Key Insights on the Big Data Phenomenon and its Impact on Healthcare
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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)
- 20. GENOME-WIDE SCAN FOR TYPE 2 DIABETES
- 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
- 28. ARCHITECTURE
- 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.
- 31. QUESTIONS? For more information about Aridhia visit www.aridhia.com Follow us on Twitter @aridhia
Editor's Notes
- 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.