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Transforming Clinical Decision Support with AI

Svansson, Axel LU and Kambugu, Herman Joseph (2022) INFM10 20221
Department of Informatics
Abstract (Swedish)
Machine learning, alongside other branches of AI have entered an era of rapid growth in several industries including healthcare, creating vast opportunities to advance several aspects of clinical practice. Specific to clinical decision support, despite the immense potential benefits of incorporating machine learning into clinical decision support systems, also known as machine learning enabled clinical decision support systems, there is a gap in literature and research shows that progress of adoption is still falling behind. This paper therefore aims to contribute to existing literature and practice by exploring influencing factors for adoption of these systems. This qualitative study used the TOE-framework as a reference to curate a... (More)
Machine learning, alongside other branches of AI have entered an era of rapid growth in several industries including healthcare, creating vast opportunities to advance several aspects of clinical practice. Specific to clinical decision support, despite the immense potential benefits of incorporating machine learning into clinical decision support systems, also known as machine learning enabled clinical decision support systems, there is a gap in literature and research shows that progress of adoption is still falling behind. This paper therefore aims to contribute to existing literature and practice by exploring influencing factors for adoption of these systems. This qualitative study used the TOE-framework as a reference to curate a conceptual research framework, including eight initial factors that guided the empirical research, from conduction of seven interviews with doctors and experts to data analysis. The study concluded that six of the ten examined factors – System Complexity, Transparency, Top Management Support, Regulations, Data Availability and Collaboration were influential, two factors – Technology Readiness and Market Trends were not influential and two factors – Financial Resources and Reliability were inconclusive for the adoption of machine learning enabled clinical decision support systems. Insights generated from this study can serve as a reference point for practice and further research. (Less)
Please use this url to cite or link to this publication:
author
Svansson, Axel LU and Kambugu, Herman Joseph
supervisor
organization
alternative title
A qualitative study of factors influencing adoption of Machine Learning enabled systems in healthcare
course
INFM10 20221
year
type
H1 - Master's Degree (One Year)
subject
keywords
Artificial Intelligence, Machine learning, Adoption, Clinical decision support systems, Healthcare
report number
INF22-13
language
English
id
9090313
date added to LUP
2022-09-07 12:58:08
date last changed
2022-09-07 12:58:08
@misc{9090313,
  abstract     = {{Machine learning, alongside other branches of AI have entered an era of rapid growth in several industries including healthcare, creating vast opportunities to advance several aspects of clinical practice. Specific to clinical decision support, despite the immense potential benefits of incorporating machine learning into clinical decision support systems, also known as machine learning enabled clinical decision support systems, there is a gap in literature and research shows that progress of adoption is still falling behind. This paper therefore aims to contribute to existing literature and practice by exploring influencing factors for adoption of these systems. This qualitative study used the TOE-framework as a reference to curate a conceptual research framework, including eight initial factors that guided the empirical research, from conduction of seven interviews with doctors and experts to data analysis. The study concluded that six of the ten examined factors – System Complexity, Transparency, Top Management Support, Regulations, Data Availability and Collaboration were influential, two factors – Technology Readiness and Market Trends were not influential and two factors – Financial Resources and Reliability were inconclusive for the adoption of machine learning enabled clinical decision support systems. Insights generated from this study can serve as a reference point for practice and further research.}},
  author       = {{Svansson, Axel and Kambugu, Herman Joseph}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Transforming Clinical Decision Support with AI}},
  year         = {{2022}},
}