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Technology in Healthcare Transformation

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Healthcare delivery is becoming an increasingly complex operation. Nurses, physicians and other allied healthcare professionals are increasingly measured on their quality of work, even with increasing patient volume and patient complexity. Technology, from sensors to analytics to software based decision support and automation, have the potential to both leverage our healthcare provider workforce to mange increasing demands and to improve quality. This presentation will focus on the key areas of opportunity for technology to improve the capabilities of healthcare providers in delivering quality care.



Published in: Healthcare

Technology in Healthcare Transformation

  1. 1. 11:00 am CALNOC Robert Mittendorff MD MBA Partner, Norwest Venture Partners TECHNOLOGY IN HEALTHCARE TRANSFORMATION
  2. 2. Robert Mittendorff MD MBA WHO AM I? Partner at Norwest Venture Partners Norwest Venture Partners is a firm with assets of $5B+ Focus on venture and growth healthcare investments in: Digital Health, Consumer Health, and Healthcare IT , Medical Devices and Diagnostics, Healthcare Services. VP of Marketing and BD in Medical Robotics Co. Led concept to commercial launch for Magellan Robot Led partnership with Philips Healthcare for $100M+ Emergency Physician (Board Certified) Emergency Physician, Bay Area hospitals Residency at Stanford Hospitals Education MD Harvard Medical School MBA Harvard Business School SM (abt) MIT BS, Biomedical Engineering, Johns Hopkins
  3. 3. (c) Robert Mittendorff MD CONFLICT OF INTEREST Ownership Interest via Norwest Venture Partners in HealthCatalyst, Omada Health, iRhythm, Telcare, CareCloud, ClearCare, TigerText, Crossover Health, iCardiac, AnalyticsMD The Views expressed herein are my own and are not attributable to any investment or employer Robert Mittendorff MD MBA
  4. 4. Intro to lorem ipsum TECHNOLOGY TRANSFORMATION AND PREDICTING THE FUTURE (c) Robert Mittendorff MDRobert Mittendorff MD MBA …there is signal and there is noise…
  5. 5. (c) Robert Mittendorff MD SIGNAL AND NOISE Robert Mittendorff MD MBA Source: CBInsights Market Report on Digital Health Trends, 2015
  6. 6. Intro to lorem ipsum ONE OF OUR ROLES AS PATRIOTS: ENTITLEMENTS (WE ARE A COST CENTER) (c) Robert Mittendorff MD 70% 60% 50% 40% 30% 20% 10% 0% Robert Mittendorff MD MBA Source: x
  7. 7. (c) Robert Mittendorff MD WHY DOESN’T HEALTHCARE (DELIVERY) INNOVATE FASTER ? Robert Mittendorff MD MBA
  8. 8. (c) Robert Mittendorff MD WHY DOESN’T HEALTHCARE (DELIVERY) INNOVATE FASTER "If things seem under control, you are just not going fast enough." -- Mario Andretti Robert Mittendorff MD MBA
  9. 9. (c) Robert Mittendorff MD WHY DOESN’T HEALTHCARE (DELIVERY) INNOVATE FASTER "If things seem under control, you are just not going fast enough." -- Mario Andretti Robert Mittendorff MD MBA ?
  10. 10. "Take calculated risks. That is quite different from being rash." -- General George Patton (c) Robert Mittendorff MD WHY DOESN’T HEALTHCARE (DELIVERY) INNOVATE FASTER: LIFE ON THE LINE Robert Mittendorff MD MBA
  11. 11. The Forces Changing Healthcare Innovations Transforming Healthcare Methods and Evidence Technology Adoption in Healthcare Summary (c) Robert Mittendorff MD AGENDA Robert Mittendorff MD MBA
  12. 12. (c) Robert Mittendorff MD FORCES IN HEALTHCARE: OPPORTUNITY OR PERIL The Rise of the Consumer Paying for Value Digital Information Factories of Providers as Employees Alternative Care Models and Mobility Analytics, AI, ML and Automation Robert Mittendorff MD MBA
  13. 13. 0% 20% 40% 60% 80% 2001 2003 2005 2007 2009 2011 2013 (c) Robert Mittendorff MD HITECH ACT FUELED THE FIRST WAVE OF HEALTH INFORMATION DIGITIZATION First, Digitization and Enumeration… 1. Clinical and operational improvements at the system level requires digitization and structuring of data 2. Once information is digitized, analytics professionals and a culture of data driven improvement must be created 3. Once an organization has the capability to organize around data, insight, and practice change, it must be continually rewarded for improving performance EMR Adoption By US Physician Practices Robert Mittendorff MD MBA Source: CMS data on EHR adoption and summary from KFF
  14. 14. (c) Robert Mittendorff MD FORCES IN HEALTHCARE: OPPORTUNITY OR PERIL The Rise of the Consumer Paying for Value Digital Information Factories of Providers as Employees Alternative Care Models and Mobility Analytics, AI, ML and Automation Robert Mittendorff MD MBA
  15. 15. (c) Robert Mittendorff MD FEWER PROVIDER ORGANIZATIONS OWN MORE PHYSICIANS THAN EVER Physician Employees 2014 2000 30% 70% Robert Mittendorff MD MBA Source: KFF and Leemor Dafny; Healthcare M&A Information Source
  16. 16. (c) Robert Mittendorff MD FORCES IN HEALTHCARE: OPPORTUNITY OR PERIL The Rise of the Consumer Paying for Value Digital Information Factories of Providers as Employees Alternative Care Models and Mobility Analytics, AI, ML and Automation Robert Mittendorff MD MBA
  17. 17. THE PURCHASER IS INCREASINGLY BECOMING FEDERAL (OR STATE) Commercial Federal State & Local 2015 Private Federal State & Local 39% 13% 20192010 48% 41% 13% 46% 37% 14% 50% (c) Robert Mittendorff MDRobert Mittendorff MD MBA Source: CMS.gov, state agency public databases for 2015, and CBO.
  18. 18. (c) Robert Mittendorff MD AT RISK ORGANIZATIONS ARE BEING CREATED (600+) TO CARE FOR 20M+ Robert Mittendorff MD MBA Source: Muhelstein, et al . Health Affairs 2013.
  19. 19. (c) Robert Mittendorff MD FORCES IN HEALTHCARE: OPPORTUNITY OR PERIL The Rise of the Consumer Paying for Value Digital Information Factories of Providers as Employees Alternative Care Models and Mobility Analytics, AI, ML and Automation Robert Mittendorff MD MBA
  20. 20. (c) Robert Mittendorff MD INDIVIDUALS AND FAMILIES SEE THEIR COSTS DOUBLED WITH SHIFTING TREND Robert Mittendorff MD MBA Source: Kaiser Family Foundation 2010 and 2016
  21. 21. (c) Robert Mittendorff MD FORCES IN HEALTHCARE: OPPORTUNITY OR PERIL The Rise of the Consumer Paying for Value Digital Information Factories of Providers as Employees Alternative Care Models and Mobility Analytics, AI, ML and Automation Robert Mittendorff MD MBA
  22. 22. MOBILE PHONES ARE A UBIQUITOUS PLATFORM (c) Robert Mittendorff MD In 1984, more than half of the world population lived in a country with less than 1 phone per 100 people, and two thirds had no access to a phone In 2014, global mobile phone penetration by country reached 96% and hit 90% in the developing world In 2014, more than 2 trillion text messages were sent in the US, and more than 8 trillion globally. (< 1 million in 1984) Robert Mittendorff MD MBA Source: (right) www.Marketer.com; 2010/2015. (left) Hall, A, et al. Anna Rev Public Health 2015 Mar 18.
  23. 23. FROM SMS TO VIDEO CHAT: REASONS WHY ALL AGES ADOPT MOBILITY Robert Mittendorff MD MBA Source: www.Marketer.com; 2010/2015
  24. 24. (c) Robert Mittendorff MD FORCES IN HEALTHCARE: OPPORTUNITY OR PERIL The Rise of the Consumer Paying for Value Analytics, AI, ML and Automation Digital Information Factories of Providers as Employees Alternative Care Models and Mobility Robert Mittendorff MD MBA
  25. 25. WHAT IS ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN A NUTSHELL? Source: left, “Her” movie website, right, “Minority Report” movie website, all rights reserved to original publishers. Robert Mittendorff MD MBA
  26. 26. WHAT IS ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN A NUTSHELL? Source: left, “Her” movie website, right, “Minority Report” movie website, all rights reserved to original publishers. Robert Mittendorff MD MBA AI and the Automation of Labor for Behavior Change AI/ML and Predictive and Prescriptive Analytics in Real Time
  27. 27. WHAT IS ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN A NUTSHELL? Robert Mittendorff MD MBA Artificial Intelligence (AI): Engineering machines to think or mimic the thinking and operations of a human. Machine Learning is frequently considered a set of technologies found in an AI. Machine Learning (ML): The use of algorithms to structure, parse, and learn from data sets (statistical or other) patterns that can be used to classify or predict in novel data sets. • Supervised learning algorithms (training data is labeled) • Unsupervised learning algorithms (training data in unlabeled) • Semi supervised learning algorithms (training data is a mix of labelled and unlabeled) • (examples include Regularization /Elastic Net, Regression and Regression Trees, Nearest Neighbor, Decision Trees, Bayesian approaches, and 50+ more) Deep Learning (a ML technique): The use of (frequently) neural networks (NN) trained on data sets to recognize patterns and features without an explicit parametric model. Neural networks can then be used on novel datasets to predict or classify. In Healthcare we like evidence to demonstrate capabilities of technology on a “VALIDATION DATASET” that the system has never seen.
  28. 28. (c) Robert Mittendorff MD IT HAS HEADROOM IN HEALTHCARE ($ OF $100 IN REVENUE SPENT ON IT) Internet Finance Healthcare Pharma Mfg Construction $6.70 $6.30 $4.20 $3.20 $1.70 $1.00 Robert Mittendorff MD MBA Source: Deloitte Touche Tomatsu, 2013, public report on IT spend
  29. 29. (c) Robert Mittendorff MD IT HAS HEADROOM IN HEALTHCARE ($ OF $100 IN REVENUE SPENT ON IT) Internet Finance Healthcare Pharma Mfg Construction $6.70 $6.30 $6.30 $3.20 $1.70 $1.00 $2.10 Robert Mittendorff MD MBA Source: Deloitte and Touche, 2013, public report on IT spend
  30. 30. (c) Robert Mittendorff MD IT HAS HEADROOM IN HEALTHCARE ($ OF $100 IN REVENUE SPENT ON IT) Internet Finance Healthcare Pharma Mfg Construction $6.70 $6.30 $4.20 $3.20 $1.70 $1.00 Automation Prescriptive Analytics Predictive Analytics Business Intelligence Customer Rel. Mgmt. Digitization & Databases Robert Mittendorff MD MBA Source: Deloitte and Touche, 2013, public report on IT spend
  31. 31. The Forces Changing Healthcare Innovations Transforming Healthcare Methods and Evidence Technology Adoption in Healthcare Summary (c) Robert Mittendorff MD AGENDA Robert Mittendorff MD MBA
  32. 32. (c) Robert Mittendorff MD INNOVATIONS TRANSFORMING HEALTHCARE Clinical Communications & Workflow Solutions Real Time Prescriptive Analytics Digital Therapeutics and Digital Vaccines Telemedicine (Semi Automated) Healthcare Services Clinical Decision Support Robert Mittendorff MD MBA
  33. 33. (c) Robert Mittendorff MD BEHAVIOR IS A BIG PROBLEM TO SOLVE IN FLATTENING THE COST CURVE Robert Mittendorff MD MBA Source: McGinnis et al, New England Journal of Medicine 2002.
  34. 34. (c) Robert Mittendorff MD BEHAVIOR IS A BIG PROBLEM TO SOLVE IN FLATTENING THE COST CURVE Robert Mittendorff MD MBA Source: McGinnis et al, New England Journal of Medicine 2002.
  35. 35. (c) Robert Mittendorff MD AUTOMATE THE LABOR INTENSIVE, CLINICALLY VALIDATED APPROACH The Diabese Adult The “Healthy” Adult High Calorie Poorly Balanced Diet Low Activity Poor Medical “Compliance” Calorie Appropriate Diet Moderate Activity Reasonable “Compliance” ? $6,500 / Year $1,000 / Year Robert Mittendorff MD MBA
  36. 36. (c) Robert Mittendorff MD BEHAVIOR MODIFICATION CAN BE MORE POTENT THAN A DRUG 31% 58% Metformin DPP (Behavior Alone) 8% of the population developed diabetes NNT of 14 5% of the population developed diabetes NNT of 6 Robert Mittendorff MD MBA Source: McGinnis et al, New England Journal of Medicine 2002, CDC DPP Data.
  37. 37. Intro to lorem ipsumRobert Mittendorff MD MBA
  38. 38. Intro to lorem ipsumRobert Mittendorff MD MBA WHAT HAPPENS WHEN YOU CROSS CANDY CRUSH WITH HEALTH RELATED BEHAVIOR CHANGE PROTOCOLS?
  39. 39. (c) Robert Mittendorff MD CAN WE USE LESSONS IN ENGAGEMENT TO SCALE BEHAVIOR CHANGE? + Robert Mittendorff MD MBA Source: YMCA (left) and king.com Candy Crush game (right)
  40. 40. DIGITAL THERAPY FOR BEHAVIOR CHANGE: AS GOOD AS A DRUG? Omada Health Prevent™ Program Robert Mittendorff MD MBA
  41. 41. DIGITAL THERAPY FOR BEHAVIOR CHANGE: AS GOOD AS A DRUG? Robert Mittendorff MD MBA
  42. 42. (c) Robert Mittendorff MD INNOVATIONS TRANSFORMING HEALTHCARE Clinical Communications & Workflow Solutions Real Time Prescriptive Analytics Digital Therapeutics and Digital Vaccines Telemedicine (Semi Automated) Healthcare Services Clinical Decision Support Robert Mittendorff MD MBA
  43. 43. TELEMEDICINE IS HERE: TECHNOLOGY, PAYMENT, CONSUMERS ALIGN New Specialties: TeleUrgent Care? TeleGeriatrician? (c) Robert Mittendorff MDRobert Mittendorff MD MBA Source: IHS (various years), and the Journal of Telemedicine and Telecare, June 2012.
  44. 44. GRANDMA SKYPING GRANDDAUGHTER: 3% OF THEM USE MOBILE VIDEO Robert Mittendorff MD MBA
  45. 45. TELEMEDICINE IS HERE: TECHNOLOGY, PAYMENT, CONSUMERS ALIGN (c) Robert Mittendorff MDRobert Mittendorff MD MBA Source: Forbes 2016
  46. 46. TELEBEHAVIORAL HEALTH COULD BE TELE THERAPEUTIC (c) Robert Mittendorff MDRobert Mittendorff MD MBA Source: TalkSpace Data (internal data; publication forthcoming)
  47. 47. (c) Robert Mittendorff MD INNOVATIONS TRANSFORMING HEALTHCARE Clinical Communications & Workflow Solutions Real Time Prescriptive Analytics Digital Therapeutics and Digital Vaccines Telemedicine (Semi Automated) Healthcare Services Clinical Decision Support Robert Mittendorff MD MBA
  48. 48. Intro to lorem ipsum …the inability to balance and steer confronts students of the flying problem… …when this one feature has been worked out the age of flying machines will have arrived, for all other difficulties are of minor importance… Source: The Wright Brothers, McCullough, and public historic references.
  49. 49. CARE COORDINATION: SMART SYSTEMS AND INTERVENTION THE AVIONICS OF POPULATION MANAGEMENT Robert Mittendorff MD MBA Source: New York Times, 1947 (left), NASA website (right top and right bottom)
  50. 50. (c) Robert Mittendorff MD MORE THAN 90 HEALTHCARE AI STARTUPS HAVE BEEN FUNDED Source: CBInsights 2016 Robert Mittendorff MD MBA
  51. 51. IMPROVING OPERATIONAL PERFORMANCE WITH AI AND MACHINE LEARNING Real time analytics, predictive & prescriptive, can guide us at the point of care from describing to forecasting to offering a program of actions within a care team Robert Mittendorff MD MBA
  52. 52. (AIR) TRAFFIC CONTROL IN PATIENT FLOW USING AI AND MACHINE LEARNING Robert Mittendorff MD MBA
  53. 53. (AIR) TRAFFIC CONTROL IN PATIENT FLOW USING AI AND MACHINE LEARNING 30% Reduction in Left Without Being Seen Rate (850 families treated instead of leaving) 14% Reduction in ER Length of Stay 20% Reduction in Door to Physician Time 40% Reduction in “Unnecessary“ Tests Moved from #29 in Patient Satisfaction to #3 in their 30 Hospital ER System within 1 year Robert Mittendorff MD MBA Source: AnalyticsMD Case Study
  54. 54. (c) Robert Mittendorff MD INNOVATIONS TRANSFORMING HEALTHCARE Clinical Communications & Workflow Solutions Real Time Prescriptive Analytics Digital Therapeutics and Digital Vaccines Telemedicine (Semi Automated) Healthcare Services Clinical Decision Support Robert Mittendorff MD MBA
  55. 55. (c) Robert Mittendorff MD THE NEW PROVIDER IN HEALTHCARE IS TEAMS SURROUNDING PATIENTS Robert Mittendorff MD MBA
  56. 56. (c) Robert Mittendorff MD INNOVATIONS TRANSFORMING HEALTHCARE Clinical Communications & Workflow Solutions Real Time Prescriptive Analytics Digital Therapeutics and Digital Vaccines Telemedicine (Semi Automated) Healthcare Services Clinical Decision Support Robert Mittendorff MD MBA
  57. 57. (c) Robert Mittendorff MD THE RHYTHM ZIO PATCH AND CLOUD BASED ANALYTICS SOLUTION Robert Mittendorff MD MBA Source: iRhythm Website
  58. 58. (c) Robert Mittendorff MD CONNECTED HEALTH IS ABOUT REAL TIME DATA AND OPERATIONAL LEVERAGE 1/28/15, 10:07 AMiRhythm - ZIO Services The Right Test, The First Time The ZIO XT Service is the only long-term, continuous cardiac monitoring option that is proven in multiple, peer-reviewed publications (healthcare-clinical-evidence.php) to produce a higher diagnostic yield and change patient management - sooner in the diagnostic pathway - compared to traditional approaches. For Reimbursement information: More ® ABOUT US Our Mission (About-us-our- mission.php) Management Team (About-us- Management.php) Board Of Directors (about-us- board-of-directors.php) Scientific Advisory Board (About-Us-Scientific- Advisory.php) Investors (about-us- investors.php) SUPPORT Contact Us Locations (Work- With-Us- location.php) Work with Us (Support-Work- With-Us.php) ! (https://www.facebook.com/i " (https://twitter.com/iRhyt + (https://plus.google.c $ (https://www.link technologies- inc.) % & 1. Automation with MD Supervision Can Increase Quality 2. Automation Can Make Things Possible That Are Otherwise Economically Infeasible 3. Technologically Enabling The Offering of a Healthcare Service (Cardiac Monitoring) Allows You to Be Best in Class Robert Mittendorff MD MBA Source: iRhythm Website
  59. 59. (c) Robert Mittendorff MD INNOVATIONS TRANSFORMING HEALTHCARE Clinical Communications & Workflow Solutions Real Time Prescriptive Analytics Digital Therapeutics and Digital Vaccines Telemedicine (Semi Automated) Healthcare Services Clinical Decision Support Robert Mittendorff MD MBA
  60. 60. (c) Robert Mittendorff MD CLINICAL DECISION SUPPORT HAS THE POTENTIAL TO MASS CUSTOMIZE Mass Customization in Healthcare Using data, analytics, decision support, and predictive and prescriptive analytics to: Right/Right/Right… Treat or diagnose the patient by the right provider using the right test/treatment with the right equipment at the right time in the right setting in an efficient and cost effective way with the right followup and right monitoring using all of the data about that patient Robert Mittendorff MD MBA Source: McKinsey; HealthCatalyst Website
  61. 61. The Forces Changing Healthcare Innovations Transforming Healthcare Methods and Evidence Technology Adoption in Healthcare Summary (c) Robert Mittendorff MD AGENDA Robert Mittendorff MD MBA
  62. 62. LEVELS OF EVIDENCE REVIEW Level 1 Systematic Reviews & Randomized Controlled Trials Level 2 Cohort Studies Level 3 Case-Controlled Studies Level 4 Case Series Level 5 Case Based Reasoning or Experts Robert Mittendorff MD MBA
  63. 63. (c) Robert Mittendorff MD DOES ‘SMART TEXTING’ / SMS LEAD TO BEHAVIOR CHANGE: THE EVIDENCE 2002: The first text messaging study published in health (1); “Mobile phone text messaging can help young people manage asthma” 2014 Househ, et al.: First Meta-analysis of reviews (umbrella): 13 systematic reviews 2015: Hall, A. et al.: 15 systematic reviews of 89 unique studies ranging from [10-5,800 patients/study] 2015 Hall, A, et al 2015 Included Studies (abridged list of the 89): • Diabetes: 16 of 16 studies reported statistically significant effects on health outcomes or health behaviors (large variation in size & design) • Smoking Cessation: 6 of 8 studies demonstrating statistically significant behavior change or outcomes (smoking cessation, self report; 7 RCT) • Weight Loss/Physical Activity: 11 of 19 had statistically significant effects on weight and/or activity • Chronic Disease Management: 3 of 4 well designed studies from a group of 16 demonstrated statistical significance in outcome or behavior • Medication Adherence: 20 of 33 had statistically significant effects on behaviors or outcomes with asthma (3/3) and HIV (5/10) Source: (1) Neville, R, et al. BMJ 2002. (2) Househ, et al. Health Inform J. 2014 (3) Hall, A, et al. Anna Rev Public Health 2015 Mar 18; Burke, et al. AHA Scientific Statement: Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention. Circa 2015. “Our review found that the majority of published text-messaging interventions were effective when addressing diabetes self-management, weight loss, physical activity, smoking cessation, and medication adherence for antiretroviral therapy” - Hall, A, et al. 2015 (3) “…low to moderate research evidence exists on the benefits of SMS interventions for appointment reminders, promoting health in developing countries and preventive healthcare…” - Househ, et al. 2014 (2) Robert Mittendorff MD MBA
  64. 64. (c) Robert Mittendorff MD IS TELEMEDICINE SAFE, EFFICACIOUS, AND DOES IT REDUCE COSTS? 2012 TeleICU Care: Systematic review of 865 citations with 11 observational studies that met selection criteria. 2016 Telestroke: Systematic review and meta-analysis evaluating 529 records, with 7 studies involving 1,863 patients that met eligibility criteria. TeleBehavioral Health 2013 Telemental Health: 14 studies met inclusion criteria in comparing telehealth modality with nontelehealth (randomized) for depression. 2016 TeleUrgent Care: CalPERS TelaDoc study of first 19 months of experience; 1.3% of enrollees used service, 1 visit/yr avg. “Our findings indicated that … tPA delivery through telestroke networks is safe and effective in the 3 hour time window.“ Kepplinger, et al. 2016 (2) “Telemedicine was associated with lower ICU & hospital mortality among critically ill patients.” Wilcox, et al. 2012 Source: (1) Wilcox, ME et al. The effect of telemedicine in critically ill patients : systematic review and meta-analysis (2) Kepplinger, et al. Safety and efficacy of thrombolysis in tele stroke. Neurology 2016. (3) Osenbach, et al. Synchronous Telehealth Technologies in Psychotherapy for Depression: A Meta-Analysis.. Depression and Anxiety, 2013. (4) Uscher-Pines, et al. Access and Quality of Care in Direct to Consumer Telemedicine. Telemedicine and e-health 2016. “Overall, we found no evidence to suggest that the delivery of psychotherapy via … telehealth…is less effective than nontelehealth means in reducing depression symptoms…” Osenbach, et al. 2013. (3) “Teladoc providers were less likely to order diagnostic testing and had poorer performance on appropriate prescribing for bronchitis…[and patients] were not preferentially located in underserved communities…” (4) Robert Mittendorff MD MBA
  65. 65. (c) Robert Mittendorff MD DOES CLINICAL DECISION SUPPORT IMPROVE OUTCOMES 2016 Clinical Decision Support in the ICU with (near) Real Time Data: 25 articles reviewed in meta-analysis of approaches of CDS in AIMS. 2012 Computerized Clinical Decision Support for Diabetes Management: 15 studies, with several at high risk of bias. 20XX Machine Learning and AI Source: (1) Simpao, et al. A systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems. J Clin Monit Comp 2016. (2) Jeffery, R. Diabetic Medicine, 2012. “Computerized clinical decision support systems in diabetes management may marginally improve clinical outcomes, but confidence in the evidence is low because of risk of bias, inconsistency and imprecision.” “ There is strong evidence for the inclusion of near real-time and point-of-care CDS in [Anesthesia Information Management Systems] to enhance compliance with perioperative antibiotic prophylaxis and clinical documentation…” Simpao, et al. 2016 Robert Mittendorff MD MBA
  66. 66. The Forces Changing Healthcare Innovations Transforming Healthcare Methods and Evidence Technology Adoption in Healthcare Summary (c) Robert Mittendorff MD AGENDA Robert Mittendorff MD MBA
  67. 67. (c) Robert Mittendorff MD THE HORSE: HOW AUTOMATION IMPROVES EFFICIENCY From Dead Horses to the Model T Robert Mittendorff MD MBA Source: Library of Congress website (top left and bottom left, Mittendorff analysis of auto registration data and horse and mule population in the US from 1900 to 1950 $0.0 M $10.0 M $20.0 M $30.0 M $40.0 M $50.0 M 1900 1910 1920 1930 1940 1950 Horse Auto Technology Substitution: US Autos and Horses
  68. 68. (c) Robert Mittendorff MD THE STORY OF THE MILKMAN: HOW TECHNOLOGY DISRUPTS JOBS Pasteurization Refrigeration in Home Ice Man Milk Man State and Federal Laws 1971 FDA requires all milk transported interstate to be pasteurized; …bye bye Milkman Robert Mittendorff MD MBA Source: Library of Congress website, Frigidaire historical photos, website.
  69. 69. Intro to lorem ipsum AMZN: NEW DISTRIBUTION MODELS LEADS TO THE SHUTTERING OF OLD (c) Robert Mittendorff MD In just five months, new Kindle replaces ‘Harry Potter and the Deathly Hallows’ as best-selling product in Amazon’s history SEATTLE, Dec 27, 2010 (BUSINESS WIRE) – (NASDAQ: AMZN)–Amazon.com today announced that the third-generation Kindle is now the bestselling product in Amazon’s history, eclipsing “Harry Potter and the Deathly Hallows (Book 7).” The company also announced that on its peak day, Nov. 29, customers ordered more than 13.7 million items worldwide across all product categories, which is a record-breaking 158 items per second. Robert Mittendorff MD MBA Source: The Wall Street Journal and Business Wire (top and bottom respectively), and images from Borders and Amazon websites.
  70. 70. Intro to lorem ipsum TECHNOLOGY ADOPTION ALSO LEADS TO OBSOLESCENCE (PCI VS CABG) (c) Robert Mittendorff MD Holmes J S et al. Health Aff 2007;26:169-177 CABG born: 1960, Goetz et al., New York PTCA born: 1977 Gruentzig et al., Zurich PCI-BMS born: 1986 Puel, Sigwart, et al. (1994 Palmaz-Schatz stent approved) PCI-DES born in US: 2003 JNJ-CYPHER approved in US. Robert Mittendorff MD MBA
  71. 71. (c) Robert Mittendorff MD WHY DOESN’T HEALTHCARE (DELIVERY) INNOVATE FASTER "Do not be too timid and squeamish about your actions. All life is an experiment. The more experiments you make the better." -- Ralph Waldo Emerson Robert Mittendorff MD MBA
  72. 72. Intro to lorem ipsum “…the rocket worked perfectly except for landing on the wrong planet…” (presumed) Wernher von Braun Source: “Wernher von Braun - The Man Who Sold the Moon.”, et al.
  73. 73. Intro to lorem ipsum TBD X (c) Robert Mittendorff MD
  74. 74. FROM PILOTS TO CREATING “BUSINESS AS USUAL” Real value is created by moving from compelling logic to evidence in outcomes or cost Clinician and patient engagement is required; think passive data collection & simple workflow The commercial model should ultimately pay for the cost of adoption Robert Mittendorff MD MBA
  75. 75. (c) Robert Mittendorff MD ADOPTION AND BUSINESS MODELS FOR HEALTHTECH Robert Mittendorff MD MBA Perpetual License (+) Aligns selling and implementation costs with revenue (+) Can demonstrate rapid revenue ramp (-) Lumpy and unpredictable sales (-) Requires maintenance re-up annually Per Click or Per Procedure (+) Aligns use with revenues (+) Rapid D2C on-boarding possible (-) Requires conversion of customer each click unless “habit” results (-) Requires training and implementation investment without known return for B2B2C Software as a Service and PMPM (+) Aligns use with revenues and cloud (+) Aligns with user or patient onboarding (-) Requires careful on boarding resourcing (-) Requires more capital commitment into company
  76. 76. Pilots are here: Pilots are becoming a mainstay in healthcare transformation with technology Pilots are useful if designed properly, with short duration, clear goals, and managed costs Pilots allow rational assessment of the risks, benefits, and operational challenges of innovation Pilots fail both the provider organization and the innovative company: If the product or service doesn’t work If there is no executive buy-in, a learning organization mentality, and provider/user buy-in If there are not clearly defined goals as to the duration, scope/size, and go-no go decisions for rollout If the executive sponsor cannot drive larger rollout from a successful pilot (c) Robert Mittendorff MD PILOT VIGOROUSLY, BUT DON’T FORGET TO SET YOUR SIGHTS TO TAKE OFF Robert Mittendorff MD MBA
  77. 77. (c) Robert Mittendorff MD AGENDA The Forces Changing Healthcare How Innovation Capital (VC) Works Themes for Innovation In Healthcare Business Models Summary Robert Mittendorff MD MBA
  78. 78. (c) Robert Mittendorff MD SUMMARY Robert Mittendorff MD MBA Take Calculated Risks and Experiment Administrative and Technologic Forces Will Drive Change or Extinction Use Data to Drive Decisions on Adoption and Look For Early Fast Wins Be Mindful of the Limited Resources (but incredible technology and teams) of Innovative Companies Labor (semi) Automation Is Coming In the Form of Analytics and AI/ML

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