predicting the future of predictive analytics in healthcare

© 2014 Health Catalyst

www.healthcatalyst.comCreative Commons Copyright

© 2014 Health Catalyst

www.healthcatalyst.comCreative Commons Copyright

Predicting The Future Of Predictive Analytics In Healthcare

There’s A 90% Chance Your Son Is Pregnant

www.healthcatalyst.comCreative Commons Copyright 2014

Presenter and Contact Information

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Dale SandersSenior Vice President, Strategy, Health Catalyst

801-708-6800

[email protected]

@drsanders

www.linkedin.com/in/dalersanders/

mailto:[email protected]

https://www.linkedin.com/profile/public-profile-settings?trk=prof-edit-edit-public_profile


Acknowledgements

David Crockett, PhD, Health Catalyst

Eric Siegel, PhD, Columbia University

Ron Gault, Aerospace Corporation, Northrup-Grumman, TRW

Wikipedia

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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

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Agenda

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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

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Gartner 2014 Hype Cycle for Emerging

Technology

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Predictive Analytics in Healthcare, according to Dale Sanders


“Beyond math, there are no facts; only interpretations.”

- Friedrich Nietzsche

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Challenge of Predicting Anything Human

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What Should We Expect In Healthcare?

Machines are predictable; humans aren’t

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“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 integrating the core data content.

Inefficient, inconsistent versions of the truth. Cumbersome internal and external reporting.

© Sanders, Protti, Burton, 2013

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Concepts & Principles of

Predictive Analytics

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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.

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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.


For Now, Just Know The Terms

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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

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A Big & Common Mistake: Over Fitting

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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

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• 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

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We Are Not “Big Data” in Healthcare Yet

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Predictive Precision vs. Data Content

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www.healthcatalyst.comCreative Commons Copyright 2014Thank 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

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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

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Predictive Analytics Outside Healthcare

Predictive Analytics Outside

Healthcare

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“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

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Thank you Sonja Star, New York Times

“Evidence Based” Sentencing

20 states use predictive analytics risk assessments

to inform criminal sentencing.

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“Evidence Based” Sentencing


Recidivism Risk Assessment: Level of Service/Case Management Inventory (LS/CMI)*

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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


“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

www.healthcatalyst.comCreative Commons Copyright 2014Thank you, Ryan Barker, Principal Software Engineering – Matching, eHarmony

29 Dimensions of Compatibility

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Predictive Analytics Inside

Healthcare

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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

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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

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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

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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)


Return on Engagement (ROE)

Socioeconomic Data Matters

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Development Partner

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Flight Path “Outcomes”

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Examples

Diabetes Cohort

1. A1c < 72. LDL < 1003. 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 outcomes


True Outcomes

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Absence of:Cardiovascular disease (angina, MI, stroke)Nephropathy/End stage renalDiabetic retinopathyGlaucomaCataractsLower extremity tissue narcosis, foot ulcersPeripheral neuropathyDiabetic ketoacidosisDiabetic preeclampsiaGI complications (nausea, constipation)Erectile dysfunction

Presence of:Cardiovascular disease (angina, MI, stroke)Nephropathy/End stage renalDiabetic retinopathyGlaucomaCataractsLower extremity tissue narcosis, foot ulcersPeripheral neuropathyDiabetic ketoacidosisDiabetic preeclampsiaGI complications (nausea, constipation)Erectile dysfunction

Diabetes Cohort

(~5 yrs and 26k patients)


Two Layers of Predictive FunctionRisk scores Simulation


Microsoft Azure: Cloud-Based Algorithms

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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

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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

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Key Questions To Ask

Of Vendors and Your Analytics Teams

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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?


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

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• Submitted prior to the webinar

• Submitted through the webinar chat box

Thank YouFor questions and follow-up, please contact me

• [email protected]

• @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

mailto:[email protected]

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