Using Big Data to Personalize the Healthcare Experience in Cancer, Genomics and Mobile


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  • Welcome. I am delighted you all could join us.
  • Personalized Medicine.  People have been talking about Personalized Medicine and changing healthcare from reactive to proactive for decades.  Now with rapidly evolving new technology , including cloud computing, computers are able to generate, store and analyze large volumes of data.  There are three main trends in harnessing these large amounts of data or “Big Data” to make leaps into personalizing the Healthcare experience.Genomics – sequencing and ultimately analyzing the human genome, and the effect of the genome on individual diseasesCancer/Clinical Trials – using EMR/EHR, along with mobile data, pharma data, and a number of types of data to look at the bigger picture of an individual’s healthMobile Health – Smartphones have permeated society, and now they are moving to healthcare. Individuals are utilizing smartphones to monitor and improve health outside the hospital.
  • First off we have Genomics.What is genomics? It is the study of the human genome. With the influx in technology, cloud computing allows researchers to store and analyze huge sets of data, like the human genome.  The primary focus of current genomics is on sequencing the genome affordably. However there are researchers and companies also looking at analyzing the genome and applying the information to causes for diseases.
  • The history of genomics research can be traced back to 1956, when Rosalie Franklin, Watson, Crick and Wilkins discovered the structure of DNA and recreated the double helix model.  Recently, researchers have used Big Data technology to sequence the human genome and better understand the role genomics plays in health and disease.  
  • Research in the field of genomics has come a long way in the past 60 years. The most significant effort in studying the genome and it’s effect on disease, was the Human Genome Project which changed sequencing form a manual process to an automated computer based one, that used the existing technology. Not only was it monumental in the study of genomics but also stands as an early example of data sharing between public and private entities.  The project began in 1990, and was completed 13 years later in 2003. Goals of the project included: sequencing, storing and improving tools for genome analysis. After successfully sequencing the genome the project will transfer the work to the private sector.  The Human Genome project was a project sponsored by the US Department of Energy and the NIH. The project developed the technology to identify, analyze, and sequence all 20,000-25,000 genes in human DNA.
  • By the time the Human Genome Project was completed the cost to sequence the Human Genome was $40 million, down from $95 million just two years before.  Academics and companies have been working hard to make sequencing affordable and therefore available to the public. Today the Human Genome can be sequenced for around $5000 consistently and accurately.
  • Today the cost is around $5000 per genome which is not affordable for the average patient. And genomic information can go a long way in personalizing medicine. However, it is clear that this is the future of medicine, and I along with many experts in the field are predicting that we will reach the $100 genome in the next couple of years.
  • Here are some of the companies that are not only working on making sequencing an affordable and routine part of healthcare, but also analyzing the genome. They are closing the gap between sequencing and analyzing taking the sequenced genome data and tuning it into information. NextBioBina TechnologiesPortable Genomics These companies work sequencing the genome and storing the information alongside other data, in order to analyze and personalize medical treatments and trials.
  • Beginning with Bina Technologies. Bina has created a platform that allows users to take genomic sequence data, move it, and analyze it. They use a hybrid architecture that keeps some data on the premises and some in the cloud, pushing computation back to where the data is in order to reduce the data 1000 fold and speed up sequencing time and facilitating movement of the data. They are not working on any specific disease, but on creating a platform that can be used for large data sets like genomic data. Bina illustrates the power of genomics to improve population health. Portable Genomics uses a mobile visualization platform for genomics that is related to the consumers well-known iTunes platform. The visualization concept brings genomics to consumers and professionals in a very simple way, immediately understandable and useable in personalized and preventative medicine. They are currently focusing on chronic diseases. Portable Genomics exemplifies bringing the power of information to the individual, Lastly we have NextBio, which uses a cloud platform that sits on top of existing health systems to aggregate the medical data. It is the epitome of a “One Stop Shop” for genomics, with a particular emphasis in Cancer. Their platform enables genetic counselors, pathologists or the tumor board to make decisions regarding patient personalized care.
  • How is genomics contributing to the Personal Healthcare ecosystem?Personal genomics is a key enabler for predictive medicine, for which a patient’s genetic profile can be used to determine the most appropriate medical treatment. People don’t come in the same shapes or sizes, so medicine should accommodate that. By combining sequenced genomic data to EMR’s and other medical data, physicians and researchers will get a better picture of disease in an individual. Subsequently, treatments will reflect an individual’s illness, and not a one treatment fits all for diseases.
  • In addition to genomics in personalizing healthcare, cancer and clinical trials are personalizing the healthcare experience.  Combining clinical trial data and genomic data, researchers have shifted focus on diagnosing and treating cancer based on the cell mutation and not on the area of the body from which it stems. For example treating the kind of mutation that causes breast cancer, instead of treating all breast cancers as breast cancer.
  • Cancer is a huge problem; in 2010 the CDC reported cancer as the leading cause of death in the United States.  Ranking second to Australia the US has the highest cancer rate in the world, making it a focal point in efforts to improve healthcare and personalize the healthcare experience.
  • Many of the deaths from cancer are due to inefficient drug treatments.  As we can see by the pie chart, cancer drugs are ineffective for 75% of the population.  
  • However we can see that this inefficiency is not only the case in cancer, but across the board for a number of diseases. (Or alternately we can see even among inefficient drug treatments cancer treatments remain the most inefficient) Despite the fact that pharma spends 50 billion dollars per year on R&D to find drugs that work, we still see high levels of inefficiency.  In an attempt to make cancer and other treatments more effective for individuals, small start-up companies have turned their attention to using Big Data and data analytics to personalize treatments.
  • Here are some of the companies focusing on personalizing treatment for Cancer:ExplorysAyasdiGNS 
  • Explorys is a cloud-based platform for storage and analysis of all clinical, financial, and operational data related to patient care. To give you an example of size, it has 14 integrated delivery networks, with 200 hospitals, 40 Million patients and 100 billion data elements.It works in Clinical Trials, the idea is to aggregate patient information and analyze it on a real time basis.  Ayasdi uses a more esoteric topological analysis, a “math of shapes”, on their Iris platform, to visualize data in a multidimensional graphic to easily show outliers and high or low-response groups in the data, even without pre-specifying the characteristics of those clusters. Their research is furthest in Cancer, where they have developed a Cancer Genome, and have used their platform to find new biomarkers in Cancer. GNS Healthcare uses standard math and statistical principles to create “what if,” scenario models. Their REF next generation machine learning cloud platform engine extracts predictive models from the data to determine comparative effectiveness and create simulations across an entire patient population and on an individual level. They have established themselves in many aspects of healthcare and are now taking their expertise to genomics.
  • By personalizing cancer treatment to the type of cell and not the area of the body, cancer deaths should decrease exponentially.  A possible example is pharmaceutical developers, who integrate population clinical data sets with genomics data, to better drugs approved in the first place and more importantly, to get the right drug to the right person at the right time.  
  • In addition to genomics, cancer and clinical trials, mobile health is a driving force in personalizing the healthcare experience. In the current digital health revolution mobile phones and social media have redefined how we communicate, and online games have redefined the gaming community drawing in a much larger audience. Now healthcare is seeking to harness this power to improve health.
  • Cheaper, faster, better technology is enabling most of us to connect with each other anytime, anywhere; while specialized networks have changed the way we live, work and play.  Mobile is ubiquitous; a 2013 Pew pool reported that 135.5 million American adults own smartphones up from 80 million in 2010 with more exponential growth expected. Mobile access anytime, anywhere through smart gadgets is putting cheap, connected mobile computing power in the hands of millions of consumers and healthcare practitioners.  Gaming has become an increasingly acceptable part of society, and now health apps are utilizing this to improve and make managing chronic conditions or complicated regimens easier.  Social applications of the apps include not just networking but crowdsourcing in healthcare. Similar in concept to Weight Watchers and Alcoholics Anonymous we see the online social networks that gives peer-to-peer support as a means to gather motivation and support health related activities.
  • Today there are over 96,000 health apps for mobile devices. Dr. Eric Topol Professor of Genomics at The Scripps Research Institute, and author of The Creative Destruction of Medicine: How the Digital Age Will Create Better Healthcare, shows us what can be done with just a few of these mobile health apps.
  • As the info graphic shows, of the 312 million people living in the US, mobile health apps can help over 124 million people with hypertension, 105 million obese adults, 21 million people with sleep apnea, 79 million pre-diabetics and 81 million adults with cardiovascular disease. These are huge numbers and this just addresses chronic disorders.
  • The potential for health apps and games are clear. Of smartphone users, 44% are looking at using health apps in the future and see it as a way to better adhere to treatment regiments. Of non-smartphone users, there is still an interest in using mobile apps to improve health.
  • Here are some of the companies that are harnessing the power of mobile to improve and personalize healthcare:Brain ResourceAchieveMintAetna CarePass, and OneHealth
  • How is Mobile contributing to the Personalized Health ecosystem?Mobile offers a way for individuals to not only keep track of their own health, but to collect information in real time. Patients can monitor their own health and be motivated to make healthy decisions about eating and exercising, along with managing medication adherence. Now patients can literally take their “health into their own hands”
  • As of date, patients aren’t seeing Genomics, Mobile. Cancer and Clinical Trial research in their trips to the hospitals. Comparatively few people have their genome sequenced or their personal health data in their hand. Although mobile health apps are catching on and there are exciting examples and success stories in Cancer and Clinical trials using genomics data, these are just a precursors of the future. But to truly make a lasting impact on healthcare, and centering medical care on the patient, companies and entities are going to have to engage in data sharing.
  • An answer to this bottleneck is data sharing.  The scientific community, researchers and even companies are incentivized and compensated based on their individual results, publications, and products. Data sharing is an idea that promotes opening up data and technologies by sharing ideas in order to improve the product. There are a number of successful examples of such data sharing, including the Human Genome Project which I talked about earlier. The Project illustrates data sharing between public – the government - and private – company- entities.  Other examples of data sharing exist between start up companies and academia, they include:Harvard Medical School & GNS HealthcareAyasdi & UCSF Medical CenterNextBio & Emory UniversityOregon Health and Sciences University & IntelMD. Anderson and Oracle
  • These are just the beginning steps in sharing data. We need data and sharing on a much larger scale and data sharing to improve the healthcare experience in the United States.
  • How can you as an individual participate in data sharing? The answer is Citizen Science. Different from Data Sharing, citizen science is a form of crowdsourcing dealing with the collection and analysis of parts of the data.  There are two layers to citizen science, collection of data from individual citizens and analysis of data by individual citizens. In healthcare, anyone can donate their data like their genomic data or EMR data for clinical trials, or collect data on themselves through mobile apps.   Stephen Friend, White House Champion of Change and President, Co-Founder & Director of Sage Bionetworks succinctly and accurately explained “Citizen science is the equivalent of a night science raid on nature. They can grab something and come back with results.”
  • Are you doing your part in data sharing? Would you like to share your data? Would you like to join the citizen science movement to catalyze the personalization of the healthcare experience? Here are three great ways to get involved:1.Stephen Friends company Sage Bionetworks is the epitome of data sharing. Working in an open environment and promoting collaboration across disciplines members can combine their knowledge and expertise to make new discoveries.2.uBiome lets you donate your xxxx for science3.Online there are a number of sites like ScieStarter with games like Fold-it that allow individuals to take part in the analysis of data
  • Data sharing and citizen science is driving this revolution, and promoting the aggregation of genomics, cancer research, drug research and mobile health to personalize the healthcare experience.  The future of Healthcare is moving to the individual. From genomics to cancer and clinical trials, to mobile health. No longer will our health data be slipped into Medical records in drawers in the far reaches of the hospital. No longer will we have to wait long hours in an Emergency room. These elements of health are finally making Personalized Healthcare a part of healthcare in the near future. In the next five years, we will be holding our health into our own hands being treated with more accurate and efficient treatments using new data streams and taking a holistic view of the patient. Using Big Data to personalize the healthcare experience.
  • Sharing song with the hands slideCome together at the end
  • Using Big Data to Personalize the Healthcare Experience in Cancer, Genomics and Mobile

    1. 1. Big Data Personalizing The Healthcare Experience
    2. 2. Using Big Data to Personalize the Healthcare Experience
    3. 3. Personal Medicine: Making Medicine Work for You • The idea has been around for over a decade • Three ways Big Data is being used to Personalize the Healthcare experience 1. Genomics 2. Cancer/ Clinical Trials 3. Mobile Health (mHealth)
    4. 4. 1. Genomics What is it? • Genomics is the study of the human genome • Sequence the Human Genome • Analyze the Human Genome • Application of the analysis in a medical setting
    5. 5. 1955 1965 1995 20051975 1985 2015 1956 Watson and Crick created the first model of a DNA molecule and discovered that genes determine heredity 1963 F. Sanger of Britain developed the first procedure to sequence DNA The Genetic Code is cracked: scientists begin to predict characteristics by studying DNA 1987 The DOE begins talk of a 15 years plan to sequence the human genome 1990: Researchers begin research on the Human Genome Project 1999 The Human Genome Project completely sequenced the first human chromosome 2000 J. Craig Venter and Francis Collins, jointly announce the sequencing of the entire human genome 2003 The successful completion of the human genome project Human Genomics Timeline 2007: GWAS to link genomics to diseases
    6. 6. The Human Genome Project The Human Genome Project was a project sponsored by the US Department of Energy and the NIH that developed the technology to identify, analyze, and sequence all 20,000-25,000 genes in human DNA. The project took 13 years, but it revolutionized the way DNA was sequenced.
    7. 7. The Cost of Sequencing the Human Genome $95,263,072 $61,448,422 $40,157,554 $18,519,312 $13,801,124 $10,474,556 $7,147,571 $343,502 $70,333 $29,092 $10,497 $7,950 $5,000 $0 $10,000,000 $20,000,000 $30,000,000 $40,000,000 $50,000,000 $60,000,000 $70,000,000 $80,000,000 $90,000,000 $100,000,000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
    8. 8. The $1000 Genome
    9. 9. Why Big Data in Genomics Now? Big Data in Genomics More rapid IT development Better analytic tools Need to personalize healthcare New data streams
    10. 10. Companies using Big Data to Analyze the Genome • Bina Technologies • Portable Genomics • NextBio
    11. 11. Summary Table of the Companies Bina Technologies Portable Genomics NextBio What · Genomic data analysis and sharing platform · System has components focused on secondary analysis and tertiary analysis that will allow companies like NextBio and others to build on top. · Mobile genomics visualization platform · Apps will aggregate genomic and epigenomic data · Similar to iTunes for genomic data · One Stop Shop: Platform that sits on top of existing system aggregating hospital data · Aggregate and analyze data on both a population and individual level Where · ALL Diseases (agnostic platform) · Chronic Diseases · Oncology, Metabolic Disease and Autoimmune Diseases How · Modifies Hadoop to make it more accurate · Builds the infrastructure for analysis of genomic data (doesn’t do the actual sequencing or analysis) · Mobile Apps: they incorporate geolocalization of healthcare specialists · Nutrigenomics: effect food has on genetic or visa versa · NextBio allows it’s customers to integrate NextBio within their application and access it from outside For Whom · Have good reviews by academic institutions · Patients · Providers · Translational research and medicine · Academic medical centers · Cancer centers Growth Trend Customers seem to be healthcare professionals and patients Trend: Started with Pharma then academic and then bigger and more pharma
    12. 12. 2. Cancer and Clinical Trials
    13. 13. Average percent of patient population for whom Cancer drugs are ineffective
    14. 14. Average percent of patient population for whom a particular drug in a class is ineffective Asthma Drugs Arthritis Drugs Cancer Drugs Antidepressants Diabetes Drugs Alzheimer’s Drugs
    15. 15. Companies Using Big Data to Personalize Cancer Treatments • Ayasdi • GNS Healthcare • Explorys
    16. 16. Explorys Ayasdi GNS Healthcare What · A cloud based platform for storage and analysis from multiple sources · Partner with different healthcare systems, aggregate data into the platform, standardize and normalize data · Topological data analysis (the math of shapes) to uncover new patterns data sets · Data analysis to identify population health and clinical trial design · Deliver causal simulations models “in a jar” that allow for a bottom up prediction · Predict costs and drivers and show optimal interventions through simulations of multiple “what if” conditions Diseases · Not specific: Research on MRSA, clinical trials, cost of care, readmissions · Their work in Cancer is most evolved: The Ayasdi Cancer Genome Atlas · Other projects include PTSD, BTI, drug toxicity, and clinical trials · Oncology How · Health data gateways (HDG): a server that sits within the healthcare system firewall collecting clinical, financial and operational data · Use topological models to find correlation in the data · Using Causal Models – “If-Then” Simulations For Whom: · Hospitals · University Hospitals/ Teaching Hospitals · Biotech companies · Pharma · Government Health departments · International Research Centers · Pharma · Providers: Research Hospitals Growth Trend · Mostly in Healthcare facilities · Mostly Research groups except their partnership with Medicaid in 2013 Summary Table of the Companies
    17. 17. 3. Mobile Health
    18. 18. • Mobile is ubiquitous with 46% of Americans owning smartphones • Crowdsourcing and Social Media Use • Gaming: the gaming community has moved their efforts to mobile Why Mobile Health Now?
    19. 19. Click Here Interview With Eric Topol
    20. 20. Companies using Big Data to Develop Apps for mHealth • Brain Resource • AchieveMint • Aetna CarePass, , OneHealth
    21. 21. Bottleneck There are three main bottlenecks that are slowing down the progress of genomics, cancer and clinical trial research, and mobile health in their pursuit to personalize the healthcare experience. 1) interoperability 2) data sharing 3) privacy concerns Genomics Cancer and Clinical Trials mHealth
    22. 22. Data Sharing
    23. 23. • “Citizen science can take a small area where there are hundreds of patients and there is very little known, and do the equivalent of a night science raid on nature. They can grab something and come back with results.” – Stephen Friend White House Champion of Change and President, Co- Founder & Director of Sage Bionetworks
    25. 25. Thank you • @DrBonnie360 • •