Complexity has been linked to health interventions in two ways: first as a property of the intervention, and secondly as a property of the system into which the intervention is implemented. The former recognizes that interventions may consist of multiple components that act both independently and interdependently, making it difficult to identify the components or combinations of components and their contexts that are important mechanisms of change.
The latter recognizes that interventions are implemented in complex adaptive systems comprised of intelligent agents who modify their behaviour including any actions required to implement the intervention in an effort to improve outcomes relative to their own perspective and objectives. Although an intervention may be intended to take a particular form, its implementation and impact within the system may deviate from its original intentions as a result of adaptation.
Complexity highlights the challenge in developing interventions as effective health solutions. The UK Medical Research Council provides guidelines on the development and evaluation of complex interventions. While mathematical modelling is included in the guidelines, there is potential for mathematical modeling to play a greater role.
The dynamic non-linear nature of complex adaptive systems makes mathematical modelling crucial. These models are deficient by not modelling the way the system reacts to the intervention via agent adaptation. Complex intervention modelling needs to capture the consequences of adaptation through the inclusion of an evolutionary dynamic to describe the long-term emergent outcomes that result as agents respond to the ecological changes introduced by intervention in an effort to produce better outcomes for themselves.
Mathematical approaches such as those found in economics in evolutionary game theory and mechanism design can inform the design and evaluation of health interventions. As an illustration, the introduction of a central screening clinic is modeled as an example of a health services delivery intervention. Complexity necessitates a greater role for mathematical models, especially those that capture the dynamics of human actions and interactions.
Health researchers have a long history of looking to ecological systems as a metaphor and framework for understanding human endeavours [ 1 — 3 ]. Here, the meaning of complex is not the opposite of simple. Rather, it is a loosely defined term used to indicate the importance of relationships and adaptive interactions of parts in the emergence of the whole.
Modeling Behavior in Complex Public Health Systems
The notion of complexity has been linked to interventions in two ways. The first recognizes that interventions can be complex, consisting of multiple components with different targets, which may produce non-linear and difficult to predict effects [ 4 ]. The second recognizes that interventions complex in themselves or not are embedded within complex adaptive systems CAS [ 5 — 7 ]. A CAS is a system comprised of intelligent agents, where each agent has a set of objectives and attempts to achieve these objectives through a process of adaptation. In this context, adaptation refers to the process of change that results as various intelligent agents - from policy maker to patient - who modify their behaviour including any actions required to implement the intervention in an effort to improve outcomes relative to their own perspective and objectives.
Thus, although an intervention is designed with one outcome effect in mind, its implementation and impact within the system may deviate from its design as a result of adaptation. This process of adaptation makes the development and implementation of effective and sustainable interventions difficult, and drives the development of increasingly complex interventions. Indeed, interventions must be adaptable both over time and across a diversity of settings to ensure that the adoption and effectiveness of the intervention is not compromised as the population responds to both the direct and indirect changes introduced by the intervention [ 8 ].
The high number of linkages between the components of the intervention and the system in which it operates means that change in long term, system-level outcomes may be non-linear, emergent and difficult to predict. As such, a broader approach is needed to design, evaluate and implement complex interventions.
The UK Medical Research Council MRC guidance on complex interventions [ 9 , 10 ] is an authoritative, frequently cited reference that continues to evolve and shape health research [ 11 , 12 ]. The guidance places emphasis on theory driven approaches that demonstrate causal mechanisms between the intervention and its outcomes [ 13 ]. While the MRC guidance highlights the value of modelling, it could go much further.
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To reach its full potential, we argue that mathematical modelling must be embraced within intervention research, but to do so modelling approaches must be expanded. In particular, mathematical modelling in intervention research must endeavor to capture the evolutionary dynamics of adaptation, which are foundational to the study of complexity. Complexity requires scientific approaches with a commitment to holism and interconnectedness. The MRC guidelines recommend the use of logic models to describe the theory of the intervention.
In this context, a logic model describes the causal pathway by detailing the operation of intervention, the expected effects of its constituent parts, the functions they fulfill alone and interactively in permutation , and the mechanisms that produce these effects [ 4 ]. Logic models certainly play an important role in capturing the theory of the intervention [ 14 , 15 ].
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However, no matter how sophisticated, a logic model alone is not sufficient, as complexity cannot be understood purely through qualitative description; hence tools such as mathematical models, are needed to test the theory of the intervention. A mathematical model can be constructed as a representation of the logic model, using mathematics to describe the relationships depicted therein and to provide the necessary tools to analyze the model while maintaining rigorous logic.
Mathematical models provide greater theoretical precision than models specified in non-mathematical terms in that their underlying assumptions are more easily examined, they are more easily verified or falsified , and they lend themselves more readily to data analysis through the generation of predictions that can be tested through observation and experimental manipulation [ 16 — 18 ].
Of course, mathematical models are far from infallible. Identify and demonstrate mechanisms through which the system can be influenced via intervention. To this end, mathematical models of complex interventions need to describe two dynamic processes: an ecological one and an evolutionary one. Modelling of health systems in these terms is common-place and capturing the ecological dynamics of a complex intervention, thus far, does not require new methods. Recent articles provide guidelines on various dynamic modelling approaches and their potential applications, highlighting examples in the evaluation of health services delivery interventions [ 21 , 22 ].
However, caution is required. Models that only consider the ecological dynamics of the system are deficient by not modelling the way the system reacts to the intervention via agent adaptation; effectively ignoring agent driven adaptation as both an explanation for why the system is failing, and as an mechanism of change that influences the realities of implementation and sustainability of the intervention and its long term effects. The evolutionary dynamic describes the process of adaptation that creates change in the ecological dynamic.
It is a description of long term emergent outcomes that result from a utility driven feedback mechanism, in which operational processes and strategies are evaluated as agents seek to produce better outcomes for themselves. This includes how agents evaluate and respond to the short-term impacts of the intervention, adapting, and adopting or discarding the intervention according to how it meets their interests and needs.
Mathematical models that incorporate adaptive processes are important to our understanding of complex interventions but such models are not typically employed in intervention development. The modelling itself is not the primary challenge. Mathematical approaches such as those found in economics in evolutionary game theory and mechanism design see [ 23 , 24 ] for examples of applications in health settings can inform the design and evaluation of health interventions.
In these approaches, the strategic interactions that occur as agents evaluate and re-evaluate the different strategies available to them are modelled as a dynamic game in which one group the group intervening may exert a certain amount of influence over the other groups.
Within the game, the intervening group is tasked with designing the rules by which the others must play, with the objective of achieving a specific outcome that is of system-wide benefit, even though each agent group has its own set of objectives. Within this framework the intervention is represented as a strategy employed by the agent group implementing the intervention to steer the system to a desired state e. Conflicting interests among different agent groups can make it difficult to implement and sustain an intervention.
Methods such as multi-criteria decision analysis [ 28 , 29 ] can be used to measure the differing priorities of stakeholder groups in decision making processes e. As such, conflict can be identified and its implications identified as risks to the intervention, allowing the intervention to be re-designed, perhaps to include formative feedback processes that are designed to ease conflict.
Agent based modelling approaches like those used in mechanism design should resonate with complex interventions. That is, the art of intervention development is in creating the conditions that encourage stakeholder behaviour that benefits the health of the population as a whole, recognising the multiple objectives, time horizons, decisions and decision makers.
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It seems that the primary challenge to the development of mathematical models of complex interventions that incorporate adaptive processes lies in acknowledging the need for the inclusion of evolutionary dynamics. To this end guidance on complex intervention modelling, like those on the development [ 9 , 10 ] and evaluation [ 11 , 12 ] of complex interventions is needed. We describe the model and the outcomes of our analysis below; the details of the model and its mathematical analysis are presented in the Additional file 1.
Central intake clinics have been proposed as a mechanism to improve the primary-specialty care interface by ensuring patients receive appropriate services in a timely manner by screening patients and subsequently scheduling consults to the next available specialist. In this example we consider a scenario in which all referrals are pooled and distributed amongst a network of n specialists. However, two options are presented for the routing of the referral. In the first option, referrals are sent to a centralized screening clinic, and in the second option the referral is sent directly to the next available specialist.
In both cases, the patient will undergo screening prior to the specialist consult. Each specialist can accept referrals from the central screening clinic, or direct referrals, or both. The specialist simply allocates a proportion of available consults to the central screening clinic for scheduling, leaving the remainder for direct referrals. Our purpose is to assess whether the central screening clinic is effective and sustainable under this scenario.
For ease of presentation, the model is intentionally simple. A key assumption of our model is that specialists are identical in their capacity to see patients.
HL7 Argentina, What to order from systems providers? A statement of compatibility with standards and specific scenarios, through implementation guides: what messages or types of documents are exchanged and at what time? The specification of vocabulary and cross-mapping of support for each item of coded data, depending on the application context, which can be expanded over time from in-hospital to regional interoperability.
These requirements for adaptation and cross-mapping of vocabularies can be increased. Accompany us in the growth of interoperability with the necessary flexibility to start with the exchange of documents or messages with a format, but with the awareness of being enabled to evolve to a higher level of interoperability over time. Finally, the compliance with the security and privacy standards as defined by the local regulations.
Regulations, In the field of standardization there is HL7 International, which provides a comprehensive framework for the exchange, integration, and retrieval of electronic health information that supports the clinical practice and the management and assessment of health services. It is the standard for the exchange of clinical documents instead of messages. CCOW: for the integration of applications at the desktop level. It allows an application vendor to integrate its HIS product with a RIS with another vendor transparently to the user, sharing the context on the desktop, at the level of user ID, patient who is being consulted and up to the study or examined report.
JavaSIG is the library that is best suited to implement the messages because, through a previous investigation, it is tailored to the particular needs of this project. Grails Framework Open Source oriented to an agile development process. It bears the paradigm of "coding by convention" to the Groovy language and it is oriented to the development of web 2. Groovy is the Java's dynamic language; it presents a dynamic extension to the Java language, providing all the features of Java API plus new builders and features such as closures a construction of functional programming.
Furthermore, Groovy is compiled to byte code which is not a big performance problem. The transmission occurs through defined channels through a graphical interface. These channels operate with input and output connectors, filters and transformers. Through the graphical interface, it is possible to select which filters and transformations are applied to the incoming message before sending it to the output HL7 Argentina, An Information System is a set of strategies that enable to carry out an efficient management of processes in an organization. HLT is specifically involved in creating standards, guidelines and flexible and cost-effective methodologies that enable the interoperability among the information systems and exchange of electronic health records.
As organization, HL7 has a formal structure and procedures based on the pursue of consensus and the balance of interests among the various sectors represented: software companies, funders of health, national states, universities, health care providers, consultants, etc. The early versions of the messaging standard 1. Since then, these were the succession of approved standards: Approved standards:. Centralized and distributed architecture is defined, and the distributed computing has been designed to solve too big problems for any supercomputer and mainframe.
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Thus, grid computing is naturally a multiuser environment; therefore, the secure authorization techniques are essential. The centralized architecture provides the computational power and an incredible internal storage. The disadvantage of this architecture is that it lacks self-contained computer processors Tanenbaum, In works carried out by ETSI and ITU, the points and interconnection interfaces are generally identified which should be available in the next generation network, highlighting the functional separation of the network in plans Strata of Transportation Infrastructure and services.
It increases the efficiency and quality of health care benefits provided by the health care units. The clinical management model used is that of Joseph, where the essential components such as data information information architecture , Hardware-Software-Networking Architecture of technology , process-tasks-applications domain architecture and user management Control of architecture is the general structure of information systems for health management Joseph, and complemented with which we will define the clinical management model that will be developed and on which we have been working so-far using the framework of causal model system See Figure 1.
It is started with the causal model as it allows to state that health problems must include the dialectical relationship between chance and necessity, between casual and causal versus the determinism and the mechanism prevailing in the analysis of health science. It is relevant to keep in mind that there is a consideration to that there is determination in indetermination and indetermination in determination.
Experimental data in principle, if the assignment to treatment is at random, the causal inference is simple, since the two groups have been drawn from the same population, and by the treatment construction and assignment it is independent from all reference variables. In an experimental setup, something can go wrong. What does the statistical adjustment require? One of the assumptions that the at random allocation by itself is not justifiable is that "observation in a unit should not be affected by the particular assignment of treatments to the other units" Cox, Rubin refers to this case as "no interference between units.
Complex systems will be used as they consist of several interconnected or interlinked parts whose ties produce non-visible additional information that makes it easier to accurately analyze the system. Complex systems have more information than that given by each independent part. Several philosophers like Hesse and Hughes have studied the traditional scientific methodology and they have proposed the same general outline as the modeling process. According to these authors, scientists models are built to develop inference processes on certain aspects of previously observed actual systems.
It is through these inference processes, that the construction and use of scientific models enhances the understanding of observed real systems. A system can be defined as "a set of elements in interaction" Bertalanffy, Complex systems i. When there is one or more intermediate layers between the real system and our formal model, the modeling basic sequence is substantially modified Edmonds et al.
Regardless of the nature or complexity of the real system, to explain each stage in the modeling process of complex systems we find it useful to distinguish three roles Drogoul et al. The agent-based simulation has proven to be an extremely useful technique for modeling complex systems, and especially social systems Conte et al.
Through agent-based simulation, the modeler explicitly recognizes that complex systems, and in particular the social system, are the outcome of individual behaviors and of their interactions. The agent-based systems are characterized by comprising multiple agents which are to a larger or lesser extent autonomous, heterogeneous and independent, each showing their own goals and objectives, which generally are able to interact with each other and with their environment Torsun, The agent-based simulation has been widely used to model systems in a wide range of scientific disciplines, i.
Walker et al. The systems dynamics is another technique for modeling complex systems. Unlike the agent-based simulation, the philosophy of system dynamics deals on the concept of feedback or circular causality between observable variables. These observables can describe some attributes of the basic components of the system. System dynamics studies observable variables of complex systems and attempts to identify the causality relationships usually circular existing among them.
Once such relationships are identified, it is easier to explain the origin of the system global behavior from their causal structure. The following is an explanation of the elements referred to the processes of the health services integration model to be developed. The data types will be quantitative and qualitative for the variables of standardization, interoperability, infrastructure, networks and service delivery quality components, in an effort to categorize each variable. This study uses the following three data gathering techniques: Observation, semi-structured interviews and the documents review or analysis.
Accordingly the Rubin Causal Model RCM is proposed, cases of observational and experimental studies of causal inference and estimation by matching methods Rubin, Indicators as variables that attempt to measure or objectify quantitatively or qualitatively collective events especially events bio demographic are identified, so as to backup political actions, evaluate achievements and goals.
WHO has defined them as "variables used to measure changes. Surveys will be carried out using sampling techniques population surveys and data from other sectors economic, political and social welfare. The method is quantitative and qualitative. Embed Size px. Start on. Show related SlideShares at end. WordPress Shortcode. Full Name Comment goes here. Are you sure you want to Yes No. Be the first to like this. No Downloads. Views Total views. Actions Shares.
Embeds 0 No embeds. No notes for slide. Description this book This graduate text is the first to present methods for modeling health behavior dynamics, using numerous online interactive simulations, downloadable programs, and examples of applications to planning interventions.
Applying these methods to public health systems, sample models introduce the dynamics of 4. Key Features: Uses interactive simulation for modeling health behavior dynamics for complModeling Behavior in Complex Public Health Systems: Simulation and Games for Action and Evaluation [FULL] This graduate text is the first to present methods for modeling health behavior dynamics, using numerous online interactive simulations, downloadable programs, and examples of applications to planning interventions.
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