CASEPLUSSimPat: An Intersectoral WebBased Case Management System for Multimorbid Dementia Patients One main challenge of health care systems in future will be the care and treatment of dementia patients. To their advanced age, dementia patients do not only have limitations in cognition, perception, and articulation, but also suffer from several other diseases (multimorbidity). These patients have sophisticated needs for assistance, care, and treatment relying on different health care sectors. Wellknown communication and coordination deficits between sectors are intensified in the case of dementia, to the above described limitations. Coordination processes concerning the transition of patients from hospitals to inpatient or outpatient aftercare pose particularly difficult. To show a possible future direction for improving intersectoral care of dementia patients, we developed the webbased case management system CASEPLUSSimPat as part of the project SimPat xe2x80x9cSecuring Integrated Care for Multimorbid Patients with Dementia using an ITbased Service Conceptxe2x80x9d. CASEPLUSSimPat allows health care professionals, hospital employees and general practitioners, to jointly access patient data and exchange information about the treatment of patients. The case management system was implemented in a threetier architecture with a rolebased authorization concept. A portal for relatives complements the system. By providing precise information and elearning services, caring relatives are integrated into the care process. In a pilot test, CASEPLUSSimPat shows a good usability. Adapting aspects, such as keywords used to structure entries will make it easier to find required information, and thus will increase usability. Next steps comprise integration of further health care professionals and care facilities as well as reallife testing in terms of feasibility and effectiveness.

Building a Risk Model for the Patientcentred Care of Multiple Chronic Diseases With the increase of multimorbidity due to population ageing, managing multiple chronic health conditions is a rising challenge. Machinelearning can contribute to a better understanding of persons with multimorbidity (PwMs) and how to design an effective framework of care and support for them. We present a risk model of older PwMs that was derived from the TILDA dataset, a longitudinal study of the ageing Irish population. This model is based on a 26nodes Bayesian network that represents patients possibly having one or more chronic conditions among diabetes, chronic obstructive pulmonary disease and arthritis, through a joint probability distribution of demographic, symptomatic and behavioral dimensions. We describe our method, give an exploratory analysis of the risk model, and assess its prediction accuracy in a crossvalidation experiment. Finally we discuss its use in supporting management of care for PwMs, drawing on comments from health practitioners on the model.