1. Machine Learning & Predictive Analytics

2. Examples of machine learning are all around us

4. What Is Machine Learning?
Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.
Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction. In commercial use, this is known as predictive analytics.

5. Machine learning is easy (or at least easier!)
The problem is…
Organizations are struggling with making machine learning routine, pervasive, and actionable
Grabber – One to three phrases that explain why the topic is important from an emotional level.
Additional examples:
Content on slide: More than on million Americans % die
Speaker comments: Unfortunately, every year, severe sepsis strikes more than a million Americans. It’s been estimated that this is more than the number of U.S. deaths from prostate cancer, breast cancer and AIDS combined (these stats are from the NIH website)
Content on slide: 200,000. Doubles a person's risk of death
Speaker comments: 200,000 young people in the US under age 20 suffer from diabetes. The growing incidence of diabetes has significant ramifications for children’s long-term health, since the disease at least doubles a person's risk of death. Furthermore, diabetes patients are at risk for serious complications, longer hospital stays, and poorer outcomes
Content on slide: 1/3 of all healthcare expenditures in the U.S.
Speaker comments: Hospital inpatient care makes up nearly one-third of all healthcare expenditures in the United States, and represents a significant impact on the country’s economy. Length of stay (LOS) is an aspect of care that can be costly for most healthcare systems if not approached the right way.

6. 2 3 1 catalyst.ai® healthcare.ai®
Our machine learning models
Our strategy for embedding machine learning into all of our products 2
healthcare.ai®
Our open source tools to automate machine learning tasks
Democratizing machine learning by releasing as open-source 3
Health Catalyst® Data Operating System
Machine Learning Foundation 1

7. Capabilities to SCALE Outcomes Improvement
What should we be doing?
How are we doing?
Healthcare.ai Toolset
Data Warehouse
Visualization Tools
SAM Designer
Catalyst.ai
Pre-built models
Feature Selection
Algorithm Selection
Education & Training
Workflow Integration
Improvement Methodology
Clinical Outcomes
Cost Outcomes
Experience Outcomes
How do we transform?

8. Discussing Predictive Models With Clinicians
Clinicians will adopt predictive analytics… insofar as they understand it

9. IU Health Risk Model for CLABSI Shows Great Potential
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.
IU Health 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, IU Health decreased the CLABSI rate by 20% over 6 months.
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 risk model AU_ROC performance is 0.871
The CLABSI predictive risk model’s true- positive rate = 0.81
CLABSI predictive risk model’s false positive rate = 0.16

10. CLABSI Analytics at IU Health – what it looks like
Key features:
Part of broader CLABSI effort
Team in place was already responding to care gaps exposed in the application
Workflow (daily huddle) was amenable to up to 24 hour latency
High risk patients easy-to-identify
Show risk factors in addition to the risk score – enables intervention

11. Models Built To Date: Built In Development Planned
Central line-associated bloodstream infection (CLABSI) Risk – Clinical Analytics and Decision Support
Congestive Heart Failure, Readmissions Risk – Clinical Analytics and Decision Support
COPD, Readmissions Risk – Clinical Analytics and Decision Support
Respiratory (COPD, Asthma, Pneumonia, & Resp. Failure), Readmission Risk – Clinical Analytics and Decision Support
Forecast IBNR claims/year-end expenditures – Financial Decision Support
Predictive appointment no shows – Operations and Performance Management
Pre-surgical risk (Bowel) – Clinical Analytics and Decision Support and client request
Propensity to pay – Financial Decision Support
Patient Flight Path, Diabetes Future Risk – Clinical Analytics and Decision Support
Patient Flight Path, Diabetes Future Cost– Clinical Analytics and Decision Support
Patient Flight Path, Diabetes Top Treatments – Clinical Analytics and Decision Support
Patient Flight Path, Diabetes Next Likely Complications (Glaucoma) – Clinical Analytics and Decision Support
Patient Flight Path, Diabetes Next Likely Complications (Retinopathy) – Clinical Analytics and Decision Support
Patient Flight Path, Diabetes Next Likely Complications (ESRD) – Clinical Analytics and Decision Support
Plus several more… (Nephropathy, Cataracts, CHF, CAD, Ketoacidosis, Erectile Dysfunction, Foot Ulcers)
In Development
Patients Like This – Clinical Analytics and Decision Support
Sepsis Risk – Clinical Analytics and Decision Support
Readmission Risk – Clinical Analytics and Decision Support
Post-surgical risk (Hips and Knees) – Clinical Analytics and Decision Support
INSIGHT socio-economic based risk – Clinical Analytics and Decision Support and client request
Native SQL/R predictive framework and standard package - Platform
Feature selection, Parallel Models, Rank and Impact of Input Variables – Platform
Predictive ETL batch load times – Platform
Planned
Composite Health Risk – Clinical Analytics and Decision Support
Composite All Cause Harm Risk – Clinical Analytics and Decision Support
Early detection of CLABSI, CAUTI, Clostridium difficile (c. diff) hospital infections – Clinical Analytics and Decision Support
Early detection of Sepsis/Septicemia (Blood Infection) – Clinical Analytics and Decision Support
Hospital Census Prediction - Operations and Performance Management
Hospital Length of Stay Prediction – Operations and Performance Management
Public data sets, benchmarks, “Catalyst Risk”, expected mortality, length of stay – CAFÉ® collaboration
Clusters of population risk (near term risk/cost) – Population Health and Accountable Care

12. Key Take Aways
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.
Catalyst is building machine learning models into every Health Catalyst application to drive outcomes.
This is catalyst.ai.
We understand outcomes improvement
We understand data and how to engineer it to provide meaningful insight
Our platform is second to none for machine learning
together, healthcare.ai and catalyst.ai represent the next generation of healthcare analytics

13. We know how clinicians use data to make decisions
We know how clinicians use data to make decisions. We understand the context in which the machine learning insight needs to be delivered, and the right time and modality to deliver that insight.