Smart ICU project. Dr.Francisco Murillo_English version

Smart ICU Project
Dr. Francisco Murillo Cabezas.
Intensive Care CMU.
Virgen del Rocio University Hospital.
University of Seville.

Clinical scenario in intensive care units

Clinical scenario in
intensive care units

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Clinical scenario in Intensive Care
Units
Critically ill
patients.
Numerous
professionals
in limited space.
Stressful
environment.
Numerous and
sophisticated
monitoring and
life support
devices
generating
multiple
streaming.
Need for hard
data to make safe
and quick
decisions.
01 02 03 04 05
A very complex and heterogeneous enviroment due to:

An avalanche of data that helps
to paralyze our decisions.
Dr. Francisco Murillo
“ ”

Why did we need to develop SMART ICU?

There are some
products that are
able to collect
and manage
data.
Most products
are exclusively
aimed at
collecting useful
data for hospital
management.
Most of these are
not user-friendly
enough for
doctors and
nurses.
Because the tool we needed was
not available on the market
1 2 3

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We wanted something more.
In short, we needed to refine our clinical
practice.
A system with added intelligence to help doctors
avoid variability in the practice and make sound
decisions.

This data come from many
varied sources.
Overall, they are not not well-
organized and lack proper
connection among them.
They are not ordered in terms of
clinical value.
1
2
3
In reality we have a lot of data available to us,
however:

What should we ask of a project of this type?

A user-friendly tool that can collect the enormous
volumes of data generated in the UCI.
Accurate information to improve the quality,
safety and efficiency of clinical events.
To organize and processe data and return it as
useful information for doctors and nurses.

To turn information into knowledge to support
decision-making.
To provide knowledge to help distinguish
effectiveness from futility.

To develop new algorithms, clinical
pathways and protocols.
To generate new knowledge that we can
offer to the scientific community.
To base itself on the real
experience of doctors and nurses
and on Big Data Analytics.

Project objectives and motivation

Project objectives and motivation
The aim of the project is to develop a predictive system for
personalized clinical decision support for acute patients in the
ICUs that uses selective learning from Big Data Analytics.
An intensive care unit (ICU)
is a special department of a
hospital or healthcare
facility that provides
intensive care treatement.

02
03
04
01 02 03 04
The modules in charge
of collecting and
presenting information
use agile interfaces,
graphs and summary
structures that make
them easier to
understand. The large
volume of data collected
in these areas (clinical
care, biosignals...) makes
for complex and
painstaking management
and interpretation in the
absence of an intuitive
tool to collect, organize
and structure the data in
an easy and agile way.
Adopts specific tools
to assist in the
management of the
processes of
intensive care
patients , generating a
more efficient
distribution of the health
resources used.
Includes an event
management system will
allow the instant
knowledge of the
patient’s health status,
allowing immediate action
to significant changes that
require require attention
from professionals.
Automated and
standardized processes
for capturing patient data
from biomedical devices,
Electronic Health Records,
laboratory, etc. to reduce
machining time and the risk
of error.
05 Discovers relationships between vital signs and
treatments through cross-validation techniques, drawing
conclusions and knowledge to design and predict future
scenarios in the care of patients in intensive care, critical
care and reanimation units.

02
03
04
06 07 08 09
Implements predictive
models that detect
behavioral patterns to
estimate the response
to the care and the
various treatments used.
Improvements in
treatment protocols to
maximize health
outcomes and optimize
resources.
Measure the
effectiveness of certain
protocols, therapies or
techniques applied in a
coordinated and
standardized manner by
the platform, allowing
comparisons and
detection of side effects
to consider proactively.
Evaluates the cost-
benefit, and even
compares different
techniques related to costs,
repayment terms, effects,
etc.

System components

Data sources, PoC details and
Clinical Experts involved

Virgen del Rocio
University Hospital (Spain)
• Reference population: over 800,000 inhabitants of Seville
• Referral hospital for the entire Region of Andalusia.
• More than 1,000 hospital beds.
• 35 doctors for a total of 70 ICU beds.
• More than 3,000 patients admitted to the ICU every year.
• 5 ICU R&D groups.
• It is the largest hospital in Spain.

Smart ICU project. Dr.Francisco Murillo_English version

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