Transcript How to innovate in healthcare through digital technologies and

How to innovate in healthcare through
digital technologies and informatics
Theodoros N. Arvanitis, RT, DPhil, CEng, MIET, MIEEE, AMIA, FSIM, FRSM
Professor of e-Health Innovation
Institute of Digital Healthcare, WMG, University of Warwick
Birmingham Children’s Hospital NHS Foundation Trust
North Locality Spoke Stakeholder Event
Professor T.N. Arvanitis - 29/11/2013
Slide 1
Digital Healthcare
• Digital healthcare is the application
of appropriate digital technologies
to the promotion of
– wellbeing & prevention;
– management of disease;
– and delivery of health services that are
effective, efficient and of high quality
• Challenges
– Healthcare is the slowest adopter of
digital technology
– Big Data is coming – West Mids
population 5.6m.
– Fragmented landscape: many
standalone digital applications from
academia, industry, NHS
– Need for interoperability
– Need for translation and adoption
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Slide 2
WM AHSN Digital Work Streams
• On-line Healthcare and Clinical Delivery
– real and tangible impact by delivering crucial on-line healthcare interventions
• Digital Innovation Management
– an environment within which all innovations can be efficiently researched,
prototyped, invested in, evaluated, commercialised and distributed
• Enabling Digital Communications
– leverage the skills and abilities of its various partners, customers and consumers at
scale
• Enabling Technology and Informatics
– local, national and international innovations
• Digital investment, Commercial and Wealth Management
– ability to attract new finance for digital investment, as well as create innovative
partnerships
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Slide 3
Exemplars of healthcare innovation
• Two local exemplars with international impact
• Translating research knowledge to practice
– TRANSFoRm
• Working with Big Data
– Multimodal Imaging for Childhood Cancer Research and
Practice
• Institute of Digital Healthcare
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Slide 4
The Institute of Digital Healthcare
• A world-class £4M 5-year funded partnership
between the NHS, WMG, WMS and other
relevant organisations
• Aims: to improve people’s health and wellbeing
through the use of innovative digital
technologies and methodologies
• We do this through high quality research,
education and training capabilities
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Slide 5
Our USP
WMG:
Industry
engagement
NHS:
Reality
check &
‘needs’
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WMS:
Clinical
need and
relevance
Slide 6
IDH – innovation and research I
Biomedical & Health
Informatics
Clinical &
Public
Health
Knowledge
Patient &
Population
Data
Clinical Trial
Systems
Deriving New
Knowledge &
Measure
Performance
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Clinical
Guidance &
Decision
Support
Systems
Slide 7
IDH – innovation and research II
Brain imaging
Clinical
Imaging
methodology
and trials
In vivo
metabolomics &
Functional
Imaging
(childhood cancer,
metabolic
disease)
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Neuro-imaging
statistics
Neuro-degenerative
disease
(epilepsy,
sleep disorders)
Modelling
Meta-analysis
fMRI clinical trials
Human Brain
Connectome
Slide 8
IDH – innovation and research III
Healthcare
Technologies
Biomedical
Signal
Processing
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Visualisation,
Modelling and
simulation
Brain
Electrophysiology
(EEG) &
BCI
Physiological
Monitoring –
Healthcare
Sensors
Slide 9
IDH – innovation and research IV
eHealth
Innovation
Apps & Social
Media
Telehealth
Evaluation
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Implementation
Slide 10
IDH – innovation and research V
Health
Systems
Engineering &
Modelling
Care Quality
Impact of
Pathway
Variation Analysis
on Quality
Threshold
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Service
Delivery
Improvements
Impact of Pathway
Variation Analysis
on Patient Care
Impact of Pathway
Variation Analysis
on cost: Hospital
Operation
Slide 11
translating research into clinical practice
• Development of Clinical Trial Management Systems
that provide, through an integrated infrastructure, a
wide-ranging set of modular, interoperable and
standards based tools designed to meet the needs of
clinical trials
• Objectives of e-trials system implementation
– To define a “computable” model representation that
supports the entire life-cycle of clinical trials protocol,
– To develop appropriate tools that use the model
representation.
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Slide 12
knowledge in healthcare
Specific
research
knowledge
•
•
•
•
EHR systems
Wide coverage
Vast quantity
May lack in detail
and quality
•
•
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Clinical trials
Controlled populations
Well-defined questions
•
Routinely
collected
knowledge
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Actionable
knowledge
•
•
Distilled scientific
findings
Usable in clinical
practice
Decision support
Slide 13
ePCRN and TRANSFoRm: US/UK perspective
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Slide 14
Informatics tower of Babel:
the grand challenge
• Overwhelming volume of data
• Multitude of sources
• Each part of the health community
speaks its own scientific “dialect”
(e.g. lab values, genetic profile,
clinical data)
• Lack of consensus on common
standards and terms
• Lack of coordination across, and
collaboration within, the cancer
research enterprise
• Integration is critical to achieve
promise of molecular medicine
(personalised medicine)
Slide courtesy of caBIG™
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Slide 15
TRANSFoRm Itegrated VS
• The TRANSFoRm Integrated Vocabulary
Service is designed to allow end users to
search and retrieve clinical vocabulary concepts
and associated content
– a web interface and a web service API
– the service uses the LexEVS (version 5.1)
technology to access a backend UMLS vocabulary
database
– the service uses direct Java Database Connectivity
(JDBC) to access other vocabulary databases (e.g.
Read Codes V2, ICPC2)
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Slide 16
RCD v2/ICPC2
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Read Codes (RCDv2) and International
Classification of Primary Care (ICPC2)
corpus of terms and their associated
mappings
– created to cater for the initial need of the
existence of specific primary care
oriented terminologies.
The UK NHS Connecting for Health
Terminology Centre - mappings from Read
Codes version 2 to SNOMED CT.
The Read Codes v2 database in Transform
VS is set up based on this mapping so that
Read Codes 2 concepts can be linked to a
UMLS search. Similar approach for ICPC2.
ICPC2-ICD10 Thesaurus and mappings Transition Project @ University of Amsterdam
The TRANSFoRm team is updating the
ICPC2-ICD 10 mapping and Thesaurus
ICPC3: new initiative
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UMLS
Metathesaurus
SNOMED CT
Codes
Read Codes v2
Codes
UMLS
Metathesaurus
ICD-10
Thesaurus/Codes
ICPC2
Codes
Slide 17
TRANSFoRm Integrated
VS Server Architecture
TRANSFoRm Integrated VS Server
Lucene Index
GWT Ajax Web
Client
GWTRPC Servlet
LexEVS Runtime
UMLS
Database
Query Service
Read Codes v2
Database
Web Services
Client
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Metro JAX-WS
Web Service
Slide 18
A screenshot of the vocabulary
service web-based interface
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Slide 19
Finding prevalent cases for trials
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Slide 20
acknowledgments: TRANSFoRm
Consortium
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Slide 21
BIG Data: multi-modal imaging
Quantitative imaging
Metabolite profiles
Investigating children’s
cancer using functional
imaging
Diffusion imaging
Tractography
Metabolite maps
Perfusion
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Slide 22
Powerful Computation
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Slide 23
large data sets
mI
Cho
NAA
w1
13C
Cr
HR-MAS
Lip+Lac
1H-13C
HSQC
w2 1H
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Slide 24
agent-based classifiers
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Slide 25
acknowledgments: cancer research
•
Investigators
– Andrew Peet – UB PI, oncology,
MRS
– Theo Arvanitis – UW bio-informatics
– Richard Grundy – UN oncology,
biology
– Martin Leach – ICR MR Physics,
perfusion
– Chris Clark – ICH MR Physics,
diffusion
– Franklyn Howe – StGUL MR
Physics, MRS
Collaborators
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•
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•
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•
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•
•
•
•
•
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Professor Dr Dorothee Auer University of
Nottingham
Dr Thomas Barrick St George's Hospital Medical
School
Mr David Collins Royal Marsden Hospital
Dr Daniel Ford University Hospital Birmingham
Dr Darren Hargrave Royal Marsden Hospital
Mr Donald Macarthur Nottingham University
Hospitals NHS Trust
Dr Lesley MacPherson Birmingham Children’s
Hospital NHS Trust
Dr Paul Morgan University of Nottingham
Dr Kal Natarajan University Hospital Birmingham
Dr Oystein Olsen Great Ormond Street Hospital
Dr Geoffrey S Payne The Institute of Cancer
Research
Professor Andy Pearson Royal Marsden
Hospital
Dr Sucheta Vaidya St George's Hospital Medical
School
Dr Tim Jaspan, University Hospital, Nottingham
Dr Dawn, Saunders, Great Ormond Street
Hospital
Research Group: Nigel Davies, Martin Wilson, Kal
Natarajan, Yu Sun, Eleni Orphanidou, Lisa Harris,
Greg Reynolds, Sim Gill, Alex Gibb, M. Saleh,
Suchada Tantisatirapong, Ben Babourina-Brooks,
Jan Novak
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Slide 26
Digital Healthcare
research and innovation
Define/identify
healthcare problem
Research/innovation
Design & develop
solution
Evaluate & generate
evidence
Support innovation
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Slide 27
realising our imagination: facing challenges
"Leave the beaten track occasionally and dive into the
woods. Every time you do so, you will be certain to
find something that you have never seen before.
Follow it up, explore all around it, and before you
know it, you will have something worth thinking
about to occupy your mind. All really big discoveries
are the result of thought."
Alexander Graham Bell: "electrical
speech machine" of 1876
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Slide 28
Thank you
[email protected]
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Slide 29