Focus on Chronic Disease through Different Lenses of Expertise Towards Implementation of Patient-Focused Decision Support Preventing Disability: The Example of Early Rheumatoid Arthritis

Sammanfattning: Introduction: Rheumatoid arthritis (RA) is a chronic inflammatory disease. Treatment strategies emphasize early multi-professional interventions to reduce disease activity and to prevent disability, but there is a lack of knowledge on how optimal treatment can be provided to each individual patient.Aim: To elucidate how clinical manifestations of early RA are associated to disease and disability outcomes, to strive for greater potential to establish prognosis in early RA, and to facilitate implementation of decision support through analyses of the decision-making environment in chronic care.Methods: Multivariate statistics and mathematical modelling, as well as field observations and focus group interviews.Results: Decision support: A prognostic tree that predicted patients with a poor prognosis (moderate or high levels of DAS-28) at one year after diagnosis had a performance of 25% sensitivity, 90% specificity and a positive predictive value of 76%. Implementation of a decision support application at a rheumatology unit should include taking into account incentive structures, workflow and awareness, as well as informal communication structures. Prognosis: A considerable part of the variance in disease activity at one year after diagnosis could be explained by disease progression during the first three months after diagnosis. Using different types of knowledge – different expertise – prior to standardized data mining methods was found to be a promising when mining (clinical) data for new patterns that elicit new knowledge. Disease and disability: Women report more fatigue than men in early RA, although the difference is not consistently significant. Fatigue in early RA is closely and rather consistently related to disease activity, pain and activity limitation, as well as to mental health and sleep disturbance.Conclusion: A decision tree was designed to identify patients at risk of poor prognosis at one year after the diagnosis of RA. When constructing prediction rules for good or poor prognosis, including more measures of disease and disability progressions showed promise. Using different types of knowledge – different lenses of expertise – prior to standardized data mining methods was also a promising method when mining (clinical) data for new patterns that elicit new knowledge.