BACKGROUND

A primary task for the IOM Subcommittee was to settle on the level of analysis it would use to anchor its work. A number of possibilities were offered: an entire health system, an integrated delivery system, an organization that delivers a particular kind of care, such as a hospital or nursing home, and so forth. The Subcommittee chose to focus on units it calls micro-systems.

This reason for this choice requires some explanation of both the origin of the term micro-unit or micro-system and the place of systems thinking in health care. Although the term micro-system is new to health care and may, at first, seem jarring, it was chosen carefully. The prefix micro- emphasizes its focus on small systems that are often embedded in larger macro-systems. The term system emphasizes that success in achieving clinical purposes requires the conscious development of systems to guide care processes.

The committee adopted the term micro-system in contrast to more traditional terms, such as team, practice, or panel to emphasize the idea that a micro-system encompasses not just the practitioners but also the patients, technologies (including information technologies), and processes of care that are integral to their work. It also emphasizes systemness as a feature that can be purposefully advanced using regular, ongoing information about the outcomes of care that indicate how well the micro-system processes meet patients' needs.

As described by Bertalanffy and others in early work on general systems theory, a system is a set of interdependent elements interacting to achieve a common aim. 1 These elements may be both human and nonhuman, such as equipment and technologies. 2 During the period following World War II, cybernetics and information theory, which originated in the disciplines of physics and biology, began to be applied across scientific disciplines to systems engineering and operations research to understand increasingly complex levels of organization, including social systems. 3 ,4 Since that time, organizational theorists, researchers, and managers have turned to systems theory for help in improving the performance of organizations. To date, however, the application of operations research has moved ahead faster and more widely in the business community than in health care. In many ways, the clinical office of today is little changed from the 1950s. The process of care is organized around individual patient visits with little clinical information technology to assist decisionmaking and very little information about performance to guide improvement, whether concerning patient health outcomes or their experience.

Despite substantial market pressure to improve both productivity and the acceptability of services, office practices and units within larger organizations (such as within hospitals) encounter substantial barriers in making threshold changes in their performance and even greater barriers in disseminating their successes within or across organizations. At the same time, the morale of health care professionals has been severely strained by efforts to do more with fewer resources even while coping with an avalanche of new technologies and knowledge.

This study began by looking beyond health care to other industries for help in framing the investigation. A primary source for the conceptual framework came from the work of James Brian Quinn. Quinn approached a study of business performance by identifying break-



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Exploring Innovation and Quality Improvement in Health Care Micro-Systems: A Cross-Case Analysis BACKGROUND A primary task for the IOM Subcommittee was to settle on the level of analysis it would use to anchor its work. A number of possibilities were offered: an entire health system, an integrated delivery system, an organization that delivers a particular kind of care, such as a hospital or nursing home, and so forth. The Subcommittee chose to focus on units it calls micro-systems. This reason for this choice requires some explanation of both the origin of the term micro-unit or micro-system and the place of systems thinking in health care. Although the term micro-system is new to health care and may, at first, seem jarring, it was chosen carefully. The prefix micro- emphasizes its focus on small systems that are often embedded in larger macro-systems. The term system emphasizes that success in achieving clinical purposes requires the conscious development of systems to guide care processes. The committee adopted the term micro-system in contrast to more traditional terms, such as team, practice, or panel to emphasize the idea that a micro-system encompasses not just the practitioners but also the patients, technologies (including information technologies), and processes of care that are integral to their work. It also emphasizes systemness as a feature that can be purposefully advanced using regular, ongoing information about the outcomes of care that indicate how well the micro-system processes meet patients' needs. As described by Bertalanffy and others in early work on general systems theory, a system is a set of interdependent elements interacting to achieve a common aim. 1 These elements may be both human and nonhuman, such as equipment and technologies. 2 During the period following World War II, cybernetics and information theory, which originated in the disciplines of physics and biology, began to be applied across scientific disciplines to systems engineering and operations research to understand increasingly complex levels of organization, including social systems. 3 , 4 Since that time, organizational theorists, researchers, and managers have turned to systems theory for help in improving the performance of organizations. To date, however, the application of operations research has moved ahead faster and more widely in the business community than in health care. In many ways, the clinical office of today is little changed from the 1950s. The process of care is organized around individual patient visits with little clinical information technology to assist decisionmaking and very little information about performance to guide improvement, whether concerning patient health outcomes or their experience. Despite substantial market pressure to improve both productivity and the acceptability of services, office practices and units within larger organizations (such as within hospitals) encounter substantial barriers in making threshold changes in their performance and even greater barriers in disseminating their successes within or across organizations. At the same time, the morale of health care professionals has been severely strained by efforts to do more with fewer resources even while coping with an avalanche of new technologies and knowledge. This study began by looking beyond health care to other industries for help in framing the investigation. A primary source for the conceptual framework came from the work of James Brian Quinn. Quinn approached a study of business performance by identifying break-

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Exploring Innovation and Quality Improvement in Health Care Micro-Systems: A Cross-Case Analysis through levels of successful performance in industries worldwide and asking how they accomplished it. 5 Quinn found that many of the world's best run organizations recognized the advantage of focusing on small functioning units to improve timeliness and cycle time, product quality, service, customer and worker satisfaction, as well as to reduce production costs. He described these small units as microunits of production, meaning that they were the smallest or minimum “replicable unit,” which for this study means a unit whose processes are repeatable with small variation in response to local conditions and that have available to them all the necessary resources to do their work. Although the approach originated with routine manufacturing and rules-based, automatable systems, it proved to be applicable, as well, to service operations where it led to large increases in customer satisfaction. Surprisingly, the larger the organization, the greater the leverage for gains because of a larger information database and greater possibility for experimentation. Using these small units as a starting place, Quinn found that highly effective service technologies were connected in a variety of new organizational forms that seemed to have some common characteristics: they had much “flatter” hierarchies than their predecessors; they were built around core service competencies typically consisting of special depth in some unique technologies, knowledge bases, skills, or other systems; and they interacted with customers using excellent information technologies and organizational design. Organizations discovered that these forms also made their workplaces more personally challenging and satisfying places to work. The micro-system study explored whether such an approach to understanding highly effective systems could be applied to professional organizations, and, in particular, to health care units—a special, form of service industry, often thought to be unique because inputs (patients) are so variable, outputs ill-defined, and the need for professional expertise so great. Health care requires a mix of rules-based action and judgment based on individual needs, and this combination seemed to defy simple notions based on manufacturing. Defining Health Care Micro-Systems Adapting Quinn's notion of the micro-unit, Batalden and coworkers 6 have described the concept of a health care micro-system that delivers the core “product” of health care—patient care. It is at this interface that patients experience care and that the quality of care is determined. Although health care is provided to patients by caregivers who work in very complex organizational arrangements, the overwhelming amount of their own daily work is as part of a small system consisting of people—the patients and practitioners —and the technologies they use. Nelson and his colleagues 7 have described the essential elements of a micro-system: a core team of health care professionals; a defined population they care for; * * Batalden notes that the population may be an enrolled population in a prepaid, capitated system or those who are seen regularly by a given set of providers who work together at a single site.

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Exploring Innovation and Quality Improvement in Health Care Micro-Systems: A Cross-Case Analysis an information environment to support the work of caregivers and patients; and support staff, equipment, and a work environment. Accordingly, for this study, we defined a micro-system as: a small, organized patient care unit with a specific clinical purpose, set of patients, technologies and practitioners who work directly with these patients. One example of a health care micro-system is a primary care or specialty practice; an office-based, physician-led practice caring for 9,800 patients with, for example, about 3,000 square feet of space in a downtown office building, six physicians, two nurse-practitioners, staff, hospital privileges, and so forth. Other micro-systems include: a cardiac care unit in a medical center, an emergency department in a community hospital, a hospice, a dialysis unit, a diabetes management program, or a back-pain treatment center. For every micro-system, clusters of tasks can be specified. Such clusters in office practice include, for example, greeting and establishing a relationship with a patient; making an initial assessment and recording findings; ordering laboratory tests and incorporating results into care plans; performing procedures, and providing instructions for self-care, next steps, and follow-up. The key components of a micro-system are not new: patients, populations, clinicians, activities, and information technology exist in every health care setting. However, most often these small systems—their elements and working dynamics—are not recognized by the larger organizations that provide the organizational context for their work, such as in the design of human resource policies and information technologies, or by groups outside health care organizations, such as third party payers devising payment policies and employers seeking accountability for the care of their employees. As a result, payment and incentives may ignore collaborative working relationships and be misdirected at too “low” or too “high” a level. For example, payment policies are typically devised to affect the behavior of physicians rather than a collaborative multidisciplinary team. Conversely, incentives and regulations may be directed at entire organizations (such as hospitals) rather than recognizing and rewarding the small work groups —micro-systems—that affect quality directly. Micro-systems do their work today along a spectrum of performance that can range from very good to very poor. We emphasize that in this study, the term micro-system is not reserved for groups that demonstrate extraordinary performance along all the dimensions of care or in their “systemness.” In part, this is because at present that would constitute an extremely small, perhaps a null, set. More importantly, it draws attention to the fact that these small care systems are ubiquitous throughout health care, and their influence on quality is key to understanding how to improve care. Batalden and his colleagues have suggested that effective micro-systems might provide (1) greater standardization of common activities and customization of care to individual patients, (2) greater use and analysis of information to support daily work, (3) consistent, measured improvement in performance, (4) extensive cooperation and teamwork within the

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Exploring Innovation and Quality Improvement in Health Care Micro-Systems: A Cross-Case Analysis micro-system, (5) and an opportunity for spread of best practices across micro-systems within their larger organizations. 8 Some previous research on teams has focused on functional and interdisciplinary workgroups and the systems that facilitate or impede the management of these workgroups. 9 For example, a firm system—parallel teams of practitioners and students and patients randomly assigned to the teams—was introduced over two decades ago at MetroHealth Medical Center in Cleveland, Ohio as a way to create and maintain longitudinal relationship of small groups of teachers, students, and patients. 10 , 11 , 12 This has been a valuable approach to evaluating different innovations in patient care and organizational design. This study continues the tradition of learning about innovation and improvement from clinical practices in dynamic settings. It used a purposive sampling of what experts in the field considered to be high performing micro-systems to learn about their organization, aims, and their approaches to measurement and improvement. Micro-systems do not, of course, function in isolation. Many work processes cut across micro-systems as well as clinical disease states such as those involving multiple chronic illnesses. Micro-systems must coordinate seamlessly with other micro-systems, and a major challenge is effectively managing the handoffs and feedback of information among micro-systems. The interaction of micro-systems is critical to ensuring that information is available when needed and is consistent, that patients receive timely services, and that waste and duplication are minimized. The larger organizations of which they are a part—which we call the macro- or umbrella organization—can help this to occur. That is, in addition to linkages among micro-systems, micro-systems may be part of a larger organization (e.g., a cardiac care unit in a hospital, a group practice that has contracts with health plans, an ophthamology practice within a multispecialty clinic), and they are embedded in and interact with a legal, financial, and regulatory environment that may foster or impede their effectiveness. Although not a focus of this study, leaders of macro-organizations interact with the environment to mediate the effect on micro-systems of such financial incentives, regulation, or workforce issues. Use of Qualitative Methods Qualitative inquiry cultivates the most useful of all human capacities —the capacity to learn from others. —Patton 1994 13 This study examined micro-systems in the context in which they exist so that meaningful inferences could be made about them. Choosing a strategy to guide the work required careful consideration of quantitative and qualitative methodologies. Both qualitative and quantitative research involves a process of inquiry into a human or social problem. The method selected, however, depends on the questions that the researcher seeks to answer. For example, small area analysis of quantitative data 14 shows that diabetic Medicare beneficiaries vary in their rates of retinal exams, HgA1c, and low-density lipoprotein (LDL) monitoring.

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Exploring Innovation and Quality Improvement in Health Care Micro-Systems: A Cross-Case Analysis Across the United States, retinal exams vary by geographic region from 25.1 percent to 66.1 percent. HbA1c monitoring varies from 8.9 percent to 70.2 percent, and LDL monitoring varies from 6.8 percent to 68 percent. Such a quantitative analysis does not explain, however, why such variation occurs or the barriers that may exist to providing these services. Nor does it reveal how to change the care to improve the outcomes. To connect the quantitative findings to small group behavior, qualitative methods can be helpful in elucidating the behavior of the system that is producing the results. Quantitative methods test theory, with an emphasis on hypothesis testing and verification. Data gathered in a quantitative study is in the form of numbers evaluated, using descriptive and inferential statistics. A quantitative approach to a study on health care micro-systems might involve a variable-oriented analysis that computes the correlation between a variable and a selected outcome. Another quantitative option would be to use regression analysis to determine the relative importance of a set of variables in determining such an outcome. These approaches, however, require clarity about the important variables going in to the study. Because this study was intended to be an exploratory look at micro-systems as a unit of analysis, the important variables were not clear at the outset but were, rather, expected to emerge as the study progressed. Neither was it clear what outcomes might be measured. We were interested in the performance of micro-systems, recognizing that in some cases this was measurable (e.g., rates of favorable or unfavorable patient outcomes), but that in other cases the outcomes of interest were subjective and not easily measured, for example, patients' experience, the professional culture, and interest in innovation and assessment of performance. For these reasons, a qualitative strategy was chosen as most appropriate for this research. Qualitative methods develop theory by emphasizing rich description and discovery. These methods assume that the phenomena under study are part of a system and cannot be reduced to a few variables with a clear cause and effect relationship. Qualitative methods build on the theme of naturalistic inquiry, which is “a discovery-oriented approach that minimizes investigator manipulation of the study setting and places no prior constraints on the outcomes of the research. ” 15 Data are in the form of words and are evaluated subjectively by systematically reducing data to themes and categories. Qualitative methods are inductive to the extent that the research design allows important themes to emerge from patterns found in the data. A criticism of qualitative methods has been the focus on individual cases, which limits the external validity of the research. In response, it can be argued that generalizability is not a goal of qualitative research in general 16 , 17 nor of this study, in particular. The qualitative methods used in this study should best be understood as descriptive, hypothesis generating and, to a limited extent, hypothesis testing (see below). Further data gathering and qualitative analysis (for example using multiple respondents at each site or negative cases for comparison) paired with quantitative analysis to test hypotheses, may be the most fruitful route to confidence in the generalizablity of study findings and their predictive value.

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Exploring Innovation and Quality Improvement in Health Care Micro-Systems: A Cross-Case Analysis Personal insights by the researchers are the essential information derived from the interview data and they are critical to understanding the complexities of micro-systems and the organizations in which they are embedded. However, the research must approach the phenomenon under study with what Patton calls “empathic neutrality.” 18 To be neutral to the findings means not approaching the phenomenon with a set of preconceived ideas. That means one approaches micro-systems with a desire to learn about them as interrelationships emerge. In qualitative research, it is important to separate the description of the data from the interpretation of the data. Geertz 19 and Denzin 20 discuss “thick” and “thin” description. “Thick description” depends on presenting descriptive data or recording verbatim comments so that researchers can make their own interpretations later. “Thin description,” on the other hand, summarizes the facts without including any of the context. Thick description sets up analysis and makes possible interpretation. 21 Appendix A shows examples of each type of description. For this research, thick description was used and later coded. Each micro-system was recorded and presented in sufficient detail so that the micro-system, or “case,” could be understood in its local context. This study used two methods: first, descriptive summaries of the interviews derived from thick description; and, second, cross-case analysis. Cross-case analysis offers a way to reconcile the need for “thick description” of uniquely individual cases yet captures the themes and patterns that emerge across cases. 22 Two approaches to cross-case analysis are available: case-oriented analysis and variable-oriented analysis. 23 A case-oriented approach starts by considering each case as its own entity. Only after understanding the relationships, configurations, associations, and the like within the case does the researcher move to a comparative case analysis. The goal is to discover the underlying themes, similarities, and associations that hold across cases. A variable-oriented approach to cross-case analysis starts with a framework of several variables or themes that cut across cases. For example, variables that may be relevant to a study of health care micro-systems may be the use of information, the role of information technology, or coordination of patient care. Although the study starts with key variables in mind, the variables may evolve and be clarified as the study progresses and as cases are included in the analysis. The variable-oriented approach is more conceptual and theory-centered from the beginning, and less emphasis is placed on the specific details of a particular case. Neither approach to cross-case analysis—case-oriented or variable-oriented—is necessarily better. As Miles and Huberman point out, the process is one of alternating, combining, or integrating methods as a study progresses. 24 They suggest a mixed strategy that combines the two approaches and uses a “stacking” technique. Such a process was used in this study. To use this technique, the researcher writes up a series of cases using a more or less standard set of variables. Matrices are used to display the data for each case. Without losing any of the individual case-level data, cases are then “stacked” in a “meta-matrix.” Analysis continues by systematically comparing the stacked cases and condensing the meta-matrix.