TY - GEN
T1 - Reengineering Clinical Decision Support Systems for Artificial Intelligence
AU - Strachna, Olga
AU - Asan, Onur
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - There has been a tremendous growth of digital health applications within healthcare. Clinicians are inundated with new types of data to synthesize in a timely manner to make a clinical decisions about the care of their patients. The overwhelming abundance of data leads to physician burnout, which is a major problem within healthcare. In parallel, there is an immense hype building up about implementation of Artificial Intelligence (AI) technologies, such as Machine Learning or Deep Learning, to augment clinician decision making processes. Healthcare is a highly regulated environment so it's imperative to involve clinicians and data scientists in the entire model development, validation and implementation lifecycle. There ought to be a mechanism in place for integration of human feedback to build trust in the AI model, through human in the loop implementation models and participatory design approaches. The overarching aim of this stury is to formulate a problem statement and propose the development a system dynamics model highlighting the feedback loops within clinical decision-making workflows leading to diffusion of innovation of AI within healthcare. We propose system dynamics modelling as a mechanism to articulate the problems that are best suited for AI models, and conceptualize how the models would fit into the current workflow.
AB - There has been a tremendous growth of digital health applications within healthcare. Clinicians are inundated with new types of data to synthesize in a timely manner to make a clinical decisions about the care of their patients. The overwhelming abundance of data leads to physician burnout, which is a major problem within healthcare. In parallel, there is an immense hype building up about implementation of Artificial Intelligence (AI) technologies, such as Machine Learning or Deep Learning, to augment clinician decision making processes. Healthcare is a highly regulated environment so it's imperative to involve clinicians and data scientists in the entire model development, validation and implementation lifecycle. There ought to be a mechanism in place for integration of human feedback to build trust in the AI model, through human in the loop implementation models and participatory design approaches. The overarching aim of this stury is to formulate a problem statement and propose the development a system dynamics model highlighting the feedback loops within clinical decision-making workflows leading to diffusion of innovation of AI within healthcare. We propose system dynamics modelling as a mechanism to articulate the problems that are best suited for AI models, and conceptualize how the models would fit into the current workflow.
KW - artificial intelligence
KW - clinical decision support systems
KW - human factors
KW - machine learning
KW - shared decision-making
KW - systems engineering
KW - systems thinking
UR - http://www.scopus.com/inward/record.url?scp=85103225981&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103225981&partnerID=8YFLogxK
U2 - 10.1109/ICHI48887.2020.9374370
DO - 10.1109/ICHI48887.2020.9374370
M3 - Conference contribution
AN - SCOPUS:85103225981
T3 - 2020 IEEE International Conference on Healthcare Informatics, ICHI 2020
BT - 2020 IEEE International Conference on Healthcare Informatics, ICHI 2020
T2 - 8th IEEE International Conference on Healthcare Informatics, ICHI 2020
Y2 - 30 November 2020 through 3 December 2020
ER -