Agent-based computational models to explore diffusion of medical innovations among cardiologists

Borracci, Raúl A.; Giorgi, Mariano

doi:10.1016/j.ijmedinf.2018.02.008

Cited by 12 publications

(10 citation statements)

References 26 publications

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“…O fator de influência social entre os profissionais médicos (Borracci & Giorgi, 2018) foi o único fator apontado na literatura e não identificado nesta pesquisa. Este resultado pode ser explicado pelo fato de a influência ser exercida muitas vezes de forma inconsciente nos indivíduos, sendo os experimentos os métodos de pesquisa mais promissores para avaliar esse tipo de fator (Borracci & Giorgi, 2018).…”

Section: Discussionunclassified

“…Veilleux et al (2017) acompanharam o desenvolvimento de uma tecnologia para tratamento de doenças inflamatórias de intestino e mostraram que a participação de médicos gastroenterologistas desde o início do processo é fator chave para a posterior aceitação de profissionais da área. Borracci & Giorgi (2018) mostram que a adoção de inovações por respeitados médicos veteranos, que agem na condição de líderes no seu meio, é fator fundamental na difusão de inovações. Mesmo na falta de maiores evidências científicas acerca da efetividade das tecnologias, a adoção dos líderes tende a proporcionar elevada confiança e difusão entre estes profissionais.…”

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Fatores Que Influenciam a Aceitação De Telemedicina Por Médicos No Brasil

Palma

Santos

Klein

2020

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O objetivo deste estudo foi identificar os fatores que influenciam a aceitação da telemedicina, mais especificamente, da teleconsulta, por médicos brasileiros. Por meio de uma revisão da literatura, foram identificados oito principais fatores que influenciam essa aceitação. Este trabalho utilizou uma abordagem qualitativa; a coleta de dados foi realizada por entrevistas semiestruturadas em uma amostra de dez profissionais, nove deles médicos de diferentes especialidades. Após, foi realizada uma análise de dados com duas metodologias distintas e apoio dos softwares NVivo 11 e Iramuteq. Os resultados sugerem que fatores relacionados com percepção de valor, resistência à mudança por parte dos indivíduos, e fatores regulatórios são os mais relevantes para aceitação das teleconsultas. Fatores não relatados na literatura, como a relação médico-paciente pregressa, a importância do exame físico presencial, as possibilidades nas especialidades médicas e o corporativismo, foram identificados e definidos. Para estudos futuros, são recomendadas análises que considerem fatores institucionais, presentes no campo da saúde, que afetam a aceitação e a difusão da telemedicina.

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Section: Discussionunclassified

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Fatores Que Influenciam a Aceitação De Telemedicina Por Médicos No Brasil

Palma

Santos

Klein

2020

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“…and also deploy them for predictive purposes. For instance, real-world data about the spread of COVID-19 in hospitals and other settings have been used to develop and deploy ABMs for use in optimizing policy measures and exploration of other epidemiological questions (Gaudou et al, 2020;Hinch et al, 2021;Park et al, 2021); also broadly notable, recent ABM-based studies of "information diffusion" have been used in the development of advanced community health resources (Lindau et al, 2021) and to examine how "medical innovation" might propagate among specific communities, such as cardiologists (Borracci and Giorgi, 2018).…”

Section: Frontiers In Systems Biologymentioning

confidence: 99%

Innovations in integrating machine learning and agent-based modeling of biomedical systems

Sivakumar

Mura²,

Peirce³

2022

Front. Syst. Biol.

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Agent-based modeling (ABM) is a well-established computational paradigm for simulating complex systems in terms of the interactions between individual entities that comprise the system’s population. Machine learning (ML) refers to computational approaches whereby algorithms use statistical methods to “learn” from data on their own, i.e., without imposing any a priori model/theory onto a system or its behavior. Biological systems—ranging from molecules, to cells, to entire organisms, to whole populations and even ecosystems—consist of vast numbers of discrete entities, governed by complex webs of interactions that span various spatiotemporal scales and exhibit nonlinearity, stochasticity, and variable degrees of coupling between entities. For these reasons, the macroscopic properties and collective dynamics of biological systems are generally difficult to accurately model or predict via continuum modeling techniques and mean-field formalisms. ABM takes a “bottom-up” approach that obviates common difficulties of other modeling approaches by enabling one to relatively easily create (or at least propose, for testing) a set of well-defined “rules” to be applied to the individual entities (agents) in a system. Quantitatively evaluating a system and propagating its state over a series of discrete time-steps effectively simulates the system, allowing various observables to be computed and the system’s properties to be analyzed. Because the rules that govern an ABM can be difficult to abstract and formulate from experimental data, at least in an unbiased way, there is a uniquely synergistic opportunity to employ ML to help infer optimal, system-specific ABM rules. Once such rule-sets are devised, running ABM calculations can generate a wealth of data, and ML can be applied in that context too—for example, to generate statistical measures that accurately and meaningfully describe the stochastic outputs of a system and its properties. As an example of synergy in the other direction (from ABM to ML), ABM simulations can generate plausible (realistic) datasets for training ML algorithms (e.g., for regularization, to mitigate overfitting). In these ways, one can envision a variety of synergistic ABM⇄ML loops. After introducing some basic ideas about ABMs and ML, and their limitations, this Review describes examples of how ABM and ML have been integrated in diverse contexts, spanning spatial scales that include multicellular and tissue-scale biology to human population-level epidemiology. In so doing, we have used published studies as a guide to identify ML approaches that are well-suited to particular types of ABM applications, based on the scale of the biological system and the properties of the available data.

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“…This methodology has gained an increasing popularity, both in academia and industry such as the study of critical infrastructures dependence [22], consumption of services of sustainable eco-systems [23], economic sciences [24,25], and management [26,27]. For adoption and diffusion studies, a number of works have used ABM for studying the adoption and diffusion of technologies such as environmental technologies [28], energy technologies [29], health and medical technologies [30][31][32], agricultural technologies [33], cinema technologies [34], transportation technologies [35], electricity production [36], alternative fuels [37,38], organic farming practices [39], automobile markets [40], fuel cell vehicles [41,42], mobile phone [43], and smart metering [44].…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling the Adoption and Diffusion of Mobile Telecommunications Technologies in Iran: A Computational Approach Based on Agent-Based Modeling and Social Network Theory

et al. 2020

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Understanding the mechanism underlying the mobile telecommunications technologies (MTTs) diffusion in a country is crucial for telecom planners to know how to accelerate their diffusion by designing appropriate scenarios. Considering the technology diffusion as a bottom-up process, this study is aimed at exploring this mechanism, drawing on insights from diffusion of innovation theory and social network theory. Accordingly, an agent-based model is proposed to investigate how MTTs are diffused in Iran over time. The results of this study show, (1) social network of Iranian society seems more similar to a Watts–Strogatz small-world network than a Barabási–Albert preferential attachment network, where the clustering coefficient is high and average path length is low, (2) compared to the compatibility parameter, the advertisement parameter not only is less influential on diffusion of a targeted MTT (i.e., 4G) but also is not necessary for it, and (3) scenarios having the least number of steps and turning points are more appropriate for continuous diffusion of 4G. The proposed study is empirically validated against real-world data ranging from 7/1/2017 to 12/31/2017. We believe it provides telecom planners insights regarding MTTs diffusion mechanism in a social complex structure and the how of scenario designing for increasing their diffusion.

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Agent-based computational models to explore diffusion of medical innovations among cardiologists

Cited by 12 publications

References 26 publications

Fatores Que Influenciam a Aceitação De Telemedicina Por Médicos No Brasil

Fatores Que Influenciam a Aceitação De Telemedicina Por Médicos No Brasil

Innovations in integrating machine learning and agent-based modeling of biomedical systems

Modeling the Adoption and Diffusion of Mobile Telecommunications Technologies in Iran: A Computational Approach Based on Agent-Based Modeling and Social Network Theory

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