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Gradually, Then Suddenly: Lessons from Silicon Valley for the Future of Healthcare

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My keynote at the Genentech Medical Affairs Summit in San Diego on March 19, 2019. Technological change happens like Hemingway's famous description of bankruptcy, first gradually, then suddenly. I illustrate this through the history of the online ride hailing market, then apply lessons from Uber/Lyft, Google, and Amazon to speculate on what changes will be required to make health data and personalized medicine have its "Suddenly" moment. Be sure to download and read the narrative in the speaker notes.

Publié dans : Technologie
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Gradually, Then Suddenly: Lessons from Silicon Valley for the Future of Healthcare

  1. Gradually, Then Suddenly Lessons from Silicon Valley for the Future of Healthcare Tim O’Reilly Founder and CEO, O’Reilly Media Genentech Medical Affairs Summit, March 19, 2019
  2. “How did you go bankrupt?” “Two ways. Gradually, then suddenly” Ernest Hemingway
  3. Sunil Paul had the idea in 1999! In July 2000, just as GPS was first opened for commercial use, Sunil Paul filed a patent that described almost everything that by 2011 would become his company Sidecar, and then Lyft and Uber. What took so long? And why was Sunil’s company the loser in the race to the future?
  4. Sometimes the world just has to catch up • 2005 – Google launches Google Maps • 2006 – Amazon launches AWS • 2007 – Apple introduces the iPhone • 2007 – Hackers jailbreak the iPhone to add custom apps • 2008 – Google launches Android • 2008 – Apple launches the App Store and Google launches Android Market • 2007- 2010 – Braintree, Stripe, Twilio etc. build out enabling services for mobile apps (payment, communications, etc.)
  5. “Framing blindness” We often begin by imagining the future in familiar terms, and our use of new technology is shaped by what we already know, and drawing within the existing lines.
  6. In 2005, we thought the connected taxicab looked like this We knew what the internet was for: It’s for showing content and ads! And we can read your credit card too!
  7. New puzzle pieces on the table 2007 – Taxi Magic uses the web to hail taxis 2007 – Zimride matches college students for long drives 2009 – Uber uses SMS to summon black cars 2012 – Sunil finally launches Sidecar – GPS enabled, with drivers who use their own cars; he is slowed by attempt to work with regulators, and lack of capital 2012 – Logan Green and John Zimmer ditch Zimride and launch Lyft 2012 – Uber launches UberX to copy the crowdsourced driver model and pours on the gas 2017 – Bikes and scooters get added to the mix
  8. “A business model is the way that all of the parts of a business work together to create competitive advantage and customer value.” - Dan and Meredith Beam
  9. A Business Model Map of Uber • A magical app that lets drivers and passengers find each other in real time • Seamless integration of data services like location, communication, and payment • A networked marketplace of drivers and passengers managed by algorithm • Augmented workers able to join the market as and when they wish
  10. Jeff Bezos calls this “the flywheel”
  11. We’re still in the “gradually” stage for healthcare • Ubiquitous smartphones • Health outcomes data availability coming together with a shift in business models, enabling payment for outcomes rather than fee for service • Big data, cloud computing, AI, genomics, proteomics, etc. enabling personalized medicine, drug discovery, and more • Video calling, remote sensing, enabling telemedicine • Remote sensing, robotics, and the Internet of Things • Patients who expect services as easy to use as their smartphone apps
  12. What will it take to get to “suddenly”?
  13. “The value proposition hasn’t yet overcome the misaligned incentives. But the value proposition continues to increase.” Jamie Heywood, PatientsLikeMe
  14. New sources of data for personalized medicine
  15. • Trillions of web pages indexed, in real time • 5.5 billion searches per day • 63,000 searches per second • 50-60 billion ad impressions per day • Response time of about half a second (reported on every query)
  16. A System of Collective Intelligence • Every web user contributes to Google’s collective intelligence • whenever we create a web page • whenever we link to a web page • whenever we click on a search result • whenever we follow maps and directions on our phone… • Humans build and manage the systems for extracting relevance, for combating spam, and for keeping things running at greater and greater scale and speed, but the system is too big and too fast for traditional “management” or decision making. Google is a giant AI. • The stakes are very high: Human minds are reflexively shaped by the knowledge and opinions aggregated there and on other similar platforms.
  17. Google is a new kind of human-machine hybrid
  18. Gradually, then suddenly Artificial Intelligence and big data are enabling fundamentally new kinds of partnerships between humans and machine
  19. It’s no longer just in the digital realm
  20. Work on stuff that matters
  21. Gradually, then suddenly The great internet services are all real-time matching marketplaces managed by algorithm. This is a radically new form of business organization.
  22. Amazon.com
  23. And then of course, there are the warehouse robots
  24. Algorithms decide “who gets what – and why” Markets are outcomes. A better designed marketplace can have better outcomes.
  25. Google is made up of two overlapping algorithmic marketplaces • Google Search: Uses 200+ ranking factors to match up users with the information they are searching for. Constantly updates its systems with new data and new, improved algorithms. What remains constant is the desired outcome: users find what they want. This is a marketplace where money plays no role in what is shown. • Google Ads: Uses an auction model in which the top ad placement doesn’t go to the highest bidder but to the best combination of price and projected likelihood that the user will click on the ad (i.e. that it too is what they want.)
  26. How might this work in healthcare? • Hundreds of factors – population health data, personalized health data, outcome data on interventions – are weighed to give the physician a set of recommendations focused on the best health results. • An economic AI engine makes recommendations on the best combination of health outcomes and costs. Providers compete for the patient’s business by being the best option.
  27. “The opportunity for AI is to help humans model and manage complex interacting systems.” Paul R. Cohen University of Pittsburgh
  28. “Comprehend Medical is helping to identify patients for clinical trials who may benefit from specific cancer therapies. Fred Hutch[inson Cancer Center] was able to evaluate millions of clinical notes to extract and index medical conditions, medications, and choice of cancer therapeutic options, reducing the time to process each document from hours to seconds.” - Dr. Taha A. Kass-Hout and Dr. Matt Wood, Amazon Taha A. Kass-Hout
  29. Better scheduling and logistics may be the first value-add “Patients who need appendectomies are typically scheduled for an hour in surgery. But young, otherwise healthy people often need less time. ‘If I look at a million patients like you, and discover we only need 25 minutes, wouldn’t that be better for society? Because now the OR is the most expensive place in a hospital,’ Halamka said.” Bloomberg, March 4, 2019 John Halamka, MD Beth-Israel Deaconess Hospital
  30. Gradually, then suddenly The system will evolve from making recommendations to doctors to a dynamic real time system designed and managed by doctors.
  31. Working at the scale and speed required by personalized medicine without the help of AI will be like asking workers to build a modern city with only picks and shovels.
  32. Gradually, then suddenly Software has become a set of ongoing business processes, not something you can buy
  33. The front-line “workers” at Google & Amazon are programs. Software developers are actually their managers. Every day, they are inspecting the performance of their workers and giving them instruction (in the form of code) about how to do a better job
  34. A new kind of management “It’s the difference between ‘playing Caesar’ (deciding which projects live and die), and ‘playing the scientist’ (being perpetually open to search and discovery.)” - Eric Ries, The Startup Way
  35. New skillsets are needed • User Centered Design • Site Reliability Engineering • Data Science • Machine Learning • API Design • Economics • Market design • Data security
  36. Gradually, then suddenly Knowledge is available on demand Neo: “Can you fly that thing?” Trinity: “Not yet.”
  37. This isn’t quite what Trinity’s got, but…
  38. What does knowledge on demand look like for healthcare?
  39. “The No. 1 thing that has made us successful by far is obsessive compulsive focus on the customer.” Jeff Bezos
  40. User-centered design is a superpower A “magical” app takes advantage of new technology to do something delightful for customers that previously seemed impossible. It doesn’t have to be complicated. Here’s One Medical, reinventing the house call – on a mobile phone
  41. Applying for food benefits in California used to be nearly impossible online and 45% of those eligible never got the benefit. Now, GetCalFresh makes it easy and with over 670,000 people helped, we’re closing the participation gap.
  42. A Future Business Model Map of Roche? • A magical app that lets patients request care as they need it. Everyone gets the right treatment. • Seamless integration of personalized patient data into a health intelligence platform • Augmented workers able to deliver the 21st century housecall • A matching marketplace managed by algorithm
  43. Drug Discovery Learning in real time Drug Discovery Personalized medicine Prices so low everyone gets a “Cadillac plan” No waiting for care Payment is invisible Access at the touch of a button Population Health Data Automatic Ticketing machines Personal Health Data A health intelligence platform So smart that paying for outcomes is more profitable Magical User Experience “Everyone gets the right treatment” Augmented Health Workers Care that shows up when you need it Managed By Algorithm The best specialists are available to everyone Physicians and other frontline health workers have AI on demand Doing now what patients need next
  44. This is the future I envision for you • A dynamic learning system with data-driven economic incentives • An internet-scale, algorithmically managed marketplace • Focused on better health outcomes • Augmented health workers serving empowered, augmented consumers
  45. The puzzle pieces are all on the table • Putting patients at the center • The shift to wellness rather than illness • Collaboration between health systems and health plans • Increased adoption of virtual care options • Greater focus on population health • The collection of patient health data rather than just health care billing data • Aligning the financial incentives with the care incentives––or disconnecting them entirely!
  46. What do the great technology platforms teach us about the future of business and the economy? The future isn’t inevitable. It doesn’t just happen. It is up to us to build the future we want. wtfeconomy.com

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