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Complexity Science and Health Care Management


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Complexity Science and Health Care Management

Reuben R. McDaniel, Jr., Ed. D.

Charles and Elizabeth Prothro Regents Chair

In Health Care Management and

Professor of Management Science and Information Systems

Graduate School of Business Administration

The University of Texas at Austin

mcdaniel@mail.utexas.edu


and

Dean J. Driebe, Ph. D.

Research Scientist

Ilya Prigogine Center for Studies in Statistical Mechanics and Complex Systems

Department of Physics

The University of Texas at Austin

dean@ physics.utexas.edu


John Blair, Myron Fottler, and Grant Savage, Eds. Advances in Health Care Management, vol. 2, pages 11-36. Copyright 2001 by Elsevier Science Ltd.


Complexity Science and Health Care Management

Complexity science offers new ways to think about health care organizations that enable one to have new insights about their nature and about their functioning (Begun, 1985; Beinhocker, 1997; Anderson & McDaniel, 2000, Arndt & Bigelow, 2000; Kiel, 1994; Lewin & Regine, 2000; Miller, Crabtree, McDaniel & Strange, 1998; Zimmerman, et al, 1998). These new insights lead to a rethinking of managerial strategies. The purpose of this paper is to identify some of the most critical insights from complexity science that affect how we view health care organizations. Based on these insights, the paper will identify some managerial changes that are indicated and some new areas for research in health care management that are indicated.

Health care organizations are, of course, members of the set of all organizations and as such share some characteristics with all organizations. However, there are some particular characteristics of health care organizations that make complexity science a particularly useful tool for studying them. For example, there is significant information asymmetry in health care organizations, particularly between professional clinician providers of services and typical patients receiving these services and these asymmetries create unusual interdependencies. There is also a weak link between service recipients and payers for services received and the weakness of this link leads to potential distortions of system’s characteristics. There is, typically, considerable technological and professional heterogeneity within any health care organization and this increases the difficulty of understanding the organization as a whole. The “mystical” nature of much of health care delivery adds another level of difficulty in understanding their management. These factors, and others like them, have made it hard to simply fit health care organizations into our general understandings of more typical organizational forms. However, complexity science offers insights that enable innovative approaches to health care management practice and research.

Complexity science is the study of systems that are characterized by nonlinear dynamics and emergent properties and it is certainly true that health care organizations are such systems. One of the types of systems often studied in complexity science is Complex Adaptive Systems (CAS). CAS are characterized by diverse agents interacting with each other and capable of undergoing spontaneous self-organization (Cilliers, 1998). For example, the multiple professionals often required to accomplish the goals of health care organizations comprise such a system. CAS are qualitatively different from linear systems so often studied in more traditional sciences. The dynamic of CAS is nonlinear, with the state of the system at a given time being a nonlinear function of the state of the system at some previous time. The history of the system matters in a fundamental way. Existing managerial and policy issues in health care are the result of, among other things; the history of health care within the cultural milieu and this contributes to the usefulness of a complexity perspective in studying health care organizations.

The state of a complex adaptive system as a whole is irreducible to a linear superposition of the states of its constituent elements. The essence of complexity science is in the study of patterns and relationships, rather than objects and substance, and in the search for characteristics of systems far from equilibrium rather that at the point of balanced stability (Capra, 1996). Complexity science looks not at the parts, but at the wholes in an effort to gain a deeper, qualitatively different understanding of phenomenon. Complexity results from the interactions between the components of a system (Cilliers, 1998) and it is manifest at a level that transcends the local dynamics among each constituent element. Its characteristics at one level cannot be understood from knowledge of its characteristics at other levels (Holland, 1998, Newman, 1996). When considering issues such as the medical error rate in hospitals, it is necessary to consider these issues at the systems level rather than simply as the failure of some individual worker to “do his/her job” (Edmondson, 1996). Complexity science helps in developing this perspective.

Complexity science transcends traditional disciplines and has been a source of new insights in physics, biology, geology, cosmology as well as the social sciences (Fontana & Ballati, 1999). As noted by Mainzer (1996, p. 272), “The crucial point of the complex systems approach is that from a macroscopic point of view the development of political, social or cultural order is not only the sum of single intentions, but the collective result of nonlinear interactions.” When we look at the world through the lens of more conventional science it may seem as though order is unnatural because the orderly arrangements of elements seems so unlikely. Complexity science attempts to explain why there is, in fact, order in the universe (Johnson, 1995; Kauffman, 1992).

While there is certainly a relationship between complexity science and chaos theory, they should not be mistaken for each other (Morel and Rananujam, 1999, Cilliers, 1998). James Gleick (1987), in his enormously popular book, Chaos, has contributed to a broad familiarity with many of the ideas of chaos theory and these have often been assumed to be the same as the notions that define complexity science. They are not. Chaotic behavior, in the technical sense of deterministic chaos, results from the nonlinear development of a relatively small number (as few as one) of variables and the study of chaos focuses on how complexity can arise from simplicity (Lewin, 1992; Cilliers, 1998). Complexity science, on the other hand, focuses on how order can emerge from a complex dynamical system (Nicolis and Prigogine, 1989). “Complexity means we have structure with variations” (Goldenfeld and Kandanoff, 1999, p. 87). It is probably most appropriate to say that chaos is a subset of complexity. Because we wish to reduce possible confusion, in this analysis we will be centering on complexity rather than chaos and the reader should be aware of this.

CAS have been difficult to study in the past because of the mathematics associated with modeling their behavior. However, recent advances in computational power and new computational techniques used in fields such as Cellular Automata and Boolean Networks and theoretical tools such as Fractal Geometry allow complexity scientists to uncover some of the common characteristics of complex systems and to understand the spontaneous self-organizing dynamics of the world (Kaplan & Glass, 1995; Capra, 1996; Waldrop, 1992; Casti, 1997).

Increasingly the science of complexity is being used as a metaphor to examine the science of organizational management (Morgan, 1997). For decades organizational scientists have labored clumsily with metaphors, myths and misunderstandings about the nature of organizations that did not fit managers’ experience of organizations (Wheatley, 1992; Stacey, 1992, 1996; McDaniel, 1997). Rational approaches to understanding organizations have worked poorly; at least when we think that to be rational is to be Newtonian in one’s logic. There are numerous inconsistencies between traditional theoretical descriptions of organizations and what participants in those organizations experience. Evidence of the level of interest by organizational theorists in complexity science is found in the wide range of articles and books that they are writing on the subject. The May-June 1999 edition of Organization Science dedicated to the Application of Complexity Theory to Organization Science, and the founding in 1999 of the new journal Emergence, A Journal of Complexity Issues in Organizations and Management, are but two examples. It has been difficult for managers and organizational scientists to truly incorporate insights from complexity science into their thinking. People are tempted to adopt the language of complexity science but ignore the logic; creating a new lexicon for old ideas (Stacey, 2001). For example, Brown and Eisenhardt (1998), in their book, Competing on the Edge, speak about complexity as associated with speed of change, but complexity science does not speak to the speed of a system’s dynamics, but rather to the nonlinearity of its unfolding over time. It is difficult to break old patterns of thinking. We are constantly tempted to try to make new models fit into our old models. But complexity science is not an extension of the Newtonian model or traditional views, nor is it caught up in the notion that health care organizations are “living systems”. Complexity science is a different way of looking at the organizational world, not just an extension of, or complement to, other ways of looking at the organizational world. For managers and researchers in health care to take complexity science seriously means to accept the idea that health care organizations are complex adaptive systems and that they share the deep characteristics (properties) of complex adaptive systems.

Applying a complexity framework suggests a different focus of attention for managerial analysis. Thoughtful organizational scientists are asking themselves these kinds of questions: How do we manage organizations in the face of the realization that they are complex adaptive systems? What would I think about health care management if I took complexity science seriously? How can you understand organizations better if you know complexity science? In order to avoid the trap of falling back to old models, we must be sure we thoroughly understand the characteristics of CAS and how those characteristics manifest themselves in health care organizations.

The remainder of this paper is organized as follows. The characteristics of CAS are delineated. The way in which these characteristics manifest themselves in health care organizations is identified. Managerial strategies rising from complexity science are identified. Lastly, suggestions for research are given.

^ Characteristics of Complex Adaptive Systems


Agents

Complex adaptive systems are made up of a large number of agents that are information processors (Zimmerman, Lindberg, and Plsek, 1998; Cilliers, 1998; Waldrop, 1992, Holland, 1998, Casti, 1997). These agents may be nerve cells, computer programs, individuals, or firms. In a health care system agents might include individual people such as clinicians, patients and administrators. Agents might also include processes such as nursing processes and medical processes, functional units such as nursing, accounting and marketing and entire organizations such as insurance companies and regulatory agencies. The one characteristic that these agents all share is that they can process information and react to changes in that information (Casti, 1997). Agents have the capacity to exchange information among themselves and with their environment and to adjust their own behavior as a function of information they process. Agents are constantly acting and reacting to what other agents are doing (Holland, 1995). It is important when thinking about health care organizations not to simply consider them as the people in the organization but to recognize the wide variety of kinds of agents in the system.

CAS agents are diverse from each other (Kauffman, 1995; Coleman, 1999). This diversity is critical to the ability of the CAS to function because diversity is a source of novelty and adaptability. If all of the agents were the same, and all processed information in the same way, there would be no potential for change and/or growth. Agents have different information about the system and none understand the system in its entirety (Casti, 1997). As noted by Cilliers, “If each element ‘knew’ what was happening to the system as a whole, all the complexity would have to be present in that element”(Cilliers, 1998, p. 5 italics in original). Each individual agent pays attention to its local environment; it is ignorant of the system as a whole and some central agent with responsibility for overall system behavior does not control it (Casti, 1997). This very diversity among agents can be a source of significant frustration. The different structure and goals of accounting processes in health care often come into conflict with the structure and goals of healing processes. Yet diversity is also the source of invention and improvisation. Although agents are elements in their own right, and are often CAS themselves, it is also true that agents at any one level in a CAS serve as building blocks for agents at a higher level (Waldrop, 1992). Different agents take different roles as the dynamic of the system unfolds. “CAS are constantly revising and rearranging their building blocks as they gain experience”(Waldrop 1992, p. 146). The health care organization as a whole consists of functional units each of which is a CAS. At the same time, each is a building block for a CAS, the health care organization itself. As these building blocks change over time, the whole organization changes.


Interconnections

While a diverse set of agents is necessary for a CAS, it is not sufficient. In fact, the essence of a CAS is captured in the relationships among agents, rather than in the agents themselves. Recently, as scientists have begun to approach questions about organizations, they have noted that their questions tend to refer to systems where there are a great many interdependent agents interacting with each other in a great many ways (Waldrop, 1992). The dynamics of these interactions makes these systems qualitatively different from static systems that may be complicated, but not complex adaptive systems. The environment for agents in a CAS is a function of the interconnections that agent has with other agents in the system and with agents in the system’s environment. Therefore, understanding a CAS requires understanding patterns of relationships among agents rather than simply understanding the nature of agents. In family medicine, it has become clear that the management of many illnesses requires attention to the relationship of the patient to others in the family, but health care organizations are ill equipped to treat the family rather than the patient. As we look at the incidence of litigation in health care, we note that the relationship of the patient and the physician may be a significant moderating factor in whether or not the patient sues the health care organization over an alleged error. And clearly, the relationship among the clinical staff of a health care organization is critical to the overall performance of the organization. We speak of well functioning surgical teams and recognize that the relationships among team members is important. Some attempts have been to manage relationship systems in health care through the adoption of increasingly sophisticated information systems. These have not been very successful. For example, computerized patient information systems have not been widely adopted in health care despite over a decade of effort (Dick, Steen and Detmer 1997). Telemedicine has been seen as a major potential strategy for increasing the quality and access to health care and to lower costs and yet it has been disappointing in its impact (Office of Technology Assessment, 1995). In both cases, failure to resolve relationship problems, rather than failure to resolve information technology issues, seems to be the major cause of difficulty (Paul, Pearlson and McDaniel, 1999; Seligman, 1999). When treated from the perspective of complexity science these difficulties are easier to understand and alternative approaches to resolving them emerge.

The relationships among agents in a CAS are non-linear in nature. Inputs are not proportional to outputs. Small changes can lead to big effects and big changes can lead to small effects. A general system has both positive and negative feedback loops and the effect of any one agent’s activity can feed back on itself as well as influence other agents.

“In the world of linear equations we thought we knew that systems described by simple equations behaved in simple ways, while those described by complicated equations behaved in complicated ways. In the nonlinear world - which includes most of the real world, as we begin to discover – simple deterministic equations may produce an unsuspected richness and variety of behavior. On the other hand, complex and seemingly chaotic behavior can give rise to ordered structures, to subtle and beautiful patterns. ... Another important property of nonlinear equations that has been disturbing to scientists is that exact prediction is often impossible, even though the equations may be strictly deterministic” (Capra, 1996, p. 123).

One characteristic of CAS is that each agent is generally connected to local agents, and the nature of these connections among diverse agents can lead to complex behavior. However, it is not simply the number of connections in a CAS that determines its character; it is also the richness of these connections. Any element in the system influences and is influenced by, quite a few other ones (Cilliers, 1998). Even though an agent’s range of interaction may be short, its range of influence is often wide. Information is carried throughout the system through feedback (Kauffman, 1995; Eisenhardt & Brown, 1999), creating patterns of interaction. “Such interactions are typically associated with the presence of feedback mechanisms in the system. These interactions in turn introduce nonlinearities in the dynamics of the system” (Morel and Ramanujam, 1999, p. 279).

These patterns of interconnections can follow fairly simple rules and complex behavior can emerge from these rules. Order is created through the patterns of interconnections, not complicated controls and rules. A large number of connections between agents is not required, and in fact can lead to random behavior (Kauffman, 1995; McKelvey, 1999). In many ways this flies in the face of conventional wisdom that suggests that everyone should participate in all activities. Sometimes, programs such as shared governance programs for nursing fail because of too much connectivity rather than too little. Research on participation in decision-making in health care organizations suggests that attention must be paid to patterns of participation, not just amount or frequency of participation (Anderson and McDaniel, 1999; Ashmos and McDaniel, 1991).

Adaptability is reflected in the ability of the CAS and its agents to change the rules through their interactions, thus changing the system. Interactions can take many forms. For example, Thompson (1979) describes interactions in terms of pooled, sequential, and reciprocal interactions. More recently, Dooley and Van de Ven (1999) have discussed the dynamic interactions among agents in terms of Chaos, Colored Noise, Periodic), and White Noise. Dooley and Van de Ven (1999) have identified appropriate mathematical techniques for distinguishing among these dynamic interactions. They suggest that complexity scientists studying organizations carefully utilize appropriate models when attempting to describe the interconnections among agents in a CAS.


Self-Organization

“Self-organization is the spontaneous emergence of new structures and new forms of behavior in open systems far from equilibrium, characterized by internal feedback loops and described mathematically by nonlinear equations” (Capra, 1996, p. 85). Self-organization arises from the changing patterns of relationships in CASs. When diagnostic related groups became the standard for prospective payment in health care, then health care organizations began to develop entire work units devoted to redefining physician’s diagnosis of illnesses in such a way as to maximize payments to the organization. This was not the intent of those who implemented drgs but it was an organizational form that emerged from changing patterns of relationships. The structure and form of CAS is not simply externally imposed from some hierarchical controller. Rather, structure and form are a function of patterns of relationships among agents and interactions of these agents with their environment (Cilliers, 1998; Mainzer, 1996). As noted by Zimmerman, Lindberg & Plsek (1998, p. 10), “ CAS have distributed control rather than centralized control.” Many health care policy makers and managers have learned, to their dismay, that their control over the organizational patterns in health care can be minimal.

Two examples that have been often used for illustrating the self-organizing properties of CASs are the flocking of birds and the schooling of fish. In neither case is there some “smart” bird or fish that “gets things organized” (Callen and Shapero, 1974). Rather the pattern of organization develops from local interactions among agents, apparently following very simple rules. This phenomenon of self-organization has been used to better understand how colonies of ants seek food and organize their living arrangements (Deneubourg, Pasteels and Verhaege, 1983; Bossomaier & Green, 1998). “The crucial point of the complex systems approach is that from a macroscopic point of view the development of political, social, or cultural order is not only the sum of individual intentions, but the collective result of nonlinear interactions” (Mainzer, 1996, p. 272). When one observes order in a system, one is tempted to assert that the order must come from some intentionally on the part of some external controller. Complexity science teaches us that order in a system may well be a result of the properties of the system itself (Nicolis and Prigogine, 1989; Kauffman, 1993). Order is a result of nonlinear interactions and the capacity for self-organization is a function of (among other things) the number of connections among agents and the intensity of these connections. It is not true that the more connections the better. Too many connections may lead to behavior that never settles into any recognizable pattern of self-organization. On the other hand, too few connections may lead to frozen behavior rather than dynamical self-organization. Kauffman (1995, p. 84) expresses the importance of this observation as follows, “ Our intuitions about the requirements for order have, I contend, been wrong for millennia. We do not need careful construction; we do not require crafting. We require only that extremely complex webs of interacting elements are sparsely coupled.” CAS consist of agents, interconnected, generating order.


Emergence

“Emergence is above all a product of coupled, context-dependent interactions. Technically these interactions and the resulting system are nonlinear: The behavior of the overall system cannot be obtained by summing the behaviors of the constituent parts” (Holland, 1998, p. 122, italics in original). Agents interacting in a nonlinear fashion may self organize and cause system properties to emerge. We see units in integrated health care systems developing patterns of behavior that make it difficult, if not impossible, for the integrated system to achieve anticipated synergies. Organizational mergers and the issues, created by these mergers need to be viewed from a complexity science viewpoint in order to detect their emerging properties (Baskin, Goldstein and Lindberg, 2000)

Because individual agents are ignorant of the behavior of the whole system of which they are a part, they cannot control emergence of the system. Rather emergence is a result of the pattern of connections among diverse agents. But it is more than connectivity alone that leads to complexity arising from emergence. The nature of the interactions among agents is critical (Casti, 1997). The global characteristics of the CAS arise from characteristics of agents and their relationships but are not reducible to these characteristics. The properties of the whole are distinctly different from the properties of the parts. The quality of a surgical team arises from the properties of the individual physicians, nurses and surgical technicians but is not reducible to these properties. A medical unit in a hospital is more than the sum of the talents of individual workers but is an emergent property of the whole unit. This means that the managerial task goes beyond getting the best employees but to facilitating the emergence of the unit itself. For example one might call into question existing human resource practices in health care that focus on individual workers and suggest more focus is needed on the emergent system of workers. Continuing education programs for clinicians might well concern themselves with the education of emergent systems as well as education of individual members of systems.

Emergence is the source of novelty and surprise in CASs (Goldstein, 1999) and the properties of the emergent system cannot be ascertained by observing the properties of lower level agents or subsystems. Complexity science focuses on dynamic states that emerge in far from equilibrium systems (Goldstein, 1999, Holland 1998). Emergence is not a provisional construct that will succumb to more powerful analytical techniques or a better theory. Rather the unpredictability of emergent systems is fundamental. What is the outcome of emergence in CAS? There are emergent structures and organizations but perhaps most importantly, there are new patterns of relationships among agents and these modify the self-organizing characteristics of CAS. These new patterns emerge from the nonlinear relationships among agents and the rules that constrain agents. Emergence is a continuous property of CAS and emergent order is always changing in unpredictable ways. “A small set of well-chosen building blocks, when constrained by simple rules, can generate an unbounded stream of complex patterns. … The most lucid examples of emergence arise when these persistent patterns obey macrolaws that do not make direct reference to the underlying generators and constraints” Holland, 1998, p. 238-9). Emergence is not the same as serendipitous novelty such as patterns of raindrops on a window pane but is the result of nonlinear dynamics generating new properties at the macro level of analysis (Goldstein, 1999; Holland, 1998)


Coevolution

CAS consist of agents interacting in a nonlinear fashion such that the system self-organizes and emerges in a dynamic fashion. But the CAS does not simply change; it changes the world around it. There is coevolution of the CAS and its environment such that each fundamentally influences the development of the other (Kauffman, 1993, 1995; McKelvey, 1999). When a major hospital system develops and implements a new pharmaceutical control system, this will change the hospital’s relationship with pharmaceutical suppliers including, possibly, changing their source of competitive advantage. Agents do not simply adapt to the environment and each other. They coevolve with each other and with the environment in a constant dance of change. A physician changes her practice pattern and nurses, therapists and clerks are affected. A new process for managing pharmacy supplies is put in place and the relative competitive advantage of pharmacy suppliers is changed. The installation of a computer system makes some processes for assuring patient safety obsolete and demands that new, unanticipated ones be put in place. The organization acts and others react, in unexpected and unpredictable ways.

Kauffman (1993, 1995) has suggested that CASs exist in “fitness landscapes” and that each seeks a point of maximum fitness with its environment. Managers have often considered the need for their organizations to adapt to the environment but when they consider that every adaptive move creates another move by another organization or set of organizations, they then can see that adaptation is not sufficient. A new hospital computer system creates new tasks for the training department as well as new functions for the purchasing department. Each of these must now rethink how it responds to its environment because the fitness landscape for each has changed. But the hospital itself has changed the environment in which other hospitals operate and they must seek to reestablish their position in the competitive field. Health care systems are constantly attempting to improve their functioning through seeking new peaks of fitness, or new places of competitive advantage on their fitness landscapes. They seek new ways to achieve better results given the circumstances in which they find themselves.

But landscapes vary in their ruggedness and, therefore, there are significant differences in the efficiency with which an agent can achieve some point of improved fitness. Some health care systems exist in a milieu where there are few health care options for their clients and others exist in a milieu where there are many such options. No agent has some global view of the world and thereby, the capacity to see the “total picture” (Cilliers, 1998). Rather each agent acts based on local information, seeking to continuously improve its fit with its environment and, therefore, usually can only achieve some local optimum. In the process of achieving this position, each agent changes the landscape for itself and for all other agents in the system. As explained by Kauffman (1993, p. 243) “In a coevolutionary system, we need to represent the fact that both the fitness and the fitness landscape of each species are a function of the other species. Thus, in general, it is necessary to couple the rugged fitness landscape for each species, such that an adaptive move by one species projects onto the fitness landscapes of the other species and alters those fitness landscapes more or less profoundly.”

For CAS, the property of coevolution signals limits in their developmental processes. Agents posses conflicting constraints within themselves and among neighboring agents and because so many of the constraints are in conflict, compromise and cooperation lead to workable solutions rather than to some grand, superb solution (Kauffman, 1995). The dynamics of the situation mean that you can’t “get it right” in some global sense. Rather, because of the emerging properties of each agent and of each CAS, the “goodness” of CAS adaptation to its environment is a moving target. An effective and efficient hospital at one moment may turn into a dinosaur of a hospital in the next moment. “The structure of the system is not the result of an a priori design nor is it determined directly by external conditions. It is the result of interaction between the system and its environment” (Cilliers, 1998, p. 91, italics in original). “Real organisms constantly circle and chase each other in an infinitely complex dance of coevolution” (Waldrop, 1992, p. 259).


^ Summary of CAS Characteristics

When the principal characteristics of complex adaptive systems – agents interconnected in self-organizing, emergent, and coevolving systems – are considered, a major insight is that the behaviors of these systems are fundamentally unknowable. No one is smart enough to figure out where the health care system is going at any level. People continue to probe for the “simple” solution but neither investors nor practitioners have been successful in predicting the future of the health care system or even which of the system’s components are likely to prosper in the future. Agents processing local information in response to simple rules can generate unpredictable behavior, even if the system is deterministic (Gleick, 1987, Prigogine and Stengers, 1984). Patterns of interconnections in CAS are nonlinear and dynamic. CAS self-organize independently of any controlling hand, but as a function of non-linear interactions among agents and patterns of self-organizations are unknowable from any analysis of present system states. Yet, the system emerges in complex and unknowable ways as a function of the self-organization that is taking place.

Complexity science and the study of CAS leads us to a deeper understanding of that portion of the universe that is not linear and additive. Health care systems are certainly in that class of things. Understanding the characteristics of CAS leads to the understanding that they are unpredictable in their trajectory but can be understood in terms of their patterns of behavior and their probabilistic nature (Waldrop, 1992; Prigogine, 1996). Agents cannot forecast total system response to their actions and, therefore, agents attempt to improve their own payoffs or fitness, not that of the system as a whole (Kauffman, 1995).


^ Health Care Organizations as CAS

Typically, when we have thought of health care organizations we have thought of them as machines that should be well run (Harris, 1997; Blair & Fottler, 1990). We have relied on Newtonian perspectives of organizations to guide our thinking (Zimmerman, et al, 1998; McDaniel, 1998, Wheatley, 1992). These perspectives have lead to a focus on getting pieces to fit together, on predicting future outcomes of managerial behavior and on controlling behavior of workers to get them to do what we want them to do. The Newtonian perspective is a reductionist perspective where understanding the whole of a system is dependent on understanding its parts. Things must be broken down into their constituent elements in order to understand them. Clockwork is the dominant metaphor and an organization that “ran like a clock” is the desired condition. The more we explored the mechanistic view of health care organizations the more we came to realize that our experiences of health care organizations did not meet our expectations of the machine-like systems we had come to define as the well run organization (Zimmerman et al, 1998; McDaniel, 1997; Stacey, 1992, Anderson & McDaniel, 2000, Chirikos, 1998).

One example of attempts to apply reductionist, Newtonian thinking to health care has been in widespread efforts to apply total quality management or continuous quality improvement to improve clinical practice. In particular, it has been used to attempt to improve the delivery of preventive services. This seems like a very straightforward objective but success has not been achieved (Solberg, et. Al. 2000). In fact a review of efforts to apply continuous quality improvements to clinical practice has generally been unsuccessful (Shortell, Bennett and Byck, 1998). These efforts all have assumed a machine-like health care system with the key issue in the failures being an inability to move the right levers to effect change. However, this may not be the real issue. The real issue may be a misspecification of the nature of the system.

A review of the characteristics of complex adaptive systems as outlined above, suggests that health care organizations are, in fact, complex adaptive systems rather than machine bureaucracies. There are many, diverse agents (Blair & Fottler, 1990) and the ability to manage these systems of agents creates major concerns (Alexander & Morrisey, 1988; Alexander, Fennell & Halpren, 1993; Begun, 1985; Bloche, 1999). Relationships and interconnections are critically important (Alexander and Morrisey, 1988; Ashmos and McDaniel 1991; Ashmos, Huonker & McDaniel, 1998; Thomas & McDaniel, 1990). Health care organizations have the capacity for self-organization and emergence and they are coevolving (Zimmerman, Lindberg, & Plsek, 1998; Lewin & Regine, 2000; Anderson & McDaniel, 2000; Kiel, 1994). As executives seek new insights for managing health care organizations in these troubled times, complexity science offers a way to re-focus attention from creating a better run organization to maximizing the potential for the organization to co-evolve in ways that increase organizational fitness. As managers take complexity science seriously, they discover new strategies for action.

The health care manager is an agent in the health care organization and not an observer (Stacey, Griffin and Shaw, 2000). Traditional views of health care administration see the manager as an external controller who manipulates the system in accordance with some well thought out logic. Complexity science teaches us that the manager must first and foremost recognize him/herself as an agent of the system whose patterns of interaction with other agents is part of the overall set of factors that is leading to the dynamic behavior of the system. The manager is within the system and his/her behavior is one of the factors leading to systems behavior but it is only one of the factors. There is nothing the manager can do to manipulate the system in a certain, explainable, predictable way. The manager can act, and his/her actions will affect the organization, but there is no guarantee of what that effect will be. Once begun, the action will be carried through the organization in a way that has no predictable outcome.

After all is said and done, the art and science of traditional health care administration has been about control. Improvement efforts in health care management have been focused on better regulation, financial restrictions and punishment of offenders. Traditional views of health care managerial theory have been focused on organizational control and the goal of the management system was to ensure that the organization and its workers did what they were supposed to do. What they were supposed to do was determined by the manager. Complexity science suggests that it is impossible to control, in the traditional sense, health care organizations or the people that work in them because of the self-organizing and emergent properties of CAS and the unknowability resulting from these properties. You cannot control that which you cannot know and you cannot know the form and direction of a CAS because these are always changing. They exist only in the moment and as potentialities, and the manager does not have control over them. This unknowability is fundamental and is not simply a lack of information. With an understanding of the unknowability of the system, the goal of management is to enable the health care organization to emerge and self-organize. The manager cannot know the entire system because the information in the system is dispersed. The whole cannot be captured in any one agent, not even the top executive, as it is impossible for any one agent to see the entire system. Instead, managers must become adept at encouraging things to happen everywhere in the system and to allow these small, local happenings to be dispersed throughout the system.

When one understands that health care organizations are complex adaptive systems and that they share the characteristics of these systems then managerial focus shifts. The manager’s focus shifts from knowing the world to making sense of the world; from forecasting the future to preparing the organization to meet an unknowable future and from controlling the system to unleashing the system’s potential. The next section of this paper details some of the managerial strategies that arise when we take complexity science seriously.


^ Managerial Strategies Rising from Complexity Science


Making Sense

In complex adaptive systems the problem is not the bounded rationality of decision-makers but the fundamental unknowability of the unfolding dynamic of the system over time. In this circumstance, sensemaking is more important than decision-making and the appropriate managerial strategy is to enhance the sensemaking capabilities of the health care organization (Thomas, Clark & Gioia, 1993). When faced with nonlinear connections and with emergent properties, people in organizations must develop a collective mind about what the situation is, who we are, why we are here and what is going on around us (Weick, 1995). Sense making is a social act that requires interaction among agents. But these very interactions create new uncertainties and ambiguities. Sensemaking is enhanced through paying attention and organizational survival is often a struggle for alertness in the face of dynamic co-evolutionary events (Weick & Roberts, 1993). Because the world is unknowable, meaning comes, not through knowing what is going on but through making sense of what is going on. Agent characteristics of information processing ability, rule following ability and, particularly, ability to connect with other agents, increases organizational capabilities for sensemaking.

Managers must create time for agents to pay attention and to interpret the events around them. Managers must also create more different ways of paying attention and interpretation. This means that they must exploit the diversity of agents in the CAS to tease out variety of ways of experiencing and interpreting events (McDaniel & Walls, 1997). Some ways of thinking about the world see homogeneity as desirable and others see heterogeneity as desirable (Glick, Miller & Huber, 1993). The characteristics of CAS suggest heterogeneity as the most fruitful managerial strategy for enriching sensemaking in the organization. When homogeneity is the focus then group think and decreased effectiveness result.

In an unknowable world, sensemaking is not a matter of doing the best we can because we are limited; rather it is the best we can do because we are smart. In a world that is constantly emerging, we cannot know the world through planning and predicting; therefore, the importance of these activities is less than we once thought (McDaniel, 1997). Managers help order emerge in CASs through sensemaking but this order is not a stable equilibrium. Agents continuously create and reenact sense and meaning because the patterns and order in CAS are always changing.


^ Remembering (and Forgetting) History

As health care managers think about the characteristics of CAS they may conclude that the history of the system is unimportant. On the contrary, the arrow of time is a key factor and the nonlinear trajectory of the system is often a function of the time dependent events that occur. Predisposition is a key factor in both enabling and inhibiting health care organizational behavior (Ashmos, Duchon & McDaniel, 1998). Because the futures of CAS are uncertain (Prigogine, 1997) success comes from capacity to learn and learning replaces control as a key managerial function (Stacey, 1995; Senge, 1990).

Critical remembering of history is not in order to know what to do before we take action. Rather we must treat the unfolding of events in real time. As noted by Stacey (1995, p. 17) “The most important learning we do flows from the trial-and-error action we take in real time and especially from the way we reflect on those actions as we take them.” CAS need to engage in learning processes that enable a pattern of action to emerge as the organization interacts with its environment (Wheatley, 1992; Kiel, 1994; Bettis & Prahalad, 1995). Because things in organizations do not recur in repetitive fashion agents must develop skills at learning from samples of one (March, Sproull & Tamuz, 1991). Included among activities necessary for such careful attention to a history that is unlikely to repeat itself are experiencing events richly and interpreting events broadly. The diversity of agents in the system enhances the system’s ability to do these things and organizations need a diverse set of stakeholders to enhance the probability that framebreaking learning will occur (Argyris, 1992).

History is not important because CAS will know what to do next time but so that they will continuously enhance their capabilities to act in the face of an uncertain unfolding of its co-evolutionary space. History informs capabilities. CAS mangers are not expected to know what is going on and then to tell others what to do. Rather the manager develops an environment where people listen to each other and value each other’s insights. It is the capacity to learn rather that the capacity to know that enhances CAS functioning (McDaniel, 1997). “Sometimes learning requires courage. It can be difficult for experts, especially, to admit candidly that they could be better at what they do if only they knew more. To become a learner is to become vulnerable. The dilemma is painful” (Berwick, 1991, p. 841).


^ Thinking about the Future

Traditional planning based on feedforward modeling and predictions of future states is not useful in CAS where the dynamic unfolding of the system is uncertain. Because of the emergent nature of CAS, reliance on forecasting and modeling of cause-effect relationships is an inappropriate managerial strategy. This does not mean that managers should not think about the future. But they must think about it in new ways. Scenario planning helps organizations to deal with surprise and it is a technique that has been widely used. The effectiveness of scenario planning in CAS is not a function of how well the manger maps the future or how likely a given scenario is. Rather, the effectiveness of scenario planning is in developing organizational capabilities for dealing with uncertainty.

Bricolage, the ability to create what is needed at the moment out of the materials at hand (Weick, 1993), is a valuable way for CAS to think about the future. Traditional managers ask the question, “What do I need to do what I want to do?” while bricoleurs ask the question, “What can I create from what I have?” Bricolage requires knowing existing situations intimately so that new and creative ways to deal with confused and mixed up situations can be invented. In CAS no one knows what is going to happen but some people are better able to create positive outcomes from what emerges.

Managers of CAS pay attention to the role of enactment and interpretation in thinking about the future (Thomas & McDaniel, 1990). CAS are systems of interconnections and they produce or construct through co-evolutionary social processes, a significant part of the environment they face (Weick, 1995). These choices are often reflected in how agents frame the world as they enact reality through patterns of action (Anderson & McDaniel, 2000). People call things problems or opportunities, sick people are patients or clients, payers are customers or stakeholders. In each case the CAS maintains capacity to think about the future through framing the future by social interaction.


^ Dealing with Surprise

“Uncertainty is an essential ingredient of progress. Surprise drives progress because innovation depends on the sort of knowledge no one can gather in a central place” (Postrel, 2000, p. 1-3). The source of surprise in a CAS can be the nonlinear trajectory of the system (Prigogine, 1996), bifurcations, or qualitative changes in behavior resulting from parameter changes (Kaplan & Glass, 1995) or sensitivity to initial conditions (Gleick, 1987). Mangers of CAS recognize that the self-organization, emergent and coevolutionary properties of CAS ensure that surprise will be a constant companion and the ability to deal with surprise will be a key source of competitive advantage. Dealing with surprise requires improvisational behavior. Crossan and Sorrenti (1997, p. 156) define improvisation as “intuition guiding action in a spontaneous way.” Action is in the moment rather than the future and there is a high use of intuition rather than reliance on detailed analysis or routine habit. As surprise emerges in a CAS managers must encourage agents to respond to unanticipated circumstances through a balance of structure with flexibility (Brown & Eisenhardt, 1998). Loose--tight coupling and order-chaos-order enable the achievement of temporary advantage in a world characterized by surprise. Dealing with surprise requires innovation and creativity at all levels and in all segments of the organization.

Because we cannot know always what resources we have to work with, or what resources we will have to work with tomorrow, we must become good improvisers. There are some trades, jobs, professions, and tasks whose workers come to be good at working with ambiguity. Good scientists are always working just beyond the edge of what they know. They feel their way, trust their instincts, and make frequent leaps of faith. Instead of assuring the workers that the ambiguity and uncertainty will go away once we “get things under control”, managers in CAS must teach them to live with ambiguity and embrace surprise.

Jazz players are another example of people who are used to living with surprise and they are often seen as role models of improvisational behavior. They know a general musical form or structure and within that they create constant surprise and very complex stuff comes out of a very simple standard form. Bad jazz occurs because one person played something that the others couldn’t build on. Note that both the player and the builder have responsibility to create good jazz. It is the responsibility of the whole system not the individual agent – it’s about the connections that lead to self-organization. Good jazz players, when they hear a surprise, don’t ask, what did you intend to do? They act on what they heard and they create. The surprising note (or phrase or passage) wasn’t the right note or the wrong one. It is right if we can use it and the central question is what can I do with what happened? Dealing with surprise involves thinking in terms of how to use whatever happens to further the development of the system. It involves building on emergent characteristics of the CAS to develop patterns of social interaction among agents that gives them confidence in each other, that leads to small wins and that enhance the capacity to learn from surprising events (McDaniel, 1997).


^ Taking Action

When health care managers have traditional beliefs about their systems they are likely to focus on “getting ready to do it right”. However, if they recognize the dynamic, nonlinear nature of organizational evolution, they will understand that they need to focus on “taking action as circumstances unfold” (Eisenhardt, 1989; Brown & Eisenhardt, 1998). Action leads to learning and learning leads to the ability to cope with the unpredictable nature of CAS. Action should be focused on small changes that can provide positive feedback to the system. The effects of both small and large inputs can be unpredictable but small inputs provide more room for learning and organizational development (McDaniel & Walls 1998).

A major component of action in CAS is the creation of connections and relationships (Casti, 1997). A lot of traditional managerial behaviors have been about reducing connections. Getting everyone in their place, doing their own thing, staying on task. Even typical organizational charts tend to fragment organizations rather than focus on the interdependencies. Managerial practices that isolate workers from each other and attempt to constrain behavior and events through rules and policies will not encourage the self-organization needed to create order. In CAS, the essence of the system is in relationships, not pieces and, therefore, the quality of connections in a CAS is more important than the quality of any one agent. Dialog, then, becomes a major mechanism for collective thinking and organizational learning (Isaacs, 1993). “Dialog can be initially defined as a sustained collective inquiry into the processes, assumptions and certainties that compose everyday experience” (Isaacs, 1993, p. 25).

It is not simply a matter of more connections. Increases in complexity are the result of increases in interdependencies rather than increases in number and differentiation. The kinds of relationships that develop are important. Managers of CAS must be careful not to simply focus on tight connections or ties, as these may often be the source of failure (Weick, Sutcliffe & Obstfeld, 1999, p. 87). There is, in fact, often strength in weak ties (Granvovetter, 1973) particularly when operating in an environment of uncertainty and surprise.

In general, in CAS, agents are guided by information flows in local connections and managers must act to enhance local connections as well as some highly centralized, systems-wide set of connections. Mangers need to develop an understanding that a person’s range of influence is very wide even though their range of interactions is small and this influence occurs through overlaps in information domains (Kauffman, 1995).

Structure and form are a function of the patterns of relationships among agents in CAS and interactions of agents with their environment. This suggests that health care managers must pay attention to processes in CASs. When we focus on process rather than simply on form and structure, we reorient the level of analysis for action and there is nothing sacred about the organizational level of analysis (Weick, Sutcliffe, & Obstfeld, 1999). Managers must help agents develop skills at paying attention to actions in their local environment. They can not simply look at the environment to see how they should adapt to it, because CAS co-evolve with their environments and the environment will change as a result of actions taken by agents in CAS and visa versa.

Participation in decision-making is an action tactic that can be effectively used by managers to enhance system functioning in health care organizations (Ashmos, Duchon, & McDaniel, 2000; Anderson & McDaniel, 1999; Ashmos, Huonker, & McDaniel, 1998). When participation is used as a complicating mechanism in CAS, the amount on information brought to the decision table is increased and the sensemaking capacity of the system is increased. Therefore, managers in CAS should be seeking ways to involve a broad range of organizational actors in many kinds of situations. But much of the traditional management analysis has too narrow a view of who should be involved in what activities (Harris, 1997). CAS can be moved from state to state by the manipulation of control parameters (Mainzer, 1996) and velocity of information flow, connectivity of agents and diversity of information models are three key parameters (Stacey, 1996). Participation in decision-making is a strategy for managing the control parameters of an organization and, thereby, moving the organization to new states (Anderson & McDaniel, 1999). Note that imposed teams as a strategy for participation are not the same as emergent networks (Goldstein, 1999) and it is the latter which are most likely to lead to organizational creativity and imaginative problem solving.


^ Developing Mindfulness

Traditional organizational analysis focuses on routines and imbedded processes. The belief is that if health care managers can “get it right”, develop information systems that revel the future state of critical variables, and understand critical cause-effect relationships then organizations will function in an efficient and effective manner (Griffith, 1994). Our understanding of health care organizations as complex adaptive systems suggests that such an outcome is hardly to be expected (Stacey, 1992; Wheatley, 1992; McDaniel, 1997; Miller, Crabtree, McDaniel & Strange, 1998). Rather the system should be understood in terms of nonlinear dynamics, self-organization, emergence and coevolution. Under these conditions, one can’t know what to do, regardless of the amount of previous understandings one has. Organizations must handle unforeseen situations in ways that work. We want them to be reliable and “reliable outcomes now become the result of stable processes of cognition directed at varying processes of production that uncover and correct unintended consequences” (Weick, Sutcliffe & Obstfeld, 1999, p. 87).

The achievement of a stable cognitive process that can enable CAS to operate in a reliable manner and achieve high quality performance requires mindfulness. Mindfulness is the “capability to induce a rich awareness of discriminatory detail and a capacity for action” (Weick, Sutcliffe & Obstfeld, 1999, p. 88). Processes that lead to mindfulness include a preoccupation with failure, reluctance to simplify interpretations, sensitivity to operations, commitment to resilience and underspecification of structures (Weick, Sutcliffe & Obstfeld, 1999, p. 89). Health care organizations that develop these processes are more likely to be able to deal with the uncertainty inherent in complex adaptive systems.

A mindful organization is one that pays attention and managers in CAS must be careful observers of the world as it unfolds (McDaniel, 1997). Organizational survival is often a struggle for alertness (Weick and Roberts, 1993). What is needed is, often, not information but attention (Simon, 1994). Managers often feel that they do not have time to pay close attention to the world around them so they can notice, in a mindful way, important changes that are occurring in their worlds (Senge, 1990; Argyris, 1992). CAS also need more diversity in the way they see and interpret the world (McDaniel & Walls, 1997). They also need the ability to sense danger at local levels while maintaining the ability to coordinate action (Weick, Sutcliffe & Obstfeld, 1999). Thus CAS managers must be mindful, and pay attention in real time to the unfolding and coevolving worlds in which they must function. They must do this while keeping in the front of their consciousness the understanding that they are not external observers of the system but are, themselves, agents in the system whose behavior is a fundamental part of the pattern of nonlinear interactions that is causing emergent behavior (Stacey, Griffin and Shaw, 2000).


^ Research Questions Suggested by Complexity Science


Complexity science is offering a host of new ideas for research in organizational science (Anderson, 1999). Likewise, new research questions arise that are likely to be of particular interest to students of health care administration. Listed below are a few of these. In no way is this list intended to be exhaustive but simply to suggest the range of research questions that emerge when one takes complexity science seriously.

How can we maintain trust in health care organizations when the fundamental behaviors of the organization itself are unpredictable?

What patterns of interactions among health care professionals is most likely to result in positive self-organization?

How can health care managers make sense of the constant change implied by complexity science?

What are the barriers to rich connections among agents in health care organizations and how can these barriers be reduced?

How can health care organizations, which by their very nature want to be high reliability organizations, manage highly uncertain emergent properties in highly reliable ways?

What is the appropriate level of interconnections for a well-functioning health care organization?

Can stakeholder analysis be used to shed light on the relevant agent population of a health care organization as a CAS?

What are the key sources of surprise when one examines health care organizations as CASs?


Conclusions


Issues in the administration of health care are becoming more and more difficult. Traditional views of organizational analysis often seem to have run their course and new ways of thinking about health care systems are constantly being sought. Complexity science is offering new ways of thinking about natural phenomenon as well as artificial systems. Increases in computer power and new computational techniques have opened the door to a richer study of all kinds of systems from genetic to political to economic. These approaches can enrich our understanding of health care organizations as we realize that they are complex adaptive systems and that they share characteristics with other complex adaptive systems.

In particular, understanding the agent-based nature of systems, the role of interconnections and the self-organizing, emergent and coevolving dynamics of complex adaptive systems can lead to new insights. We have suggested five specific managerial strategies that seem to rise from complexity science. They are, making sense, remembering (and forgetting) history, thinking about the future, dealing with surprise and taking action. When complexity science is taken seriously, these managerial strategies become the focus of attention.

One must offer a word of caution. There is to date no well-developed theory of the complex (Casti, 1997; Anderson, 1999, Cohen, 1999). We are just beginning to scratch the surface of the study of complex adaptive systems and research methods are in their infancy. Managerial inferences from current information on general characteristics of complex adaptive systems are coarse at best but they do represent a new point of view that deserves serious attention as we attempt to unravel the nature of health care systems (Begun, 1994).

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