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Big BioData: The Story of Us, a talk by Emanuel Brown @ Webvisions PDX 2014
 

Big BioData: The Story of Us, a talk by Emanuel Brown @ Webvisions PDX 2014

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Slides from Big BioData: The Story of Us, presented by Emanuel Brown at Webvisions Portland, May 9th, 2014. ...

Slides from Big BioData: The Story of Us, presented by Emanuel Brown at Webvisions Portland, May 9th, 2014.

Full video of the talk at https://vimeo.com/95333857

http://metadata.pluscitizen.com
http://pluscitizen.com

The healthcare industry is under enormous pressure to transform in order to meet the needs of increasing numbers of people while at the same time reigning in costs to allow all of them access to the best possible care.

The key to this transformation is known as Evidence-Based Medicine (EMB) which holds the potential to open up a level of personalized care unseen in human history. EBM is not without its detractors and it is not a salve that aids all wounds. Still, as more of medical practice – from patient records to insurance and beyond – move into digital formats, the massive amounts of data accumulated opens up the possibility of moving past preventive to predictive care.

Much like Big Data in other industries, getting the most out of these data sets is both a major puzzle and a frenetic footrace as institutions, businesses and individuals all scramble to crack open the hidden value within.

Big BioData, a term that has been floating about in academic circles, is being brought to the fore by Citizen as a way to contextualize what is happening in the health industry for a variety of perspectives. At the same time, with its white paper series focused on Big BioData, Citizen seeks to foster a more comprehensive and yet concise conversation around what is achievable in the near term as well as the long term through the collection, storage, analysis and application of Big BioData.

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    Big BioData: The Story of Us, a talk by Emanuel Brown @ Webvisions PDX 2014 Big BioData: The Story of Us, a talk by Emanuel Brown @ Webvisions PDX 2014 Presentation Transcript

    • WEBVISIONS PORTLAND / MAY 9, 2014 Big BioData: The Story of Us
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 2
    • What’s the Big Deal? 3
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 4 ! US HEALTH INDUSTRY $3 
 TRILLION
 2015
    • © 2014 Citizen, Inc. Proprietary and confidential. Fee-For-Service doesn’t scale, taxes innovation. Current Model is Unsustainable 5 Significant amounts of data collected for use Federal incentives to use Big BioData Pushing for evidence- based practices 5 Cost Pressure Healthcare expenditure reached 17.6% of GDP, with over $600B inefficient spending ! Moving to risk sharing model instead of 
 fee-for-service More Supply In 2005 <30% of physicians used EMRs. Today, 75% hospitals use 
 e-record systems and 50% of US hospitals participate in information exchange Government $40B payment incentives for EMR-using providers. ! Over $500M invested to release Big BioData housed within government science institutions.
    • © 2014 Citizen, Inc. Proprietary and confidential. Healthcare has lagged behind other industries in the use of big data. 6
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 7 AVERAGE BRAIN SCAN 10MB/S
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 8 TYPICAL 
 20 MINUTE SCAN GIGABYTESOF DATA PER PERSON
    • © 2014 Citizen, Inc. Proprietary and confidential. Common Characteristics Big Data Overview 9 VOLUMEVARIETY VELOCITY
    • © 2014 Citizen, Inc. Proprietary and confidential. Additional Characteristics Big Data Overview 10 VOLUMEVARIETY VELOCITY VERACITY VALUE
    • © 2014 Citizen, Inc. Proprietary and confidential. The business case has been widely accepted. Big Data Deployments are Increasing 11 70% ENTERPRISES 56% 
 SMB
    • © 2014 Citizen, Inc. Proprietary and confidential. 71% of current big data analytics rely on data visualization not advanced intelligence engines. 12
    • Big BioData 13
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 14 ! HEALTH IT $57 
 BILLION
 2017
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 15 IN 2001 GENOME SEQUENCING $10,000/MB
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 16 IN 2014 GENOME SEQUENCING <1¢/MB
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 17 Patient Data Medical Technology Scientific Research
    • © 2014 Citizen, Inc. Proprietary and confidential. Domains of Big BioData Data From the Clinic Patient Records Bedside information. This data is collected from patients for the purpose of building clinical histories and treatment plans. Drug History The prescriptions of patients are tracked and stored with their medical files along with dosage and disease history. Clinical Outcome Whether from surgical, rehabilitative, or drug-based treatment, clinical outcomes of patients are vital to health. 18
    • © 2014 Citizen, Inc. Proprietary and confidential. Domains of Big BioData Medical Technologies Point of Care Medicine practiced at the point where - and when - treatment is required. Lab-on-a-Chip The evolution (and shrinkage) of decades-old clinical technologies Human-Computer Interface Technologies that interact with humans for the purpose of treating or augmenting function. 19
    • © 2014 Citizen, Inc. Proprietary and confidential. Domains of Big BioData Scientific Research Prospective Research All research that seeks to investigate novel phenomena add to the existing ocean of scientific information. Biology and Physiology Includes the studies of: neuroscience, genetics, molecular biology, and systems physiology. Publications A scientific paper is the medium to communicate and pass down knowledge through scientific discovery. 20
    • © 2014 Citizen, Inc. Proprietary and confidential. Multidimensional information about the self Combining the Domains PATIENT RECORDS This data is collected from patients for the purpose of building clinical histories and treatment plans. DRUG HISTORY The prescriptions of patients are tracked and stored with their medical files along with dosage and disease history. CLINICAL OUTCOME Whether from surgical, rehabilitative, or drug-based treatment, clinical outcomes of patients are vital to health. POINT OF CARE Medicine practiced at the point where - and when - treatment is required. LAB-ON-A-CHIP The evolution (and shrinkage) of decades-old clinical technologies HUMAN-COMPUTER INTERFACE Technologies that interact with humans to treat or augment function. PROSPECTIVE RESEARCH All research that seeks to investigate novel phenomena add to the existing ocean of scientific information. BIOLOGY AND PHYSIOLOGY Includes the studies of: neuroscience, genetics, molecular biology, and systems physiology. PUBLICATIONS A scientific paper is the medium to communicate and pass down knowledge through scientific discovery. 21
    • © 2014 Citizen, Inc. Proprietary and confidential. Heterogenous Big BioData is being acquired in huge volumes too fast for humans to understand. 22
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 23 Biological Biometric Biographical
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 24 3.2 MILLION BASE PAIRS IN EVERY HUMAN CELL
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 25 37 TRILLION CELLS IN AVERAGE HUMAN ~10 EXABYTES OF BASE PAIRS TO ANALYZE
    • © 2014 Citizen, Inc. Proprietary and confidential. Machine learning aims to identify 
 and exploit hidden patterns in data 
 to do useful tasks. 26
    • © 2014 Citizen, Inc. Proprietary and confidential. Implement Supervised Learning CLASSIFICATION Discrete Supervised learning algorithms that classify data perform a discrete mapping between features and outputs. Example: Hand-writing recognition REGRESSION Continuous Algorithms with a continuous output, rather than finite, are regressions that map features 
 to an output value. Example: Predicting stock price 27 Training Data: Output Values: • Exploratory data analysis • Select appropriate model Given new , predict Training Selection
    • © 2014 Citizen, Inc. Proprietary and confidential. Unsupervised Learning 28 Clustering consumer purchasing history Habit 2 Habit 1 Clustering Find latent structures in data without clear labels or assumptions. Hidden features can be explored post hoc.
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2013 Citizen, Inc. Proprietary and confidential. 29
    • © 2014 Citizen, Inc. Proprietary and confidential. Over 43,000 mobile health apps available yet majority receive fewer than 500 downloads globally. 30
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 31
    • © 2014 Citizen, Inc. Proprietary and confidential. Approximately 42% of all 
 US hospitals currently utilize 
 electronic medical records systems. 32
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2013 Citizen, Inc. Proprietary and confidential. 33
    • © 2014 Citizen, Inc. Proprietary and confidential. After 20 years nearly 80% of originally sourced data is either 
 gone or unusable. 34
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 35 Purpose Vision Methodology
    • © 2014 Citizen, Inc. Proprietary and confidential. The need to establish data integrity should be at the core of every 
 Big BioData application, service, business model or policy. 36
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 37 metadata.pluscitizen.com
    • © 2013 Citizen, Inc. Proprietary and confidential.© 2014 Citizen, Inc. Proprietary and confidential. 38 @PLUSCITIZEN @EMANUELBROWN