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Healthcare Analytics: Right-Brain Advice in a Left-Brain World


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U.S. healthcare is badly missing the soft, human side of healthcare analytics, especially as it impacts clinicians. How do we fix that? This webinar explores those ideas.

You won’t hear Dale talk about SQL, inner joins, outer joins, R, Python, logistic regression, random forest, or convolutional neural networks but instead, in this webinar he talks about the principles and philosophy of analytics.

For the most part, we’ve figured out the technology of analytics. That is all left-brain thinking—analytical, logical and methodical in nature—and it is literally getting easier every day with new data technology. But, in healthcare, we’re missing the right-brain thinking—creative and artistic in nature—that has almost nothing to do with technology but has everything to do with the human side of pursuing “data driven healthcare.”

Right-brain thinking is required for the oddities and shortcomings of healthcare data, and how to manage those shortcomings in the context of delivering data to the humans who we hope will consume it. The right-brain relates to the personality characteristics of the people who are leading your analytics strategy. It relates to the leadership culture of the organization and where that culture resides on a scale of transparency, internally and externally. The right-brain relates to behavioral economics, evolutionary psychology, human decision making theories, and the fundamental factors that motivate or demotivate human behavior. The right-brain relates to concepts like experimental design and PICO—patients, interventions, comparisons, and outcomes—that, if followed, can make your analytics more truthful and believable. It has to do with the way we negotiate and structure performance-based contracts that are loaded with quality metrics that either measure things that can’t be measured accurately or may measure the wrong thing, altogether.

You see, right-brained thinking in this left-brain world of analytics relates to a bunch of things, but mostly it relates to the Golden Rule of Data. Do unto others with data as you would have them do unto you.

Published in: Healthcare
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Healthcare Analytics: Right-Brain Advice in a Left-Brain World

  1. 1. Healthcare Analytics: Right-Brain Advice in a Left-Brain World Dale Sanders Chief Technology Officer Health Catalyst July 2019 Creative Commons Copyright
  2. 2. © 2018 Health Catalyst Health Catalyst Company Confidential • If healthcare analytics is fundamentally about decision support and changing or maintaining human behavior based on those decisions • And healthcare data quality, other than imaging, is overall very poor, given the criticality of the mission and complexity of decision making • There’s a big gap between the data we do have vs. data we should have to support the criticality of the mission • And changing human behavior with poor data is incredibly difficult, if not worse … poor data, if presented as good, entrenches the wrong behavior • Then, healthcare analytics success must be accompanied by an inordinately heavy emphasis on human engagement Today’s Assertion 2
  3. 3. Agenda • The State of Healthcare Data and Analytics • Reading List for Human-Side of Analytics • Lessons Learned from My Career • Air Force, Space & Defense • Intermountain Healthcare • Northwestern Medicine • Cayman Islands • Health Catalyst
  4. 4. Healthcare Data & Analytics: Brief State of Affairs
  5. 5. © 2018 Health Catalyst Health Catalyst Company Confidential5 Why have we not seen the analytics revolution that I predicted in 2002?
  6. 6. © 2018 Health Catalyst Health Catalyst Company Confidential The Human Health Data Ecosystem • The digital world of health is largely stuck in the lower left two clouds. • Our digital understanding of the patient requires the entire data ecosystem. 6
  7. 7. © 2018 Health Catalyst Health Catalyst Company Confidential7 Quality of Healthcare Data Relative Ease of Human Behavioral Change This is where we should be, given the criticality of the mission. This is where we are.
  8. 8. © 2018 Health Catalyst Health Catalyst Company Confidential Let’s kick-off a $175B, 10-year mission to digitize the patient … not the billing process. That feels like our generation’s moonshot. 8 IEEE Future Directions; “Can We Have a Digital Twin?”
  9. 9. © 2018 Health Catalyst Health Catalyst Company Confidential Acknowledge Our Poor Data Quality to Physicians “Here’s the truth: We know that all of these quality measures are a giant pain in the neck. We know they are burning you out and stripping you of your sense of mission, autonomy and purpose. We also know that most of these measures are clinically irrelevant. It’s a spiral of frustration. You are forced to collect irrelevant data, then we measure your performance according to that irrelevant data. Going forward, we will minimize all of that burden, including the pressure we put on you, AND we commit to giving you the data you want, when you want it, and how you want it so that you can practice to the top of your profession.” If I were still a C-level in the trenches of healthcare analytics 9
  10. 10. © 2018 Health Catalyst Health Catalyst Company Confidential The State of Healthcare Data Quantitative Data Examples • Lab results • Some measurements in diagnostic imaging • Vital signs • Dosing • Genomics Qualitative Data Examples • Diagnosis codes • Clinical notes Significant amounts of qualitative data And we only sample data about 3x per year, and on patients seeking treatment in a traditional clinical encounter, none from those. 10
  11. 11. © 2018 Health Catalyst Health Catalyst Company Confidential • The maturity of any body of science and engineering is directly proportional to the mathematic models which describe that body’s predictability and reproducibility. • Observation, measurement, and data collection precede the development of mathematical models. • We must acknowledge the overall lack of data and maturity of mathematical models in healthcare and adjust our approach to decision making according to the reality of our current state. Acknowledge Our Lack of Mathematical Maturity 11
  12. 12. © 2018 Health Catalyst Health Catalyst Company Confidential12 Maturity of Mathematical Models Economics Psychology Biology Quantum Physics Electrical, Computer, Aeronautical, and Aerospace Engineering Newtonian Physics Healthcare If not for randomized clinical trials, which have problems of their own with predictability and reproducibility, our “Mathematical Maturity” would score even lower. Meteorology Facebook Ads Feed J
  13. 13. © 2018 Health Catalyst Health Catalyst Company Confidential • Mouse models != human models • Cell mis-identity and contamination • p-values of 0.05 vs. 0.005 • Lack of open data sharing to allow scrutiny and reproducibility A Sobering Assessment of Published Research 13
  14. 14. © 2018 Health Catalyst Health Catalyst Company Confidential This our CTO’s analog life. This is healthcare’s digital view of our CTO’s life. 14 The Patient View: An Unclear Picture
  15. 15. © 2018 Health Catalyst Health Catalyst Company Confidential On Top of Poor Data We have the usual complexities of human behavior 15 Psychology of Change Tribalism Social Norms of the Tribe Homo Economicus & Evolutionary Biology Behavioral Economics “Truth” According to Data “Truth” According to Perception This tends to be the most dominant influence on behavior.
  16. 16. A Reading List: Decision Making, Human Behavior, and More 16
  17. 17. © 2018 Health Catalyst Health Catalyst Company Confidential17 Undergrad student: “What’s the most valuable class you’ve ever taken?” Me: “Without a doubt, an epistemology philosophy class, “What is Truth?” taught by Dr. Paul Pixler, because it taught me to think, and peel away the false layers of belief until reaching fundamental truths. But it also confused the hell out of me. I’ve never been too sure of anything since then.”
  18. 18. • What are we doing with analytics and decision support to satisfy these basic human needs in clinicians, patients, and administrators? • In the US, our national analytics strategy is detracting from these basic human needs. Mastery, Autonomy, Purpose 1818
  19. 19. 19 History doesn’t repeat itself. Human nature does, which leads to repeated history. “Therefore the laws of biology are the fundamental lessons of history. We are subject to the processes and trials of evolution, to the struggle for existence and the survival of the fittest to survive.” 1963-1969 True “Moon Shot” = Incredibly Complex Organizational Challenges x Unprecedented Engineering Challenges x Political Challenges x Human Lives at Stake x Hopes of the World • 410,000 employees and contractors • 20,000 companies and universities 19
  20. 20. © 2018 Health Catalyst Health Catalyst Company Confidential One of the Best Single References… 20
  21. 21. © 2018 Health Catalyst Health Catalyst Company Confidential Give people freedom of choice, but nudge, don’t shove, with data towards the preferred behavior. This book is mostly about personal self-awareness, but can readily apply to empathy and awareness in others. Data is persuasive when it fits your world-view. If presented insensitively, data entrenches the opinion that’s targeted for change. 21
  22. 22. © 2018 Health Catalyst Health Catalyst Company Confidential Attributes of a Change Negotiator Believability: Is this person honest, sincere, trustworthy, and transparent? If I don’t know them well enough, is there evidence of that in their background? Relatability: Is this person similar to me? Can this person empathize directly with my situation and/or role? Can I relate to this person along other dimensions of empathy, e.g., upbringing, religion, age, gender, race, ethnicity, experiences, hobbies, education, etc.? Credibility: Even though we’re similar, does this person bring a diverse view, expertise or knowledge that I don’t have; and that I value, respect, and need? Inspired by a RAND Corp study, ~1985…? 22
  23. 23. Lessons Learned From the Field 23
  24. 24. © 2018 Health Catalyst Health Catalyst Company Confidential Air Force, Space, Defense Like Healthcare • Strong personalities • Life and time critical • Subjective and objective information Not like healthcare • Leadership insistence on more data, more intel, before making high-risk decisions • Digitizing aircraft, satellites, and ICBMs, is easier than digitizing humans Military Decision Making 24
  25. 25. © 2018 Health Catalyst Health Catalyst Company Confidential25 Boeing 777 Data Ecosystem • 11,600 reportable faults • 270 prognostic alerts • >/=1Tbyte per flight • Aircraft Health Maintenance System (AHMS) • Prognostics and Health Maintenance (PHM) The aircraft industry started formally planning their strategic data acquisition strategy for aircraft health maintenance in the early 1990s, which drove the development of sensors.
  26. 26. © 2018 Health Catalyst Health Catalyst Company Confidential Vested Contracting • Funded by US Air Force • Traditional, legalese-style of contracting and treaty negotiations was not working • Leaner, faster agreements based on shared principles, collaborative economic interests, and conflict governance and resolution • Open books financials Contracting and negotiating like humans, not robots 1. Focus on outcomes, not transactions. 2. Focus on the WHAT, not the HOW. 3. Agree on clearly defined and measurable outcomes. 4. Pricing model with incentives that optimize both businesses. 5. Insight and resolution versus oversight governance structure. 26
  27. 27. © 2018 Health Catalyst Health Catalyst Company Confidential Lessons Learned: Intermountain Healthcare • The economics of an IDN create a healthy balance and shared incentives between hospitals, clinics, and insurance. • That economic balance makes behavioral change easier. More importantly: • Utah’s state symbol is the beehive, and that’s not about honey. It’s about working hard, together, selflessly for the common good. • Intermountain’s culture is communal. • Reducing clinical variation fit the culture. 27
  28. 28. © 2018 Health Catalyst Health Catalyst Company Confidential Lessons Learned: Northwestern Medicine • My first executive level meeting was a disaster. • The Intermountain-centric message for analytics didn’t fly. • Dean Landsberg: “Dale, you just told all those physician researchers that they should reduce their variation in care. They came here to experiment with controlled variation. Come back with a different message.” • Pivoted the analytics strategy to research, and we became Data Heroes. 28
  29. 29. © 2018 Health Catalyst Health Catalyst Company Confidential Make the Best Out of the Data with Good Process PATIENT OR PROBLEM How would you describe a similar group of patients? What are the most important characteristics of the patient? INTERVENTION What main intervention are you considering? What do you want to do with this patient? COMPARISON What is the main alternative being considered, if any? OUTCOME What are you trying to accomplish, measure, improve or affect? PICO: Ask the questions 29
  30. 30. 30 In 2007, this was our approach to “Meaningful Use” at Northwestern Medicine. Purposely constrained to one page, based on principles that could be measured, but we didn’t make measurement the core message. We made common sense the core message. This was the seed that turned into the weeds of Meaningful Use. 30
  31. 31. © 2018 Health Catalyst Health Catalyst Company Confidential Drained the analytics energy out of the industry. We are obligated to unwind the legacy of MU as quickly as we created it. Start over. Starting in 2009, Meaningful Use Became Meaningless Use 31
  32. 32. © 2018 Health Catalyst Health Catalyst Company Confidential • It was a nudge, not a push, to embrace analytics. • The autonomy of government-employed physicians in a small labor pool, made behavioral change difficult. • The culture was caught between the US and UK influence. • Culturally, they treated me very well, and I loved them, but there was always a sense that the American Guy had his own agenda, and would only be around for a short time. Lessons Learned: Cayman Islands 32
  33. 33. © 2018 Health Catalyst Health Catalyst Company Confidential Lessons Learned: Health Catalyst • Our Intermountain skills play well in some places, but not in others. • Most clients are not in an IDN economic model. • Many cultures are not as ”beehive” as Intermountain. • Clinical variation reduction has taken a back seat to quality of care analytics tied to reimbursements, and preventive care. 33
  34. 34. Market Segment Core Behavioral and Cultural Economic Drivers Integrated Delivery Network Higher quality, lower cost for their covered lives; but they can’t distinguish between covered and uncovered lives, so they operate most closely to our value proposition. Good combination of care optimization plus care prevention. Academic Medical Center Heads in beds for high acuity, complex and rare patients; grant funding and research; clinical trials enrollment; publications. They typically have reimbursement levels from payers that are 300-400% higher than CMS, but there is a shift to reference pricing that they are starting to feel. Quality measures as it relates to brand and status. Community Hospital Payer mix frequently dominated by Medicare/Medicaid (60-70%) and commercial payers with reference-based pricing; higher bad debt ratios. Typically an important employer in the community so reducing staff through efficiencies is not naturally appealing. Cutting costs through more efficient operations is a high priority. Direct to Employer Healthcare This is the segment that is naturally aligned with maximizing health for the lowest cost possible. Risk Management Entities, including PHMOs This segment is naturally aligned with our mission, but in the big scheme of the economic layers, it’s yet another layer that someone is paying for. Benefits accrue to payers and physicians/hospitals through split margins on MLR savings with the Risk Management entity. ACOs/CINs Performance on Quality Measures and upside risk contracts; downside risk is almost non-existent right now in today’s market. Large Physician Practices Physician productivity and per physician revenue. Life Sciences Pharma, rare disease biotechs and digital therapeutics: risk-based contracts, adoption of guidelines in areas where they are market leaders, pragmatic trials, filling more prescriptions through medication adherence; pharmacovigilance: distinguishing via root cause analysis true adverse events; pragmatic trials; patient selection algorithms; clinically identifying and justifying off-brand use; clinical trials acceleration/adoption; finding rare disease patients to treat. Adjust Your Analytics Strategy to the Cultural Economic Drivers
  35. 35. © 2019 Health Catalyst Wrapping Up • Find your Data Empathy, especially for physicians. • Acknowledge the limitations of existing data and analytics, then adjust your decisions accordingly. • Give physicians some form of analytic hope… give them some data that they want and need. • Look for and adapt to the social norms of the tribe… avoid head-on collisions. • Nudge, don’t push. • Campaign for digitizing the patient. 35
  36. 36. © 2019 Health Catalyst Sept. 10-12, Salt Lake City, Grand America Hotel • National Keynotes • Digital Innovation Showcase • 28 Educational, Case Study, and Technical Breakouts • 24 Analytics Walkabout Projects • Machine Learning Marketplace • Networking (bringing back ”Braindate”) • CME Accreditation for Clinicians • 5-Star Grand America Hotel Experience • 96 Total Presentations Healthcare Analytics Summit 19 36