Multi-agent modelling of decision support systems based on visual data mining

Ellouzi, Hamdi; Ltifi, Hela; Ayed, Mounir Ben

doi:10.3233/mgs-170260

Cited by 14 publications

(4 citation statements)

References 20 publications

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“…The study designs varied considerably ( Table 1 ); the most frequent designs were pre-post designs (15/42, 36%) and case studies or case series (11/42, 26%). There were 3 lower-middle–income countries, as defined by the World Bank [ 62 ] (Tunisia [ 63 , 64 ], Nepal [ 65 ], and Tanzania [ 66 ]) represented in the 42 included full-text studies.…”

Section: Resultsmentioning

confidence: 99%

“…Rationales included removing the burden from clinicians in complex situations (eg, monitoring antibiotic use [ 77 ]). Other rationales cited for implementing a CDSS included the following: the CDSS as a separate intervention to address a clinical need or context (eg, reducing the incidence of hospital-acquired infections [ 64 ]), increasing workflow efficacy [ 79 ], improving adherence to clinical guidelines [ 82 ], transforming time-consuming paper versions of a CDSS to a digital version [ 81 , 87 , 104 ], engaging or collaborating with health care workers [ 78 ], and decreasing duplication of tests or health resource waste (eg, duplicates of magnetic resonance imaging scans) [ 84 ]. Other authors noted that their study was a scale-up or modification of an initial pilot of a CDSS [ 80 , 82 , 98 ], that there was existing evidence that validated the use of a CDSS [ 66 ], and contextual factors of the health system (eg, rapid turnover of clinicians) required the implementation of a CDSS [ 70 ].…”

Section: Resultsmentioning

confidence: 99%

“…Studies that reported on the strengths of applying a specific theory, model, or framework referred to the lessons learned [ 80 , 90 , 97 ], implementation plans developed [ 102 ], ability to address barriers and facilitators for implementation within the intervention [ 92 , 94 ], adaptability of an intervention or increased scale-up [ 72 , 74 , 93 ], buy-in from stakeholders [ 65 , 76 ], use of interdisciplinary knowledge [ 64 ], and synergy with other implementation tools such as the ERIC [ 100 ].…”

Section: Resultsmentioning

confidence: 99%

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Using Theories, Models, and Frameworks to Inform Implementation Cycles of Computerized Clinical Decision Support Systems in Tertiary Health Care Settings: Scoping Review

Fernando,

Abell,

Tyack

et al. 2023

J Med Internet Res

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Background Computerized clinical decision support systems (CDSSs) are essential components of modern health system service delivery, particularly within acute care settings such as hospitals. Theories, models, and frameworks may assist in facilitating the implementation processes associated with CDSS innovation and its use within these care settings. These processes include context assessments to identify key determinants, implementation plans for adoption, promoting ongoing uptake, adherence, and long-term evaluation. However, there has been no prior review synthesizing the literature regarding the theories, models, and frameworks that have informed the implementation and adoption of CDSSs within hospitals. Objective This scoping review aims to identify the theory, model, and framework approaches that have been used to facilitate the implementation and adoption of CDSSs in tertiary health care settings, including hospitals. The rationales reported for selecting these approaches, including the limitations and strengths, are described. Methods A total of 5 electronic databases were searched (CINAHL via EBSCOhost, PubMed, Scopus, PsycINFO, and Embase) to identify studies that implemented or adopted a CDSS in a tertiary health care setting using an implementation theory, model, or framework. No date or language limits were applied. A narrative synthesis was conducted using full-text publications and abstracts. Implementation phases were classified according to the “Active Implementation Framework stages”: exploration (feasibility and organizational readiness), installation (organizational preparation), initial implementation (initiating implementation, ie, training), full implementation (sustainment), and nontranslational effectiveness studies. Results A total of 81 records (42 full text and 39 abstracts) were included. Full-text studies and abstracts are reported separately. For full-text studies, models (18/42, 43%), followed by determinants frameworks (14/42,33%), were most frequently used to guide adoption and evaluation strategies. Most studies (36/42, 86%) did not list the limitations associated with applying a specific theory, model, or framework. Conclusions Models and related quality improvement methods were most frequently used to inform CDSS adoption. Models were not typically combined with each other or with theory to inform full-cycle implementation strategies. The findings highlight a gap in the application of implementation methods including theories, models, and frameworks to facilitate full-cycle implementation strategies for hospital CDSSs.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Using Theories, Models, and Frameworks to Inform Implementation Cycles of Computerized Clinical Decision Support Systems in Tertiary Health Care Settings: Scoping Review

Fernando,

Abell,

Tyack

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

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“…Combining decision support systems, data mining techniques, and multi agent systems provide promising solutions for decision making. Recently, multi agent systems combined with decision support systems and knowledge management techniques have been widely used in intensive care systems, including hospital-acquired infection surveillance in the ICU ( 25 ), modeling ICU processes using Intuitionistic Fuzzy Expert Decision Support System ( 23 ), remote health care monitoring for risk detection and decision making ( 24 ), and dynamic aggregation of medical research data from distributed sources applied in neonatal intensive care ( 26 ). The proposed architecture in this study is also considered a multi-disciplinary approach to support decision making and process management in the neonatal intensive care complex environment.…”

Section: Discussionmentioning

confidence: 99%

An agent based architecture for high-risk neonate management at neonatal intensive care unit

et al. 2018

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BackgroundIn recent years, the use of new tools and technologies has decreased the neonatal mortality rate. Despite the positive effect of using these technologies, the decisions are complex and uncertain in critical conditions when the neonate is preterm or has a low birth weight or malformations. There is a need to automate the high-risk neonate management process by creating real-time and more precise decision support tools.ObjectiveTo create a collaborative and real-time environment to manage neonates with critical conditions at the NICU (Neonatal Intensive Care Unit) and to overcome high-risk neonate management weaknesses by applying a multi agent based analysis and design methodology as a new solution for NICU management.MethodsThis study was a basic research for medical informatics method development that was carried out in 2017. The requirement analysis was done by reviewing articles on NICU Decision Support Systems. PubMed, Science Direct, and IEEE databases were searched. Only English articles published after 1990 were included; also, a needs assessment was done by reviewing the extracted features and current processes at the NICU environment where the research was conducted. We analyzed the requirements and identified the main system roles (agents) and interactions by a comparative study of existing NICU decision support systems. The Universal Multi Agent Platform (UMAP) was applied to implement a prototype of our multi agent based high-risk neonate management architecture.ResultsLocal environment agents interacted inside a container and each container interacted with external resources, including other NICU systems and consultation centers. In the NICU container, the main identified agents were reception, monitoring, NICU registry, and outcome prediction, which interacted with human agents including nurses and physicians.ConclusionManaging patients at the NICU units requires online data collection, real-time collaboration, and management of many components. Multi agent systems are applied as a well-known solution for management, coordination, modeling, and control of NICU processes. We are currently working on an outcome prediction module using artificial intelligence techniques for neonatal mortality risk prediction. The full implementation of the proposed architecture and evaluation is considered the future work.

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