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Clinical Decision Support: Driving the Last Mile

Self-driving cars have become the most visible form of computer-aided decision support in society. What can we learn from these innovations—both good and bad, technically and culturally—about computer-aided decision support for clinicians? The adoption of EHRs provided a foundation; what and how do we build on that foundation to help clinicians, and patients, benefit from meaningful, precise decision support?

Scott Weingarten, MD, MPH, and Dale Sanders explore clinical decision support in a joint webinar. Dr. Weingarten is recognized throughout the U.S. and international healthcare space as a physician and for his contributions to decision support, including his role in founding Zynx and Stanson Health. Dale brings a technologist’s viewpoint to the conversation, informed by his background in computer-aided decision support in the healthcare, military, and national intelligence sectors.

During this webinar, learn more about the following topics:
-How clinical decision support can improve the quality, safety, and value of care.
-How developments in the field of artificial intelligence will impact clinical decision support.
-The conceptual framework for digitizing an industry.Tradeoffs in artificial intelligence models between data volume and algorithm complexity.
-The approach to digitization in the automobile and aerospace industries.
-Shortcomings in current healthcare data.Future aspirations and plans for further digitization of healthcare.

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Clinical Decision Support: Driving the Last Mile

  1. 1. Clinical Decision Support: Driving the Last Mile January 16, 2020 Dale Sanders Chief Technology Officer, Health Catalyst Scott Weingarten, MD, MPH Chief Executive Officer, Stanson Health
  2. 2. © 2020 Health Catalyst On a scale of 1-5, rate your organization’s clinical decision support effectiveness. Poll Question #1 2 1. Not effective at all – 12% 2. Somewhat effective – 29% 3. Moderately effective – 38% 4. Very effective – 14% 5. Extremely effective – 7%
  3. 3. © 2020 Health Catalyst In your opinion, what is the greatest barrier to better clinical decision support? • Technology of EHRs – 20% • Uncertainties in evidence-based medicine – 13% • Clinician cultural resistance – 30% • Fundamentally poor data quality in healthcare – 30% • Other – 7% Poll Question #2 3
  4. 4. © 2020 Health Catalyst • Spend lots of time getting the Concepts right, then explore options for Implementation • I’m not talking about requirements… I’m talking about Concepts • There are only a few good Concepts for solving a problem, but there are usually lots of options for Implementation • If you don’t nail the Concepts, your Implementation will forever underperform or fail • Example • Historically, the conceptual center of EHR design was the Encounter, but it should have been the Patient • That conceptual miss has dogged all of us for years, forcing all sorts of workarounds in software, data, and workflow Solving Problems, Building Systems
  5. 5. © 2020 Health Catalyst • The human mind works like a filing system • Give it the General file folders, then fill those file folders with Specifics • The military called this “Gen-Spec” learning Teaching and Informing the Human Mind 5
  6. 6. • Dale: 20 minutes – Concepts and frameworks for decision support in healthcare – Current and future state of data in US healthcare • Scott: 20 minutes – The opportunities and potential for better decision support – Examples of clinical decision support in the real world of EHRs • Q&A: 20 minutes Today’s Agenda
  7. 7. © 2020 Health Catalyst Underground Command Center for US Nuclear Forces 7
  8. 8. © 2020 Health Catalyst US National Emergency Airborne Command Post System “Doomsday Planes” 8
  9. 9. © 2020 Health Catalyst Splashdown of inert warheads in Kwajalein Atoll from Peacekeeper Intercontinental Ballistic Missile, ~1995 Telemetry data, galore 9
  10. 10. © 2020 Health Catalyst • Our data quality (Completeness x Validity) in healthcare is not that great • The data is still useful, but beware of these current data quality limitations in decision support Employ Decision Support, Cautiously 10 Time Data Quality 2008
  11. 11. © 2020 Health Catalyst Airplane Pilot Decision Support “The airframe, the hardware, should get it right the first time and not need a lot of added bells and whistles to fly predictably.” “Boeing’s solution to its hardware problem was software.” Choose your use cases carefully… 11
  12. 12. Decision Support Concepts and Frameworks in Healthcare
  13. 13. © 2020 Health Catalyst 1313
  14. 14. © 2020 Health Catalyst Closed Loop Analytics Loop C: Populations • MTTI: Years, decades • SPA: Millions, several hundred thousand • Analytic consumers: Board of Directors, executive leadership team, Strategic plans and policy Loop B: Protocols • MTTI: Weeks, months • SPA: Subsets of patients– hundreds, thousands • Analytic consumers: Care improvement teams, clinical service lines Loop A: Patients • MTTI: Minutes, hours • SPA: Individual patients • Analytic consumers: Physicians and patients at the point of care MTTI: Mean Time To Improvement, SPA: Span of Population Affected 14
  15. 15. © 2020 Health Catalyst Improve Health Level 9 Direct-to-Member Analytics & Artificial Intelligence Level 8 Personalized Medicine & Prescriptive Analytics Level 7 Clinical Risk Intervention & Predictive Analytics Reduce Variation Level 6 Population Health Management & Suggestive Analytics Level 5 Waste & Care Variability Reduction Improve Efficiency Level 4 Automated External Reporting Level 3 Automated Internal Reporting Level 2 Standardized Vocabulary & Member Registries Level 1 Enterprise Data Operating System Level 0 Fragmented Point Solutions The Healthcare Analytics Adoption Model 15
  16. 16. © 2020 Health Catalyst Creating the Patient’s Digital Twin Developing three fundamental AI pattern recognitions in healthcare 16 Patients like this [pattern] Who were treated like this [pattern] Had these outcomes and costs [pattern] Less about predictions, more about patterns
  17. 17. © 2020 Health Catalyst Sanders’ Predictive Analytics Postulate 17 Predictions without interventions are a liability to the decision maker, not an asset.
  18. 18. Digitizing an Industry for Decision Support Aerospace and Automotive Role Models
  19. 19. © 2020 Health Catalyst What’s Required to become “Digitized?” 1. Digitize the assets you are trying to manage and optimize Airplanes Air traffic control, baggage handling, ticketing, maintenance, manufacturing 2. Digitize your operations for managing the assets you are trying to understand and optimize 19 Patients Registration, scheduling, encounters, diagnosis, orders, billing, claims
  20. 20. © 2020 Health Catalyst Data Volume is Key to AI “The Unreasonable Effectiveness of Data”, March 2009, IEEE Computer Society; Alon Halevy, Peter Norvig, and Fernando Pereira, of Google “Invariably, simple models and a lot of data trump more elaborate models based on less data.” 20
  21. 21. © 2020 Health Catalyst • Every 10 hours, Tesla collects 1 million miles of driving data • 25Gbytes per car per hour • “We can fix problems in your car and make it safer, long before you know you need it.” • “10,000 fatalities and 500,000 injuries per year will be prevented.” – Ram Ramachander, Chief Commercial Officer, Social Innovation Business at Hitachi Vehicle Health Monitoring – Human Health Monitoring 21
  22. 22. © 2020 Health Catalyst Properties of Satellite (and Human) Telemetry Data • High Dimensionality: Hundreds to thousands of data variables • Multimodality: Day and night modes; pediatric & adult • Heterogeneity: Continuous, real values; discreet, categorical values • Temporal Dependence: At what time you collect the data matters; the temporal dimension between heterogeneous data also matters • Missing Data: Is the missing data expected to be missing, or not? Spacecraft Health Monitoring – Human Health Monitoring 22 TRW/Northrup Grumman DSP Satellite
  23. 23. © 2020 Health Catalyst 23 “…newest generation aircraft… five-to-eight terabytes per flight” “Airplanes like the 787 and A350 collect 10,000 times more data than 1990s or early 2000s-era aircraft. That is because more parameters are being measured at higher frequencies, using broader transmission pipelines.” – Joel Reuter, Vice President of Public Affairs, Rolls-Royce North America
  24. 24. Current State The Data for Decision Support in Healthcare
  25. 25. © 2020 Health Catalyst Our Digital Understanding of Patients is Poor This is my life. This is healthcare’s digital view of my life. 25
  26. 26. © 2020 Health Catalyst We Are Not “Big Data” in Healthcare, Yet 26 Citation: Dale Sanders, CIO, Northwestern Medicine. Calculating annual storage requirements for the Northwestern electronic health record, 2011 5-8TB per 4 hrs. 30TB in 8 hrs. 26 100 MB per year
  27. 27. © 2020 Health Catalyst Turn this into your strategic data acquisition roadmap • In the US, our digital view of the patient is stuck in the lower left quadrant • On average, we collect data on patients about 3x per year in the US, during visits to the clinic or hospital • We collect almost no data on healthy patients, who rarely visit the healthcare system The Human Health Data Ecosystem 27
  28. 28. © 2020 Health Catalyst • July 2019 • U of Toronto, Microsoft, Johns Hopkins, Harvard, MIT, New York University 28 “…diseases in EHRs are poorly labeled, conditions can encompass multiple underlying endotypes, and healthy individuals are underrepresented. This article serves as a primer to illuminate these challenges and highlights opportunities for members of the machine learning community to contribute to healthcare.”
  29. 29. © 2020 Health Catalyst Clinical Text Data: Questionable Quality 29 In a typical note, 18% of the text was manually entered; 46% copied; and 36%, imported
  30. 30. © 2020 Health Catalyst EHR Documentation = Observed Physician Behavior 30 • 38.5% of review of systems (ROS) were confirmed (61.5% of the time, the EHR data did not reflect reality) • 53% of physical exams (PE) were confirmed (47% of the time, the EHR data did not reflect reality) • Sept 2019 • UCLA, Stanford, UC Santa Cruz Perception Reality
  31. 31. © 2020 Health Catalyst 49% of randomized clinical trails were deemed high risk for wrong conclusions because of missing or poor measurement of outcomes data The Importance of Outcomes Data 31 18 Sep 2019
  32. 32. Future State What’s a better state look like? How do we get there?
  33. 33. © 2020 Health Catalyst Enabling the Digital Healthcare Conversation 33 "I can make a health optimization recommendation for you, informed not only by the latest clinical trials, but also by local and regional data about patients like you; the real-world health outcomes over time of every patient like you; and the level of your interest and ability to engage in your own care. In turn, I can tell you within a specified range of confidence, which treatment or health management plan is best suited for a patient specifically like you and how much that will cost.”* Between a physician and their patient… or patient and their avatar *—Inspired by the Learning Health Community We are parsing this statement for outcomes and cost data, predictive analytics, machine learning, social determinants of health data, recommendation engines
  34. 34. © 2020 Health Catalyst A National Healthcare Goal 34 By 2030, every citizen will possess at least 10,000x more data, coupled with analytics and AI, to support their health optimization, than exists in 2020 In the US, that means going from 100MB to 1TB per year
  35. 35. © 2020 Health Catalyst 35 Feb 2019
  36. 36. © 2020 Health Catalyst Microns-thin, one-inch skin- pliable sensors with integrated Bluetooth antenna, CPU, physiologic monitors, and wireless power 36
  37. 37. © 2020 Health Catalyst Rise of The Digitician and Patient Data Profiles 37 • Different patient types have different data profiles required for the active management of their outcomes and health • I’m not talking about quality measures • I’m talking about telemetry, diagnostics and functional status about the state of the patient, not the state of healthcare processes • It’s the Digitician’s job to prescribe the right sensors and proactively collect this data for patients in their panel, and feed the analytics of that to the care team and patient
  38. 38. © 2020 Health Catalyst In Closing… 38 • Be humble healthcare… look for role models, borrow concepts and hire engineers from military, aerospace, and automotive • The volume and quality of healthcare data is lower than the hype would lead you to believe • The good news: Much is left to achieve, and transformation is truly ahead in front of us
  39. 39. 39 Scott Weingarten, MD, MPH Chief Executive Officer, Stanson Health
  40. 40. © 2020 Health Catalyst • IOM/NAM – 17-year gap • Evidence-based care 50% of the time – Female physicians have lower patient mortality rates than male physicians • 1/3rd of health care costs = waste • In 1 hour…. – There may be approximately 28 deaths in the United States because of medical errors – There may be $22 million spent on medical over-treatment Opportunity 40
  41. 41. © 2020 Health Catalyst 41 Medical Education: Information Acquisition, Application NIH research: $39 billion in FY 2019 US medical research budget 2016, $172 billion Medical Research Funding 20,000 biomedical journals 6,000 articles per day 1 article every 30 seconds 75,000 lab tests 878% health care data growth since 2016 Doubling time of medical information 73 days in 2020 Output Brain The Cloud Point of Care
  42. 42. © 2020 Health Catalyst Changing Care Predictors of Success Adjusted OR Automatic provision of decision support as part of workflow 112 Provision of decision support at the time and location of decision making 15 Provision of recommendation rather than just an assessment 7 Computer-based generation of decision support 6 Source: Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005 Apr 2;330(7494):765. PMID: 15767266 42
  43. 43. © 2020 Health Catalyst CDS 1.0 43 Note: CDS alert displays using EHR’s native best practice alerts; EHR’s do not allow use of actual screenshots Physician starts order in EHR Likely appropriate Order placed Inappropriate order cancelled Likely unnecessary Logic evaluates 30+ elements Choosing Wisely: Don't perform population based screening for 25-OH-Vitamin D deficiency. 1, 2, 3 (American Society for Clinical Pathology) Reasons for override: malabsorption syndromes meds (glucocorticoids, antifungals, etc.) diet excludes dairy products monitoring of known vitamin D deficiency dark skin complexion Hyperlink: Choosing Wisely – American Society for Clinical Pathology Information for Patients: Vitamin D Tests (ASCP) Comments: see comments remove order keep order
  44. 44. © 2020 Health Catalyst CDS 1.0 44 >$400,000 savings/yr. Analytics July 6 – Aug 3, 2019 High Cost Lab Reminder >$500
  45. 45. © 2020 Health Catalyst “Making it easier to do the right thing, harder to do the wrong thing” 45
  46. 46. © 2020 Health Catalyst Comprehensive CDS 46 Update to Practice Standards for Electrocardiograp hic Monitoring in Hospital Settings: A Scientific Statement From the American Heart Association. Circulation 2017; Oct 3:[Epub ahead of print]. Standards for Inpatient Electrocardiographic Monitoring Oct. 04, 2017 $3.7M Annualized Savings “Hard Green” 1. Identified cardiac monitoring/telemetry in order sets & preference lists 2. Utilized BPAs to guide practice 3. No change in mortality, rapid response times or code blues
  47. 47. © 2020 Health Catalyst Order Sets and Preference Lists 47
  48. 48. © 2020 Health Catalyst Order Sets and Preference Lists There was nearly a 50% increased odds of dementia associated with total anticholinergic exposure of more than 1095 TSDDs within a 10-year period, which is equivalent to 3 years’ daily use of a single strong anticholinergic medication at the minimum effective dose recommended for older people Analytics, 1/1/2019-7/28/201948
  49. 49. © 2020 Health Catalyst References: 1. Arch Intern Med. 2009 Nov 23;169(21):1952-60. doi: 10.1001/archinternmed.2009.357. 2. Neuroepidemiology. 2016;47(3-4):181-191. doi: 10.1159/000454881. Epub 2016 Dec 24. 3. Pharmacoepidemiol Drug Saf. 2010 Dec;19(12):1248-55. doi: 10.1002/pds.2031. Epub 2010 Oct 7. Additional sources: *Cancellation of inappropriate benzo or sedative-hypnotic order Choosing Wisely (American Geriatrics Society): “Don’t use benzodiazepines or other sedative-hypnotics in older adults as 1st choice for insomnia, agitation or delirium.” CDS Intervention #92 AMB live, #119 INPT live Prevented 35 falls1 63 dementia2 10 hip fractures3 723 follows*/year Based on HFHS data: CDS analytics 07/01/18 – 06/30/19 Current Benefits: Harm Avoided 49
  50. 50. © 2020 Health Catalyst Peer-comparison Feedback Randomized controlled trial Low value care - Antibiotics for URIs • 248 providers • 14,753 patient visits Results • Control (24.1% to 13.1%) • Peer-comparison feedback (19.9% to 3.7%,p<0.001) 1 year later • Drift • Peer-comparison feedback still has some impact Reinforcing Medical Education 50
  51. 51. © 2020 Health Catalyst 5151
  52. 52. © 2020 Health Catalyst Affiliation % of potentially low-value care (n=47) Physician 1 62% Physician 2 5% 52
  53. 53. © 2020 Health Catalyst 53 CDS 2.0 Avoiding Alert Fatigue and Increasing Impact
  54. 54. © 2020 Health Catalyst 54 Decision Support 2.0
  55. 55. © 2020 Health Catalyst 5555 Decision Support 2.0
  56. 56. © 2020 Health Catalyst Decision Support 2.0 56
  57. 57. © 2020 Health Catalyst • Patient EHR structured data often incomplete, fragmented, and sometimes erroneous1 • Recent survey of U.S. hospitals with advanced EHRs, about 35 % of clinical data was captured in structured format, 65% in unstructured text1 • Use of 1/3 of available clinical data can cause false-positives and missed opportunities • More effective CDS will require NLP, ML, and AI CDS 2.0 1 Meystre SM, Lovis C, Bürkle T, Tognola G, Budrionis A, Lehmann CU. Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress. Yearb Med Inform. 2017;26(1):38–52. doi:10.15265/IY-2017-007 57
  58. 58. © 2020 Health Catalyst Version 2.0, CDS Requires NLP, ML, and AI 58
  59. 59. © 2020 Health Catalyst NLP, AI 59 Fact extraction Extract clinical facts from unstructured EHR elements Fact inference Infer additional clinical facts from the entire EHR record Return CDS Recommend ation ✓ CDS scoring Determine the appropriateness of a provider’s action codified facts “… to r/o pulmonary embolism …” pregnancy status yes no patient record ✓ ✓ ✓ Physician signs order
  60. 60. © 2020 Health Catalyst Need NLP, AI 60
  61. 61. © 2020 Health Catalyst Trajectory 61
  62. 62. © 2020 Health Catalyst 62
  63. 63. © 2020 Health Catalyst • CDS that includes interpretation of free text should provide more effective CDS with less fatigue • Evolution… – 2019 - Clinical, physiological, lab, images, patient preferences, etc. – 2020 - Clinical, physiological, lab, images, patient preferences, social determinants, genetics, proteomics, microbiome, etc. CDS 63
  64. 64. © 2020 Health Catalyst Based upon Mr. Jones’ genetic profile, microbiome information, symptoms, vital signs, laboratory values, personal preferences, social determinants… The Future What tests and treatments are appropriate for Mr. Jones?After review of… Mr. Jones, I would recommend the following…. 64
  65. 65. © 2020 Health Catalyst • Evidence-based care 50% of the time – Female physicians have lower patient mortality rates than male physicians • 1/3rd of health care costs = waste • During the hour that we are spending together today – There may be approximately 28 deaths in the United States because of medical errors – There may be $22 million spent on medical over-treatment Opportunity 65
  66. 66. © 2020 Health Catalyst 66
  67. 67. Q&A 67 Scott Weingarten, MD, MPH Chief Executive Officer, Stanson Health Dale Sanders Chief Technology Officer, Health Catalyst
  68. 68. Thank You!