Applying AI to Healthcare’s Biggest Opportunity: Clinical Variation Dr. Todd Stewart, Mercy Lonny Northrup, Intermountain November 18, 2018

The challenges associated with healthcare data are tailor made for AI.

The Characteristics of Healthcare Data Big + getting bigger Noisy + sparse Complex

Intermountain’s Definition of Big Data Using additional data sources and new analytic tools to produce superior, actionable analytic insights (not previously possible or cost effective) leading to: Improved Healthcare Outcomes Reduced Cost Healthier People Value = Results / Costs NOTE: Volume, Variety and Velocity (and sometimes Veracity) are frequently used to describe big data. For Intermountain, our primary measure is Value. Confidential

What is Machine Learning in Healthcare Machine learning, cognitive computing, artificial intelligence and deep learning are related terms. Collectively, they refer to the ability of computers to learn from data how to replicate and improve human predictions and decisions. In healthcare this means consuming a variety of data (clinical, cost, claims, patient characteristics, etc.) to produce actionable insights leading to lower cost and more effective healthcare outcomes.

The Challenge of Clinical Variation “ An inefficient, broken system based on perverse incentives that cripples innovation and wastes $800 billion annually on health care that doesn’t make us healthier.” – Bernard J. Tyson PAYMENT METHODS Type of Waste % of all waste Cost-plus Fee for service Per case Population- based payment Production level Inefficient production of individual care units, such as drugs, tests, nursing support 5% Payer Provider Case level Use of unnecessary or suboptimal services in treating a case 50% Population level Unnecessary or avoidable patient cases 45% Clinical variation is a $400B problem for healthcare with no principled way to determine best practices across a hospital Ayasdi’s award winning application surfaces best practices from across the entire healthcare data chain Learns complex patterns within patient data to create nuanced patient cohorts, leading to better, more accurate prediction Collaboratively create standard pathways tailored to organization’s patient population, physicians, and specific care parameters to reduce costs and improve outcomes. Source: The Case for Capitation, Harvard Business Review

The Value of CVM is Broad Unbiased approach surfaces all variation, good + bad across the care continuum. Care paths are only the beginning – measurement/ adherence matters. CVM needs to reflect the mission of the organization and how it practices medicine Always remember – it is about enhancing patient outcomes

What Makes CVM So Difficult? Multi-Event time series for every procedure Sparsity of individual events Physician buy-in hampered by “my patients are different” problem Tracking adherence to care process models immensely complex Confidential

A Framework for Intelligence Discover Predict Justify Act Learn Confidential

Discover Analyze all EMR + financial data, representing thousands of patient procedures + millions of individual events to automatically surfaces groups of similar patient procedures.

Predict Powerful prediction draws on discovery and in the case of clinical variation management allows healthcare organizations to accurately predict the quality and cost for the desired treatment outcomes. 

Justify For AI to deliver on its promise in healthcare it must be able to justify and document its recommendations. 

Act Integrate with EMR systems to facilitate the rapid deployment of intelligence across the organization.

Learn Intelligent applications are always looking at new data as it comes in, finding emerging patterns that reflect the current practice within the organization.  

Mercy’s Application of Intelligence: CVM A Truven top 5 hospital, Mercy operates 43 acute care + specialty hospitals, employing over 700 physicians. Mercy has developed over 40 clinical carepaths since our first work together on total knee replacement. Mercy saves $1M per year on TKR and projects savings of $45M over 3 years ending in FY2018 on other carepaths.

Driving Variance Out of Care Process Models w/ AI Nearly 60 CPM’s Today Behavioral health Cardiovascular Collaborative Pharmacy Imaging Services Intensive Medicine Musculoskeletal Oncology Pain Services Pediatric Primary Care Surgical Services Women and newborns Etc. Reducing variance from knee and hip replacement surgeries alone resulted in $60M savings over 3 years AND delivered improved outcomes * Link to Intermountain Care Process Models: https://intermountainphysician.org/clinical/Pages/Care-Process-Models-(CPMs).aspx Confidential

Colon Surgery – Developing Care Models with AI Alvimopan Ketorolac Ambulation Foley Catheter Oral fluids well tolerated Intermountain 2010-2015 Colon Surgery (~4500) Fac: 128 Lowest LOS Found Ayasdi 2015 Colon Surgery (~530) 3 groups >4 day LOS (~200) Found in Best group More frequent ambulation Removed earlier in best group Found – trend to earlier fluids for best group

AI Requires Analytical Structure and Organizational Buy In Confidential

Intermountain’s Data + Analytics Structure Data Warehousing and Integration Data Governance Data Semantics and Clinical Modeling Analytics and Data Science Analytic Technology Services Data & Analytics Advancement Data Stewardship / Ownership – train and enable business to engage in data management processes Data Quality Management Tools for defining business terms + metrics (metadata) Data Standardization, Shared Data Services Reference Data (ICD codes, Zip codes, etc.) Management Master Data (provider, patient, locations) Management Clinical Data Modeling Organizing Data for Analysis Integration of Disparate Data (EDW) Data Movement Enterprise Data Analyst Coordination Data Analyst Best Practices Data Science = predictive analytics, integration into decision processes Technologies & Tools to support data analysts Enterprise-level dashboard and analytic solution dev. Coordinate innovation efforts for data and analytics

Intermountain’s Infrastructure

Augmented Medical Intelligence Machine Learning (cognitive computing, artificial intelligence, deep learning, etc.) assists humans to make better decisions and take better actions, but cannot completely replace people in the processes of achieving the very best outcomes.

Machine Learning in Healthcare – Additional Results Machine Learning driven Patient Engagement Type 2 Diabetes Prevention: 58% to 85% risk reduction for over 70% of participants (Omada) Diabetes: Average 3.2 drop in HbA1c in 3 months (typical drop is 0.5 to 1.0 in 1 year) (Twine Health) Congestive Heart Failure: 4% readmission rate compared to national average of 26-28% (Sensely) Remote Patient Monitoring: 89% reduction in inpatient visits, 70% reduction in emergency department visits (Vivify) Chronic Obstructive Pulmonary Disease (COPD): 87% adherence to care plan, 92% medication adherence and more than 70% reduction in hospitalizations (Senscio) Real Time Machine Learning driven Emergency Department Optimization 20% reduction in door to doc time (Qventus) 30% reduction in Leave Without Being Seen (LWBS) rate (Qventus) 13% reduction in length of stay - (Qventus)

Machine Learning in Healthcare – Additional Results Machine Learning driven Personalized Treatment Recommendations $895,000 in savings in under 3 months and significantly reduced readmissions (Health First, Florida - Jvion) $4M in savings from readmission reductions (University of Tennessee Medical Center - Jvion) Reduced Catheter Associated Urinary Tract Infections (CAUTI) (Jvion) Over 60 areas of clinical outcomes improvement (Jvion)

$$$ 24/7 N of 1 Where Do We Go Next? Individual Cost Prediction Individual “Next Best Action” Care Plan Continuous Connection to Care Team $$$ N of 1 24/7 The promise of personalized medicine Confidential

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