Presentation Looks into the Future of Oncology Nursing in a Digital Age


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In the opening keynote address for 160 attendees at the 34th annual University of Iowa Scofield Advanced Oncology Nursing Conference in Iowa City, PYA Principal Kent Bottles, MD, explored “The Future of Oncology in a Digital Age”—a thought-provoking analysis of what lies ahead in the field of medicine.

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Presentation Looks into the Future of Oncology Nursing in a Digital Age

  1. 1. The Future of Oncology in a Digital Age Kent Bottles, MD Chief Medical Officer, PYA Analytics Thomas Jefferson University School of Population Health April 1, 2014 Scofield Advanced Oncology Nursing Conference Coralville, Iowa
  2. 2. The End of Illness David Agus, New York: Free Press, 2011 • “Take a moment to imagine what it would be like to live robustly to a ripe old age of one hundred or more. Then, as if your master switch clicked off, your body just goes kaput. You die peacefully in your sleep after your last dance that evening. You don’t die of any particular illness, and you haven’t gradually been wasting away under the spell of some awful, enfeebling disease that began years or decades earlier.”
  3. 3. Jeff Goldsmith on Digital Future • “David never spent a day in the hospital, and had one home and two office visits with his physicians during the course of treatment, which consisted in its entirety of six weeks’ worth of home infusion therapy.
  4. 4. Jeff Goldsmith on Digital Future • The bill for all these services was created, evaluated, and paid electronically, with David’s nominal portion of the cost billed to his Visa card, per agreement with his health plan. He never saw a paper bill, though he could view the billing process in real time on his health plan’s website.”
  5. 5. Traditional Medicine • Diagnose and treat • Health is defined as absence of disease • Patient story is subjective and untrustworthy • Lab results are objective and true • Pathologists are the most important doctors • Clinicians are paralyzed until lab provides dx
  6. 6. Traditional Medicine • Biomedical model reduces every illness to a biological mechanism of cause and effect • Attention on acute episodic illness • Generalists replaced by specialists • Focus on individuals • Cure as uncompromised goal • Focus on disease • Antibiotics & infectious disease
  7. 7. Digital Medicine of Present & Future • Predict and Prevent • Health is a state of complete physical, mental, and social well-being and not merely absence of disease • Patient story is essential for development of personal metrics which will be unique to each individual • Pathologist sadly becomes less important
  8. 8. Systems Biology Yields New Therapies diabetes.html?_r=1&pagewanted=print • Michael Snyder sequenced his genome that showed he was at high risk for Type 2 Diabetes • Blood tests every 2 months of 40,000 molecules • After 7 months showed he had developed DM • Early detection, early treatment • “This study is a landmark for personalized medicine.” Eric Topol
  9. 9. Digital Medicine • Digitizing a human being – Genome – Remotely, continuously monitor vital signs, mood, activity – Image any part of body, 3D reconstruction, print an organ – Readily available on your smartphone, integrated with traditional medical record, constantly updated
  10. 10. Digital Medicine Convergence • Genomics • Wireless sensors • Imaging • Information Systems • Social networks • Ubiquity of smartphones • Unlimited computing power via cloud server farms
  11. 11. Rules vs. Complex Tasks • Tasks that require application of rules by use of algorithms • Information processing tasks that cannot be boiled down to rules – Pattern recognition – Complex communication • The New Division of Labor by Frank Levy and Richard Murnane, 2004
  12. 12. The Digital Age • Exponential • Digital • Combinatorial
  13. 13. The Digital Age • Exponential – “The greatest shortcoming of the human race is our inability to understand the exponential function.” Albert A. Bartlett – Chess invented in sixth century CE, Gupta Empire – “Place one single grain of rice on first square of the board, two on the second, four on the third, and so on.” – 18 quintillion grains of rice; taller than Mt. Everest – Numbers so big they are inconceivable
  14. 14. The Digital Age • Exponential – ASCI Red fastest computer in world in 1996 ($55 million and 1600 square feet of floor space) – 1.8 teraflops of computer speed – Sony PlayStation 3 in 2005 ($500 and less than a tenth of a square meter): Sold 64 million units – 1.8 teraflops of speed
  15. 15. The Digital Age • Exponential – Second machine age – Second half of the chess board – “into a time when what’s come before is no longer a particularly reliable guide to what will happen next”
  16. 16. The Digital Age • Digitalization of everything – Waze tells you what route is best right now due to network effort – Information is non-rival and close to zero marginal cost of reproduction – Products are free, perfect, and instant – “Information is costly to produce but cheap to reproduce.” Carl Shapiro and Hal Varian – “I keep saying that the sexy job in the next ten years will be statisticians. And I’m not kidding.”
  17. 17. The Digital Age • Digitalization of everything – Waze tells you what route is best right now due to network effort – Information is non-rival and close to zero marginal cost of reproduction – Products are free, perfect, and instant – “Information is costly to produce but cheap to reproduce” Carl Shapiro and Hal Varian – “I keep saying that the sexy job in the next ten years will be statisticians. And I’m not kidding.”
  18. 18. Digital Age • Combinatorial – Combining things that already exist – Kary Mullis 1993 Nobel Prize in Chemistry PCR – “I thought it had to be an illusion…It was too easy…There was not a single unknown in the scheme. Every step involved had been done already.” – Crowdscourcing with Innocentive or Kaggle – Waze
  19. 19. The Digital Age • The emergence of AI and connection of most of the people on globe via common digital network • Computers can now demonstrate broad abilities in pattern recognition and complex communication
  20. 20. Moravec’s Paradox • “It is comparatively easy to make computers exhibit adult-level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year old when it comes to perception and mobility.” • “Contrary to traditional assumptions, high-level reasoning requires very little computation, but low-level sensorimotor skills require enormous computational resources.”
  21. 21. The Digital Age • Watson, Jeopardy, and Medicine • Human doctor would need to read 160 hours every week to keep up with relevant new literature • Freestyle chess tournaments: teams have any combination of human and digital players • Weak human + machine + better process superior to strong computer or strong human + machine + inferior process
  22. 22. Something Computers Cannot Do • Ideation • Coming up with new good ideas or concepts • Partnership between Dr. Watson and a human doctor will be far more creative and robust than either of them working alone • “You’ll be paid in the future based on how well you work with robots.” Kevin Kelly
  23. 23. Something Computers Cannot Do • Jeffrey Dyer and Hal Gregersen interviewed 500 prominent innovators • Disproportionate number went to Montessori • Not following rules • Self motivation • Questioning • Doing things a bit differently
  24. 24. Big Data Viktor Mayer-Schonberger & Kenneth Cukier, 2013 • Big data refers to things one can do at a large scale that cannot be done at a smaller one, to extract new insights or create new forms of value, in ways that change markets, organizations, the relationship between citizens and governments. • Causality is replaced by correlation • Not knowing why but only what
  25. 25. The Amount of Data Available is Truly Big • The International Data Corporation reported that the amount of digital data exceeded 1 zetabyte in 2010. • In 2011 this number was almost 2 zetabytes. • Google’s Eric Schmidt claims that every two days we create as much information as we did from the dawn of civilization up until the year 2003.
  26. 26. Sizing Up Big Data Steve Lohr, NY Times, June 20, 2013 • Bundle of technologies – Web pages, browsing habits, sensor signals, social media, GPS location data, genomic information, surveillance videos – Advances in data storage and processing – Machine learning/AI software to find actionable correlations from the big data
  27. 27. Sizing Up Big Data Steve Lohr, NY Times, June 20, 2013 • Philosophy about how decisions should be made – Decisions based on data and analysis – Less based on experience and gut intuition – Eliminates anchoring bias and confirmation bias • Revolution in measurement – Digital equivalent of the telescope – Digital equivalent of the microscope
  28. 28. Jeffrey Hammerbacher • All industries are being disrupted – Moneyball, 538, Large Hadron Collider • McKinsley: Big Data: The Next Frontier for Competition – $338 billion potential annual value to US healthcare – $165 billion in clinical operations – $105 billion in research and development
  29. 29. Jeffrey Hammerbacher • Oracle: From Overload to Impact – Healthcare executives say collecting & managing more business information today than 2 years ago – Average increase 85% per year • Frost & Sullivan: US Hospital Health Data Analytics Market – 2011 10% of US hospitals use data analytic tools – 2016 50% of US hospitals will use data analytic tools
  30. 30. Jeffrey Hammerbacher on Moneyball • Triple Crown in MLB: Batting average, RBI, HR • OPS (on base plus slugging) • GPA (gross production average) • TOB (times on base) • The outcome is how many runs we score and allow; A’s have big, fat, slow Matt Stairs who is terrible outfielder. Need stat that reflects both runs produced at bat & runs saved by defense • WAR (“Wins above replacement”)
  31. 31. New York City’s Office of Policy & Strategic Planning • 1 terabyte of data flows into office every day • 95% success rate in identifying restaurants dumping cooking oil into sewers • Doubled the hit rate of finding stores selling bootleg cigarettes • Sped removal of trees toppled by Sandy • Guided building inspectors to increase citation rate from 13 to 80% for buildings likely to have catastrophic house fires
  32. 32. Big Data Viktor Mayer-Schonberger & Kenneth Cukier, 2013 • To analyze & understand the world we used to test hypotheses driven by theories • Big data discards theories & causality for correlations • Univ. of Ontario premature baby studies • 1,260 data points per second • Diagnose infections 24 hours before apparent • Very constant vital signs indicate impending infection
  33. 33. Big Data Viktor Mayer-Schonberger & Kenneth Cukier, 2013 • Google Nature article predicts flu spread in USA • Compared 50 million search terms with CDC data on spread of flu from 2003 to 2008 • 450 million different mathematical models • 45 search terms had strong correlation with spread of flu • H1N1 crisis in 2009 Google approach worked
  34. 34. Big Data for Cancer Care Ron Winslow, WSJ, March 27, 2013 • ASCO • Database of hundreds of thousands of patients • Prototype has collected 100,000 breast cancer patients from 27 groups who have different EMRs • “Recognition that big data is imperative for the future of medicine” Lynn Etheredge • Less than 5% of adult cancer patients participate in randomized clinical trials
  35. 35. Big Data Viktor Mayer-Schonberger & Kenneth Cukier, 2013 • Recombinant data • Danish Cancer Society study on cell phone/cancer • Cellphone users from 1987 to 1995 (358,403) • Brain cancer patients (10,729) • Registry of education and disposable income • Combining the three databases found no increase in risk of cancer for those who used cell phones • Not based on sample size; based on N=all
  36. 36. Digital Medicine of Present & Future • Agus consulted on treatment of Steve Jobs • Jobs had both his cancer and normal cells sequenced for molecular targeted therapy • Oncologists customized his chemotherapy to target specific defective molecular pathways in his tumor • Treatment changed when tumor mutated during therapy
  37. 37. Digital Medicine of Present & Future • One of Steve Jobs’ doctors said there was hope that his cancer would soon be considered a manageable chronic disease, which could be kept at bay until he died of something else. • “I’m either going to be one of the first to be able to outrun a cancer like this, or I’m going to be one of the last to die from it. Either among the first to make it to shore, or the last to get dumped.”
  38. 38. Cancer Is Not a Disease of Organs: Turned on Genes • Adenocarcinomas with driver mutation for EGFR gene • Clinical response with oral med Gefitinib • Adenocarcinomas with driver mutation Alk+ gene • Clinical response with Crizotinib • SCC of lung • Breast Cancer
  39. 39. Melanoma • Sixty percent of patients have specific point mutation (V600E) in the driver mutation BRAF gene • 80% response rate when treated with orally active BRAF mutation directed drug that specifically binds the mutated protein
  40. 40. Problems with Personalized Medicine WY • Tumor’s genetic makeup varies significantly within same tumor sample • Sampling error may miss genetic mutations that affect course of disease • Complicates personalized medicine strategy • Only 1/3 of 128 mutations were present in all sites sampled of 4 patients with RCC • Differences in mutations: primary vs. mets
  41. 41. Systems Biology Yields New Therapies treatment-for-leukemia-glimpses-of-the-future.html?pagewanted=all • Dr. Lukas Wartman of Washington University developed Adult Acute Lymboblastic Leukemia • Sequenced cancer cells & healthy cells • Discovered normal gene in overdrive producing huge amounts of protein • Drug for kidney cancer shut down the malfunctioning gene • Whole genome sequencing
  42. 42. A Catalog of Cancer Genes That’s Done Carl Zimmer, NY Times, Feb 6, 2014 • Cancer Genome Atlas NIH 2005 • $375 million • 500 samples from each of 20 cancer types • Discovered new genes associated with cancer • Tarceva approved by FDA for lung cancers with EGFR mutation (10% of nonsmall cell cancers)
  43. 43. A Catalog of Cancer Genes That’s Done Carl Zimmer, NY Times, Feb 6, 2014 • “The Cancer Genome Atlas has been a spectacular success, there’s no doubt about that.” Bruce Stillman of Cold Springs Harbor Lab
  44. 44. A Catalog of Cancer Genes That’s Done Carl Zimmer, NY Times, Feb 6, 2014 • Broad Institute of MIT and Harvard propose completing the atlas • 100,000 cancer samples need to be analyzed • “How could we think of beating cancer in the long term without having the whole catalog?” Eric Lander of MIT
  45. 45. A Catalog of Cancer Genes That’s Done Carl Zimmer, NY Times, Feb 6, 2014 • Broad Institute of MIT and Harvard propose completing the atlas • 100,000 cancer samples need to be analyzed • “How could we think of beating cancer in the long term without having the whole catalog?” Eric Lander of MIT
  46. 46. A Catalog of Cancer Genes That’s Done Carl Zimmer, NY Times, Feb 6, 2014 • Broad Institute of MIT and Harvard propose completing the atlas • 4742 samples from 21 cancer types • Identified 33 new genes identified associated with cancer that were missed in the original Cancer Genome Atlas
  47. 47. A Catalog of Cancer Genes That’s Done Carl Zimmer, NY Times, Feb 6, 2014 • “Whether we need to know every cancer gene, I’d like to see an argument for how that’s going to help the advancement of new therapy.” Stillman • “There’s no question that it would be valuable. The question is whether it’s worth it.” Bert Vogelstein of Johns Hopkins
  48. 48. Algorithms Mine Public Data • Atul Butte combined data from 130 studies of gene activity levels in diabetic & healthy tissue • Butte identified new gene associated with Type 2 DM because stood out in 78/130 studies • Algorithm looking for drugs & diseases that had opposing effects on gene expression – Cimetidine for lung adenocarcinomas – Topiramate for Chrohn’s Disease
  49. 49. Algorithms Mine Public Data • Russ Altman used algorithms to mine Stanford Translational Research Integrated Database Environment & FDA adverse event reports database • Patients taking SSRI antidepressants and thiazide are at increased risk for long QT syndrome, a serious cardiac arrhythmia
  50. 50. Predictive Analytics Eric Siegel, Wiley, 2013 • Predicting sepsis – Sisters of Mercy Health System predicts septic shock based on vital signs observed over time. Detected 71% of cases with low false positive rate • Predicting death – US health insurance company predicts likelihood person will die within 18 months to trigger end-of-life counseling on living wills and palliative care – predicts your risk of death in surgery based on your condition
  51. 51. Predictive Analytics Eric Siegel, Wiley, 2013 • UPMC – Predicts patient’s risk of readmission within 30 days of discharge • Heritage Provider Network – $3 million competition to predict number of days patient will spend in hospital over next year • BYU & University of Utah – Correctly predicted 80% of premature births based on peptide biomarkers found as early as 24 weeks
  52. 52. Predictive Analytics Eric Siegel, Wiley, 2013 • Blue Cross Blue Shield of Tennessee – Claims data analysis predicts which health resources individual member will need in the coming year • Multicare Health System in Washington State – $2 million in missed charges a year identified using algorithm
  53. 53. Big Data Viktor Mayer-Schonberger & Kenneth Cukier, 2013 • Multiple uses of same database • Data exhaust: digital trail people leave in their wake
  54. 54. Big Data Viktor Mayer-Schonberger & Kenneth Cukier, 2013 • Paralyzing privacy – Notice and consent – Cannot give informed consent for secondary uses – Anonymization does not work • AOL 2006 20 million search queries from 657,000 users: NY Times identified user number 4417749 as Thelma Arnold (“My goodness, it’s my whole personal life. I had no idea somebody was looking over my shoulder.”) • Netflix Prize 100 million rental records from 500,000 users; Mother and closeted lesbian in Midwest was reidentified
  55. 55. Big Data Viktor Mayer-Schonberger & Kenneth Cukier, 2013 • Probability and punishment – Minority Report: People are imprisoned not for what they did, but for what they are foreseen to do, even though they never actually commit the crime – Blue CRUSH (Crime Reduction, Utilizing Statistical History in Memphis, Tennessee – Homeland Security FAST (Future Attribute Screening Technology) – Big data based on correlation unsuitable tool to judge causality and thus assign individual culpability
  56. 56. Big Data Viktor Mayer-Schonberger & Kenneth Cukier, 2013 • Dictatorship of Data – Relying on numbers when they are far more fallible than we think – Robert McNamara’s body count numbers in Viet Nam – Michael Eisen tried to buy The Making of a Fly on Amazon in April 2011. Two established sellers offering the book for $1,730,045 and $2,198,177. Two week escalation to a peak of $23,698,655.93 on April 18 – Unsupervised algorithms priced the books for the two sellers.
  57. 57. Big Data Viktor Mayer-Schonberger & Kenneth Cukier, 2013 • Regulatory shift from “privacy by consent” to “privacy through accountability” • “Differential privacy” through deliberately blurring the data so hard to reidentify people • Openness, Certification, Disprovability • Algorithmists to perform “audits”
  58. 58. What Big Data Can’t Do David Brooks, NY Times, February 26, 2013 • Data struggles with the social • Data struggles with context • Data creates bigger haystacks (spurious correlations that are statistically significant) • Data has trouble with big problems • Data favors memes over masterpieces • Data obscures values
  59. 59. What Big Data Will Never Explain • “To datafy a phenomenon,” they explain, “is to put it in a quantified format so it can be tabulated and analyzed.” • Sentiment analysis mathematical model for grief called Good Grief Algorithm • “The mathematization of subjectivity will founder upon the resplendent fact that we are ambiguous beings. We frequently have mixed feelings, and are divided against ourselves.”
  60. 60. The Hidden Biases of Big Data • Big Data vs. Data with Depth • “With enough data, the numbers speak for themselves.” Chris Anderson • Can numbers actually speak for themselves? Sadly, they can't. Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations. • Hidden biases in both the collection and analysis stages
  61. 61. The Hidden Biases of Big Data • Google Flu Trends vs. CDC – 11% vs. 6% of US population infected – Media coverage affected Google Flu Trends • Boston’s StreetBump smartphone app – 20,000 potholes a year need to be patched – Poor areas have less cell phones, less service • Hurricane Sandy 20 million tweets + 4square – Grocery shopping day before – Night life peaked day after – Illusion Manhattan was hub of disaster
  62. 62. Automate This Christopher Steiner, 2012 • Dr. Bot – Always be convenient and available – Know all your strengths and weaknesses – Know every risk factor past conditions might signal – Know your complete medical history – Know medical history of last 3 generations of family – Never make careless mistake in prescription
  63. 63. Automate This Christopher Steiner, 2012 • Dr. Bot – Always be up to date on treatments and discoveries – Never fall into bad habits or ruts – Monitor you at all times – Always be searching for the hint of a problem by monitoring pulse, cholesterol, blood pressure, weight, lung capacity, bone density, changes in the air you expel
  64. 64. Vinod Khosla (Sun Microsystems) • Being part of the healthcare system is a disadvantage to disrupting the status quo • Machine learning system will be cheaper, more accurate, and more objective than physicians • Machine expertise would need to be in the 80th percentile of human physician expertise
  65. 65. Vinod Khosla (Sun Microsystems) • Do we need doctors or algorithms • “Health is like witchcraft and just based on tradition” • 80% of physicians will be replaced by machines • 80% of doctors are below the top 20% • We will not need average doctors. • Still need “doctors like Gregory House who solve biomedical puzzles beyond our best input ability”
  66. 66. Will Robots Steal Your Job? ml • “At this moment, there's someone training for your job. He may not be as smart as you are—in fact, he could be quite stupid—but what he lacks in intelligence he makes up for in drive, reliability, consistency, and price. He's willing to work for longer hours, and he's capable of doing better work, at a much lower wage. He doesn't ask for health or retirement benefits, he doesn't take sick days, and he doesn't goof off when he's on the clock.What's more, he keeps getting better at his job.”
  67. 67. How Robots Will Replace Doctors doctors/2011/08/25/gIQASA17AL_blog.html • “We’re not sitting in that room wrapped in a garment made of the finest recycled sandpaper because we were hoping for a good conversation. We’re there because we’re sick…, and we’re hoping this arrogant, hurried, credentialed genius can tell us what’s wrong. We go to doctors not because they’re great empaths, but because we’re hoping medical school has made them into the closest thing the human race has developed into robots.”
  68. 68. References • The Second Machine Age by Erik Brynjolfsson & Andrew McAfee, Norton, 2014 • Big Data by Viktor Mayer-Schonberger & Kenneth Cukier, Houghton Mifflin Harcourt, 2013 • The Creative Disruption of Medicine: How the Digital Revolution Will Create Better Health Care by Eric Topol, Basic Books, 2012 • The End of Illness by David Agus, Simon Schuster, 2013