title: Generating personalized advice for schizophrenia patients.
abstract: The results of routine patient assessments in psychiatric healthcare in the Northern Netherlands are primarily used to support clinicians. We developed Wegweis, a web-based advice platform, to make this data accessible and understandable for patients.
  
  
  OBJECTIVE
  We show that a fully automated explanation and interpretation of assessment results for schizophrenia patients, which prioritizes the information in the same way that a clinician would, is possible and is considered helpful and relevant by patients. The goal is not to replace the clinician but rather to function as a second perspective and to enable patient empowerment through knowledge.
  
  
  METHODS
  We have developed and implemented an ontology-based approach for selecting and ranking information for schizophrenia patients based on their routine assessment results. Our approach ranks information by severity of associated schizophrenia-related problems and uses an ontology to decouple problems from advice, which adds robustness to the system, because advice can be inferred for problems that have no exact match.
  
  
  RESULTS
  We created a problem ontology, validated by a group of experts, to combine and interpret the results of multiple schizophrenia-specific questionnaires. We designed and implemented a novel ontology-based algorithm for ranking and selecting advice, based on questionnaire answers. We designed, implemented, and illustrated Wegweis, a proof of concept for our algorithm, and, to the best of our knowledge, the first fully automated interpretation of assessment results for patients suffering from schizophrenia. We evaluated the system vis-à-vis the opinions of clinicians and patients in two experiments. For the task of identifying important problems based on MANSA questionnaires (the MANSA is a satisfaction questionnaire commonly used in schizophrenia assessments), our system corresponds to the opinion of clinicians 94% of the time for the first three problems and 72% of the time, overall. Patients find two out of the first three advice topics selected by the system to be relevant and roughly half of the advice topics overall.
  
  
  CONCLUSIONS
  Our findings suggest that an approach that uses problem severities to identify important problems for a patient corresponds closely to the way a clinician thinks. Furthermore, after applying a severity threshold, the majority of advice units selected by the system are considered relevant by the patients. Our findings pave the way for the development of systems that facilitate patient-centered care for chronic illnesses by automating the sharing of assessment results between patient and clinician.
authors: University of Groningen, Nijenborgh 9, 9747 AG Groningen, The Netherlands. a.c.emerencia@rug.nl
authors: Ando
authors: A
authors: Emerencia
authors: Lian
authors: L
authors: van der Krieke
authors: Sjoerd
authors: S
authors: Sytema
authors: Nicolai
authors: N
authors: Petkov
authors: Marco
authors: M
authors: Aiello
mesh_terms: D000465:Algorithms; D020000:Decision Support Systems, Clinical; D020407:Internet; D051188:Knowledge Bases; D012559:Schizophrenia / Q000175:diagnosis* / Q000628:therapy*; D012648:Self Care; D014584:User-Computer Interface