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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.

http://dx.doi.org/10.1016/j.eurpsy.2016.01.861

S46

Value-based healthcare delivery in the

digital era

G. Seara

1 ,

, A. Payá

2

, J. Mayol

3

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

[1] .

However, organizational structures and information

systems are focused on delivery of care, rather than providing value

[2] .

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.

http://www.euro.who.int/en/health-topics/noncommu- nicable-diseases/mental-health/data-and-statistics .

[2] Muir Gray JA. How to build healthcare systems. Oxford: Offox

Press; 2011.

http://dx.doi.org/10.1016/j.eurpsy.2016.01.862

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.

http://dx.doi.org/10.1016/j.eurpsy.2016.01.863

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