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.
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
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
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
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
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