What can healthcare executives learn from military decision-making, as it relates to predictiveanalytics in healthcare? As it turns out, quite a lot. Dale Sanders, senior vice president for strategy at Salt Lake City, Utah-based Health Catalyst, drew some surprising parallels between these two vital sectors of the economy during a concluding session at the Plante Moran Healthcare Executive Summit on June 5 in Chicago. His main theme was to remember that in predictive analytic analytics, it's the intervention that matters, noting that much of the industry is seduced by flashy predictive analytics "objects" without thinking through the needed interventions which are needed to get the proper ROI.
1. Predictive Analytics:
It’s the Intervention That Matters
P L A N T E M O R A N H E A LT H C A R E E X E C U T I V E S U M M I T
June 4-5, 2014
2. What Motivates Human Beings?
Like it or not, fast or slow, your company now
adapts to change, at the speed of software.
The decisions you make as executives and
leaders about the software that your company
uses to run its operations will determine your
company’s long long term success or failure. It’s
not just facilities, people, and products anymore.
3. The Agenda
Alignment
Human, societal, and organizational motives with
software strategies
General overview of predictive analytics
Nuclear delivery, counter-terrorism, and
healthcare delivery
The odd parallels
Predictive analytics in healthcare
When does it work and when doesn’t it?
How much should we expect from it and when?
What about Long Term Care?
4. Before Healthcare:
An Oddly Relevant Career Path
US Air Force CIO
• Nuclear warfare operations
TRW
Credit risk scoring, nuclear ballistic missile
maintenance and engineering
• NSA
• Nuclear Command & Control Counter Threat
Program
• Joint Chiefs of Staff
• Strategic Execution Decision Aid
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5. Key Messages & Themes
1. Predictions without interventions are useless-- and
potentially worse than useless
And those interventions better align with your economic model
2. Some of the most valuable predictions don’t need a
computer algorithm
Nurses and physicians can tell you
We already know what the interventions should be
3. Missing data = Poor predictions
4. When it comes to analytics, there is lowering
hanging fruit than predictive analytics
Target wasteful healthcare, first
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7. What Motivates Human Beings?
Mastery: The opportunity to master a
skill and be recognized for it
Autonomy: An environment in which
people are given the tools and support
to work under their own authority
Purpose: Living and working for
something larger than themselves
Economics: Enough material wealth to
at least live safely and comfortably, if
not more
With influence from Daniel Pink
8. Homo Economicus vs. Homo Reciprocans?
Motivated by self-interest or
motivated by cooperation?
“…the individual [and company]
seeks to attain very specific and
predetermined goals to the greatest
extent, with the least possible cost.”
“When times are tight, good will
takes flight.”
13. Sampling Rate vs. Predictability
The sampling rate and volume of data in an
experiment is directly proportional to the
predictability of the next experiment
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21. Where And How Can A Computer Help?
Reduce variability in decision making & improve outcomes
22. Desired Political-Military Outcomes
1. Retain US society as described in the
Constitution
2. Retain the ability to govern & command US
forces
3. Minimize loss of US lives
4. Minimize destruction of US infrastructure
5. Achieve all of this as quickly as possible
with minimal expenditure of US military
resources
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23. Can We Learn From Nuclear Warfare
Decision Making?
“Clinical” observations
• Satellites and radar indicate an enemy
launch
Predictive “diagnosis”
• Are we under attack or not?
Decision making timeframe
• <4 minutes to first impact when enemy
subs launch from the east coast of the US
“Treatment” & intervention
• Launch on warning or not?
25. Patient Fight Path Profiler
The Goal: Predictable, fast turnaround of patients to a good life
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26. Healthcare As a Battle Field…??
The Order of Battle and the Order of Care
Demand forecasting: What do we need and when?
27. NSA, Terrorists, and Patients
The Odd Parallels of Terrorist Registries and Patient Registries
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28. Predicting Terrorist Risk
Risk = P(A) × P(S|A) × C
• Probability of Attack
• Probability of Success if Attack occurs
• Consequences of Attack (dollars, lives, national psyche,
etc.)
• What are the costs of intervention and
mitigation?
• Do they significantly outweigh the Risk?
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31. *Apologies for non-attribution. This diagram was taken from a text book many years ago and the specific reference has been lost.
COLLECTIVE
CONSUMPTION
COLLECTIVE CONSUMPTION
NATURAL PROGRESSION OF
DISEASE
PERSONAL CONSUMPTION
LOW RISK
AT RISK
EARLY SIGNS
AND SYMPTOMS
DISEASE
DISABILITY
(BODY STRUCTURE
AND FUNCTION)
CHRONIC CONDITION
AND FUNCTIONAL
DECLINE
DEPENDENCY
FOR SELF CARE
DEATH
GOVERNANCE
AND HEALTH
SYSTEM
ADMINISTRATION
COLLECTIVE
PREVENTION:
Epidemiologic
Surveillance and
Risk and Disease
Control Program
Management
CURE, TREATMENT,
REHABILITATION
MAINTENANCE,
LTC, PALLIATIVE CARE
PERSONAL PREVENTION:
Information & Counseling,
Immunization, Early Case
Detection, Health Condition
Monitoring
The Healthcare Ecosystem*
32. True Population Risk
Management
32
Robert Wood Johnson
Foundation, 2014
Requires a collaborative
strategy between leaders in
healthcare, politics, charity,
education, and business
33. Healthcare Analytics Adoption Model
Level 8
Cost per Unit of Health Payment & Prescriptive
Analytics
Contracting for & managing health. Tailoring
patient care based on population outcomes.
Level 7
Cost per Capita Payment &
Predictive Analytics
Diagnosis-based financial reimbursement &
managing risk proactively
Level 6
Cost per Case Payment
& The Triple Aim
Procedure-based financial risk and applying “closed
loop” analytics at the point of care
Level 5 Clinical Effectiveness & Accountable Care Measuring & managing evidence based care
Level 4 Automated External Reporting Efficient, consistent production & agility
Level 3 Automated Internal Reporting Efficient, consistent production
Level 2 Standardized Vocabulary & Patient Registries Relating and organizing the core data
Level 1 Integrated, Enterprise Data Warehouse Foundation of data and technology
Level 0 Fragmented Point Solutions Inefficient, inconsistent versions of the truth
34. What Are Trying To Predict and Why?
In the current economic model
Those patients and situations that maximize our
revenue
In the future economic model
Those patients and situations that maximize our
margin
Healthcare predictive analytics vendors are,
for the most part, selling concepts that are
suited for the latter, not the reality of the
former
35. What Are We Trying to Predict? Why?
Common applications being marketed today
Identifying preventable readmissions
Risk management of decubitus ulcers
LOS predictions in hospital and ICU
Cost per patient per inpatient stay
Likelihood of inpatient mortality
Likelihood of ICU admission
Appropriateness of C-section
Emerging: Genomic phenotyping
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36. Example Variables: Readmission Drivers
Newborn delivery
Multiple prior admissions
High creatinine
High ammonia
High HBA1C
Low Oxygen Sats
Age
Admitting physician is
pulmonologist or
infectious diseases
Prior admission for CHF
Prior traumatic stupor &
coma
Prior nutritional disorders
Diabetic drugs
36Swati Abbott
Weighted
Predictive
Model
Now
what?
Risk of
Readmission
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37. Most Common Causes for Readmission
Robert Wood Johnson Foundation, Feb 2013
1. Patients have no family or other caregiver at home
2. Patients did not receive accurate discharge instructions,
including medications
3. Patients did not understand discharge instructions
4. Patients discharged too soon
5. Patients referred to outpatient physicians and clinics not
affiliated with the hospital
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38. 38
Forecasting:
Process Model Structural Model: Bill of Resources
Patient Seen in
Emergency Dept
Admit Patient:
Presumptive
Diagnosis:
Pneumonia
Discharge
Monitor
Care
Delivery
Standard
Order Sets
Equipment
Labor
Materials
Facilities
Nursing Orders:
Respiratory Therapy:
Medication Orders:
Resource
Demand
Day 1 Day 2 Day 3 Day 4 Day 5
Edgewater Consulting
39. Predictive Analytics: Socioeconomic
Data Matters In Healthcare
Not all patients can participate in a protocol
At Northwestern, we found that 30% of patients
fell into one or more of these categories
Cognitive inability
Economic inability
Physical inability
Geographic inability
Religious beliefs
Contraindications to the protocol
Voluntarily non-compliant
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40. Accounting For These Patients
30% of your patients will have to be treated and/or
reached in a unique way
• Your predictive algorithms must be adjusted these
attributes, especially for readmission
• These patients are a unique numerator in the overall
denominator of patients under accountable care
• You need a data collection & governance strategy for
these patient attributes
• You need a different interventional strategy for each
of the 7 categories
• Your physician compensation model must be
adjusted for these patient types
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42. Start Within Your Scope of Influence
We are still learning how to manage outpatient populations
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43.
44.
45.
46.
47. Where Do We Start, Clinically?
We see consistent opportunities, across the
industry, in the following areas:
• CAUTI
• CLABSI
• Pregnancy management,
elective induction
• Discharge medications
adherence for MI/CHF
• Prophylactic pre-surgical
antibiotics
• Materials management,
supply chain
• Glucose management in
the ICU
• Knee and hip replacement
• Gastroenterology patient
management
• Spine surgery patient
management
• Heart failure and ischemic
patient management
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49. The State of Long Term Care
12 million: The number of Americans expected to
need long-term care in 2020.
40%: The percentage of the older population with
long-term care needs who are poor or near-poor
(income below 150% of the federal poverty level).
78%: Percentage of the elderly in need of long-term
care who receive that care from family members and
friends.
2.44 years: Average length of stay for current nursing-
home residents
Morningstar, 2012
The Pending Tsunami
51. State of Healthcare IT in LTC
HIT is used primarily for state or federal payment
and certification requirements.
There is minimal use of clinical HIT applications.
HIT systems are not integrated.
HIT systems are underused.
California Health Care Foundation
No Data, No Predictions
52. Summary
1. Alignment of human, societal, company motives
with software strategies is CRITICAL
2. Predictions without interventions are useless
3. Some of the most valuable predictions don’t need a
computer algorithm
We already know what the interventions should be
4. Missing data = Poor predictions
5. When it comes to analytics, there is lowering
hanging fruit
Target wasteful variability, first
Deming: Where there is variability, there is opportunity
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53. Many Thanks…!
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• Contact information
• dale.sanders@healthcatalyst.com
• @drsanders
• www.linkedin.com/in/dalersanders
54. Group Discussion
1. What would you like to predict in today’s economic
model and why?
2. What would you like to predict in tomorrow’s
economic model and why?
3. What data do you need to support precise predictive
analytics?
4. What types of new intervention strategies do you
need to complement these predictive models?
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What about Long Term Care?