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Developing e-health applications to promote a patient-centered approach to medically unexplained symptoms

PhD ceremony:dr. A. (Anne) van Gils
When:July 03, 2019
Start:16:15
Supervisors:prof. dr. J.G.M. (Judith) Rosmalen, prof. dr. R.A. (Robert) Schoevers
Where:Academy building RUG
Faculty:Medical Sciences / UMCG
Developing e-health applications to promote a patient-centered
approach to medically unexplained symptoms

Medically unexplained symptoms (MUS) are physical symptoms that cannot be (fully) explained by organic pathology despite adequate diagnostic testing. MUS are highly prevalent and range from single, self-limiting complaints to constellations of chronic and disabling symptoms. Apart from the suffering and impairments MUS impose on patients, they are very costly for society due to the associated productivity losses and burden on healthcare. Anne van Gils’ PhD research aimed to develop e-health applications, offering solutions to three obstacles in the treatment of MUS. Communication problems between patients and healthcare providers are the first obstacle. Since many healthcare providers find it difficult to manage patients with MUS, an online course (‘e-learning’) was developed to improve their knowledge, skills, and attitude. Healthcare providers found this an effective and satisfying way to learn about MUS. The second obstacle are the limited availability of and stigma surrounding psychological treatment. Even though psychotherapy is one of few evidence-based treatments for MUS, only a small proportion of patients benefits from this type of treatment. Online self-help can form a widely available and acceptable alternative. Self-help reduces physical symptoms and improves quality of life. This led to the development of Grip: an online, guided self-help intervention, offered in general practice. Grip also addresses the third obstacle: the large variation in symptom characteristics and etiological factors of MUS. Personalizing the contents of psychological treatments may thus improve their effectiveness. Grip uses algorithms, based on results of online questionnaires and diaries to personalize treatment.