Predictive Analytics: It’s the Intervention That Matters

P L A N T E M O R A N H E A L T H C A R E E X E C U T I V E S U M M I T June 4-5, 2014

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.

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?

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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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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 predictions4. When it comes to analytics, there is lowering

hanging fruit than predictive analytics Target wasteful healthcare, first

Alignment of MotivesHuman, Societal, Corporate, and Software

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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

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.”

Fee-for-Service vs. Fee-for-QualityPercentage of healthcare dollars spent on fee-for-quality, fixed-fee contracting

General Concepts of Predictive Analytics

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Challenge of Predicting Anything Human

The Basic Process of Predictive Analytics

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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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The Human Data Ecosystem

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Predictive Precision vs. Data Content

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Our Healthcare Sampling Rate

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We Are Not “Big Data” in Healthcare, Yet

The Odd Parallels

Nuclear Weapons Delivery, Terrorism, and Healthcare Delivery

Where And How Can A Computer Help?Reduce variability in decision making & improve outcomes

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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 lives4. Minimize destruction of US infrastructure

5. Achieve all of this as quickly as possible with minimal expenditure of US military resources

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?

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Sortie Turnaround TimesThe Goal: Predictable, fast turnaround of aircraft to a successful battle

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Patient Fight Path ProfilerThe Goal: Predictable, fast turnaround of patients to a good life

Healthcare As a Battle Field…??The Order of Battle and the Order of Care

Demand forecasting: What do we need and when?

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NSA, Terrorists, and PatientsThe Odd Parallels of Terrorist Registries and Patient Registries

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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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Predicting Patient Risk

Predictive Analytics in Healthcare

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*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 CONSUMPTIONNATURAL 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*

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True Population RiskManagement

Robert Wood Johnson Foundation, 2014

Requires a collaborative strategy between leaders in healthcare, politics, charity, education, and business

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

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

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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

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

Swati Abbott

Weighted Predictive

Model

Now what?

Risk of Readmission

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Most Common Causes for Readmission

Robert Wood Johnson Foundation, Feb 2013

1. Patients have no family or other caregiver at home2. Patients did not receive accurate discharge instructions,

including medications3. Patients did not understand discharge instructions4. Patients discharged too soon5. Patients referred to outpatient physicians and clinics not

affiliated with the hospital

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Forecasting: Process Model Structural Model: Bill of Resources

Patient Seen inEmergency Dept

Admit Patient:Presumptive

Diagnosis:Pneumonia

DischargeMonitor

CareDelivery

StandardOrder Sets

Equipment

LaborMaterials

Facilities

Nursing Orders:

Respiratory Therapy:

Medication Orders:

ResourceDemand

Day 1 Day 2 Day 3 Day 4 Day 5

Edgewater Consulting

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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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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

How Do You Get Started?

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Start Within Your Scope of InfluenceWe are still learning how to manage outpatient populations

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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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What About Long Term Care?

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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

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Economics of Long Term Care

Kaiser Family Foundation

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

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Summary

1. Alignment of human, societal, company motives with software strategies is CRITICAL

2. Predictions without interventions are useless3. Some of the most valuable predictions don’t need a

computer algorithm We already know what the interventions should be

4. Missing data = Poor predictions5. 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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Many Thanks…!

• Contact information• dale.sanders@healthcatalyst.com• @drsanders• www.linkedin.com/in/dalersanders

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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?

What about Long Term Care?