From “Big Data” to Digital Medicine--PYA Explores Innovations in Healthcare

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With reform in healthcare and advancements in technology, the future of medicine is in a state of flux. What it all means can be heard in discussions from coast-to-coast, in the halls of hospitals, at conferences, and in board rooms.

Among the thought leaders who have broached this timely subject is PYA Principal Kent Bottles, MD, who is also PYA Analytics’ Chief Medical Officer. He recently spoke at The North American Menopause Society Annual Meeting on the topic: “The Perils and Prospects of Practicing Medicine in a Digital Era.”

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From “Big Data” to Digital Medicine--PYA Explores Innovations in Healthcare

  1. 1. Perils & Prospects of Practicing Medicine in a Digital Era Kent Bottles, MD Chief Medical Officer, PYA Analytics Thomas Jefferson University School of Population Health kent@kentbottlesmd.com; 610 639 4956 NAMS 2013 Annual Meeting Plenary Symposium 1
  2. 2. The Digital Revolution in Medicine
  3. 3. 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
  4. 4. 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
  5. 5. 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.
  6. 6. 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 web site.”
  7. 7. 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.”
  8. 8. Eric Topol on MI prevention • “Monitoring would ideally use an implanted nanosensor, smaller than a grain of sand and capable of finding its targets in even one-millionth of a liter of blood, communicating with a patient’s smartphone. Individuals who would get the nanosensors would be those whose genome sequence or other biomarkers had already put them at risk for a heart attack.
  9. 9. Eric Topol on MI prevention • Well before the horse was out of the barn, the nanosensor could alert the individual to seek attention; therapy then would consist of both anti-clotting and anti-inflammatory medications. At some point in the future, nanosensors will likely have the capacity to release medications on their own in response to high levels of circulating cells or nucleic acids”
  10. 10. Digital Medicine Convergence • • • • • • • Genomics Wireless sensors Imaging Information Systems Social networks Ubiquity of smartphones Unlimited computing power via cloud server farms makes Big Data Analytics possible
  11. 11. Data Breaches http://healthitsecurity.com/2013/07/23/healthcare-data-breaches-reviewing-theramifications/ • Texas Health Harris Methodist Hospital – Microfiche not destroyed by paper shredder vendor winds up in public park • SC Department of Health (230,000) – 17 Excel spreadsheets illegally copied • Emory HealthCare (315,000) – Unlocked storage room 10 backup disks lost • Utah Department of Health (780,000) – Weak passwords
  12. 12. Data Breaches http://healthitsecurity.com/2013/07/23/healthcare-data-breaches-reviewing-theramifications/ • 495 breaches • Average: 42,659 • Average time to id: 84.78 days • $4.1 billion cost • Average cost: $8 mill • Average time to notify: 68.31 days
  13. 13. Data Breaches http://healthitsecurity.com/2013/07/23/healthcare-data-breaches-reviewing-theramifications/ • If more than 500 pts affected must report • Devastating Public Relations impact • One class action lawsuit is seeking $4.9 billion in damages • $1000 per patient affected by breach
  14. 14. EHR Associated Medical Errors • National Ambulatory Medical Care Survey found EHRs were not associated with better quality ambulatory care • PA Pt Safety Authority found 324 medical errors due to HER – Default settings – Wrong time errors – Automated stop medications errors
  15. 15. EHR Associated Medical Errors • Vendor contract barriers don’t allow discussion of errors between providers • Usability problem of too many clicks • Coding software problems • System glitches • Keys/bars too close together • Backfiring features
  16. 16. EMR Facilitate Data Mining • OIG interest in cardiac procedures • Heart stents, implantable cardioverterdefibrillators, pacemakers • St. Joseph Medical Center in Towson, MD • Stent rates well above state average
  17. 17. EMR Facilitates Data Mining http://articles.baltimoresun.com/2013-10-04/health/bs-md-co-stent-trial-openings-20131004_1_mark-midei-midei-and-st-catholichealth-initiatives • • • • • • Mark Midel, MD has settled 250 cases 45 malpractice cases pending Hospital sold due to financial difficulties Hospital paid $22 million in fines to CMS Hospital revenues dropped 25% Dr. Midel state medical license revoked
  18. 18. 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
  19. 19. Digital Medicine of Present & Future • Human body and disease is complex emergent system that may never be fully understood • Attention on chronic diseases • Managing chronic diseases rather than cure • Focus on person and the disease
  20. 20. 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
  21. 21. 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.”
  22. 22. Systems Biology Yields New Therapies • Zoledronic acid affects bone metabolism and is used to reduce fractures but does nothing to cancer cells. • Zoledronic acid decreases breast cancer recurrence by 36% presumably because it changes the environment of bones so cancer cells cannot spread.
  23. 23. Systems Biology Yields New Therapies http://www.nytimes.com/2012/06/03/business/geneticists-research-finds-his-owndiabetes.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
  24. 24. Systems Biology Yields New Therapies http://www.nytimes.com/2012/07/08/health/in-gene-sequencing-treatment-forleukemia-glimpses-of-the-future.html?pagewanted=all • Dr. Lukas Wartman of Washington University developed Adult Acute Lymphoblastic 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
  25. 25. 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
  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. Jeffrey Hammerbacher http://www.youtube.com/watch?v=OVBZTDREg7c • 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
  28. 28. Jeffrey Hammerbacher http://www.youtube.com/watch?v=OVBZTDREg7c • 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
  29. 29. 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
  30. 30. Algorithms Mine Public Data • Atul Butte combined data from 130 studies of gene activity levels in diabetic & healthy tissue • Butte identified new gene associate 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
  31. 31. 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
  32. 32. 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
  33. 33. Big Data Viktor Mayer-Schonberger & Kenneth Cukier, 2013 • Datafication of acts of living • Zeo large database of sleep patterns • Asthmapolis sensor to inhaler that tracks location via GPS identifies environmental triggers • Fitbit and Jawbone • iTrem monitors Parkinson’s tremors almost as well as the tri-axial accelerometer used in specialized office medical equipment
  34. 34. Big Data Viktor Mayer-Schonberger & Kenneth Cukier, 2013 • Paralyzing privacy – Notice and consent – Consent for secondary uses impossible – Anonymization does not work • AOL 2006 20 million search from 657,000 users: NY Times 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.
  35. 35. 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 Vietnam – 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.
  36. 36. The Hidden Biases of Big Data http://blogs.hbr.org/cs/2013/04/the_hidden_biases_in_big_data.html • 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
  37. 37. The Hidden Biases of Big Data http://blogs.hbr.org/cs/2013/04/the_hidden_biases_in_big_data.html • 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
  38. 38. 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

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