Clinical Decision Support for Rheumatoid Arthritis & Primary Antibody Deficiencies

Abstract

A clinical decision support system (CDSS) generates a patient-specific medical recommendation, which is then presented to the clinician for consideration. Recommendations can be made for e.g. diagnoses, additional exams or medical treatment. A CDSS can be rule-based (e.g. derived from literature or clinical guidelines), or it can be based on a prediction model. This thesis is about the development and implementation of CDSSs for rheumatoid arthritis (RA) and primary antibody deficiencies (PAD). RA is a chronic auto-immune disease that is characterized by inflammation of the synovial joints. PAD are a heterogeneous group of immunodeficiencies that can result in recurrent infections and auto-immune symptoms, among other complaints.

This thesis has several parts. The first part described the RA patient perspective on the use of prediction models as a support in their medical treatment. In the second part we focus on diagnostic decision support. We describe different methods for the identification of patients with difficult-to-treat RA in routine care data, and we describe the development of a CDSS for the early recognition of PAD in primary care. The third part focuses on treatment decision support. We describe the development of a CDSS that aims to reduce the risk of disease flares when tapering RA medication. The discussion addresses challenges and possible solutions for the effective development and implementation of CDSSs.