Optimum Healthcare IT A physician’s perspective on Big Data, Predictive Analytics & Business Intelligence (BI) tools

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Optimum Healthcare IT A physician’s perspective on Big Data, Predictive Analytics & Business Intelligence (BI) tools

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Optimum Healthcare IT A physician’s perspective on Big Data, Predictive Analytics & Business Intelligence (BI) tools

  1. 1. Optimum Healthcare IT A physician’s perspective on Big Data, Predictive Analytics & Business Intelligence (BI) tools Professor Steven Boyages JCMIT 2013 TAIWAN
  2. 2. 10/31/2013 2
  3. 3. 10/31/2013 3
  4. 4. Summary • Healthcare organizations face growing, complex cost-containment pressures. Increasing regulation, diminishing reimbursement, changes in the patient mix, and other challenges mean providers must maximize operational and financial efficiency through best practices and analytics-based decision making. • A combination of insufficient information, poor incentives for cost control along with deficient monitoring and inefficiencies in health care processes—all of those factors have led to much waste of time and funds. • The ability to contain costs while delivering quality healthcare outcomes is eminently possible, but will require a better understanding of what today’s data technologies can achieve. Most people in healthcare are aware of BI tools and analytics but generally its benefits are under-appreciated. As a result, BI in healthcare remains under-implemented. • This presentation will outline uses of BI for clinical and non clinical purposes, how to govern such activity using real world examples. 10/31/2013 4
  5. 5. Bridging the Gap
  6. 6. Objectives of Investment in IT • • • • Improve patient experience Improve patient safety Improve provider experience Create value for money 10/31/2013 8
  7. 7. Health work matrix High Touch Team and Workflow Platform High Tech Technology Platform
  8. 8. New Technology Triggers The Nexus of IT Forces: Social, Mobile, Cloud & Big Data/Information Source: Gartner, 2013
  9. 9. Nearly every transaction or interaction leaves a data signature
  10. 10. Information Someone somewhere is capturing and storing
  11. 11. Sheer scale has far exceeded human sensemaking capabilities
  12. 12. At these scales patterns are often too subtle and relationships too complex or multi dimensional to observe by simply looking at the data
  13. 13. Data mining is a means of automating the process to detect interpretable patterns
  14. 14. It helps us see the forest without getting lost in the trees 10/31/201 3 ©2011 Healthcare Information and Management Systems Society 16
  15. 15. Summary • Big data describes the way we deal with the astonishing accumulation of digital information which is often stored in large unstructured data repositories. • New tools such as business intelligence (BI) have emerged to organise and interpret this vast array of information with benefits in public health, research, patient care and hospital operational systems. 10/31/201 3 ©2011 Healthcare Information and Management Systems Society 17
  16. 16. 10/31/201 3 ©2011 Healthcare Information and Management Systems Society 18
  17. 17. Looking for patterns • The trend of looking for commonalities and overlapping interests is emerging in many parts of both academia and business • At the ultra small nanoscale examination of a cell, researchers say, the disciplines of biology, chemistry and physics begin to collapse in on each other • Online marketers look at your behaviour in a number of contexts to sell you something you may not even know you wanted. 10/31/201 3 19
  18. 18. Algorithms • When it comes to algorithms, “if I can do a power grid, I can do water supply,” said Steve Mills, I.B.M.’s senior vice president for software and systems. Even traffic, which like water and electricity has value when it flows effectively, can reuse some of the same algorithms. • “leveraging the cost structure of new mathematics.” • Patient flow 10/31/201 3 20
  19. 19. Potential areas for use • MGI studied big data in five domains—healthcare in the United States, the public sector in Europe, retail in the United States, and manufacturing and personal-location data globally. Big data can generate value in each. • If US healthcare were to use big data creatively and effectively to drive efficiency and quality, the sector could create more than $300 billion in value every year. • Two-thirds of that would be in the form of reducing US healthcare expenditure by about 8 percent. 10/31/201 3 21
  20. 20. Five broad areas in which big data can create value • First, big data can unlock significant value by making information transparent and usable at much higher frequency • Secondly, as organizations create and store more transactional data in digital form, they can collect more accurate and detailed performance information on everything from product inventories to sick days, and therefore expose variability and boost performance. • Using data for basic low-frequency forecasting to highfrequency nowcasting to adjust their business levers just in time. 10/31/201 3 y 22
  21. 21. Five broad areas in which big data can create value • Third, big data allows ever-narrower segmentation of customers and therefore much more precisely tailored products or services • Fourth, sophisticated analytics can substantially improve decision-making • Finally, big data can be used to improve the development of the next generation of products and services. Eg data obtained from sensors embedded in products to create innovative after-sales service offerings 10/31/201 3 23
  22. 22. Google trends, Google analytics USING DATA 10/31/201 3 24
  23. 23. Google Trends Michael Jackson 10/31/201 3 25
  24. 24. Facebook can predict your breakups 10/31/201 3 ©2011 Healthcare Information and Management Systems Society 26
  25. 25. Your personality can be predicted 10/31/201 3 ©2011 Healthcare Information and Management Systems Society 27
  26. 26. Eating Habits 10/31/201 3 ©2011 Healthcare Information and Management Systems Society 28
  27. 27. Google flu trends 10/31/201 3 ©2011 Healthcare Information and Management Systems Society 29
  28. 28. Health Intelligence: Keeping Score in Health 30
  29. 29. Moving from Analogue to Digital Scoreboards 31
  30. 30. The Next Level: Health Intelligence Systems • Definition  Responsive  Agile  Available  Flexible  Timely  Real time  Near Real time Capability Patient Care  Safety  Decision support  Outcomes Research  Patient Logistics Performance Management  State  Area based  Hospital/cluster/network  Modality (scheduling)  Bedside 32
  31. 31. Mix of Patient & Performance Management tools to support patient care / flow The Next Level: Health Intelligence Systems (a) Patient Care • Bed Board (including LOS enhancements) • Ward Activity and Nursing Display (WAND) • eConsults • iHandover (b) Performance Mgmt • CareFirst meetings run 3 times per week with all senior clinical management • Uses up to date (near real time information) through CareFirst Dashboard – which includes: • Subject Area Dashboards (Patient • Transport booking Safety, Mental • Infectious Diseases Alerts Health, Surgery, Nursing, Costing, ED etc.) • Pharmtrack • Links to hundreds of pre-populated Business Objects Reports (no performance issues) 33
  32. 32. Bed Board Web Based Delivered by legacy PAS Real Time Predictive ED performance Network performance eg cardiology Load Management Patient Placement Length of Stay Features 34
  33. 33. Bed Board: Length of Stay and Inter-hospital Transfer 35
  34. 34. 10/31/201 3 37
  35. 35. New methods of scientific inquiry Big Data Big Question 10/31/2013 38
  36. 36. New methods of scientific inquiry • While it is attractive to contemplate the way everything may become connected to everything else, it presents a number of large challenges. • The lab research model has been important for over a century in both scientific advancement and product development; soon it may also have to accommodate a search for truth based only on pattern-spotting. 10/31/201 3 ©2011 Healthcare Information and Management Systems Society 39
  37. 37. Patterns of care 10/31/2013 40
  38. 38. 10/31/201 3 41
  39. 39. 10/31/201 3 ©2011 Healthcare Information and Management Systems Society 42
  40. 40. Rate% 100000% per% individuals Rate% 100000% per% individuals Figure% Rate% Services% 100% for% 1:% of% per% 000% vitamin% (25?hydroxyvitamin% full% D% D),% blood% count% (FBC)% bone% and% densitometry% quarter% by% between% 2000% 2011 and% Year% Quarter and% QLD,% Queensland;% Northern% NT,% Territory;% NSW,% New% South% Wales;% ACT,% Australian% Capital% Territory;% Western% WA,% Australia;% South% SA,% Australia;% Victoria;% VIC,% TAS,% Tasmania;% FBC,% blood% Full% count 10/31/201 3 ©2011 Healthcare Information and Management Systems Society 43
  41. 41. Table 2: Annual benefit paid by Medicare for 25-hydroxyvitamin D testing and percentage increase since 2000 Year 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 10/31/201 3 Annual Benefit ($) 1,021,784 1,670,597 2,318,770 3,216,543 5,269,951 7,592,467 12,149,112 22,621,733 42,358,509 67,643,016 96,746,203 % Increase 100% 163% 227% 315% 516% 743% 1189% 2214% 4146% 6620% 9468% 44
  42. 42. b) Frequency of testing in individuals with more than one test Frequency of testing per individual 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 10/31/201 3 Number of individuals 1026483 496225 251306 132173 71534 39857 22717 13165 7790 4665 2881 1826 1196 809 555 390 288 221 176 145 126 106 93 79 65 55 47 43 37 Cumulative Percentage 49.5 73.4 85.5 91.8 95.3 97.2 98.3 98.9 99.3 99.5 99.7 99.8 99.8 99.9 99.9 99.9 99.9 99.9 99.9 100 100 100 100 100 100 100 100 100 100 Frequency of testing per individual 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 Number of individuals 12 11 11 10 8 6 5 5 5 4 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 Cumulative Percentage 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 100 45
  43. 43. Google trends for vitamin D 10/31/201 3 ©2011 Healthcare Information and Management Systems Society 46
  44. 44. Google Analytics-Google Trends 10/31/201 3 ©2011 Healthcare Information and Management Systems Society 47
  45. 45. New Technology Triggers The Nexus of IT Forces: Social, Mobile, Cloud & Big Data/Information Source: Gartner, 2013
  46. 46. We need to embrace social media
  47. 47. apps4nsw collaborative solutions for Health
  48. 48. The Two Hype Cycle Views of the Electronic Health Record Usability Value expectations Positive Hype Technology Trigger Negative Hype Peak of Trough of Inflated Disillusionment Expectations time Slope of Enlightenment Plateau of Productivity Source: Gartner, 2013
  49. 49. Adoption framework for an effective HER Needs Benefits • Complexity • Chronicity • Severity • Improved experience • Improved safety • Improved clinical outcomes • Urgency EHR Adoption Risks Trust • Increased clinical risk • Privacy breach • Design • Credibility of information • Security • Privacy • Ease of use
  50. 50. Source: The Lancet (DOI:10.1016/S0140-6736(10)61854-5) Terms and Conditions
  51. 51. What Do We Need To Get There? New-Style Leadership New Skills • • • • • Next Generation of ICT-engaged Clinical Leaders Executive Level-performing CIOs Medical/Clinical Informatics Patient Informatics Enterprise data warehouse architects/data modelers, master data managers • Data Scientists (epidemiologists are the “new cool”) • Analysts - both business & clinical systems and clinical data analysts • Project managers Source: Gartner, 2013
  52. 52. High High Tech Touch
  53. 53. High Touch • “I tell them that their first reflex should be to look at the patient, not the computer,” • Dr. Heineken said. And he tells the team to return to each patient’s bedside at day’s end. “I say, ‘Don’t go to a computer; go back to the room, sit down and listen to them. And don’t look like you’re in a hurry.’ ”
  54. 54. George Bernard Shaw • “The reasonable man adapts himself to the world: the unreasonable one persists in trying to adapt the world to himself. • Therefore all progress depends on the unreasonable man”

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