Research Project: An intelligent and patient-specific clinical decision support system aiming to enhance the effectiveness of interdisciplinary specialist care in patients with chronic pain

Project leader
Björn Äng
Project Members
Johan Ärnlöv
Tony Bohman
Roger G Nyberg
Elena Tseli
Linda Vixner
Jerker Westin
Riccardo Lo Martire, Centrum för klinisk forskning Dalarna (CKF) Region Dalarna
Project Period
Project Status
With the purpose of enhancing the effectiveness of specialised interdisciplinary treatment (IDT) in patients with chronic pain we will develop and validate a new intelligent Clinical Decision Support System (CDSS) and, with the support of a registry based randomised trial, evaluate and implement the system in future IDT. Chronic pain is a leading cause of disability worldwide and has a huge impact on public health. IDT is the established form of treatment but only modest effects have been reported. Both patient selection processes and individual treatment precision need improvement. We will create a CDSS using artificial intelligence and clinical data from the FRIDA-database, which includes over 60,000 patients with chronic pain across 40 specialist units in Sweden. FRIDA synchronises unique and longitudinal data from the National Register of Pain Rehabilitation with four other national registers. The CDSS will facilitate patient selection and guide individualised treatment strategies with the use of systematic machine-learning loops to identify patient-specific patterns. This support system has the potential to improve clinical praxis, which will result in enhanced quality of life and reduced sick leave, emotional suffering and pain medication in patients with chronic pain,
and hence be of great socio-economic value to society. Our multi-professional research team has
the expertise to successfully complete the project, working in close collaboration with stakeholders.
Kronisk smärta, prediktion, artificiell intelligens, interdiciplinär smärtrehabilitering, Chronic pain, prediction, artificial intelligence, interdisiplinary pain rehabilitation, machine learning
Research Profile
Health and Social Welfare
Computer Engineering
Medical Science
Högskolan Dalarna