scholarly journals Irrational use of antibiotics in Iran from the Perspective of Complex Adaptive Systems: redefining the challenge

2020 ◽  
Author(s):  
Zahra Sharif ◽  
Farzad Peiravian ◽  
Jamshid Salamzadeh ◽  
Nastaran Keshavarz Mohammadi ◽  
Ammar Jalalimanesh
Keyword(s):  
Health Systems ◽  
Information Flow ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Emergent Behavior ◽  
System Level ◽  
Antibiotics Resistance ◽  
Policy Document ◽  
Complex Adaptive ◽  
Antibiotics Use

Abstract Background: Irrational use of antibiotics is proving to be major concern to the health systems globally. It results in antibiotics resistance and increases health care costs. In Iran, many years of research, appreciable efforts and policy making have been of little avail and indicators still show suboptimal use of antibiotics, pointing to an urgent need for adopting an alternative approach to understanding the problem and generating new solutions. Applying the Complex Adaptive Systems theory, to explore and research in health systems and their challenges has become popular. Therefore, this study aimed to better understand the complexity of irrational use of antibiotics use in Iran and to propose potential solutions. Method: This research utilized a CAS observatory tool to qualitatively collect and analyse data. Twenty interviews and two Focus Group Discussion were conducted. The data was enriched with policy document reviews to fully understand the system. MAXQDA software was used to organize and analyze the data. Result: We could identify several diverse and heterogeneous, yet highly interdependent agents operating at different levels in the antibiotics use system in Iran. The network structure and its adaptive emergent behavior, information flow, governing rules, feedback and values of the system, and the way they interact were identified. The findings describe antibiotics use as an emergent behavior which is formed by an interplay of many factors and agents over time. Insufficient and ineffective interaction and information flow regarding antibiotics between agents were among key causes of irrational antibiotics use in Iran. Results showed that effective rules to minimize irrational use of antibiotics are missing or can be easily disobeyed. The gaps and weakness of the system which needs redesigning or modification were recognized as well. Conclusion: The study suggests re-engineering the system by implementing several system-level changes including establishing strong, timely, and effective interactions between identified stakeholders, which facilitate information flow and provision of on-time feedback, and create win-win rules in a participatory manner with stakeholders and the distributed control system.

scholarly journals Irrational use of antibiotics in Iran from the perspective of complex adaptive systems: redefining the challenge

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zahra Sharif ◽  
Farzad Peiravian ◽  
Jamshid Salamzadeh ◽  
Nastaran Keshavarz Mohammadi ◽  
Ammar Jalalimanesh
Keyword(s):  
Health Systems ◽  
Information Flow ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Emergent Behavior ◽  
System Level ◽  
Antibiotics Resistance ◽  
Policy Document ◽  
Complex Adaptive ◽  
Antibiotics Use

Abstract Background Irrational use of antibiotics is proving to be a major concern to the health systems globally. This results in antibiotics resistance and increases health care costs. In Iran, despite many years of research, appreciable efforts, and policymaking to avoid irrational use of antibiotics, yet indicators show suboptimal use of antibiotics, pointing to an urgent need for adopting alternative approaches to further understand the problem and to offer new solutions. Applying the Complex Adaptive Systems (CAS) theory, to explore and research health systems and their challenges has become popular. Therefore, this study aimed to better understand the complexity of the irrational use of antibiotics in Iran and to propose potential solutions. Method This research utilized a CAS observatory tool to qualitatively collect and analyse data. Twenty interviews and two Focus Group discussions were conducted. The data was enriched with policy document reviews to fully understand the system. MAXQDA software was used to organize and analyze the data. Result We could identify several diverse and heterogeneous, yet highly interdependent agents operating at different levels in the antibiotics use system in Iran. The network structure and its adaptive emergent behavior, information flow, governing rules, feedback and values of the system, and the way they interact were identified. The findings described antibiotics use as emergent behavior that is formed by an interplay of many factors and agents over time. According to this study, insufficient and ineffective interaction and information flow regarding antibiotics between agents are among key causes of irrational antibiotics use in Iran. Results showed that effective rules to minimize irrational use of antibiotics are missing or can be easily disobeyed. The gaps and weaknesses of the system which need redesigning or modification were recognized as well. Conclusion The study suggests re-engineering the system by implementing several system-level changes including establishing strong, timely, and effective interactions between identified stakeholders, which facilitate information flow and provision of on-time feedback, and create win-win rules in a participatory manner with stakeholders and the distributed control system.

scholarly journals Irrational use of antibiotics in Iran from the Perspective of Complex Adaptive Systems: redefining the challenge

2020 ◽  
Author(s):  
Zahra Sharif ◽  
Farzad Peiravian ◽  
Jamshid Salamzadeh ◽  
Nastaran Keshavarz Mohammadi ◽  
Ammar Jalalimanesh
Keyword(s):  
Health Systems ◽  
Information Flow ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Antibiotic Use ◽  
Analysis Data ◽  
Antibiotic Consumption ◽  
System Level ◽  
Antibiotics Resistance ◽  
Complex Adaptive

Abstract Background: Irrational use of antibiotics proves major concerns to the health systems globally. It results in antibiotics resistance and increases health care costs. In Iran, many years of research, appreciable efforts and policy making have been of little avail and indicators still show suboptimal use of antibiotics, pointing out an urgent need to alternative approach to understand the problem and generate new solutions. Applying the Complex Adaptive Systems theory, to explore and research in health systems and their challenges and has become popular. Therefore, this study was aimed to better understand the complexity of irrational use of antibiotic use in Iran and the potential solutions. Method: This research utilized a CAS observatory tool to qualitatively collect and analysis data. Many interviews with key informants were conducted. The data was enriched with documents reviews in order to fully understand the system. MAXQDA software was applied to organize and analyze the data. Result: We could identify several diverse and heterogeneous, yet highly interdependent agents in the antibiotic consumption system in Iran, operating at different levels. The network structure and its adaptive emergent behaviour, information flow, governing rules, feedbacks and values of the system and the way they interact were identified. The gaps and weakness of the system which needs redesigning or modification were recognized as well. Findings describe antibiotic use as an emerge behavior of the system which is formed by interplay of many factors and actors over time.Conclusion: The study suggests re-engineering the system by implementing several system level changes including establishing strong, timely and effective interactions between identified stakeholders which facilitate information flow and provision of on time feedbacks, create win-win rules in participatory manner with stakeholders and distributed control system.

scholarly journals Performance management in complex adaptive systems: a conceptual framework for health systems

2021 ◽  
Vol 6 (7) ◽  
pp. e005582
Author(s):  
Tom Newton-Lewis ◽  
Wolfgang Munar ◽  
Tata Chanturidze
Keyword(s):  
Performance Management ◽  
Health Systems ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Collective Responsibility ◽  
System Level ◽  
Empirical Literature ◽  
Current Evidence ◽  
Current Evidence Base ◽  
Complex Adaptive

Existing performance management approaches in health systems in low-income and middle-income countries are generally ineffective at driving organisational-level and population-level outcomes. They are largely directive: they try to control behaviour using targets, performance monitoring, incentives and answerability to hierarchies. In contrast, enabling approaches aim to leverage intrinsic motivation, foster collective responsibility, and empower teams to self-organise and use data for shared sensemaking and decision-making.The current evidence base is too limited to guide reforms to strengthen performance management in a particular context. Further, existing conceptual frameworks are undertheorised and do not consider the complexity of dynamic, multilevel health systems. As a result, they are not able to guide reforms, particularly on the contextually appropriate balance between directive and enabling approaches. This paper presents a framework that attempts to situate performance management within complex adaptive systems. Building on theoretical and empirical literature across disciplines, it identifies interdependencies between organisational performance management, organisational culture and software, system-level performance management, and the system-derived enabling environment. It uses these interdependencies to identify when more directive or enabling approaches may be more appropriate. The framework is intended to help those working to strengthen performance management to achieve greater effectiveness in organisational and system performance. The paper provides insights from the literature and examples of pitfalls and successes to aid this thinking. The complexity of the framework and the interdependencies it describes reinforce that there is no one-size-fits-all blueprint for performance management, and interventions must be carefully calibrated to the health system context.

scholarly journals Next Generation Data Infrastructures: Towards an Extendable Model of the Asset Management Data Infrastructure as Complex Adaptive System

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Paul Brous ◽  
Marijn Janssen ◽  
Paulien Herder
Keyword(s):  
Complex Adaptive Systems ◽  
Asset Management ◽  
Adaptive Systems ◽  
New Technologies ◽  
Complex Adaptive System ◽  
Emergent Behavior ◽  
Data Infrastructure ◽  
Management Domain ◽  
Complex Adaptive ◽  
Data Infrastructures

Organizations are increasingly looking to adopt the Internet of Things (IoT) to collect the data required for data-driven decision-making. IoT might yield many benefits for asset management organizations engaged in infrastructure asset management, yet not all organizations are equipped to handle this data. IoT data is collected, stored, and analyzed within data infrastructures and there are many changes over time, resulting in the evolution of the data infrastructure and the need to view data infrastructures as complex adaptive systems (CAS). Such data infrastructures represent information about physical reality, in this case about the underlying physical infrastructure. Physical infrastructures are often described and analyzed in literature as CASs, but their underlying data infrastructures are not yet systematically analyzed, whereas they can also be viewed as CAS. Current asset management data models tend to view the system from a static perspective, posing constraints on the extensibility of the system, and making it difficult to adopt new data sources such as IoT. The objective of the research is therefore to develop an extensible model of asset management data infrastructures which helps organizations implement data infrastructures which are capable of evolution and aids the successful adoption of IoT. Systematic literature review and an IoT case study in the infrastructure management domain are used as research methods. By adopting a CAS lens in the design, the resulting data infrastructure is extendable to deal with evolution of asset management data infrastructures in the face of new technologies and new requirements and to steadily exhibit new forms of emergent behavior. This paper concludes that asset management data infrastructures are inherently multilevel, consisting of subsystems, links, and nodes, all of which are interdependent in several ways.

A Complex Adaptive Systems-Based Enterprise Knowledge Sharing Model

2011 ◽  
pp. 35-59
Author(s):  
Cynthia T. Small ◽  
Andrew P. Sage
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Knowledge Sharing ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Knowledge Workers ◽  
Emergent Behavior ◽  
Agent Simulation ◽  
Complex Adaptive ◽  
Multi Agent

This paper describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid knowledge management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.

A Complex Adaptive Systems-Based Enterprise Knowledge Sharing Model

2009 ◽  
pp. 281-300
Author(s):  
Andrew P. Sage ◽  
Cynthia T. Small
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Knowledge Sharing ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Knowledge Workers ◽  
Emergent Behavior ◽  
Agent Simulation ◽  
Complex Adaptive ◽  
Multi Agent

This chapter describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid Knowledge Management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.

scholarly journals The World is More Than Complicated

2011 ◽  
Vol 133 (11) ◽  
pp. 30-35
Author(s):  
Ahmed K. Noor
Keyword(s):  
Complex Systems ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Emergent Behavior ◽  
Global Response ◽  
Emergent Complexity ◽  
Engineering Designs ◽  
Complex Adaptive ◽  
Engineered Systems ◽  
The Individual

This article discusses the need of complex systems to be adaptive, and various innovative technologies that are required to engineer these systems. Complex adaptive systems consist of several simultaneously interacting parts or components, which are expected to function in an uncertain, complex environment, and to adapt to unforeseeable contingencies. The defining characteristics of complex adaptive systems are that the components are continually changing, the systems involve many interactions among components, and configurations cannot be fully determined in advance. Studies have shown that complex systems of the future will require a multidisciplinary framework—an approach that has been called emergent (complexity) engineering. Emergent engineering designs a system from the bottom-up by designing the individual components and their interactions that can lead to a desired global response. Although significant effort has been devoted to understanding complexity in natural and engineered systems, the research into complex adaptive systems is fragmented and is largely focused on specific examples. In order to accelerate the development of future diverse complex systems, there is a profound need for developing the new multidisciplinary framework of emergent engineering, along with associated systematic approaches, and generally valid methods and tools for high-fidelity simulations of the collective emergent behavior of these systems.

scholarly journals A health systems resilience research agenda: moving from concept to practice

2021 ◽  
Vol 6 (8) ◽  
pp. e006779
Author(s):  
Dell D Saulnier ◽  
Karl Blanchet ◽  
Carmelita Canila ◽  
Daniel Cobos Muñoz ◽  
Livia Dal Zennaro ◽  
...  
Keyword(s):  
Health System ◽  
Health Systems ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Dynamic Performance ◽  
Performance Outcomes ◽  
Theoretical Research ◽  
Systems Research ◽  
Complex Adaptive ◽  
System Resilience

Health system resilience, known as the ability for health systems to absorb, adapt or transform to maintain essential functions when stressed or shocked, has quickly gained popularity following shocks like COVID-19. The concept is relatively new in health policy and systems research and the existing research remains mostly theoretical. Research to date has viewed resilience as an outcome that can be measured through performance outcomes, as an ability of complex adaptive systems that is derived from dynamic behaviour and interactions, or as both. However, there is little congruence on the theory and the existing frameworks have not been widely used, which as diluted the research applications for health system resilience. A global group of health system researchers were convened in March 2021 to discuss and identify priorities for health system resilience research and implementation based on lessons from COVID-19 and other health emergencies. Five research priority areas were identified: (1) measuring and managing systems dynamic performance, (2) the linkages between societal resilience and health system resilience, (3) the effect of governance on the capacity for resilience, (4) creating legitimacy and (5) the influence of the private sector on health system resilience. A key to filling these research gaps will be longitudinal and comparative case studies that use cocreation and coproduction approaches that go beyond researchers to include policy-makers, practitioners and the public.

A Complex Adaptive Systems-Based Enterprise Knowledge Sharing Model

2010 ◽  
pp. 831-849
Author(s):  
Cynthia T. Small ◽  
Andrew P. Sage
Keyword(s):  
Decision Making ◽  
Knowledge Management ◽  
Knowledge Sharing ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Knowledge Workers ◽  
Emergent Behavior ◽  
Agent Simulation ◽  
Complex Adaptive ◽  
Multi Agent

This paper describes a complex adaptive systems (CAS)-based enterprise knowledge-sharing (KnS) model. The CAS-based enterprise KnS model consists of a CAS-based KnS framework and a multi-agent simulation model. Enterprise knowledge sharing is modeled as the emergent behavior of knowledge workers interacting with the KnS environment and other knowledge workers. The CAS-based enterprise KnS model is developed to aid knowledge management (KM) leadership and other KnS researchers in gaining an enhanced understanding of KnS behavior and its influences. A premise of this research is that a better understanding of KnS influences can result in enhanced decision-making of KnS interventions that can result in improvements in KnS behavior.

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