HeathXL report on use cases for Big Data and AI

HeathXL report on use cases for Big Data and AI

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  HOW AI CANIMPROVE RESEARCH & CARE MODELS August 2017 H E A L T H X L B I G D A T A & A I W O R K I N G G R O U P
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  About HealthXL The HealthXLPlatform brings together key market stakeholders in digital health and empowers them to collaborate and learn from each other. HealthXL engages leading companies such as … T H E L E A D I N G P L A T F O R M F O R C O L L A B O R A T I O N
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  OVERVIEW
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  First, what do wemean by AI? Defining AI and its various methods is a subject of high scrutiny and debate. At the risk of being overly simplistic, we’ve taken a practical approach for this report. Further, we’ve focused the report on select AI applications in the following areas: life sciences, care delivery, payor & consumer. Artificial intelligence (AI) is that activity devoted to making machines intelligent. Machine learning refers to a process in which computers use algorithms to analyze large data sets in non-linear ways, identify patterns, and make predictions that can be tested and confirmed. Deep learning is the application of artificial neural networks to learning tasks that contain more than one hidden layer. A R T I F I C I A L I N T E L L I G E N C E M A C H I N E L E A R N I N G D E E P L E A R N I N G Source: "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University (2016), UCSF & GE White Paper: Big Data, Analytics & Artificial Intelligence (2016)
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  Data quality is ofthe utmost importance Source: "Artificial Intelligence and Life in 2030." One Hundred Year Study on Artificial Intelligence: Report of the 2015-2016 Study Panel, Stanford University (2016), UCSF & GE White Paper: Big Data, Analytics & Artificial Intelligence (2016) Source: The AI Hierarchy of Needs (http://bit.ly/2wI8oMW) In pharma and care delivery applications in particular, understanding the context and setting of data collection can provide clarity in how the data should be utilized or interpreted. While access to many data types is increasing, often times data remains filled with gaps and lacks a level of completeness necessary for analysis. AI projects should ideally incorporate a prospective data collection methodology to ensure the appropriate type of data is collected from the onset.
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  Why now? Source: Internet Association,Jeff Bezos Fireside Chat (May, 2017) •  Data access and computing power are enabling AI solutions that were unimaginable in years prior, improving both research processes and care delivery. Access to high quality, “complete” data remains a challenge however in many instances. •  Tech giants alongside innovative AI startups are diving head first into various applications - ranging from general platforms (IBM) to niche application areas (cancer imaging). There remains a desire for increased transparency into the algorithm development process. •  Early results from validation studies and initial use cases indicate AI is augmenting human intelligence instead of replacing it. As result, individuals are becoming more efficient and able to focus on more creative tasks. •  A strong commitment, dedication, and a mindset of deep partnerships is needed at this time to maximize the value of AI approaches. Stakeholders are similarly experimenting with collaboration models to better reach partnership objectives. - J E F F B E Z O S C E O , A M A Z O N “ ” “AI [artificial intelligence] … this is a renaissance, this is a golden age … ML [machine learning] and AI is a horizontal enabling layer, it will empower and improve every business - every government organization, every philanthropy - there is no institution in the world that cannot be improved with ML.”
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  A N DR E W N G Former Chief Scientist Baidu R A Y K U R Z W E I L Founder & Futurist Multiple Companies as a serial entrepreneur A T U L B U T T E Director of the Institute of Computational Health Sciences UCSF Received $10M from Mark Zuckerburg & Priscilla Chan to advance health research. Raising a $150M fund for AI startups; established Coursera Deep Learning course. Continues to advance understanding of natural language at Google. Leaders in the field continue to make progress in applying AI methods Visit HealthXL.co for access to leaders in the field of Big Data & AI.
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  AI has along history, but today’s enablers are distinct from years prior E X P E R I M E N T A T I O N M I N D S E T Ecosystems and centers of excellence are emerging, facilitating pilot opportunities and novel research partnerships. C O M P U T I N G P O W E R The gaming industry has enabled computing power to increase, particularly companies like Nvidia’s GPUs (graphical processing units). D A T A A C C E S S New technologies and biological discoveries are expanding the available pool of data without traditional access challenges. - A T U L B U T T E I N S T I T U T E O F C O M P U T A T I O N A L H E A L T H S C I E N C E S , U C S F “ ” “Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world.”
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  AI has broadutility across a number of use cases B U S I N E S S P R O C E S S O P T I M I Z A T I O N S E L E C T U S E C A S E S L I F E S C I E N C E S •  Disease Understanding •  Drug Repurposing •  Drug Discovery C A R E D E L I V E R Y •  Care Management Plans •  Treatment Selection •  Remote Monitoring P A Y O R •  Risk Stratification •  Patient Engagement •  Customer Service C O N S U M E R S •  Nutrition •  Care Management •  Novel Experiences T h e r e a r e a n u m b e r o f u s e c a s e s t h a t h e l p b u s i n e s s e s i m p r o v e t h e i r c o r e o p e r a t i o n s . S u c h u s e c a s e s i n c l u d e p r e d i c t i v e i n v e n t o r y m a n a g e m e n t , a u t o m a t e d r i s k & s e c u r i t y a s s e s s m e n t s , a n d m e t h o d s t h a t i m p r o v e d a t a s t a n d a r d i z a t i o n , a m o n g m a n y o t h e r s . T h e s e t y p e s o f a p p l i c a t i o n s w i l l b e f u r t h e r d i s c u s s e d i n t h e f u t u r e w i t h i n H e a l t h X L ’ s B i g D a t a & A I W o r k i n g G r o u p . Note: While imaging is a major application, other use cases are starting to gain traction.
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  Flourishing startup scene AC R O S S M A N Y M A R K E T S E G M E N T S Note: The companies listed above are meant to be representative, not exhaustive. Visit HealthXL.co for more detailed company information including partners, funding, and publications. C A R E D E L I V E R Y / P A Y O R R E S E A R C H C O N S U M E R
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  Investments into AI companies Estimates vary,but total VC investment in health or research related AI companies is in the billions, in part fueled by projections of the AI in healthcare market surpassing $6 billion by 2021. Some funds invest in a number of industries and prioritize robustness of tech approach; in other cases, funds are focused on healthcare and see their AI investments as an extension of their thesis. $30 $75 $493 $292 $748 2012 2013 2014 2015 2016 A N N U A L F U N D I N G H I S T O R Y ( $ M , U S D ) A C T I V E V C s Source: CB Insights, Accenture, Company Websites A C R O S S A D I V E R S E I N V E S T O R B A S E
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  News headlines vary,but leaders believe we’re still in early phases of AI - S T E P H E N K R A U S B E S S E M E R V E N T U R E P A R T N E R S “ ” “It’s all for real - this isn’t about putting out vaporware in order to boost stock prices. This is hard. It’s not happening today, and it might not be happening in five years. And it’s not going to replace doctors.” HOW MACHINE LEARNING, BIG DATA AND AI ARE CHANGING HEALTHCARE FOREVER FDA ASSEMBLES TEAM TO OVERSEE AI REVOLUTION IN HEALTH NHS MEMO DETAILS GOOGLE / DEEPMIND’S FIVE YEAR PLAN TO BRING AI TO HEALTHCARE MICROSOFT ANNOUNCES NEW AI-POWERED HEALTH CARE INITIATIVES TARGETING CANCER IN SURVEY ACROSS EMEA, UK MOST SKEPTICAL OF ROBOTS, AI FOR HEALTHCARE - A N D R E W N G S T A N F O R D ( F O R M E R L Y B A I D U ) “ ” “We still have work ahead to get these algorithms into the healthcare system's workflow. But I think health care 10 years from now will use a lot more AI and will look very different than it does today.” HEADLINES
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  Imaging has beenthe focus of many innovators, however use cases are growing - D R . A N D R E W B E C K P R E S I D E N T & C E O , P A T H A I “ ” “The implications of this work are large, suggesting that in the future we’ll see more examples of AI being used with traditional pathology to make diagnoses more accurate, standardized and predictive” PathAI is engineering and applying proprietary deep learning technology to massive aggregated sets of pathology data to help physicians and scientists more effectively understand, diagnose and treat disease. Its models have been improved through trained experts in pathology, and have now surpassed human accuracy. P A T H O L O G I S T A L O N E E R R O R R A T E 3 . 5 % A I M O D E L A L O N E E R R O R R A T E 2 . 9 % C O M B I N E P A T H O L O G I S T S + A I M O D E L E R R O R R A T E 0 . 5 % BREAST CANCER Source: PathAI Website, Deep Learning Drops Error Rate for Breast Cancer Diagnoses by 85% (Nvidia Blog, 2016)
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  SELECT USE CASES
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  Life Sciences Source: CompanyWebsites. Visit HealthXL.co for more detailed company information including partners, funding, and publications. - B R E N D A N F R E Y U N I V E R S I T Y O F T O R O N T O ( A N D F O U N D E R O F D E E P G E N O M I C S ) “ ” “There’s going to be this really massive shake- up of pharmaceuticals. In five years or so, the pharmaceutical companies that are going to be successful are going to have a culture of using these AI tools.” R E D E F I N I N G B I O L O G I C A L U N D E R S T A N D I N G O F D I S E A S E D I S E A S E U N D E R S T A N D I N G Breaking down biochemical processes and physiology to better map natural history of health, disease, and diagnostic process. D R U G R E P U R P O S I N G Mapping relationships between known drugs to novel indications by creatively leveraging compound libraries. D R U G D I S C O V E R Y With an understanding of structural biology, creating new classes of drug categories and interventions.
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  Care Delivery Care deliverycan be viewed as a complex process with many interdependencies, AI approaches can help streamline the delivery of care and how clinical insights are discovered. G A I N I N G 3 6 0 º V I E W O F P A T I E N T N E E D C A R E M A N A G E M E N T P L A N S Optimizing care management plans and creating guidelines to manage follow ups, intakes, readmissions, and more. T R E A T M E N T S E L E C T I O N Identifying methods to provide better treatments, early switch rates, and improve adherence. R E M O T E M O N I T O R I N G Medical grade sensors and clinical algorithms track high-risk patients beyond facility walls S E L E C T H E A L T H S Y S T E M S W I T H A I I N I T I A T I V E S Source: Company Websites. Visit HealthXL.co for more detailed company information including partners, funding, and publications.
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  S E LE C T P A Y O R S W I T H A I I N I T I A T I V E S Payors Payors are aiming to strike a balance between broad population coverage and meeting member expectations how they expect to interact with technology. Further, AI approaches can help facilitate value-based reimbursement strategies. R E T H I N K I N G R I S K S T R A T I F I C A T I O N & P O P U L A T I O N H E A L T H R I S K S T R A T I F I C A T I O N Applied analytics to predict patient outcomes and inform treatment recommendations. P A T I E N T E N G A G E M E N T Machine learning to tailor member outreach based on clinical, claims, and contextual data. C U S T O M E R S E R V I C E Chatbots to help members navigate their benefits quickly and efficiently. Source: Company Websites. Visit HealthXL.co for more detailed company information including partners, funding, and publications.
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  Consumers N O VE L C H A T I N T E R F A C E S & V I S U A L E X P E R I E N C E S N U T R I T I O N Chatbots, food image analyses, and personalized nutrition based on microbiome and other biological determinants. C A R E M A N A G E M E N T Enabling personalized medicine, often through use of genomics and research models. N O V E L E X P E R I E N C E S Interactive technology and new engagement models via robotics. Consumer-facing applications of AI are emerging across every major health segment. Advances in natural language processing (NLP), sensors, voice recognition, augmented reality (AR), sentiment analysis, and more are raising the sophistication of digital interaction and reshaping consumer experiences. Source: Company Websites. Visit HealthXL.co for more detailed company information including partners, funding, and publications.
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  Prevention & timelyintervention I S R E S H A P I N G O V E R A L L H E A L T H M A N A G E M E N T ( E X : D I A B E T E S ) Source: Company Websites C A R E D E L I V E R Y / P A Y O R R E S E A R C H C O N S U M E R A R T I F I C I A L P A N C R E A S Closed-loop insulin dosing and blood glucose management. S M A R T P O P U L A T I O N M A N A G E M E N T Real-time insulin pump adjustments based on patient-specific care plans. C O G N I T I V E P A T I E N T E N G A G E M E N T Patient decision-making aids based on insulin, diet, lifestyle. S M A R T E A T I N G A S S I S T A N T S Foster healthy diets by turning knowledge into know-how with AR, NLP, decision support tools.
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  IBM Watson Healthis a large ecosystem player, with dozens of partners spanning oncology, pharma, payers, medical device, and health systems. Partnerships largely focus on ingesting partners’ proprietary data to train Watson to strengthen applied cognitive computing tools. Tech giants with deep pockets A R E N O W S I G N I F I C A N T P L A Y E R S Seamless workflow integration into complex settings. B A C K G R O U N D C H A L L E N G E S DeepMind, Google’s AI company, signed a 5-year deal with the UK’s National Health Service for access to 1.6M patient records. Goals include workflow automation and optimization to enable the detection and intervention of avoidable conditions like sepsis or acute kidney failure. Privacy and security concerns in the public dialogue. B A C K G R O U N D C H A L L E N G E S Source: Company Websites
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  Within research, strategicmulti-stakeholder partnerships are becoming the norm Source: Company Websites iCarbonX has created the Digital Health Alliance, bringing together various technologies, proprietary data sources, patient access, and drug development capabilities into a comprehensive research ecosystem. WuXi Next Code and AbbVie entered 15 year partnership to sequence the genomes of 45,000 participants across Ireland to identify novel targets of disease. GSK and Exscientia partnered to accelerate small molecule drug discovery. The deal could total upwards of $42.7 million (USD).
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  Partnerships are similarlycommon in care delivery, often with a focus on specific diseases Source: Company Websites IBM Watson Health has a growing ecosystem, followed by Microsoft & UPMC, and GE & Partners Healthcare. Specialization across diseases such as Ginger.io’s coaching platform in behavioral health, Flatiron’s oncology focus across care and research, and Cyft’s precision care platform across diseases. Payers play a key role in experimenting with new benefit design, Aetna particularly around substance abuse (top) and outcomes based reimbursement for insulin pumps (bottom).
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  Future thought R ED E S I G N I N G S O C I E T Y F O R H E A L T H Y L I V I N G Thinking beyond generally defined “health data” and including novel data sets will increase our understanding of biology, behaviors, and outcomes management. As AI methods become more sophisticated, there’s a real opportunity to ensure we keep tackling the problems that really matter to society. HealthXL looks forward to enabling global collaborations between leading players to create a better future. As predictive capabilities increase, ensuring that as a society we’re creating the incentives where avoidance of risk is economically rewarded is crucial. With novel methods, we can begin to solve social and structural problems, in part by better understanding social determinants of health and everyday living conditions.
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  Stay connected &learn about HealthXL’s working groups
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  Authors C A RL O S R O D A R T E Founder & Managing Director Volar Health, LLC + HealthXL Advisor N A V E E N R A O Founder & Managing Partner Patchwise Labs, LLC + HealthXL Advisor J U L I E C A R T Y Chief Operating Officer HealthXL
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  HOW AI CANIMPROVE RESEARCH & CARE MODELS August 2017 H E A L T H X L B I G D A T A & A I W O R K I N G G R O U P