There Is A 90% Probability That Your Son Is Pregnant: Predicting the Future of Predictive Analytics in Healthcare

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Predicting The Future Of Predictive Analytics In Healthcare
There’s A 90% Chance Your
Son Is Pregnant

Presenter and Contact
Information
2
Dale Sanders
Senior Vice President, Strategy, Health Catalyst
801-708-6800
dale.sanders@healthcatalyst.com
@drsanders
www.linkedin.com/in/dalersanders/

Acknowledgements
David Crockett, PhD, Health Catalyst
Eric Siegel, PhD, Columbia University
Ron Gault, Aerospace Corporation, Northrup-Grumman, TRW
Wikipedia
3

The Goal Today
I hope you leave this webinar with…
Informed Expectations and Opinions: Be generally aware of the
realistic possibilities for predictive analytics in healthcare, over the next
few years
The Right Questions: To be conversant in the concepts of predictive
analytics and be able to ask reasonably well-informed questions of
your analytics teams, especially vendors, during the strategic process
of developing your organization’s predictive analytics strategy
4

5
Agenda
5
Basic Concepts, Fundamental Assertions
Predictive Analytics Outside Healthcare
Predictive Analytics Inside Healthcare
Key Questions To Ask Vendors And Your Analytics Teams

Sampling of My Background In
Predictive Analytics
6

7
Gartner 2014 Hype Cycle for
Emerging Technology
7
Predictive Analytics in
Healthcare, according to Dale
Sanders

“Beyond math, there are no facts; only
interpretations.”
- Friedrich Nietzsche
8

Challenge of Predicting Anything Human
9

What Should We Expect In
Healthcare?
Machines are predictable; humans aren’t
10
“People are influenced by their environment in innumerable ways. Trying to understand
what people will do next, assumes that all the influential variables can be known and
measured accurately. People's environments change even more quickly than they
themselves do. Everything from the weather to their relationship with their mother can
change the way people think and act. All of those variables are unpredictable. How they will
impact a person is even less predictable. If put in the exact same situation tomorrow, they
may make a completely different decision. This means that a statistical prediction is only
valid in sterile laboratory conditions, which suddenly isn't as useful as it seemed before.”
Gary King, Harvard University and the Director of the Institute for Quantitative Social
Science

Healthcare Analytics Adoption Model
Level 8
Level 7
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0
Personalized Medicine
& Prescriptive Analytics
Clinical Risk Intervention
& Predictive Analytics
Population Health Management
& Suggestive Analytics
Waste & Care Variability Reduction
Automated External Reporting
Automated Internal Reporting
Standardized Vocabulary
& Patient Registries
Enterprise Data Warehouse
Fragmented Point Solutions
Tailoring patient care based on population outcomes and genomic data. Fee-
for-quality rewards health maintenance.
Organizational processes for intervention are supported with predictive risk
models. Fee-for-quality includes fixed per capita payment.
Tailoring patient care based on population metrics. Fee-for-quality includes
bundled per case payment.
Reducing variability in care processes. Focusing on internal optimization and
waste reduction.
Efficient, consistent production of reports & adaptability to changing
requirements.
Efficient, consistent production of reports & widespread availability in the
organization.
Relating and organizing the core data content.
Collecting and integratingthe core data content.
Inefficient, inconsistent versions of the truth. Cumbersome internal and external
reporting.
© Sanders, Protti, Burton, 2013
11

Concepts & Principles of Predictive Analytics
12

Semantics, Ssschmantics
Predictive Analytics and Predictive Models: These terms have their origins in
statisticians; e.g., understanding real-world phenomena such as healthcare, retail
sales, customer relationship management, voting preferences, etc.
Machine Learning Algorithms: This term has its origins in computer scientists; e.g.,
natural language processing, speech recognition, image recognition, adaptive
control systems in manufacturing, robots, satellites, automobiles and aircraft, etc.
13
As it turns out, the latter can be applied to the former, so the two schools
of thought are now generally interchangeable. Don’t let vendors fool
you into thinking that “machine learning” is more sophisticated or better
than predictive modeling.

14
For Now, Just Know The Terms
14
And Know Where To Go For Details

For Now, Just Know The Terms
And Know Where To Go For Details
MachineLearningMastery.com

The Basic Process of Predictive Analytics
16

17
A Big & Common Mistake: Over
Fitting
17
You train the model to be very specific on a given data set, but the model cannot adapt
to a new, unknown data set

Specificity vs. Sensitivity:
Trading One For Another
Specificity:
The true negative rate. For example, the percentage of diabetic
patients identified who will not have a myocardial infarction
Sensitivity:
The true positive rate. For example, the percentage of diabetic
patients that will have a myocardial infarction

Receiver Operating
Characteristic (ROC) Plot
19
• Tuning radar receivers in WWII
• Maximum radar receiver sensitivity led
to many false positives… too many
alarms
• Lower radar receiver sensitivity led to
many false negatives… missed threats
• Same challenge in airport security
screening systems and spam filters
• Concept has been applied heavily in
diagnostic medicine
• True Positive Rate vs. False Positive
Rate

Data Volume vs. Predictive
Model
“But invariably, simple models and a lot of data trump more elaborate
models based on less data.”
“The Unreasonable Effectiveness of Data”, March 2009, IEEE Computer Society; Alon
Halevy, Peter Norvig, and Fernando Pereira, Google

The Human Data Ecosystem
21

We Are Not “Big Data” in Healthcare Yet
22

Predictive Precision vs. Data Content
23

Thank you for the graphs, PreSonus
Healthcare and patients are
continuous flow, analog
process and beings
But, if we sample that analog
process enough, we can
approximately recreate it with
digital data
24
Remember Your Calculus Digital Sampling
Theory?

We are asking physicians and nurses to act as
our “digital samplers”… and that’s not going to
work
25

26

“Mr. Sanders, while your 9-year tenure as an inmate has been stellar,
our analytics models predict that you are 87% likely to become a repeat
offender if you are granted parole. Therefore, your parole is denied.”
- 2014, 80% of parole boards now use predictive analytics for case
management*
* The Economist, “Big data can help states decide whom to release from prison” April 19, 2014
27

Thank you Sonja Star, New York Times
“Evidence Based” Sentencing
20 states use predictive analytics risk assessments
to inform criminal sentencing.
28
“Evidence Based” Sentencing

Recidivism Risk Assessment:
Level of Service/Case Management Inventory (LS/CMI)*
29
15 different scales feed the PA algorithm
1. Criminal history
2. Education/employment
3. Family/marital
4. Leisure/recreation
5. Companions
6. Alcohol/drug problems
7. Antisocial patterns
8. Pro-criminal attitude orientation
9. Barriers to release
10. Case management plan
11. Progress record
12. Discharge summary
13. Specific risk/needs factors
14. Prison experience - institutional
factors
15. Special responsivity consideration
42.2% of high-risk offenders recidivate within 3 years
*Nov. 2012, Hennepin County, Minn. Department of Community Corrections and Rehabilitation

30
“Since the publishing of Lewis' book, there has
been an explosion in the use of data analytics to
identify patterns of human behavior and
experience and bring new insights to fields of
nearly every kind.”

eHarmony Predictions
“Heart”  of the system: Compatibility Match
Processor (CMP)
• 320 profiling questions/attributes per user
• 29 dimensions of compatibility
• ~75TB
• 20M users
• 3B potential matches daily
• 60M+ queries per day, 250 attributes
31Thank you, Thod Nugyen, eHarmony CTO

Follow Us on Twitter #TimeforAnalyticsThank you, Ryan Barker, Principal Software Engineering – Matching, eHarmony
29 Dimensions of Compatibility
32

Predictive Analytics Inside Healthcare
33

What Are We Trying to Predict?
Common applications being marketed today
• Identifying preventable re-admissions: COPD, MI/CHF,
Pneumonia, et al
• Sepsis
• Risk of decubitus ulcers
• LOS predictions in hospital and ICU
• Cost-per-patient per inpatient stay
• Cost-per-patient per year by disease and comorbidity
• Risk of ICU mortality
• Risk of ICU admission
• Appropriateness of C-section
• Emerging: Genomic phenotyping
34

True Population
Predictive Risk
Management
Thank you, for the diagram, Robert Wood
Johnson Foundation, 2014
Very Little ACO
Influence
Very Little ACO
Influence
>/=30% Waste*
100% ACO Influence
*Congressional Budget Office, IOM,
“Best Care at Lower Cost”, 2013
True Population
Health Management
35

Not all patients can functionally participate in a protocol
At Northwestern (2007-2009), 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
Socioeconomic Data Matters
36

37
The key to predictive analytics in the future of health care will be the
ability to answer this two-part question:
What’s the probability of influencing this patient’s
behavior towards our desired outcome and how
much effort (cost) will be required for that
influence?
Return on Engagement (ROE)

38
Return on Engagement (ROE)

Socioeconomic Data Matters
39

40
Development Partner
40

Flight Path “Outcomes”
41
Examples
Diabetes
Cohort
1. A1c < 7
2. LDL < 100
3. BP < 130/80
1. A1c > 7
2. LDL > 100
3. BP > 130/80
$ COST Per Member Per Year (Charges)
For > 1 year of encounters
(~5 yrs and 26k patients)
These aren’t really outcomes… they are proxies for outcom

True Outcomes
42
Absence of:
Cardiovascular disease (angina, MI,
stroke)
Nephropathy/End stage renal
Diabetic retinopathy
Glaucoma
Cataracts
Lower extremity tissue narcosis, foot
ulcers
Peripheral neuropathy
Diabetic ketoacidosis
Diabetic preeclampsia
GI complications (nausea, constipation)
Erectile dysfunction
Presence of:
Cardiovascular disease (angina, MI,
stroke)
Nephropathy/End stage renal
Diabetic retinopathy
Glaucoma
Cataracts
Lower extremity tissue narcosis, foot
ulcers
Peripheral neuropathy
Diabetic ketoacidosis
Diabetic preeclampsia
GI complications (nausea, constipation)
Erectile dysfunction
Diabetes
Cohort
(~5 yrs and 26k patients)

43
Two Layers of Predictive Function
Risk scores Simulation

44
Microsoft Azure: Cloud-Based
Algorithms
44

Allina Health Readmissions
Model*
Variables Considered
*- Thank you, Jonathan Haupt

Allina Compared To Other
Models
Multiple logistic regression
5.2% of discharged
patients in high risk
category

Allina’s Intervention To
Reduce Risk
Transition of Care “Conferences”
• Patients, families, care givers
• 15% reduction in readmissions
• 100+ APR-DRGs affected
• More patients utilizing post-acute care
‒ Skilled Nursing Facility
‒ Home Health
‒ TCU

Antibiotic
Protocol
Dosage Route Interval Predicted
Efficacy
Average
Cost/Patient
Option 1 500mg IV Q12 98% $7,256
Option 2 300mg IV Q24 96% $1,236
Option 3 40mg IV Q6 90% $1,759
• Predictive and prescriptive (suggestive) analytics in the same user interface
• The efficacy and costs of antibiotic protocols for inpatients
Thank you, Dave Claussen, Scott Evans, et al, Intermountain Healthcare
48
The Antibiotic Assistant

The Antibiotic Assistant
Impact
• Complications declined 50%
• Avg. number of doses declined from 19 to 5.3
• The replicable and bigger story
‒ Antibiotic cost per treated patient: $123 to $52
‒ By simply displaying the cost to physicians
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Wrapping Up
50

Key Questions To Ask
Of Vendors and Your Analytics Teams
51
1. What is your formal training, education, and practical
experience in this field?
2. What are the input variables to the model?
3. What model and/or algorithms are you using and why?
4. How are you going to train the model?
5. Are you using our data or other organizations’ data for
training? Why?
6. If you are using other organizations’ data, how are you going
to customize the model to our specific data environment?

Follow Us on Twitter #TimeforAnalytics 52
1. Action matters: What is the return in investment for intervention?
Are we prepared to invest more... or say “no”… to patients who
score low on predicted engagement?
2. Human unpredictability: The mathematical models of human
behavior are relatively immature.
3. Socio-economics: Can today’s healthcare ecosystem expand to
make a difference?
4. Missing data: Without patient outcomes, the PA models are open
loop.
5. Social controversy: How much do we want to know about the
future of our health, especially when the predictive models are
uncertain?
6. Wisdom of crowds: Suggestive analytics from “wise crowds”
might be easier and more reliable than predictive analytics, until
our data content improves
Closing Thoughts and Questions

Q & A
53
• Submitted prior to the webinar
• Submitted through the webinar chat box

Thank You
For questions and follow-up, please contact me
• dale.sanders@healthcatalyst.com
• @drsanders
Upcoming Educational Opportunities
An Overview of the Healthcare Analytics Market
Date: January 21, 2015, 1-2pm, EST
Host: Jim Adams, Executive Director, The Advisory Board
A Pioneer ACO Case Study: Quality Improvement in Healthcare
Date: January 28, 2015, 1-2pm, EST
Hosts:
Robert Sawicki, MD, Senior Vice President of Supportive Care, OSF HealthCare
Roopa Foulger, Executive Director Data Delivery, OSF HealthCare
Linda Fehr, RN, Division Director of Supportive Care, OSF HealthCare

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