Literature DB >> 23232759

A task-based support architecture for developing point-of-care clinical decision support systems for the emergency department.

S Wilk1, W Michalowski, D O'Sullivan, K Farion, J Sayyad-Shirabad, C Kuziemsky, B Kukawka.   

Abstract

OBJECTIVES: The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter.
METHODS: The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate.
RESULTS: The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE--a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED.
CONCLUSIONS: The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs.

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Mesh:

Year:  2012        PMID: 23232759     DOI: 10.3414/ME11-01-0099

Source DB:  PubMed          Journal:  Methods Inf Med        ISSN: 0026-1270            Impact factor:   2.176


  6 in total

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Authors:  R Haux; C U Lehmann
Journal:  Appl Clin Inform       Date:  2014-10-29       Impact factor: 2.342

2.  Comparing predictions made by a prediction model, clinical score, and physicians: pediatric asthma exacerbations in the emergency department.

Authors:  K J Farion; S Wilk; W Michalowski; D O'Sullivan; J Sayyad-Shirabad
Journal:  Appl Clin Inform       Date:  2013-08-07       Impact factor: 2.342

3.  A Framework for Incorporating Patient Preferences to Deliver Participatory Medicine via Interdisciplinary Healthcare Teams.

Authors:  Craig Kuziemsky; Davood Astaraky; Szymon Wilk; Wojtek Michalowski; Pavel Andreev
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

4.  Using Semantic Components to Represent Dynamics of an Interdisciplinary Healthcare Team in a Multi-Agent Decision Support System.

Authors:  Szymon Wilk; Mounira Kezadri-Hamiaz; Daniela Rosu; Craig Kuziemsky; Wojtek Michalowski; Daniel Amyot; Marc Carrier
Journal:  J Med Syst       Date:  2015-11-21       Impact factor: 4.460

Review 5.  Grading and assessment of clinical predictive tools for paediatric head injury: a new evidence-based approach.

Authors:  Mohamed Khalifa; Blanca Gallego
Journal:  BMC Emerg Med       Date:  2019-06-14

6.  Machine learning in critical care: state-of-the-art and a sepsis case study.

Authors:  Alfredo Vellido; Vicent Ribas; Carles Morales; Adolfo Ruiz Sanmartín; Juan Carlos Ruiz Rodríguez
Journal:  Biomed Eng Online       Date:  2018-11-20       Impact factor: 2.819

  6 in total

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