Multivariate data-driven decision guidance for clinical scientists
- Authors: Burstein, Frada , De Silva, Daswin , Jelinek, Herbert , Stranieri, Andrew
- Date: 2013
- Type: Text , Conference paper
- Relation: 29th International Conference on Data Engineering Workshops, ICDEW 2013; Proceedings - International Conference on Data Engineering p. 193-199
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- Description: Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards utilising better information management for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges created for effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. A Data-driven Decision Guidance Management System (DD-DGMS) architecture can encompass solutions into a single closed-loop integrated platform to empower clinical scientists to seamlessly explore a multivariate data space in search of novel patterns and correlations to inform their research and practice. The paper describes the components of such an architecture, which includes a robust data warehouse as an infrastructure for comprehensive clinical knowledge management. The proposed DD-DGMS architecture incorporates the dynamic dimensional data model as its elemental core. Given the heterogeneous nature of clinical contexts and corresponding data, the dimensional data model presents itself as an adaptive model that facilitates knowledge discovery, distribution and application, which is essential for clinical decision support. The paper reports on a trial of the DD-DGMS system prototype conducted on diabetes screening data which further establishes the relevance of the proposed architecture to a clinical context.
- Description: E1
A participatory information management framework for patient centred care of autism spectrum disorder
- Authors: De Silva, Daswin , Burstein, Frada , Stranieri, Andrew , Williams, Katrina , Rinehart, Nicole
- Date: 2013
- Type: Text , Conference paper
- Relation: Information systems: Transforming the future 24th Australasian Conference on Information p. 2-11
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Addressing the complexities of big data analytics in healthcare : The diabetes screening case
- Authors: De Silva, Daswin , Burstein, Frada , Jelinek, Herbert , Stranieri, Andrew
- Date: 2015
- Type: Text , Journal article
- Relation: Australasian Journal of Information Systems Vol. 19, no. (2015), p. S99-S115
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- Description: The healthcare industry generates a high throughput of medical, clinical and omics data of varying complexity and features. Clinical decision-support is gaining widespread attention as medical institutions and governing bodies turn towards better management of this data for effective and efficient healthcare delivery and quality assured outcomes. Amass of data across all stages, from disease diagnosis to palliative care, is further indication of the opportunities and challenges to effective data management, analysis, prediction and optimization techniques as parts of knowledge management in clinical environments. Big Data analytics (BDA) presents the potential to advance this industry with reforms in clinical decision-support and translational research. However, adoption of big data analytics has been slow due to complexities posed by the nature of healthcare data. The success of these systems is hard to predict, so further research is needed to provide a robust framework to ensure investment in BDA is justified. In this paper we investigate these complexities from the perspective of updated Information Systems (IS) participation theory. We present a case study on a large diabetes screening project to integrate, converge and derive expedient insights from such an accumulation of data and make recommendations for a successful BDA implementation grounded in a participatory framework and the specificities of big data in healthcare context. © 2015 De Silva, Burstein, Jelinek, Stranieri.
Association of ankle brachial pressure index with heart rate variability in a rural screening clinic
- Authors: Jelinek, Herbert , De Silva, Daswin , Burstein, Frada , Stranieri, Andrew , Khalaf, Kinda , Khandoker, Ahsan , Al-Aubaidy, Hayder
- Date: 2013
- Type: Text , Conference paper
- Relation: 40th Computing in Cardiology Conference, CinC 2013; Vol. 40, p. 755-758
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- Description: Peripheral vascular disease (PVD) can be associated with atherosclerosis and/ or peripheral neuropathy, which can be characterized by impairment of sensory, motor or autonomic nervous system. A noninvasive test to detect PVD is the ankle brachial pressure index (ABPI). Autonomic nervous system function can be determined by assessing heart rate variability from an ECG recording. No clear association between PVD and cardiac autonomic dysfunction has been demonstrated to date. © 2013 CCAL.
Group decision making in health care : A case study of multidisciplinary meetings
- Authors: Sharma, Vishakha , Stranieri, Andrew , Burstein, Frada , Warren, Jim , Daly, Sharon , Patterson, Louise , Yearwood, John , Wolff, Alan
- Date: 2016
- Type: Text , Journal article
- Relation: Journal of Decision Systems Vol. 25, no. (2016), p. 476-485
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- Description: Abstract: Recent studies have demonstrated that Multi-Disciplinary Meetings (MDM) practiced in some medical contexts can contribute to positive health care outcomes. The group reasoning and decision-making in MDMs has been found to be most effective when deliberations revolve around the patient’s needs, comprehensive information is available during the meeting, core members attend and the MDM is effectively facilitated. This article presents a case study of the MDMs in cancer care in a region of Australia. The case study draws on a group reasoning model called the Reasoning Community model to analyse MDM deliberations to illustrate that many factors are important to support group reasoning, not solely the provision of pertinent information. The case study has implications for the use of data analytics in any group reasoning context. © 2016 Informa UK Limited, trading as Taylor & Francis Group.
Gestalt based evaluation of health information diagrams
- Authors: Sharma, Vishakha , Stranieri, Andrew , Burstein, Frada , Warren, Jim , Firmin, Sally
- Date: 2020
- Type: Text , Conference paper
- Relation: 24th International Conference Information Visualisation, IV 2020 Vol. 2020-September, p. 195-201
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- Description: Diagrams for four different health care settings have been proposed: Snapshot Diagram, Diagnosis Diagram, Strength of Evidence Diagram and Patient Pathway Diagram The availability of large amount of digital health care data and potential to utilize its benefits led to the development of these diagrams. This paper presents an analysis of the diagrams based on the selection of a subset of Gestalt principles deemed relevant for each diagram. Although Gestalt and human-computer interaction principles are advanced to apply to all diagrams or user interfaces, in practice a sub-set of principles must be selected to evaluate a diagram or interface The selection of a subset of principles to use on a diagram has not been widely studied. This paper presents an approach for identifying a subset of relevant Gestalt principles tailored for each of the four diagrams advanced for health care settings. © 2020 IEEE.