Introducing catalyst.ai and MACRA Measures & Insights
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Transcript of 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
2
© 2016 Health CatalystProprietary and Confidential
A.I.: Artificial Intelligence
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General Artificial Intelligence“Narrow” Artificial Intelligence
© 2016 Health CatalystProprietary and Confidential
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.
© 2016 Health CatalystProprietary and Confidential
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
© 2016 Health CatalystProprietary and Confidential 7
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.
© 2016 Health CatalystProprietary and Confidential 8
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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Ana
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Sup
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Man
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Pat
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Rel
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Machine Learning
© 2016 Health CatalystProprietary and Confidential
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
© 2016 Health CatalystProprietary and Confidential
What is the World’s Best Predictive Engine in Healthcare?
© 2016 Health CatalystProprietary and Confidential
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Why Do We Need Machine Learning Models?
© 2016 Health CatalystProprietary and Confidential 13
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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catalyst.ai
© 2016 Health CatalystProprietary and Confidential 14
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
© 2016 Health CatalystProprietary and Confidential 15
What Does It Look Like?
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catalyst.ai
© 2016 Health CatalystProprietary and Confidential
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
© 2016 Health CatalystProprietary and Confidential
COPD ReadmissionsPowered by
catalyst.ai
© 2016 Health CatalystProprietary and Confidential
Practice Management Explorer
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catalyst.ai
© 2016 Health CatalystProprietary and Confidential
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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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catalyst.ai
© 2016 Health CatalystProprietary and Confidential
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
© 2016 Health CatalystProprietary and Confidential
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.
© 2016 Health CatalystProprietary and Confidential
Health Catalyst’s Two-Part Machine Learning Strategy
22
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
© 2016 Health CatalystProprietary and Confidential
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
© 2016 Health CatalystProprietary and Confidential28
Algorithm 1 Algorithm 2 Algorithm 3
Model & Accuracy Report
Model &Accuracy Report
Model &Accuracy Report
Model & Accuracy Report
Model & Accuracy Report
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.
© 2016 Health CatalystProprietary and Confidential 29
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.
© 2016 Health CatalystProprietary and Confidential 30
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’
© 2016 Health CatalystProprietary and Confidential 31
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.
© 2016 Health CatalystProprietary and Confidential
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!
© 2016 Health CatalystProprietary and Confidential35
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
© 2016 Health CatalystProprietary and Confidential
Payer and Regulatory MeasuresMACRA Measures & Insights
© 2016 Health CatalystProprietary and Confidential38
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
© 2016 Health CatalystProprietary and Confidential
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
© 2016 Health CatalystProprietary and Confidential40
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
© 2016 Health CatalystProprietary and Confidential41
• 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
© 2016 Health CatalystProprietary and Confidential42
• 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???
© 2016 Health CatalystProprietary and Confidential43
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.
© 2016 Health CatalystProprietary and Confidential44
MACRA Measures & Insights Demo
© 2016 Health CatalystProprietary and Confidential45
Features coming soon:
Quadrants
© 2016 Health CatalystProprietary and Confidential46
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.
© 2016 Health CatalystProprietary and Confidential
Q&A
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