Streamlining patient consultations for sleep disorders with a knowledge-based CDSS

Blake, Jacqueline; Kerr, Donald; Gammack, John

doi:10.1016/j.is.2015.08.003

Cited by 11 publications

(14 citation statements)

References 42 publications

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“…The study reported in this paper builds upon our previous work (Blake, Kerr & Gammack, 2016) which was focused on reporting a specific case of CDSS design and evaluation. The aim of design knowledge development from the specific CDSS design case was based upon an artefact called a knowledge based CDSS (KB-CDSS), shown in figure 1.…”

Section: Methodsmentioning

confidence: 99%

“…Poor sleep hygiene, or insufficient time made available for sleep, may be a cause of excessive daytime sleepiness, the main symptom of a sleep disorder. The tool is used by health professionals, such as physicians and sleep specialists, as a primary diagnostic tool for identifying insomnia (Blake, Kerr, & Gammack, 2016). side effects such as reporting misleading patient history.…”

Section: Communication Guideline 4 Andmentioning

confidence: 99%

“…For the purpose of checking acceptance of the technology, evaluation focused firstly on the ease-of-use aspects, particularly addressing readability and layout, and secondly on the usefulness of the reports themselves for supporting professional decisions. The comments made by the physicians were grouped under these themes (Blake, Kerr, & Gammack, 2016). Various presentations and stakeholder focus groups affirmed the utility and ease of use within normal workflows.…”

Section: Communication Guideline 4 Andmentioning

confidence: 99%

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Meta-design knowledge for Clinical Decision Support Systems

Miah

Blake

Kerr

2020

AJIS

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Knowledge gained from a Decision Support Systems (DSS) design should ideally be reusable by DSS designers and researchers. The majority of existing DSS research has mainly focused on empirical problem solving rather than on developing principles that could inform solution approaches for other user contexts. Design Science Research (DSR) has contributed to effective development of various innovative DSS artefacts and associated knowledge development, but there has been limited progress on new knowledge development from a practical problem context, going beyond product and process descriptions. For DSS applications such as Clinical Decision Support Systems (CDSS) design and development, relevant reusable prescriptive knowledge is of significance not only to understand mutability but also to extend application of theory across domains. In this paper, we develop new design knowledge abstracted from the approach taken in a representative case of innovative CDSS development, specified as an architecture and six design principles. The CDSS design artefact was initially designed for a specific clinical need is shown to be flexible for meeting demands of knowledge production both for diagnosis and treatment. It is argued that the proposed general strategy is applicable to designing CDSS artefacts in similar problem domains representing an important contribution of design knowledge both in DSS and DSR fields.

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Section: Methodsmentioning

confidence: 99%

Section: Communication Guideline 4 Andmentioning

confidence: 99%

Section: Communication Guideline 4 Andmentioning

confidence: 99%

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Meta-design knowledge for Clinical Decision Support Systems

Miah

Blake

Kerr

2020

AJIS

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“…The objective of the physicians' investigation was to ensure that the sleep tool web application had value in diagnosing sleep disorders, and met the sleep physicians' needs that they had detailed during the phase of requirements discovery. The objectives of the end user evaluation were to ensure that the sleep tools web application was robust and that it was reliable and usable (Blake, Kerr & Gammack 2016).…”

Section: Design Evaluationmentioning

confidence: 99%

“…The results of this research have been promulgated to the information systems community by publishing in journals (Blake & Kerr 2010Blake, Kerr & Gammack 2016). Communication of the research within the sleep community has been undertaken at conferences (Blake & Kerr 2007) and in person at the sleep clinics under study.…”

Section: Communication Of Researchmentioning

confidence: 99%

Development of a DSS and Online Tools to Support Sleep Disorder Consultations using Design Science.

Blake

Kerr

Gammack

2016

AJIS

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Medical decision making takes place in a complex and dynamic environment, involving various individual and organisational stakeholders. In such contexts supportive information systems are required to be designed for practical efficacy, emphasising transparency, usability, and usefulness to their primary users. At the same time, they need to demonstrate improved efficiency and diagnostic accuracy. In addition, they also need to provide effective communication of relevant information. This paper describes the use of design science to develop and evaluate a DSS incorporated into online tools to support sleep physician's diagnosis of sleep disorders. The design science approach offers guidance for developing this solution, that ensure the artefacts fit into their usage context, and that development and evaluation principles that generalise to similar problem domains are identified. How this project fits in the design science guidelines is specified, thus demonstrating how this philosophy and methodology advances theory particularly relevant to other similar medical diagnostic domains. The usability and technical evaluation of the set of tools indicated provide clear practical benefits at patient, physician and organisational levels.

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Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks

Naiseh

Jiang

et al. 2020

Research Challenges in Information Science

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With the increase in data volume, velocity and types, intelligent human-agent systems have become popular and adopted in different application domains, including critical and sensitive areas such as health and security. Humans' trust, their consent and receptiveness to recommendations are the main requirement for the success of such services. Recently, the demand on explaining the recommendations to humans has increased both from humans interacting with these systems so that they make an informed decision and, also, owners and systems managers to increase transparency and consequently trust and users' retention. Existing systematic reviews in the area of explainable recommendations focused on the goal of providing explanations, their presentation and informational content. In this paper, we review the literature with a focus on two user experience facets of explanations; delivery methods and modalities. We then focus on the risks of explanation both on user experience and their decision making. Our review revealed that explanations delivery to end-users is mostly designed to be along with the recommendation in a push and pull styles while archiving explanations for later accountability and traceability is still limited. We also found that the emphasis was mainly on the benefits of recommendations while risks and potential concerns, such as over-reliance on machines, is still a new area to explore.

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Streamlining patient consultations for sleep disorders with a knowledge-based CDSS

Cited by 11 publications

References 42 publications

Meta-design knowledge for Clinical Decision Support Systems

Meta-design knowledge for Clinical Decision Support Systems

Development of a DSS and Online Tools to Support Sleep Disorder Consultations using Design Science.

Explainable Recommendations in Intelligent Systems: Delivery Methods, Modalities and Risks

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