Philosophies for the Design and Development of Clinical Decision-Support Systems

Heathfield, Heather; Wyatt, Jeremy C.

doi:10.1055/s-0038-1634896

Cited by 106 publications

(71 citation statements)

References 29 publications

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“…The use of standard clinical trial regime for evaluating new healthcare technologies has been subject to much criticism in recent years [23][24][25][26]. One problem that has been highlighted is that trial designs inevitably ignore the contextual nature of the work being supported, raising doubts about extrapolating trial results to real settings of use.…”

Section: Methodological Implications For the Evaluation Of Computer Aidsmentioning

confidence: 99%

Use of computer-aided detection (CAD) tools in screening mammography: a multidisciplinary investigation

Alberdi

Povyakalo²,

Strigini³

et al. 2005

BJR

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent repository link SHORTENED VERSION OF THE TITLE:Multidisciplinary study of CAD use in mammography FUNDING:The work described in this paper has been partly funded by the UK Engineering and Physical Sciences Research Council (EPSRC) through DIRC, the Dependability Interdisciplinary Research Collaboration, a project investigating the dependability of computer based systems. KEYWORDS:Breast cancer screening, digital imaging, mammography 2 ABSTRACTWe summarise a set of analyses and studies we have conducted to assess the effects of the use of a Computer Aided Detection (CAD) tool in breast screening. We have used an interdisciplinary approach which combines: a) statistical analyses inspired by reliability modelling in engineering, b) experimental studies of mammography experts' decisions using the tool, interpreted in the light of human factors psychology;and c) ethnographic observations of the use of the tool both in trial conditions and in everyday screening practice. Our investigations have shown patterns of human behaviour and effects of computer-based advice that would not have been revealed by a standard clinical trial approach. For example, we found that the negligible measured effect of CAD could be explained by a range of effects on experts' decisions, beneficial in some cases and detrimental in others. There is some evidence of the latter effects being due to the experts using the computer tool differently from the developers' intentions. We integrate insights from the different pieces of evidence and highlight their implications for the design, evaluation and deployment of this sort of computer tool.3

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Section: Methodological Implications For the Evaluation Of Computer Aidsmentioning

confidence: 99%

Use of computer-aided detection (CAD) tools in screening mammography: a multidisciplinary investigation

Alberdi

Povyakalo²,

Strigini³

et al. 2005

BJR

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“…43 What has changed dramatically in recent years is the ability to store and manage huge amounts of data. This is when "Big Data" sets in.…”

Section: Example Of Big Data Applicationmentioning

confidence: 99%

Where Chemical Sensors May Assist in Clinical Diagnosis Exploring “Big Data”

et al. 2014

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An overview is presented of the many opportunities and challenges for using “Big Data” concepts to treat data from chemical sensors. Issues discussed include the need to make data machine readable and how to integrate distinct types of data that can be acquired and stored in different places. As a concrete example of possible applications, we propose a framework for an expert system dedicated to clinical diagnosis, where data from various types of sensors can be combined with text to provide better-informed diagnostics.

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“…', and 'where do we go now?' [26,32,51,61,93,105]. Within the community there is a general agreement on several reasons for the so-called lack of success.…”

Section: Aim Research: the Ashesmentioning

confidence: 99%

AI in medical education—another grand challenge for medical informatics

Lillehaug

Lajoie

1998

Artificial Intelligence in Medicine

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The potential benefits of artificial intelligence in medicine (AIM) were never realized as anticipated. This paper addresses ways in which such potential can be achieved. Recent discussions of this topic have proposed a stronger integration between AIM applications and health information systems, and emphasize computer guidelines to support the new health care paradigms of evidence-based medicine and cost-effectiveness. These proposals, however, promote the initial definition of AIM applications as being AI systems that can perform or aid in diagnoses. We challenge this traditional philosophy of AIM and propose a new approach aiming at empowering health care workers to become independent self-sufficient problem solvers and decision makers. Our philosophy is based on findings from a review of empirical research that examines the relationship between the health care personnel's level of knowledge and skills, their job satisfaction, and the quality of the health care they provide. This review supports addressing the quality of health care by empowering health care workers to reach their full potential. As an aid in this empowerment process we argue for re6i6ing a long forgotten AIM research area, namely, AI based applications for medical education and training. There is a growing body of research in artificial intelligence in education that demonstrates that the use of artificial intelligence can enhance learning in numerous domains. By examining the strengths of these educational applications and the results from previous AIM research we derive a framework for empowering medical * Corresponding author. E-mail: svili@ida.liu.se 0933-3657/98/$19.00 © 1998 Elsevier Science B.V. All rights reserved. PII S 0 9 3 3 -3 6 5 7 ( 9 7 ) 0 0 0 5 4 -7 S.-I. Lillehaug, S.P. Lajoie / Artificial Intelligence in Medicine 12 (1998) 197-225 198 personnel and consequently raising the quality of health care through the use of advanced AI based technology.

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Philosophies for the Design and Development of Clinical Decision-Support Systems

Cited by 106 publications

References 29 publications

Use of computer-aided detection (CAD) tools in screening mammography: a multidisciplinary investigation

Use of computer-aided detection (CAD) tools in screening mammography: a multidisciplinary investigation

Where Chemical Sensors May Assist in Clinical Diagnosis Exploring “Big Data”

AI in medical education—another grand challenge for medical informatics

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