, and failures and evaluating the robustness of a product design (McDonough et al., 2004). FMEA can be used to evaluate systems, product designs, processes, and services and is essential to finalizing the design of a product or identifying how a part, subsystem, or system might fail, as well as the impact of failure on safety and effectiveness. Thus, FMEA provides an opportunity to design a potential failure mode out of a product or process. In the health care delivery system, FMEA can be helpful for designing systems (e.g., the seamless transfer of information, the implementation of electronic health records [EHRs], potential failures in the regional response to a public health emergency, etc.) on the level of health care provider teams and on the organizational level. In addition to identifying potential design flaws, FMEA has several other benefits: identification of areas that require more testing or inspection to ensure high quality identification of areas where redundancies are justified prioritization of areas that require further design, testing, and analysis identification of areas where education could minimize the misuse or inappropriate use of a product foundation for reliability assessment and risk analysis effective communication and decision making FMEA can be done using a bottom-up or a top-down approach, or both. A bottom-up analysis (called a failure mode, effects, and criticality analysis, or FMECA) starts at the component level, is carried through the subsystem level, and finally is used at the system level. Failure of an individual component is important, but it is equally important to understand possible failure modes when components are assembled into subsystems or systems. Wherever possible, the probability of failures and their criticality are quantified. A FMECA is redone every time a design is changed or new information from testing or preliminary field use becomes available. FMECA is used at each step until the final design meets design criteria and satisfies quality and reliability goals. A top-down approach, called fault-tree analysis (FTA), is used to identify consequences or potential root causes of a failure event. With FTA, an undesirable event is identified and then linked to more basic events by identifying possible causes and using logic gates. FTA is an essential tool in reliability engineering for problem prevention and problem solving. Root-cause analysis (RCA) is a qualitative, retrospective approach that is widely used to analyze major industrial accidents. An RCA can reveal latent or systems failures that underlie adverse events or near misses. In 1997, the Joint Commission on the Accreditation of Healthcare Organizations (JCAHO) mandated that RCAs be used to investigate sentinel events in its accredited hospitals. Key steps in an RCA include: (1) the creation of an interdisciplinary team; (2) data collection; (3) data analysis to determine how and why an event occurred; and (4) the identification of administrative and systems problems that should be redesigned. Although RCAs are retrospective, they identify corrections of systems problems that can prevent future errors or near misses. One caveat about RCAs is that they may be tainted by “hindsight bias,” that is, after an accident, individuals tend to believe that the accident should have been considered highly likely, if not inevitable, by those who had observed the system prior to the accident (McDonough, et al., 2004). In the past five years, the Veterans Health Administration (VHA) and JCAHO have taken several steps toward promoting the adaptation and application of FMEA, FMECA, FTA, and related tools of proactive hazard analysis and design to health care (McDonough, 2002) (see Box 3-2). In 2000, the VHA published a patient safety handbook that included instructions on FMEA and developed a health care failure-mode and effects analysis (HFMEA), “a systemic approach to identify and prevent product and process problems before they occur” (McDonough, 2002; Weeks and Bagian, 2000). In 2000, JCAHO encouraged the use of FMEA/HFMEA and related tools in its new standards that require all accredited hospitals to conduct at least one proactive risk assessment of a high-risk process every year. In 2002, JCAHO published a book specifically about FMEA for health care, which includes a step-by-step guide through the process and examples of FMEAs conducted by health care organizations (JCAHO, 2002).

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Building a Better Delivery System: A New Engineering/Health Care Partnership BOX 3-2 Proactive Hazard Analysis To address hazard and safety concerns in health care delivery, some have looked to other industries (e.g., aviation, manufacturing, food service, nuclear power plants, aircraft carriers) for models that can be applied to medical systems. From these sources, health professionals found frameworks for strategies and tools consistent with the needs of large clinical institutions. One prominent approach, called failure-mode and effects analysis (FMEA), which has been used in manufacturing for more than 30 years, was adapted for health care organizations. The health care failure-mode and effects analysis (HFMEA) is now being used by the Veterans Administration (VA) National Center for Patient Safety (NCPS). In 2000, the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) issued a new standard stipulating that all accredited hospitals must complete at least one “proactive risk assessment” of a high-risk process each year. JCAHO did not specify whether FMEA or HFMEA should be used, but its own approach to fulfilling this requirement is based on the terminology and structure of these two models. The first surveys were to be completed by July 1, 2002. Hazard analysis and critical control points (HACCP), another form of proactive hazard analysis, also provides a useful framework for improving safety. HACCP, which is used in food production and food services worldwide, is now being used by medical-device manufacturers. Although FMEA, HFMEA, and HACCP differ in significant ways, they also have striking similarities. The table below shows the basic steps in performing an HFMEA analysis and an HACCP. The five steps in the HFMEA are described in materials produced by the VA NCPS. The HACCP procedure has been slightly modified from a 14-step process to highlight the similarities with HFMEA. Both tools involve the selection of a process and/or product, the selection of a team, the creation of a process flow chart, the identification of hazards or failures, corrective or preventive action, ongoing monitoring and assessment, and process review. Both rely on decision making based on data, cross-functional teams, and, most important, a preventive approach to hazard/failure mode identification and elimination or reduction. Because these analysis tools were developed for use in different sectors, they also have some differences in emphasis. In FMEA, the hazard is a failure mode in a process, and the principal goal is to redesign the process to reduce or eliminate the risk of the failure mode recurring. In HACCP, the hazard is unsafe food, and the primary goal is to control the process at critical points to eliminate or reduce the risk of the hazard. Thus the goal of FMEA/HFMEA is to reduce process failure. The goal of HACCP is to detect and control process failure to eliminate or reduce bad effects. TABLE HFMEA and HACCP Steps Step HFMEA HACCP 1. Define the HFMEA topic. Identify the hazard category. 2. Assemble the team. Assemble the team. 3. Graphically describe the process: Develop a flow diagram. Number each process step. Identify the key process step. Identify sub-processes. Create a flow diagram of the subprocesses. Describe the product or process: Identify the intended use. Construct a flow diagram from point of entry to departure. Confirm accuracy of flow diagram. 4. Conduct a failure analysis: List all potential failure modes. Determine the severity and probability of each failure mode. Use the HFMEA decision tree to determine if the failure mode requires further action. Where the decision is to proceed, list all causes for each failure mode. Conduct a hazard analysis: Identify all relevant hazards and preventive measures. Identify critical control points and apply a decision tree to determine if intervention is needed. Establish target levels and critical limits for critical control points. 5. Action and outcome measures: Determine if you want to eliminate, control, or accept the failure mode cause. Identify a description of action for each failure mode to be eliminated or controlled. Identify outcome measures to test the redefined process. Identify an individual to complete the recommended action. Indicate whether top management concurs with recommended action. Action and outcome measures: Establish a monitoring system to ensure proper .implementation Establish verification procedures. Establish documentation and record keeping. 6. Review HACCP plan: Conduct reviews at predetermined intervals to determine whether working and still appropriate. Source: McDonough, 2002.

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Building a Better Delivery System: A New Engineering/Health Care Partnership SYSTEMS-ANALYSIS TOOLS Engineers use system analysis to help themselves and others understand how complex systems operate, how well systems meet overall goals and objectives, and how they can be improved. On one level, a systems analysis may focus on the performance of a single unit in a large system (e.g., the flow of patients through a facility or the allocation of resources in an emergency room). The results of these studies can be used to evaluate how changes in procedures might improve performance (e.g., reduce patient delays, improve safety, eliminate nonessential steps). At a higher level, a systems analysis may consider interactions among elements in a large system, such as a hospital, a regional medical enterprise, or even the national health care delivery system. Obviously, the larger the system, the more complex and the more difficult the analysis. But a careful analysis of systems at all levels can reveal interactions and opportunities for improvement that might otherwise be missed. Table 3-4 shows the levels for which various systems-analysis tools are most useful. Systems-analysis tools are generally used to analyze existing systems for improvement. Mathematical analyses of system operations include queuing theory, which could be used, for example, to understand the flow of patients through a system, the average time patients spend in the system, or bottlenecks in the system. Discrete-event simulation could be used for a more detailed examination of performance, such as an analysis of surges of patients on particular days or during emergencies or the scheduling of ambulances. With enterprise-management tools, a system can be managed as a whole across the entire spectrum of elements, rather than at the level of individual patients. In spite of the fragmented nature of the health care system, interactions among all elements in the total chain can be clarified and managed. Supply-chain management tools, for example, are useful for determining the physical and informational resources necessary to the delivery of a product to a customer (e.g., reducing inventory, eliminating delays, reducing cost, etc.). Economic and econometric models, based on historical data, are useful for bringing to light causal relationships among system variables. These tools include game theory, systems-dynamics modeling, data-envelopment analysis, and productivity modeling. Financial engineering, risk management, and market models, which are used to evaluate and manage risks, can be useful for examining financial risks to an organization, as well as for understanding the risks of certain actions for/by patients. Knowledge discovery in databases is a method that can be used to examine large databases (e.g., a database of patient reactions to groups of drugs). It might be used, for example, to examine the history of particular drugs or treatments or to examine procedures for patients with particular life styles or health histories. With knowledge-discovery tools, one might search historical records for an effective procedure or identify outlier events, such as a small number of patients who share a condition and experience unexpected side effects from a medication. Because system analyses must describe an existing system (or one that reasonably approximates an existing system), it is essential that data be available (or obtainable) for that system. The nature of the data depends on the problem being addressed. Analyzing a system to improve the efficiency of a surgical operation requires very different data from an analysis to assess the effectiveness of a disease-management program. TABLE 3-4 Systems-Analysis Tools Tool/Research Area Patient Team Organization Environment Modeling and Simulation Queuing methods   X X   Discrete-event simulation   X X X Enterprise-Management Tools Supply-chain management   X X X Game theory and contracts   X X X Systems-dynamics models   X X X Productivity measuring and monitoring   X X X Financial Engineering and Risk Analysis Tools Stochastic analysis and value-at-risk     X X Optimization tools for individual decision making   X X X Distributed decision making (market models and agency theory)     X X Knowledge Discovery in Databases Data mining     X X Predictive modeling   X X X Neural networks   X X X

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Building a Better Delivery System: A New Engineering/Health Care Partnership Modeling and Simulation Models and simulations are important tools for analyzing systems. Models are mathematical constructs that describe the performance of subsystems. Interactions among subsystems in a larger system, combined with the constraints within which the system operates, influence the performance of the total system and represent the overall system model. Using these models and simulations, it becomes possible to analyze the expected performance of a system if systemic changes are made. For example, would a change in inventory location and levels improve or reduce the effectiveness of the nursing staff? Would a change in scheduling of the emergency room increase or decrease the number of patients that must be diverted and at what cost? Models have been developed for a variety of health care applications that do not directly involve physical facilities. For instance, multiple models have been developed to examine the effectiveness of screening and treatment protocols for many diseases, including colorectal cancer, lung cancer, tuberculosis, and HIV (Brandeau, 2004; Brewer et al., 2001; Eddy et al., 1987; Fone et al. 2003; Mahadevia et al., 2003; Neilson and Whynes, 1995; Ness et al., 2000; Phillips et al., 2001; Schaefer et al., 2004; Walensky et al., 2002). In addition, many models have implications for health care policy; for example, models might suggest that efforts to reduce tobacco use in adults would be most beneficial in the short term, whereas blocking the introduction of tobacco to young people is more likely to have long-term benefits (Levy et al., 2000; Teng et al., 2001). Hospitals and clinics have used simulations to improve staffing and scheduling (Dittus et al., 1996; Hashimoto and Bell, 1996), and models have been used to help clinicians distinguish injuries caused by falls down stairs from those resulting from child abuse (Bertocci et al., 2001). Virtual-reality patients have been used for training in psychiatry, the social sciences, surgery, and obstetrics (Letterie, 2002). Queuing Theory Queuing theory deals with problems that involve waiting (queuing), lines that form because resources are limited. The purpose of queuing theory is to balance customer service (i.e., shorter waiting times) and resource limitations (i.e., the number of servers). Queuing models have long been used in many industries, including banking, computers, and public transportation. In health care, they can be used, for example, to manage the flow of unscheduled patient arrivals in emergency departments, ORs, ICUs, blood laboratories, or x-ray departments. Queuing models can be used to address the following questions: How long will the average patient have to wait? How long will it take, on average, to complete a visit? What is the likelihood that a patient will have to wait for more than 20 minutes? How long are providers occupied with an average patient? How many personnel would be necessary for all patients to be seen within 10 minutes? Would flow be improved if certain patients were triaged differently? What resources would be necessary to improving performance to a given level or standard? What is the likelihood that a hospital will have to divert patients to another hospital? Queuing is a descriptive modeling tool that “describes” steady-state functioning of the flow through systems. Although health care is rarely in a steady state, from a mathematical point of view, queuing models provide useful approximations that are surprisingly accurate. Queuing models are generally based on three variables that define the system: arrival rate; service time; and the number of servers. The arrival rate, λ, describes the frequency of the arrival of patients. The most common type of unscheduled arrival pattern can be described with the Poisson distribution (Huang, 1995). Service time, T, is the average time spent serving a particular type of patient at a given station. In health care, the service time is most often random and is most commonly described by an exponential probability distribution. Number of servers, n, is the number of stations doing similar tasks for all patients who approach those stations. For a station with a single server, average arrival rate of patients (λ) multiplied by the average time patients spend with a given server (T) must be less than or equal to unity (i.e., λT ≤ 1). Otherwise the queue would continue to build up without relief. If n servers are present, λT ≤ n. In the absence of variability, no queues would build up and the flow through the station would be regular. In the presence of variability, which always exists, queues will build up. The closer λT is to 1, the longer the queues for that station. The bottleneck station in the network can be identified by locating the station with the largest λT. For a single station with the probability distributions described above, the response time for the station (the average time for a patient to pass through the station) is given by Response Time = T/ (1 – λT). As λT approaches unity, the response time becomes very long. To manage flow well, service areas must measure critical indices derived from the model; these may include, but are not limited to, utilization (percentage of time servers are busy, waiting time, length of waiting lines), probability of diversion (rejection), abandonment rates, bottlenecks, and door-to-door time (time of actual arrival to time of actual departure). It is critical that the full variability of the metrics be measured and displayed. Often the data mean or median is calculated and graphed, but this does not give a true picture

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Building a Better Delivery System: A New Engineering/Health Care Partnership of variability. If the measures were constant and could be predicted by the mean, the problem of managing flow would not exist! Queuing theory can provide analytical expressions for a single station, but analytical expressions for a network of stations require computer programs that can approximate the performance of a network. Once the network description has been entered, the performance of the network can usually be analyzed quickly. The law that applies to systems with queues, Little’s law, enables one to determine either the number of patients being served in a facility, for example a clinic or a hospital, or the average time a patient spends in the facility. If L is the average number of entities (patients) in a system that contains a variety of locations at which procedures are performed, that is, servers, Little’s law states that L = λ W where λ is the average arrival rate into the system and W is the average time each patient spends in the system (the sum of the average time patients spend waiting plus the average time they spend with caregivers). If either L or W is known, the other can be calculated easily. One problem in health care today is that the number of facilities that have unscheduled patient flows is increasing, while the number of people available to treat them is decreasing. This situation requires new management approaches, methods of reducing waiting times and keeping emergency departments from turning away patients, such as building in segmentation, matching capacity to demand using queuing theory, and creating surge capacity and backup plans for exigencies. Because of variabilities in patient demand, fixed bed and staffing levels are almost always either too high or too low, which has ramifications for both the quality and cost of care. Queuing models allow for natural variabilities, which leads to greater predictability and control and, ultimately, more timely and safer patient care. Queuing theory has been used (although infrequently) to analyze a variety of clinical settings, including emergency departments, primary care practices, operating rooms, nursing homes, and radiology departments (Gorunescu et al., 2002; Huang, 1995; Lucas et al., 2001; Murray and Berwick, 2003; Reinus et al., 2000; Siddharthan et al., 1996). Discrete-Event Simulation In discrete-event simulation, the dependent variables are “actors” in, or are developed by, the system. In a health care system, these can include patients, caregivers, administrators, inventory, capital equipment, and others. The independent variable is time. In this type of simulation, it is expected that events take place at discrete points in time (e.g., the arrival of two patients at Station C, one at time t1, the second at a later time, t2). A key aspect of a discrete-event simulation is the system-state description, which includes values for all of the variables in the system. If any variable changes, it changes the system state. In a simulation, the dynamic behavior of the system can be observed as entities (e.g., patients, staff, inventory) move through the nodes and activities (e.g., registration desk, nurse’s preliminary examination, physician’s examination, laboratory tests, etc.) identified in the model. The rules governing the motion of entities and the paths they follow are peculiar to the specific model and are specified by the modeler. Describing systems that involve human interactions requires the use of mathematics based on probability theory and statistics, which can describe the variabilities and discreteness of events. Computers are necessary to analyze the many states in complex systems. In most cases, the initial system state must first be specified, that is, values must be supplied for the variables and their variances based on observations of an existing system or a system sufficiently similar. The model can then be tested to see if it describes the performance of the existing system. If it does not, it must be adjusted, perhaps by including different variables or by treating interactions among the variables in different ways. Once the model has been validated, it can be used to explore the consequences of different actions. If each variable had only one possible value (e.g., the number of nurses available in the prenatal clinic at 10:05 a.m.), a single calculation would be sufficient to describe a system. But most system variables have a distribution of values, such as the differences in the number of nurses needed throughout the day in Surgical Ward 2 of the hospital. Thus, many computer runs must be made to explore combinations of values of the variables. Tools are readily available for determining how various computer outputs should be grouped and interpreted. Discrete-event simulation has been used to analyze a number of health care settings, such as operating rooms, emergency rooms, and prenatal-care wards (Klein et al., 1993), and a variety of workforce planning problems. The overall objective has been to improve or optimize the safety, efficiency, and or effectiveness of processes and systems. Kutzler and Sevcovic (1980) developed a simulation model of a nurse-midwifery practice. Duraiswamy et al. (1981) simulated a 20-bed medical ICU that included patient census, patient acuity, and required staffing on a daily basis for one year. A simulation of obstetric anesthesia developed by Reisman et al. (1977) was used to determine the optimal configuration of an anesthesia team. Magazine (1977) describes a patient transportation service problem in a hospital; queuing analysis and simulation were used to determine the number of transporters necessary to ensure availability 95 percent of the time. Bonder (see paper in this volume), describes a simulation for a very large-scale, level-four analysis of a regional health care system in the Puget Sound area of Washington. Pritsker (1998) describes the development

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Building a Better Delivery System: A New Engineering/Health Care Partnership BOX 3-5 The Chronic Care Model The Chronic Care Model, developed by Dr. Ed Wagner, director of the MacColl Institute for Healthcare Innovation at the Group Health Cooperative of Puget Sound, is based on the premise that good outcomes in health care (e.g., better clinical control, more self-confidence and a better quality of life for patients, lower costs, etc.) require productive interactions between prepared, proactive provider teams and active patients, families, or caregivers who are ready to participate in their care. To make those productive interactions happen, the model identifies six fundamental areas of interconnected activity and support that encourage high-quality management of patients with chronic diseases. These six qualities should be the hallmarks of health care delivery systems: Self-management support involves empowering patients through motivational interviewing and integrating assessment, tailoring, problem solving, and goal setting into everyday care. Delivery system design addresses the questions of who should be on a care team, what kind of interaction each member of the team should have with the patient (e.g., delivering the services that are known to work, such as case management, group visits, planned visits, and follow-up), how team members should interact with each other, and how patients should telephone or e-mail caregivers. Decision support provides health care professionals with guidelines to ensure that best-practice, evidence-based health care is delivered. Clinical information systems provide a means of making the information about an entire patient population (a registry) available to the patient and provider when it is needed. The health care organization, which subsumes and supports the office practice, determines senior leaders’ goals for health care quality and how the business plan makes the goals actionable. The community, within which the whole health care system exists, provides resources and policies that influence the patient’s interactions with the extended care delivery system. Source: Davis, 2005. Originally published in Wagner, 1998. APPLYING SYSTEMS TOOLS TO HEALTH CARE DELIVERY The systems tools described in this chapter can be applied to all four levels of the health care system, with the caveat that they must be adapted to the specific conditions and circumstances of this unique patient-centered environment. Patient Level In the past, systems tools have not been widely applied to individual patients, but they should be. The ultimate purpose of using these tools should be to improve patient care and ensure that the system is responsive to patients’ needs and wishes. Concurrent engineering tools like QFD can be used most effectively in the design/redesign of care delivery systems in the hospital and ambulatory clinics and, as information/ communications technologies advance, in virtual settings, such as patients’ homes. Human-factors expertise focused on care provider-patient relationships can help modify care instructions to ensure that they are meaningful to patients and encourage patients to participate in care processes. Indeed, human-factors engineering will be critical in moving toward remote care delivery and viable self-care systems,

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Building a Better Delivery System: A New Engineering/Health Care Partnership BOX 3-6 Institute for Healthcare Improvement The Institute for Healthcare Improvement (IHI), a not-for-profit research center, was established in 1991 by Dr. Donald Berwick for the purpose of improving the quality and efficiency of health care. IHI’s 15-member board of directors is drawn from leading health care institutions and the academic community. IHI researchers identify specific problems and bring together multidisciplinary teams of experts from across the country to work on them. IHI then creates a collaborative group of 30 to 50 health care institutions that agree to implement the new processes and share their findings. Each collaborative project lasts from nine months to three years. Problem areas tackled by IHI groups have included: reducing medication errors; reducing waiting times in emergency rooms; reducing surgical infection rates; improving the performance of supply chains; and reducing ventilator-induced pneumonia. IHI has had considerable success, both nationally and internationally, and now has an annual operating budget or more than $30 million, most of it from the health care industry. More than 200 faculty members from different academic institutions lead the project teams, and a network of more than 175 health care institutions are working together to solve specific problems. Because the projects are funded by the participating institutions, they have a vested interest in implementing the new ideas and procedures that are developed. The annual IHI National Forum now attracts more than 4,000 attendees. Source: IHI, 2005. ensuring the usability and reliability of information/ communications systems and other systems patients will have to use for professionally guided, self-instructed care in their homes, and maintaining communications and relationships of trust with care providers. Modeling and simulation tools can be used to improve patient access to care providers (e.g., more efficient scheduling of appointments), reduce patient waiting times in care centers, and ensure that laboratory test results are available on demand. Patients will also benefit directly from improved scheduling of personnel, from the development of predictive models for treating particular diseases, and from improved regimes for administering medication. The use of systems tools at the patient level will require detailed data on patient flows, delay times, and service times by caregivers, laboratories, support staff, and so on. Some of these data can be collected from computer records, but much of it will require individual measurements of, for example, time spent in accomplishing various tasks. Significant differences among facilities will require that data be collected for particular environments. One advantage of systems tools is that they are sufficiently general that they can be applied in very diverse environments. Frontline Care Team Level In this section, we highlight the benefits of these same tools for caregiver teams. Benefits to caregivers and patients lead, in turn, to benefits for organizations and the overall health care environment by improving the efficiency of operations throughout the entire system. A health care system designed to meet the needs and wants of both patients and caregiver teams can provide a smoothly operating environment that is best for both caregivers and patients. Human factors might be used to assess the effectiveness of cross-checks among care groups. Analyses that can reveal where a system can fail, either by predicting errors or by identifying inefficiencies, generally depend more on interactions among individuals who work in the system and understand all of its aspects and components than on large amounts of data. However, modeling and simulation tools do require good data. These tools can focus on improving the clinical and administrative operation of a practice, including the scheduling of personnel, the allocation of physical resources, and the reduction or elimination of tasks that require substantial time but may be of limited value to the team or the patient. Simulation of an operating room can improve the organization of facilities, personnel, and supplies to ensure the highest level of safety and effectiveness. The simulation of nurses’ stations can ensure that supplies are available when needed and that support is provided to reduce unnecessary tasks. These analyses can also identify ways of automating some tasks and reducing unnecessary repetitions of tasks (e.g., data entries). Modeling and simulation of back-office operations can help reduce the time spent by physicians and nurses in data recording and improve communications with patients. The proper scheduling of team members can reduce overload and improve the quality of the workplace for the team as a whole. The data for some of these analyses must be collected locally through detailed observations. These data can then be supplemented with data from a comprehensive information technology system designed to provide detailed records of events, personnel, and resources.

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Building a Better Delivery System: A New Engineering/Health Care Partnership Enterprise-management tools address interactions between the caregiver team and the enterprise. Supply-chain management is intended to reduce inventory and ensure that needed supplies are available when required. It can also reduce inventory costs without compromising the availability of the means and personnel to handle emergencies. The significant data necessary for these analyses can involve a number of operating units of the system. Experience in other industries suggests that these data needs can only be provided by an information system that connects all elements of the enterprise. Game-theory tools, contracts, and system-dynamic models can enable caregiver teams to explore “what-if” questions to predict the consequences of taking very different actions, such as the consequences of a major emergency or different ways of managing and controlling large fluctuations that might be introduced into a local system. For example, what actions should be taken if an emergency room is suddenly overburdened? How should nurses be allocated if only 10 percent are unavailable on a given day? How should priorities be set for using an operating room? Optimization tools for decision making can help answer the same questions. Longer-term efforts to optimize the care team’s efforts can be addressed by predictive, rather than descriptive, models. Predictive models, such as neural networks, require an understanding of the causes and effects of unexpected changes in the operational environment. The data requirements for predictive analyses are complex and require historical knowledge of the operation of the care team, as well as information about the operation of the enterprise, at least as it affects the care team. Large-scale databases on patients, diseases, and treatments are also necessary. Collecting the necessary data for these analyses without a comprehensive information system would be practically impossible. Even if it could be done, the cost would be exorbitant. Organizational Level At the organizational level, analyses and other systems approaches become more complex. Analyses and other studies at this level must address interactions among many elements of a system. Questions may relate to cost, overall organizational efficiency, trade-offs among departments, and organizational responses to major emergencies. Human-factors studies might be used to ensure that new software-intensive systems promote continuity of care (e.g., avoid fragmentation and complexity). Health care provider organizations have the large, complex task of providing all of the support functions for both clinical care (e.g., radiology, laboratories, operating rooms, etc.) and infrastructure (e.g., finance, administration, accounting, etc.). In the current health care system, clinical and infrastructural needs are addressed separately. Although each clinical support function and each infrastructural need requires a high level of reliability and standardization, a truly patient-centered system will require high-performance systems at all levels. At the organizational level, some of the more traditional engineering approaches (e.g., supply-chain management) are readily applicable. Indeed, some of the larger health care institutions have already adopted them. Systems-engineering techniques are critical to analyzing data and using modeling and simulation strategies to improve outcomes (e.g., interactions among reimbursement policies, regulations, improved care, etc.). All of these tools (i.e., systems tools, analysis, modeling, and simulation) are applicable, not only at this level, but also at the environmental level. Data needs for these analyses can place a heavy burden on information systems, and data must be available on activities outside the boundaries of the organization (e.g., IPAs, drug suppliers, rehabilitation centers, emergency response units, etc.). To meet these needs, information systems will require interconnectivity of various elements of the overall health care delivery system. Environmental Level Questions at this level concern overall trends and system responses, such as regulation and oversight, reimbursement strategies, cost trends for the treatment of various diseases, the supply of caregivers, the availability of evidence-based medical information, research on the development of predictive models, and system responsiveness to major outbreaks of disease. The data requirements for addressing these and other high-level system questions depend on the issue being investigated, but, in general, information must be available from a host of institutions and organizations. To ensure that information from these many sources is available, there must be a comprehensive information system that facilitates communication and encourages information exchange among entities in the health care delivery system. The use of systems engineering to investigate and improve the overall health care system will reflect an important change in the way reforms and changes are approached and a movement away from the old, entrenched cultures that have characterized the system historically. The hope is that systems-engineering tools can bring these deeply entrenched structures to the surface where they can be investigated and evaluated in terms of the needs of a twenty-first century health care delivery system. Up to now, most health care professionals have not understood the relevance of systems-engineering tools to the safety and quality of patient-centered care. One of the objectives of this report is to encourage a conversation on this subject between the engineering community and health care professionals at all levels. Working together, these two communities can take advantage of the benefits of systems-engineering tools to manage and optimize costs; ensure high-quality, timely production processes; improve the safety and quality

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Building a Better Delivery System: A New Engineering/Health Care Partnership of care; and, ultimately, provide a truly patient-centered health care delivery system. BARRIERS TO IMPLEMENTATION Significant barriers to the widespread diffusion and implementation of systems-engineering tools in health care include impediments related to inadequate information technology and economic, policy, organizational, and educational barriers. Inadequate Information and Information Technology In general, at the tactical or local level, data gathering and processing and associated informational needs do not present significant technical or cost barriers to the adoption of systems-engineering tools (e.g., SPC, discrete-event simulation, queuing methods). By contrast, there are significant structural, technical, and cost-related barriers at the organization, multi-organization, and environmental levels to the strategic implementation of tools for modeling and simulation, enterprise management, financial engineering and risk analysis, and knowledge discovery in databases. The use of these tools requires integrated clinical, administrative, and financial information systems (e.g., clinical data repositories, etc.) that are expensive to install and maintain, and only a relatively small number of large integrated provider organizations or networks (e.g., Veterans Health Administration, Kaiser-Permanente, Mayo Clinic, Group Health Cooperative of Puget Sound, etc.) have such information systems in place. Without access to integrated clinical information systems, it is extremely difficult for small, independent elements of highly distributed, loosely connected care provider networks to take advantage of tactical systems tools and virtually impossible for them to take advantage of enterprise-management and other systems-analysis tools. In principle, with the advance of computerization and automation in health care delivery, the cost of capturing relevant data for design, analysis, and control of processes and systems should come down. However, the health care system does not have interoperability standards for information/communication systems that would make it possible to connect the myriad pieces of the fragmented, distributed delivery system. This absence of interoperability presents a formidable barrier to the use of strategic, data-intensive systems tools at the organizational and environment levels. (Information/ communications-related challenges to patient-centered, high-performance health care delivery are addressed at greater length in Chapter 4.) Policy and Market Barriers In the present system, reimbursement practices and rules, regulatory frameworks, and the lack of support for research continue to discourage the development, adaptation, and use of systems-engineering tools to improve the performance of the health care delivery system. The current “market” for health care services does not reward care providers who improve the quality of their processes and outcomes through investments in systems engineering, information/communications technologies, or other innovations (Hellinger, 1998; Leape, 2004; Leatherman et al., 2003; Miller and Luft, 1994, 2002; Robinson, 2001). The lack of comparative quality and cost data and the corresponding lack of quality/cost transparency in the market for health care services prevent patients from making informed choices on the basis of quality or value (quality/cost) (see Safran, in this volume, and Rosenthal et al., 2004). In the prevailing payment/reimbursement climate, care providers are not reimbursed on the basis of the quality of care they provide (IOM, 2001). Care providers have little incentive to invest in systems tools in support of quality improvement, unless they generate revenue directly or demonstrate immediate improvements in operating efficiency. In recent years, several experiments with new reimbursement approaches have been tried to change the prevailing practice of reimbursing discrete units by a “reasonable cost” method to include fixed-price reimbursement for a definable bundle of services or a care episode. The object of these changes is to give providers an incentive to improve the effectiveness and efficiency of their processes and procedures. For example, the introduction of diagnostic related groups shifted the reimbursement for hospitalization to a fixed price (adjusted for regional labor costs). Severity-adjusted capitation for patients covered under the new Medicare HMO coverage applies the same principles. Some insurers have experimented with linking reimbursement explicitly to quality measures (for example, selected health care organizations may receive a fixed price for organ transplants based on quality, that is, the success rate of the procedure). These are promising first steps toward changing reimbursement to encourage high-quality, efficient care and a systems approach. However, for the vast majority of care providers, there are no such incentives. Organizational and Managerial Barriers Other barriers to the widespread use of systems tools in health care are related to the culture, organization, and management structure of most health care provider organizations and the lack of confidence in systems tools and technologies by those who will be called upon to use them. As discussed in Chapter 1, cultural, organizational, and policy-related factors (e.g., regulation, licensing, etc.) have contributed to rigid divisions of labor in many areas of health care, which has impeded the widespread use of systems tools and related innovations that are likely to have significant, disruptive effects on organizational structures and work processes at all four levels of the health care system (see

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Building a Better Delivery System: A New Engineering/Health Care Partnership Bohmer this volume and Christensen et al., 2000). Organizational changes are difficult under any circumstances, and inflexibility in roles and responsibilities can increase the difficulties. There is ample documentation of tools and technologies that were poorly integrated with/accommodated by existing processes of care delivery that generated additional work for frontline providers and very little apparent reward (Boodman, 2005; Durieux, 2005; Garg et al., 2005; Wears and Berg, 2005) Ultimately, the benefits of systems tools and technologies can only be realized if their introduction is carefully managed and the people who must use them are adequately prepared, technically and mentally, to change their work practices and organization. First, as Nelson and colleagues observed in their assessment of successful clinical microsystems and as IHI has demonstrated in its successful collaboratives, management must change its philosophy (IHI, 2005; Nelson et al., 2002). Once management is committed to change, the participation of professional caregivers can be enlisted from the outset in the analysis of processes and systems and in the design and implementation of system improvements. In short, there must be mutual trust between health care management and the health care professionals who work with management. Educational Barriers Prevailing approaches to the education and training of health care, engineering, and management professionals also present significant barriers to the implementation and diffusion of systems-engineering tools, information/communications technologies, and associated innovations in the health care sector. Currently, very few health care professionals or administrators are equipped to think analytically about health care delivery as a system. As a result, very few appreciate the relevance, let alone the value, of systems-engineering tools. And of these, only a fraction are equipped to work with systems engineers to tailor and apply them to the needs of the health care delivery system. Students of engineering and management are much more likely to be trained in systems thinking and the uses and implications of systems-engineering tools and information/ communications technologies for the management and optimization of production and delivery systems. However, students in most U.S. engineering and business schools are unlikely to find courses that address operational challenges in the quality and productivity of health care delivery. (Educational barriers to the application of systems engineering to health care delivery and the steps necessary to overcome them are addressed at length in Chapter 5.) The culture of the health care enterprise will have to undergo a seismic change, a so-called paradigm shift, for systems thinking and the health of populations to become integral factors in health care decision making. Even at that point, it will take a tremendous effort and a great deal of flexibility for organizations to implement fundamental changes based on the optimization of interactions among all elements of the system. Ultimately, the whole must be greater than the sum of its parts. To date, organizations with corporate structures and management have been most successful in accomplishing this. FINDINGS Finding 3-1. The health care delivery system functions not as a system, but as a collection of entities that consider their performance in isolation. Even within a given organization (e.g., a hospital), individual departments are often isolated and behave as functional and operational “silos.” Finding 3-2. A systems view of health care cannot be achieved until the organizational barriers to change are overcome. Management and professionals must be committed to removing silos and focusing on optimizing contributions of professionals at all levels. Finding 3-3. Systems-engineering tools have been used to improve the quality, efficiency, safety, and/or customer-centeredness of processes, products, and services in a wide range of manufacturing and services industries. Finding 3-4. Health care has been very slow to embrace systems-engineering tools, even though they have been shown to benefit the small fraction of health care organizations and clinicians that have used them. Most health care providers do not understand how systems engineering can help solve health care delivery problems and improve operating performance. Many do not even know the questions systems tools and techniques might address or how to take advantage of the answers Only when people trained in the use of systems-engineering tools are integral to the health care community will the benefits become fully available. Finding 3-5. Systems-engineering tools for the design, analysis, and control of complex systems and processes could potentially transform the quality and productivity of health care. Statistical process control, queuing theory, human-factors engineering, discrete-event simulation, QFD, FMEA, modeling and simulation, supply-chain management, and knowledge discovery in databases either have been or can be readily adapted to applications in health care delivery. Other tools, such as enterprise management, financial engineering, and risk analysis, are the subjects of ongoing research and can be expected to be useful for health care in the future. Finding 3-6. Neither the engineering community nor the health care research community has addressed the delivery aspects of health care adequately. Although clinical applications of new medicines, procedures, and devices have been

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Building a Better Delivery System: A New Engineering/Health Care Partnership widespread, improving the processes by which care is delivered has been mostly disregarded. The adaptation and improvement of existing systems tools and the creation of new tools to address health care delivery have not been primary objectives of federal agencies or public or private research institutions. Finding 3-7. Information/communications systems will be critical to taking advantage of the potential of existing and emerging systems-design, -analysis, and -control tools to transform health care delivery. These tools can provide timely collection, analysis, and sharing of process and outcome data that would benefit all stakeholders in the enterprise. Although such systems are available in other industries, meeting the unique requirements of the health care community will require active research. Finding 3-8. The current organization, management, and regulation of health care delivery provide few incentives for the use or development of systems-engineering tools that could lead to improvements. Finding 3-9. The widespread use of systems-engineering tools will require determined efforts on the part of health care providers, the engineering community, federal and state governments, private insurers, large employers, and other stakeholders. RECOMMENDATIONS Recommendation 3-1. Private insurers, large employers, and public payers, including the Federal Center for Medicare and Medicaid Services and state Medicare programs, should provide more incentives for health care providers to use systems tools to improve the quality of care and the efficiency of care delivery. Reimbursement systems, both private and public, should expand the scope of reimbursement for care episodes or use other bundling techniques (e.g., disease-related groups, severity-adjusted capitation for Medicare Advantage, fixed payments for transplantation, etc.) to encourage the use of systems-engineering tools. Regulatory barriers should also be removed. As a first step, regulatory waivers could be granted for demonstration projects to validate and publicize the utility of systems tools. Recommendation 3-2. Outreach and dissemination efforts by public- and private-sector organizations that have used systems-engineering tools in health care delivery (e.g., Veterans Health Administration, Joint Commission on Accreditation of Healthcare Organizations, Agency for Healthcare Research and Quality, Institute for Healthcare Improvement, Leagfrog Group, U.S. Department of Commerce Baldrige National Quality Program, and others) should be expanded, integrated into existing regulatory and accreditation frameworks, and reviewed to determine whether, and if so how, better coordination might make their collective impact stronger. Recommendation 3-3. The use and diffusion of systems-engineering tools in health care delivery should be promoted by a National Institutes of Health Library of Medicine website that provides patients and clinicians with information about, and access to, systems-engineering tools for health care (a systems-engineering counterpart to the Library of Medicine web-based “clearinghouse” on the status and treatment of diseases and the Agency for Healthcare Research and Quality National Guideline Clearinghouse for evidence-based clinical practice). In addition, federal agencies and private funders should support the development of new curricula, textbooks, instructional software, and other tools to train individual patients and care providers in the use of systems-engineering tools. Recommendation 3-4. The use of any single systems tool or approach should not be put “on hold” until other tools become available. Some system tools already have extensive tactical or local applications in health care settings. Information-technology-intensive systems tools, however, are just beginning to be used at higher levels of the health care delivery system. Changes must be approached from many directions, with systems engineering tools that are available now and with new tools developed through research. Successes in other industries clearly show that small steps can yield significant results, even while longer term efforts are being pursued. Recommendation 3-5. Federal research and mission agencies should significantly increase their support for research to advance the application and utility of systems engineering in health care delivery, including research on new systems tools and the adaptation, implementation, and improvement of existing tools for all levels of health care delivery. Promising areas for research include human-factors engineering, modeling and simulation, enterprise management, knowledge discovery in databases, and financial engineering and risk analysis. Research on the organizational, economic, and policy-related barriers to implementation of these and other systems tools should be an integral part of the larger research agenda. CONCLUSION Information/communications systems will be critical to the effectiveness of existing and emerging systems-design, -analysis, and -control tools in the transformation of health care delivery. Information/communications systems can provide timely collection, analysis, and sharing of process and outcome data that would benefit all stakeholders in the enterprise. Although these systems are available in other industries, meeting the unique requirements of the health

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