European Psychiatry - Volume 33 - March 2016

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24th European Congress of Psychiatry / European Psychiatry 33S (2016) S18–S55 S33
During this symposium, results from a randomized controlled
trial investigating the effect of smartphone based electronic self-
monitoring on the severity of depressive and manic symptoms will
be presented and discussed.
Further, wewill present and discuss the use of automatically gener-
ated objective smartphone data on behavioral activities (e.g. social
activities, mobility and physical activity) as electronic biomarkers
of illness activity in bipolar disorder.
Disclosure of interest The authors have not supplied their decla-
ration of competing interest.
S46
Value-based healthcare delivery in the
digital era
G. Seara ∗, A. Payá , J. Mayol
1 Hospital Clínico San Carlos, Innovation Unit, Madrid, Spain
2 Servicio Madrile˜no de Salud SERMAS, Dirección General de Sistemas
de Información Sanitaria, Madrid, Spain
3 Hospital Clínico San Carlos, Universidad Complutense, Innovation
Unit, Department of Surgery, Madrid, Spain
∗ Corresponding author.
Introduction Mental disorders are a major cause of disability in
Europe However, organizational structures and information
systems are focused on delivery of care, rather than providing value
In the digital era, we have the capacity to change priorities
through the analysis of heterogeneous databases that could support
patients’ and professionals’ decisions.
Objectives to analyse the contradictions between the design and
the theoretical structure of mental health services and the possi-
bilities to evaluate the actual value of the delivered care.
Aims To reflect on changing the trend using a different concep-
tualization of objectives and evaluating methods.
Methods We used a tool provided to clinicians by the Madrid’s
Regional Health Service SERMAS (‘ConsultaWeb’) combining pri-
mary care, pharmacy and hospital data ( n = 395,073 patients for the
catchment area), and a set of hospital-based data (patients attended
by psychiatrists at the ER, n = 13,877, and patients admitted to the
Psychiatric Inpatient unit n = 3318), to explore some of the present
professional information resources.
Results Currently used healthcare databases only describe the
diagnostic or therapeutic categories of patients and might be used
to detect abnormal behaviours. However, they are neither able to
show the functional status of patients nor designed to predict their
clinical course.
Conclusions A clearer definition of value in patient outcomes is
needed. This might help to organize the healthcare delivery and to
create a new information system that would allow to asses health
outcomes.
Disclosure of interest The authors have not supplied their decla-
ration of competing interest.
References
[1] WHO.
[2] Muir Gray JA. How to build healthcare systems. Oxford: Offox
Press; 2011.
S47
New platform of data analytics for
mental health
K. Suzuki
Fujitsu Spain, Department of Innovation, Madrid, Japan
Introduction Mental disorder is a key public health challenge and
a leading cause of disability-adjusted life years (DALYs) due to its
high level of disability and mortality. Therefore, a slight improve-
ment on mental care provision and management could generate
solid benefits on relieving the social burden of mental diseases.
Objectives This paper presents a long-termvision of strategic col-
laboration between Fujitsu Laboratories, Fujitsu Spain, andHospital
Clinico San Carlos to generate value through predictive and pre-
ventive medicine improving healthcare outcomes for every clinical
area, benefiting managers, clinicians, and patients.
Aims The aim is to enable a data analytic approach towards a
value-based healthcare system via health informatics. The project
generates knowledge from heterogeneous data sources to obtain
patterns assisting clinical decision-making.
Methods This project leverages a data analytic platform named
HIKARI (“light” in Japanese) to deliver the “right” information, to
the “right” people, at the “right” time. HIKARI consists of a data-
driven and evidence-based Decision Support and Recommendation
System (DSRS), facilitating identification of patterns in large-scale
hospital and open data sets and linking data from different sources
and types.
Results Using multiple, heterogeneous data sets, HIKARI detects
correlations from retrospective data and would facilitate early
interventionwhen signs and symptoms prompt immediate actions.
HIKARI also analyses resource consumption patterns and suggests
better resource allocation, using real-world data.
Conclusions With the advance of ICT, especially data-intensive
computing paradigm, approaches mixing individual risk assess-
ment and environmental conditions become increasingly available.
As a key tool, HIKARI DSRS can assist clinicians in the daily man-
agement of mental disorders.
Disclosure of interest The author has not supplied his declaration
of competing interest.
Is schizophrenia a disorder of brain connectivity?
S48
Disintegration of sensorimotor brain
networks in schizophrenia
T. Kaufmann∗, K.C. Skåtun , D. Alnæs , C.L. Brandt , N.T. Doan ,
I. Agartz , I.S. Melle , O.A. Andreassen , L.T. Westlye
University of Oslo, Norwegian Centre for Mental Disorders Research,
Oslo, Norway
∗ Corresponding author.
A large body of literature reported widespread structural and
functional abnormalities throughout the brain in schizophrenia
spectrum disorders (SZ). Corresponding with the typical sym-
ptomatology in SZ where sensory dysfunctions contribute to the
core social and cognitive impairment, converging evidence sug-
gests a disturbed interplay between higher-order (cognitive) and
lower-order (sensory) regions. This talk will discuss the results of
several recent studies, investigating brain connectivity in SZ using
functional magnetic resonance imaging data from large samples.
Within-network sensorimotor as well as sensorimotor-thalamic
aberrations in SZ robustly appear among the core findings across
studies, both during performance of cognitive tasks and during
rest. We utilized machine learning to distinguish SZ from healthy
controls based on connectivity profiles.When classifying on sensor-
imotor connections alone, not only can we reach accuracies largely
above chance but also, these accuracies are as good as when incor-
porating whole brain connectivity in the classification. Whereas
the overall accuracy levels in distinguishing SZ from controls are
not useful in a clinical context, these results underline the robust-
ness of the sensorimotor findings on the individual subject level.
Targeting the sensory and perceptual domains may thus be key for