Big data is having a disruptive impact on healthcare. Chronic diseases account for a large portion of healthcare spending that is projected to increase dramatically in the coming decades. Integrating and analyzing big healthcare data can help improve patient outcomes and reduce costs by enabling more precise diagnosis and targeted treatment through stratified medicine approaches. However, healthcare systems remain fragmented with data silos that limit comprehensive analysis. Emerging technologies now make it possible to integrate diverse healthcare data sources including genomics, but cultural and regulatory challenges must be addressed to fully realize the benefits of big data for patients and healthcare systems.
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Key Insights on the Big Data Phenomenon and its Impact on Healthcare
1. KEYNOTE #1:
INTRODUCING THE BIG DATA
PHENOMENON AND EXPLORING THE
IMPLICATION OF THIS DISRUPTIVE
FORCE ON THE STATUS QUO
Big Data Insights Group Forum
November, 2012
2. ABOUT ARIDHIA
Clinically led, technology driven
Founders:
Dr David Sibbald, Professor Andrew Morris,
University of Dundee & NHS Scotland
Focus:
Integrated chronic disease management,
healthcare analytics for system
Aim: improvement and stratified medicine
To improve patient and public health
outcomes by improving quality of health
services and R&D, while driving down costs
Multi-disciplinary Team:
In-house team includes 60+ clinicians,
computer, data & life scientists working
with external Clinical Faculty
3. TACKLING HEALTHCARE & TECHNOLOGY CHALLENGES
Integration and analysis of big data accelerates the ability to solve
complex healthcare problems and enables stratified medicine
Disease Registry
Accurate, real-time disease specific data at patient, organisational or population level
Shared Care Clinical Record
Connected data solution for chronic disease management across healthcare sectors
Healthcare Analytics
Data integration and analysis for quality improvement, performance management, governance and assurance
Research Safe Haven
Repository and complex data analysis for linked, de-identified clinical, bioimage, genomic and proteomic data
Patient Self Management
Condition specific symptom management, self-reporting, monitoring and risk stratification
4. EXPLOSION OF DIGITAL DATA
35% of all
2011 2020
digital
1.8
zettabytes
data is
healthcare
90
zettabytes
related
Source: IDC, Digital Universe Study, June 2012
5. Diabetes
Affects 366 million
CHRONIC DISEASE IMPACT 2010 annual cost: $500 billion
2030 annual cost: $6.0 trillion
75% of the population has one chronic disease Cancer
13.3 million new cases/year
and 50% have two or more conditions
2010 annual cost: $290 billion
Patients with a chronic disease use > 60% of 2030 annual cost: $458 billion
hospital bed days Cardiovascular disease
32 million MIs & CVAs/year
75% of patients admitted as medical emergencies 2010 annual cost: $863 billion
have an exacerbation of a chronic condition 2030 annual cost: $1.04 trillion
The 15% of patients with 3+ chronic conditions COPD
account for 30% of total inpatient days Affects 210 million
2010 annual cost: $2.1 trillion
10% patients account for 55% of total inpatient days 2030 annual cost: $4.8 trillion
The World Economic Forum
estimates that chronic diseases
will cost the world economy
$47 trillion
over next 20 years
6. CHALLENGES TO INTEGRATED CARE
Fragmented services across Lack of data sharing
primary and secondary care agreements
Data silos make it difficult Clinical focus on individual
to assess quality of care and diseases, not multiple diseases
outcomes across health system simultaneously
Organisation-centric rather than Little or no chronic disease
patient-centric surveillance
Reactive rather than proactive Data often not integrated into
clinical management national information systems
7. SYSTEM FRAGMENTATION
“ chronically ill patients receive episodic care
System fragmentation means that
from multiple providers who rarely
coordinate the care they deliver.
Because of this structural deficiency,
patients with chronic illnesses receive only
56 percent of clinically recommended care.”
K. THORPE, ET AL: “CHRONIC CONDITIONS ACCOUNT FOR RISE IN MEDICARE SPENDING FROM 1987 TO 2006”;
HEALTH AFFAIRS 29 NO. 4 (2010)
8. MAKING SENSE OF DISEASE-SPECIFIC BIG DATA WORKS
Scottish Care Information Diabetes Collaboration
• Nationwide real-time, web-based national IT solution in support of diabetes
patient and clinical activity
• All 247,768 patients with type I and type II diabetes in Scotland have a SCI-DC
electronic record
• 8,265 of these patients have agreed to take part in research on diabetes,
including clinical trials
• Single care record for all 5,000+ primary, secondary and tertiary clinical care
users at the point of care and 4 university research departments
• Integrates data from 1,015 GP practices, 39 hospital- based diabetes clinics, 7
lab systems, national diabetic retinopathy screening system, master patient
index plus multiple specialist forms & direct data entry
• Patient self-management via “My Diabetes My Way” website.
9. EVIDENCE OF IMPROVED CLINICAL OUTCOMES
43% reduction in diabetic retinopathy
40% reduction in amputations
Source: Diabetic Medicine 2009 Source: Diabetes Care 2008
10. JOIN THE REVOLUTION
“If you live in Scotland and suffer from diabetes, you have
recently been taking part in a medical revolution.”
SIR MARK WALPORT, THE TIMES, MAY 2011
11. INFORMATICS CAN HELP….
“..the Department [of Health] estimates that
24,000 people with diabetes die prematurely each
year because their diabetes has not been
managed effectively.”
“An estimated 80% of the costs of diabetes in the
NHS are attributable to the treatment and
management of avoidable diabetic complications.
Fewer than one in five people with diabetes have
achieved the recommended levels for blood
glucose, blood pressure and cholesterol. Failure
to carry out these simple checks heightens the
risk of diabetic patients developing complications.
If people develop complications they are more
likely to die early and also cost the NHS more
money.”
“…information is not being used effectively by the
NHS to assess quality and improve care...”
Public Accounts Committee - Seventeenth Report
Department of Health: The management of adult diabetes services in the NHS (22 October 2012)
12. CONSIDERATIONS: SAFETY & REGULATORY
Increasing recognition of the need for safe clinical systems
Data needs to be presented in a clear, unambiguous manner
Clinicians should be aware of data quality and completeness
so they can make an informed decision about interpretation
Data should be presented in most appropriate format to
avoid misinterpretation
Anything that is seen as clinical decision support will require
future regulation – in the interests of patient safety
13. CONSIDERATIONS: CULTURAL AND PATIENT
Move away from data control by clinical teams/organisations
towards patients providing access to information
IT companies traditionally very reluctant to share knowledge
and information - need for more openness and transparency
Improve bench to bedside time - need for flexible systems
that can be adapted to include up to date research findings
and translation into clinical care
Enable patients to take more control of conditions - access
to their own data; self monitoring/reporting; feedback on
delivery of care
Encourage end user feedback so that systems continue to
meet needs
14. THE WORLD • Number of people with chronic disease will rise
substantially in coming decades
POPULATION IS
GROWING & • Changing demographic with ageing population
GETTING OLDER • Chronic disease disproportionately affects those >
60 years
• Increasing prevalence of key risk factors for
developing chronic disease
smoking
obesity
alcohol
lack of exercise
Source: United Nations Population Division 2011
15. STRATIFIED MEDICINE = BETTER PATIENT OUTCOMES
It will allow us to offer
• The right drug
Prevent premature
• To the right patient
deaths
• For the right disease
• At the right time Deliver positive
experiences of care
• With the right dosage
Enhance quality of
life for chronic
• .Minimise adverse reactions disease patients
.to medications
Prevent avoidable
harm
• .Reduce the costs of clinical
.trials by enabling pre-screening
Enable faster
.of potential trial participants and recovery
.enabling the faster identification
.of possible failures
16. WHERE IT ALL STARTED
• In 1951 James Watson travelled from the United States to work with Francis
Crick at Cambridge University
• Watson and Crick used the “Model Building” approach
• They physically built models out of wire, sheet metal, nuts and bolts to come
up with the structure of DNA.
Why did they build models?
“Sometimes the fingers
can grasp what the mind cannot”
(Biology the Science of Life)
17. FROM TRIAL & ERROR TO PERSONALISED MEDICATIONS
100% Response Rate (%)
75%
50%
25%
0%
Treatment A
Treatment B
Treatment C
Given limited ability to predict
responders, doctors practice
trial-and-error medicine
Adapted from Vaidyanathan, Cell 2012;148:1079
18. INNOVATIVE The convergence of big data and
TECHNOLOGIES life sciences enables healthcare to
become truly patient-centric:
MAKE THIS
POSSIBLE • integrate data-intensive biology with medicine
• understand clinical & genetic correlations
• genomics has a network effect to catalyze changes
in information technology, medicine, and society
Transform health data into actionable information
Support research genomics and beyond
Support patient self-reporting & management
Enable providers to improve patient care
Build a more responsive healthcare delivery infrastructure
19. TECHNOLOGY IS THE ENABLER
Single Variant
(100 Snps; 103 Genotypes)
Detailed Study Of Individual Genes
(102 Snps; 105+ Genotypes)
Regional Studies
(104 Snps; 108 Genotypes )
Genome-wide Association
(106 Snps; 1010 Genotypes)
Complete Resequencing
(108 Snps / 1012 Genotypes)
21. IS IT WORTH
STUDYING
GENETICS FOR
CHRONIC
DISEASES?
Diabetes Life Time Risk
0 Parent 10%
1 Parent 30%
Brother/sister 40%
Both parents 70%
Identical twin 80-100 %
22. WE ARE THE START OF THE GENOMICS JOURNEY
Current Resolution Future Resolution
23. OPEN & COMPREHENSIVE COLLABORATION IS KEY
Industry • A strong scientific informatics
Bioinformatics infrastructure with vibrant PHD and
post doctorate communities
Diagnostics
Clinical Research
• Academic health science centres
Biotechnology with a tripartite mission and
NGS significant infrastructure investment
Pharmaceuticals
Therapeutics • A commitment to linking
information from medical and non-
Academia medical sources using electronic
Health Informatics patient records to support better
Genetics treatment, safety and research
Clinical
Biostatistics • A new pathway for the regulation
Skilled Workforce Training and governance of health research
Government • Collaborative arrangements with
Healthcare Agencies the biotechnology pharmaceutical
Policy Makers and medical devices industries.
24. AS COSTS DROP, WE FACE A TIDAL WAVE OF DATA
Current Costs
• Full genome sequence ~£3,000 [2012]
• Dropping in price 10x every 2-4 years
• Existing NHS genetic test ~£1,000
• Disk cost to store raw sequence ~£100
• Disk cost to store individuals variations ~10p
Future Approaches
• Needed for accessing, manipulating, visualizing
• Requires entirely new perspective
• Emergent evidence for clinical validation, clinical utility
and patient stratification
Hokusai, K. The Great Wave
25. NOW WE HAVE THE GENES…
CLINICAL MEDICINE STRATIFIED MEDICINE
Do the variants allow us to predict The right medicine to
disease progression and the the right person
effect of lifestyle interventions? at the right time
GENETIC EPIDEMIOLOGY MICROBIOLOGY
Confirmed
How does variation variants What are the pathogenic
here interact with variation organisms?
at other sites?
PHARMACOGENETICS PHYSIOLOGY
Do these variants also influence What are the physiological
complication risk, or response to correlates of these variants?
available treatments?
EPIDEMIOLOGY
What is the population risk
and are there important
interactions with exposures?
26. THE COMPLEX BIG DATA ENVIRONMENT OF MEDICINE
High throughput
screening HTA
Biomarkers
BRUs AHSCs Stem cells
Molecular
Trial Methodology pathology Cohorts
Imaging
Cyclotrons Biologics
Preclinical models CRFs
Stratification
Biobanks
Regulation
RNAi
Enabling GMP facilities
technology
Chemistry
Genetics
Technology
transfer Large trials
27. INTEGRATION OF PATIENT & HETEROGENEOUS DATA
Laboratory Genomic
data data
E-health Record
Imaging GP Hospital
records admissions
29. 2011: STRATIFIED MEDICINES INNOVATION PLATFORM
Technology Strategy Board invests £5.6m in collaborative R&D projects in in partnership with
“tumour profiling and data capture to improve cancer care by
providing cancer specialists with information specific to the patient’s
tumour which will enable more targeted treatment to be provided.”
Inclusion of breast, lung, colorectal, prostate, skin & ovarian cancer patients
30. DR LUKAS WARTMAN’S STORY
Lukas Wartman, 25 was finishing medical school when he was first
diagnosed with acute lymphoblastic leukaemia.
Disease RegistryAccurate, up to date information about a disease at patient, organisational or population levelShared Clinical Care Record Longitudinal health record for chronic disease management shared across healthcare sectorsHealthcare AnalyticsQuality improvement, performance management, predictive modelsPatient Self-Management Condition specific symptom management, monitoring and risk stratification Research Safe HavenRepository for linked, de-identified clinical, bio-image, genomic and proteomic data sets for cohort analysisChallenges: Project is huge in both scope and sizeNo real infrastructure [i.e. no health records management systems, no awareness of system admin/data quality requirements etc)Huge data volumes Patients act as conduits of information [i.e. no discharge or clinic letters]Paper lab results collected by patients Patients shop around for care & medicationsComplex political backdrop
With this leap comes an explosion in the amount of digital data generated.Storing genetic information is a data nightmare--genotyping a single individual can produce up to 1.5 GB of data.The breadth of data output created by research is introducing new challenges to analyze and store this information.
Chronic diseases are the leading cause of mortality in the world, accounting for 36 million deaths in 2008 – 63% of the total global deathsThe WHO has warned that the number of deaths from these diseases will increase by 15% to reach 44 million deaths by 2020, and 52 million by 2UN Summit 2011 declared chronic diseases to be a global threat to future sustainability and affordability of healthcare deliveryWorld Economic Forum placed chronic diseases amongst most severe threats to economic growth and developmentInstitute of Medicine study found that chronic diseases currently cost developed countries 0.02 – 6.77% of GDPWorld Economic Forum estimates that chronic diseases will cost world economy $47 trillion over next 20 yearsChronic disease management estimated to cost 75% of GDP by 2030.
The big stumbling block for many health systems is their inability to properly analyze the vast stores of data they have, either because the data are isolated in disparate and incompatible systems around the organization, or because the analytical tools at hand are simply not powerful enough and sophisticated enough to handle these complex data challenges.Big data is a transformational enabler for the healthcare industryHealthcare systems are often poorly optimised to meet the demands of managing patients with chronic diseases, with services fragmented across primary, secondary, tertiary and community care. Since healthcare expenditure on chronic disease management continues to rise, when many countries are faced with significant economic constraints, it is vital that services should be efficient and treatments cost effective. The current disjointed provision of services and fragmented sources of clinical information do not readily support delivery of high quality clinical care or assessment of treatment on clinical outcomes.
SCI-DC - crucial tool in enabling rapid and accurate clinical research, particularly in completing study feasibilities and delivery of stratified medicine studiesEnables population based overview of where changes in care have had an impact – the availability of longitudinal data makes this possible, where fragmented care makes this almost impossible.Reveals year on year results rarely available on a nationwide basis.Over 6,000 patients have consented to be part of an electronic database of patients who have agreed to be contacted about research for which they are eligible. This research register uses the latest clinical data on each patient to identify suitable patients for studies, thus increasing the recruitment rate and decreasing the screen failure rate. In addition to incoming feeds, SCI-DC data is also transferred to external systems National Diabetic Retinopathy Screening System (to maintain the call-recall system) My Diabetes My Way: Patient Access (patients accessing their own information) Back-Population of 700 GP systems (in support of a single-point of data entry). The
Better engagement and understanding between eHealth, clinical, academic and senior management teams so that technology is an enabler to delivery of care
key risk factors -smoking, obesity, alcohol, lack of exercise
Talking Points:Medicine today is imperfectResponse to current therapies low (graph)Leads to trial and error medicineTransition (if applicable):Solution to these issues lies in personalized medicine
Talking Points:Cost of sequencing is dropping, currently can sequence exomes for ~$1000 and whole genomes ~$4000 expect this to continue dropping on the road to a $1000 genomeThroughput increasing can sequence more on one day on just one machine today, than the human genome project was able to sequence in 10 yearsWith this decrease in price and increase in throughput has come an explosion of genetic knowledge
The gene links cancer pathways, metformin pathways and type 2 diabetes
The UK holds a favourable position in the development of stratified medicines through strong scientific innovation, robust biotechnology and pharmaceutical industries and comparatively simple regulatory and reimbursement processes. Translation, and not just vertically from the bench to the bedside, but also horizontally from academic clinical research into applied clinical research in pharmaceutical and diagnostic companiesDECIPHER as example
essential for the future of medicine
The BCD allowed us to apply for this competition with the knowledge that we could deliver
Stop video at 4.18Wartman's colleagues were able to use 26 gene sequencing machines to form a comparison between Wartman's healthy cells and the leukaemia cells that were affecting him. This map of Wartman's genetic composition helped his research partners identify the gene responsible for producing excessive amounts of protein, which was causing the leukaemia cells to spread.Without these gene sequencing tools, that gene would likely not have been discovered.And nowhis cancer is in remission and has been since autumn last yearNote that just before the end of the video, they state that this is not routine, this is research.