Introducing catalyst.ai and MACRA Measures & Insights

Introducing Two New Products From Health Catalyst · catalyst.ai· MACRA Measures & Insights Thursday, February 161-2:30 PM EST

Eric Just, Senior Vice President of Product Development

Dorian Dinardo, Vice President of Product Development

© 2016 Health CatalystProprietary and Confidential

Agenda

1-1:35 – catalyst.ai

1:35-2:05 – MACRA Measures & Insights

2:05 – Q&A

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A.I.: Artificial Intelligence

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General Artificial Intelligence“Narrow” Artificial Intelligence


Machine Learning

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Machine learning is a large reason for the recent progress in artificial intelligence.

Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.https://en.wikipedia.org/wiki/Machine_learning

Predictive analytics, or making predictions based on past data, is one of the artificial intelligence tasks that machine learning can solve.


We believe machine learning can accelerate outcomes improvement and save lives

Why is Machine Learning Important to Us?

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© 2016 Health CatalystProprietary and Confidential 6

Predictive Analytics in Healthcare

• Mortality predictionThe Charlson Index was introduced in 1987 in the Journal of Chronic Disease as mortality risk score.

• Readmission predictionThe LACE Index was introduced in the Canadian Medical Association Journal in 2010 to predict early death or unplanned readmission after discharge.

“Classic” Approaches


Shortcomings…

Using the LACE index to predict hospital readmissions in congestive heart failure patients

By Wang et. al, BMC Cardiovascular Disorders , 2014

Predicting readmissions: poor performance of the LACE index in an older UK population

By Cotter et al., Age Aging , 2012

CONCLUSION: The LACE Index may not accurately predict unplannedreadmissions within 30 days from hospital discharge in CHF patients. TheLACE high risk index may have utility as a screening tool to predict high risk ED revisits after hospital discharge.

CONCLUSION: The LACE Index is a poor tool for predicting 30-day readmission in older UK inpatients.the absence of a simple predictive model may limitthe benefit of readmission avoidance strategies.


Machine learning is easy(or at least easier!)

The problem is…

Organizations are struggling with making machine learning routine, pervasive, and actionable

© 2016 Health CatalystProprietary and Confidential9

Pervasive Use of Machine Learning

Health Catalyst Data Operating SystemData Operating System

ApplicationsAccelerate insight

ServicesInstall technology and ignite change

Clin

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Machine Learning


Health Catalyst’s Two-Part Machine Learning Strategy

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catalyst.ai

Machine learning models in Health

Catalyst applications to drive outcomes

healthcare.aiEducation and open

source software initiative to accelerate machine learning in healthcare nationally


What is the World’s Best Predictive Engine in Healthcare?


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Why Do We Need Machine Learning Models?


Discussing Predictive Models With Clinicians

Clinicians will adopt predictive analytics… insofar as they understand it

catalyst.ai includes performance reports for every model we bring

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Case Study: Central Line Associated Bloodstream Infection (CLABSI)• Approximately 41,000 patients with central lines will end up

with a blood stream infection (CLABSI)

• One in four patients with a CLABSI will die

• CLABSI improvement team looking at compliance with evidence-based guidelines

• Retrospective analysis led to increased insight into problem areas and associated interventions

• Team wanted more pro-active notification of high-risk patients

• Developed predictive algorithm based on 16 features


What Does It Look Like?

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catalyst.ai


Risk Model for CLABSI Shows Great Potential

The CLABSI predictive risk model’s true-positive rate = 0.81

Central line-associated bloodstream infections (CLABSIs) are serious and sometimes fatal. According to the Centers for Disease Control and Prevention (CDC), about one in 20 patients get an infection while receiving medical care. Nationally, one in four patients with a CLABSI die.

Health Catalyst developed and implemented a CLABSI predictive risk model to identify which patients with a central line are at greatest risk for developing a CLABSI. Informed by their risk factor analysis, as well as using education and focused interventions with staff caring for patients with central lines, client decreased the CLABSI rate by 20% over 6 months.

CLABSI risk model AU_ROC performance is 0.871

CLABSI predictive risk model’s false positive rate = 0.16


COPD ReadmissionsPowered by

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Practice Management Explorer

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Propensity to Pay Magnitude of Problem

$50+ BILLION lost annually to bad debt

What We Predicted Likelihood of making a payment on an outstanding debt: 85% positive predictive rate

Important Variables Leveraging ACTUAL patient payment history + demographic data (most just use credit history and demographic) Highest impact levers are: payment history (payments made and # of times sent to collections), age (older more likely to pay), and balance size (almost

nobody will pay a $6,000 bill).

Expected Interventions Outreach for people that are likely to pay but are close to collections. Do we have the right address and does patient know they have a bill? Quickly give charity care when needed: For individuals that have a low likelihood of paying, a high balance and have been on Medicaid or charity care in the

past

Expected Results Improved collection rates and lower cost to collect for health systems Less patients being sent to bad debt inadvertently = better patient satisfaction Seamless path for patients that need charity care

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Roadmap: Preventing Chronic Disease

2020

Managing chronically ill patients

Prevent need for inpatient care

Monitoring and managing patients at high risk for developing disease

Prevent progression to disease state

Managing inpatient populations


c

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Roadmap: Fully Optimized Closed Loop Architecture

Data Acquisition closer to real-time – leveraging API’s (Smart on FHIR)

Data Science algorithms leveraging machine learning and real-time data pushes data back to the workflow engine for integrated display

Workflow engine context passed to analytics engine via API’s to customize analytics to workflow.

NLP and closer to real-time data acquisition allows precise customization of cohort by context.

Customizable widgets present end-users with most important analytics relevant to this patient at this time.

‘Check list’ Surveillance.

Display of relevant information not part of the workflow engine can be accommodated.

Allows immediate action.

Closer to real-time data acquisition and context passing allows analytics to be hosted directly in workflow and allow immediate action.


Health Catalyst’s Two-Part Machine Learning Strategy

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catalyst.ai

Machine learning models in Health

Catalyst applications to drive outcomes

healthcare.aiEducation and open

source software initiative to accelerate machine learning in healthcare nationally


We believe:

• Machine learning can greatly increase the pace of improved outcomes in healthcare nationally

• The rate of adoption of this technology is too slow. Barriers cited include not having the right technology or people.

• We can increase the adoption through • Education• Collaboration• Better tools

Why healthcare.ai?

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Machine Education and Community

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Thursday, February 23, 2017 – 3 PM EST

Machine Learning Software


Algorithm 1 Algorithm 2 Algorithm 3

Model & Accuracy Report

Model &Accuracy Report

Model &Accuracy Report






Features (i.e. age, comorbidities, polypharmacy)

Result:

• Handful of best (most predictive) features

• Best algorithm that computes the relationships between input features to generate prediction

• Performance report summarizing best ‘model’

Algorithms (i.e. Lasso, Random Forest, k-means)

Definition: Simply put, a feature is an input to a machine learning model

Definition: Algorithms are complex mathematical processes that discover the relationship between features (input) and the outcome being predicted.


Typical ‘Current State’ for Predictive Analytics

Data Source

Predictive Model ?

Gnarly SQL Query

Data Manipulation

Tools/Algorithms

SAS | Weka | R | Python

Deploy

Even organizations that have good data scientists often struggle to operationalize machine learning.


healthcare.ai Open Source Software

Our open-source machine learning software product

Automates key tasks in developing

models, or customizing existing models using local

data

Makes deployment in an analytics

environment easy and ‘production

quality’


Scaling PeopleData Architects

Great domain knowledge Often looking for opportunities to advance

career/skills

With the right tools…

Data architects make great feature engineers Data architects can easily get started in predictive

analytics.

With healthcare.ai, you have the people to do data science right now.


Health Catalyst Data Science Core Team

Levi Thatcher, PhD, Director

Mike Mastanduno, PhD, Data Scientist

Taylor Miller, PharmD, Data Scientist

Taylor Larsen, MA, Data Science Engineer

ChangSu Lee, PhD, Data Scientist (Adjunct)

Scores of additional data scientists throughout the organization!


Key Take Aways

Catalyst is building machine learning models into every Health Catalyst application to drive outcomes.

This is catalyst.ai.

We know technology is not enough to improve outcomes. We understand the human factor – the context in which the machine learning insight needs to be delivered, and the right time and modality to deliver that insight.

This is Health Catalyst

Catalyst is stimulating the adoption of machine learning in healthcare nationally by creating an open source repository for machine learning tools and expertise.

This is healthcare.ai.

MACRA Measures & InsightsFebruary 15, 2017

Dorian DiNardoVice President - Operations and Performance Management Product Development


Payer and Regulatory MeasuresMACRA Measures & Insights


Measures: Where is the pressure coming from?

Competition

Government Regulations

DSRIP

MACRA Meaningful Use

NHSN

Registries

Risk Contracts

Pay

ers

Joint CommissionMarketing

…the list goes on

Reduced Payments SurveysIncreasing Costs

Increasing Audits

Workload Management

Impr

ove

Qua

lity

Impr

ove

Car

e

Improve Safety


Reporting Burden• Physicians and their

staff spend between 6 and 12 hours per week processing and reporting quality metrics to the government1

• $15.4 billion spent annually1

• Burden expected to significantly increase2

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1) Casalino et al. Health Aff March 2016 vol. 35 no. 3 401-4062) Health Catalyst/Peer 60 Survey


Problem: Aligning Financial and Clinical• Measures are only increasing in number, data sources,

financial implications• With this product: Capture the various quality incentives and

financial terms across payers.

• Many challenges to managing measures across departments• With this product: Identify areas of overlap and determine relative

financial importance to inform initiative selection.• We provide a ‘quick and dirty’ assessment of what to go at risk for

under the new CMS legislation– Remains in Qlik/Excel for small and mid-size clients


• For the MIPS track, payment adjustments begin in 2019 and range from a -4% penalty to a 12% bonus

• This range grows within first few years

• An estimated 712,000 clinicians will be impacted in the 2017 performance year

• CMS calculates 83-90% of eligible clinicians will be part of the MIPS track

Implications of MACRA


• 2017 performance will dictate the first payment adjustments

• Yet, only 35% of respondents to a recent Health Catalyst survey said “we have a strategy and are well on our way to being ready”

• A key decision health systems need to make is which measures to go at risk on under the Quality Payment Program• Performance on these measures will be worth 50% of the total score in the

initial year

Are we ready???


What is MACRA Measures & Insights?MACRA Measures & Insights is the product that will pinpoint the measures you should take risk on.

1. Help you clearly identify the measures you should go at risk on.

2. Integrate and align your organization on measures.

3. Measure Surveillance.

Surveillance is the monitoring of the behavior, activities, or other changing information, usually of people for the purpose of influencing, managing, directing, or protecting them.


MACRA Measures & Insights Demo


Features coming soon:

Quadrants


MACRA Measures & Insights Timeline and Availability

Jan ‘17

Feb ‘17 Mar ‘17 Apr ‘17

May ‘17

Jun ‘17 Jul ‘17 Aug ‘17

Sep ‘17

Oct ‘17 Nov ‘17 Dec ‘17

Beta Client Focus

General Availability including Excel friendly model

Integration of other measures into Framework

Integrate into web applications such as MBL

and API’s into EMR’s

MACRA enhancements for additional

quadrants, rule changes, etc.


Q&A

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