Engineering a Learning Healthcare System A Look at the Future: Workshop Summary (2011) / Chapter Skim
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2 Engaging Complex Systems Through Engineering Concepts
2 Engaging Complex Systems Through Engineering Concepts
Pages 63-116
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From page 63... ...
The engineering disciplines presented as possible opportunity areas for improving healthcare delivery and management included systems engineering, industrial engineering, operations research, human factors engineering, financial engineering, and risk analysis. William B
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From page 64... ...
Sorenson discussed the principles of an "integrated perspective" for managing complex systems. He outlined the questions that typically apply in engineering complex enterprises, and he described typical approaches that a systems engineer might use to articulate the nature of a problem and to design an appropriate architecture to address it.
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Engineering Approach Determination of the best way to control healthcare costs should be approached as an engineering problem rather than as a medical science problem. The potential of engineering to enhance health care has, of late, received increasing attention (NAE/IOM, 2005)
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There simply are not enough healthcare systems to achieve statistical significance in a study of the large, systemic changes likely to be needed to control costs and enhance quality to the extent outlined earlier. Engineering approaches to solving large-scale problems typically rely on models as a basis for prediction.
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Healthcare Illustration As discussed earlier, the past four decades have seen enormous increases in healthcare costs. Specifically, real healthcare costs tripled as a percentage of GDP in the period from 1965 to 2005, with half of this growth due to technological innovation (CBO, 2008)
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In parallel, increased efficiency through production learning (discussed further below) leads to decreased cost per use, although not enough to keep pace with the product's growing use in health care.
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In particular, the model is quite limited in that it provides no mechanism for achieving cost reductions and does not differentiate between the various elements of the healthcare delivery process. Thus we need to elaborate on model 1.
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Figure 2-2 shows learning curves for the three learning rates in Table 2-2, assuming a 10 percent annual rate of growth in usage. Note that the initial conditions were 100 uses at $100 per use, yielding an initial total expenditure of $10,000.
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A rich experience base allows us to define the learning rates for technology. For present purposes we, somewhat optimistically, set the technology learning rate at 70 percent.
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The worst case is for 0 percent GDP growth and 15 percent usage growth, which would require a learning curve of greater than 40 percent. This level of learning has never been achieved in any domain.
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Achieving these savings will, however, be a significant challenge since learning rates of less than 70 percent are difficult to achieve. Sources of Learning In industries in which production learning curves have long been used, the sources of learning include labor efficiency, changes in personnel mix, standardization, specialization, method improvements, better use of equipment, changes in the resource mix, product and service redesign, and shared best practices.
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FIGURE 2-6 The architecture of healthcare delivery.
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Other physicists founded OR in Great Britain. According to the seminal book Methods of Operations Research, OR is an applied science that uses all known scientific techniques as tools to solve a specific problem (Morse and Kimball, 1951)
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Those systems can bring many relevant applications to bear on health care. A special issue of OR's flagship journal, Operations Research, was recently devoted entirely to such considerations.
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More recently still, Kaplan and his colleague Larry Wein have received national acclaim for their OR-based ideas, presented in papers and in congressional testimony, on how best to respond to bioterrorism and its associated health risks. The city of Stockholm was a 2008 finalist for the Edelman Prize for the project "Operations Research Improves Quality and Efficiency in Social Care and Home Help." The program led to an annual savings of €20 million to €30 million ($30 million to $45 million)
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Although additional research concerning how OR can improve the healthcare system is available, more is needed. Most notably, studies that connect OR and the social sciences (e.g., understanding how physicians and patients view uncertainty in healthcare delivery)
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In general, services are carried out with knowledge-intensive agents or components that work together as providers and consumers to create or coproduce value. Indeed, anyone performing the engineering design of a healthcare system must recognize that the system is a complex integration of human-centered activities that is increasingly dependent on information technology and knowledge.
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The purpose of this paper, then, is to highlight the critical importance of integration and adaptation when designing, operating, or refining a complex service system such as health care. On Services Before discussing a healthcare service system as an integrated system, an adaptive system, and a complex system, it is helpful to start by defining services and discussing their uniqueness, especially in contrast to goods.
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The remainder of this section focuses on three overarching influences. First, the emergence of electronic services is totally dependent on information technology; examples include financial services, banking, airline reservation systems, and consumer goods marketing.
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. Consequently, while traditional services -- such as traditional manu TABLE 2-5 Comparison of Traditional and Electronic Services Service Enterprises Issue Traditional Electronic Coproduction medium Physical Electronic Labor requirement High Low Wage level Low High Self-service requirement Low High Transaction speed Low High requirement Computation requirement Medium High Data sources Multiple homogeneous Multiple nonhomogeneous Driver Data driven Information driven Data availability/accuracy Poor Rich Information Poor Poor availability/accuracy Economic consideration Economies of scale Economies of expertise Service objective Standardized Personalized Service focus Mass production Mass customization Decision time frame Predetermined Real time
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The goods sector requires material as input, is physical in nature, involves the customer at the design stage, and employs mainly quantitative measures to assess its performance. By contrast, the services sector requires information as input, is virtual in nature, involves the customer at both the production and delivery stages, and employs mainly qualitative measures to assess its performance.
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, they are focused on being "personalizable," they are expectation related in terms of customer satisfaction, and they are reusable in their entirety. On the other hand, manufactured goods are preproduced, quite identical or standardized in their production and use, physically tangible, "inventoryable" if not consumed, focused on being reliable, utility related in terms of customer satisfaction, and recyclable with regard to their parts.
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. Mass customization implies meeting the needs of a segmented customer market, with each segment being a single individual (e.g., a tailor who laser scans an individual's upper torso and then delivers a uniquely fitted jacket)
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or algorithmic (data mining, decision modeling, systems engineering, etc.) in structure, or sometimes both.
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that, as indicated earlier, enable the delivery of effective and high-quality services (e.g., road travel, air travel, global positioning, electronic services)
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Moreover, real-time mass customization occurs when supply and demand chains are simultaneously managed. The shift in focus from mass production to mass customization (whereby a service is produced and delivered in response to a customer's stated or imputed needs)
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Adaptation is a uniquely human characteristic, based on a combination of three essential components: decision making, decision informatics, and human interface. (Indeed, designing a healthcare system is essentially an exercise in making decisions or choices about the system's characteristics or attributes.)
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Unfortunately, for the most part the literature does not distinguish between data and information. Economists claim that because of the astounding growth in information -- really, data -- technology, the United States and other developed countries are now part of a global "knowledge economy." Although electronic data technology has transformed large-scale information systems from being the "glue" that holds the various units of an organization together to being the strategic asset that provides the organization with its competitive advantage, the United States is far from having reached the level of a knowledge economy.
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The feedback loops in Figure 2-8 are within the context of systems engineering; they serve to refine the analysis and modeling steps. Continuing with the decision informatics paradigm in Figure 2-8, it should be noted that decision modeling includes the information-based modeling and analysis of alternative decision scenarios.
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When working with patients, for example, sensors that monitor the patients' vital signs are essential, as are verbal inputs from the patients themselves. More recently, data warehouses have been proliferating, and data mining techniques have been gaining popularity.
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When developing real-time, adaptive data processors, one must consider several critical issues. First, as shown in Figure 2-8, these data processors must be able to combine (i.e., fuse and analyze)
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Third, inasmuch as the data processors must function in real time and be able to adapt to an ongoing stream of data, genetic algorithms, which have equations that can mutate repeatedly in an evolutionary manner until a solution emerges that best fits the observed data, are becoming the tools of choice in this area. Feedback adaptation can be defined by the degree of expected actions based on standardized (e.g., prestructured, preplanned)
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In such a situation and as indicated earlier, the issue is not simply how to speed up steady-state models and their solution algorithms; indeed, steady-state models become irrelevant in real-time environments. Instead, learning adaptation concerns reasoning under both uncertainty and severe time constraints.
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, and interdependent (infrastructures, supply chains, demand chains, etc.) relationships are difficult to determine.
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, and real-time customized management (RTCM, which can occur when both demand and supply are flexible, thereby allowing for real-time mass customization)
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are combined and dealt with simultaneously. Thus, a combined integration/adaptation research effort is synonymous with an RTCM activity, which can occur when both demand and supply are flexible and thereby allow for real-time mass customization.
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In time, smart agents representing both providers and consumers will be the service coproducers; they will employ decision informatics techniques to accomplish their tasks. It should be noted that such smart agents may never be appropriate for certain situations, especially, for example, when nuanced patient behavior is critical or when a catastrophic surgical consequence is a possibility.
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On the other hand, with the encoding of Web pages in a semantic Web format, the evolving Web will make it possible for the above-mentioned smart or decision informatics−supported agents to undertake semantic analysis of user intent and Web content, to understand and filter their meaning, and to respond adaptively in light of user needs. The semantic Web, then, would be an ideal complex service system for which integration and adaptation would constitute the basis for its functionality.
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, it should be noted that a number of other engineering approaches can be applied to health care and related biological issues. Grossman (2008)
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102 ENGINEERING A LEARNING HEALTHCARE SYSTEM TABLE 2-11 Technobiology Examples Discipline Examples Scope Biomedical 1.
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Data mining and analysis of past including false discovery treatments can point to effective rate protocols, including minimization of false positives linking diseases and DNA genes 2. Adaptive clinical trials 2.
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The result can be new and more effective ways to deliver health care through the rapid fielding of enhanced capabilities based on a close working relationship among all stakeholders, including healthcare administrators and practitioners, enterprise architects, and enterprise systems engineers. Consequently, the culture, practice, and delivery of patient care can change in fundamentally important ways.
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By transforming data and information into knowledge, enterprises gain an asset that can be used to address current deficiencies in healthcare delivery directly and to allow more benefits to be delivered to the patient. In summary, knowledge must become an enterprise asset.
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General Approach When we approach the problem of engineering complex enterprise systems, we typically start by asking a series of interrelated questions. How do we think about the problem?
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A product of the competitive world in which we live, systems thinking began with the ideas of Henry Ford regarding mass production. His model posited that people and parts are interchangeable, a way of thinking that Multiminded Mindless System Uniminded System System SHIFT Social Model OF Purposeful Machine Model Biological Model PARADIGM Society Organization Members Interchangeability Diversity and Analytical Participative of Growth Approach Management Parts and Labor Self-organizing Systems Independent Variables Social systems are Henry Ford's Alfred Sloan's information-bonded Mass Production Divisional Structure Tavistock Institute's System Socio-Tech Model Systems Approach Joint Optimization Flexibility and Redesign Control Interdependent Ford's Whiz Kids Ohno's Lean Ackoff's Interactive Variables Production Management Operations Research Cybernetics Model Choice FIGURE 2-9 Evolution of systems thinking.
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As people first experimented with mass production, they learned that the factors they regarded as being independent actually were not, but rather were interdependent. This interdependence led to a systems approach, which can be said to be the roots of operations research.
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Learning healthcare systems can be described as being information bonded and operating through social networks. The social model basically says that if one is purposeful and seeks to reach some kind of enterprise objective, one should take a holistic view that includes society, the organization, and, finally, the various members of that society or organization who may be involved in what one is trying to do.
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. The current approach for managing enterprise complexity, which is continuing to evolve and mature, is based on three interdependent variables: structure, function, and process.
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Users interact with the system using a portal, sometimes referred to as the human–system interface. For our architecture construct, users, through the portal, have access to the service registry and the elements of the system they are interested in using.
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Having defined a framework for managing the complexity, it is logical to ask, "How do we develop the problem solution? " First, one identifies a function or mission that is to be provided by the healthcare delivery system.
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" The development of complex systems such as healthcare delivery advances because of feedback and communication among all the participants. The architect serves as the feedback controller during the development of the architecture.
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2004. Operations research and health care.
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1951. Methods of Operations Research.
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2003. Towards a decision informatics paradigm: A real-time, information-based ap proach to decision making.
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