OPPORTUNITY AND IMPACT OF AI IN MEDICINE AND HEALTH DELIVERY

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Robert Mittendorff MD MBA, Partner, Norwest Venture Partners
11 January 2018
OPPORTUNITY AND IMPACT OF AI IN MEDICINE
AND HEALTH DELIVERY

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Robert Mittendorff MD (2018)
AI in Healthcare
Speaker Introduction
Robert Mittendorff, MD, MBA
Partner
Norwest Venture Partners
Venture & growth equity investor, and
emergency physician focused on companies
in the digital health, healthcare IT,
healthcare services, and medical device and
diagnostics spaces.

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AI in Healthcare
Conflict of Interest
Robert Mittendorff MD MBA
Ownership Interest via Norwest Venture Partners in HealthCatalyst, Omada
Health, iRhythm, CareCloud, ClearData, TigerText, Crossover Health,
iCardiac, Qventus (fka AnalyticsMD), TalkSpace, Visitpay
The Views expressed herein are my own and are not attributable to any
company investment or employer

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Agenda
• The Labor of Healthcare: Leverage, Automation, and AI
• AI, ML and Data Science: Terminology and Technology
• Engagement and Persuasive Design: From Psychology to Digital Health
• Interventional Approaches in Outcomes
• State of the Evidence
• Summary

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AI: The Three Impacts
• Leverage Human Labor to Increase Capacity to Treat by 10x-100x
• Allow Patients to Self Manage Their Conditions With Persuasive Software
• Enable New Interventions, Like Finding Problems in Real Time and
Nudging Providers or Managers with Solutions

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Agenda
• Summary

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THE HORSE: HOW AUTOMATION IMPROVES EFFICIENCY
From Dead Horses to the Model T
Source: Library of Congress website (top left and bottom left, Mittendorff analysis of auto registration data and horse and mule population in the US from 1900 to 1950
Technology Substitution: US Autos and Horses
Technology and Labor Substitutes: From Horse to Car

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THE STORY OF THE MILKMAN: HOW TECHNOLOGY DISRUPTS
JOBS
Pasteurization Refrigeration in Home
Ice Man
Milk Man
State and Federal Laws
1971 FDA requires all milk
transported
interstate to be pasteurized;
…bye bye Milkman
Source: Library of Congress website
Technology and Labor Substitutes: Milk Man & Pasteur

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Source: Library of Congress website
Technology and Labor : H&R Block and Turbotax

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TECHNOLOGY ADOPTION ALSO LEADS TO OBSOLESCENCE (PCI
VS CABG)
Technology and Labor Substitutes: Surgery To Stents

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THE HORSE: HOW AUTOMATION IMPROVES EFFICIENCY
Source: NHDS 2009; MarketRealist 2016.
Labor is 50% of a Healthcare System’s Expense
And Half of Admissions are Unscheduled (from ED)
Share of Hospital Admissions by Source
50% = ER
Percent of Hospital Operating Expenses By Source
49% = Salary

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FEWER PROVIDER ORGANIZATIONS OWN MORE PHYSICIANS
THAN EVER
Physician Employees
2014
2000
30%
70%
Source: Kaiser Family Foundation 2015 reports and NEJM Policy Center 2016
Labor Centralization Leads to Scalability

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HITECH ACT FUELED THE FIRST WAVE OF HEALTH
INFORMATION DIGITIZATION
1. Clinical and operational improvements at the system
level requires digitization & structuring of data
2. Once information is digitized, analytics professionals
and a culture of evidence based improvement
must be created
3. Once an organization has the capability to organize
around data, insight, and practice change, it must be
continually rewarded for improving performance
EMR Adoption By US Physician Practices
INSERT
Source: ONC for HIT, Health IT Dashboard, https://dashboard.healthit.gov
Digitization Leads to Mass Customization of
Interventions

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A Little Background on Other Relevant Work
Source: (left) (right) Brown, E, et al. Nature Neuroscience 2004. Multiple neural spike train data analysis: state-of-the-art and future challenge.
Record from the Brain
Use
Neural Network
Model
Predict Location Based on
Neural Signals

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Human Neural Systems and The Practice of Medicine

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Radiology
Dermatology
Endocrinology
Emergency Medicine
Cardiac Surgery

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Radiology Cardiac Surgery

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Agenda
• Summary

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AI Will Be Everywhere

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MORE THAN 90 HEALTHCARE AI STARTUPS HAVE BEEN
FUNDED
Source: CBInsights 2016
Hunting the Blockbuster in AI

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AI is a Set of Technologies – It is Not a Company

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“Gives computers the ability to learn
without being explicitly programmed"
- Arthur Samuel
Machine Learning: Finding Patterns in Data

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Hard vs. Easy Problems in Machine Learning

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Train: Find the patterns in the data
Price
50,000
100,000
150,000
200,000
250,000
300,000
1000 2000 3000
Size
Fitted line plot
Test: Let's use the patterns we have
found to do the prediction
House size Estimated
price
2000 ?
2200 ?
1450 ?
Train and Test in Machine Learning Models

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Should the model focus on being right 100% of the time or it ok to be wrong
sometimes but find more of the ‘high priced homes’?
Price
50,000
100,000
150,000
200,000
250,000
300,000
1000 2000 3000
Size
Fitted line plot
Precision (PPV) and Recall (Sens) in Performance

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WHAT IS ARTIFICIAL INTELLIGENCE AND MACHINE
LEARNING IN A NUTSHELL?
Machine Learning (ML): The use of algorithms to structure, parse, and learn from data
sets (statistical or other) patterns that can be used to classify or predict in novel data
sets.
Supervised learning algorithms (training data is labeled)
Unsupervised learning algorithms (training data in unlabeled)
Semi supervised learning algorithms (training data is a mix of labelled and unlabeled)
(examples include Regularization /Elastic Net, Regression and Regression Trees,
Nearest Neighbor, Decision Trees, Bayesian approaches, and 50+ more)
Deep Learning (a ML technique): The use of (frequently) neural networks (NN) trained
on data sets to recognize patterns and features without an explicit parametric model.
Neural networks can then be used on novel datasets to predict or classify.
AI, ML, Deep Learning, and the Jargon

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Artificial Intelligence (AI): Engineering machines to think or mimic the thinking and
operations of a human. Machine Learning is frequently considered a set of
technologies found in an AI.
In Healthcare we like evidence to demonstrate capabilities in a “VALIDATION
DATASET” that the system has not been exposed to in training.
AI, ML, Deep Learning, and the Jargon

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A Simple Neural Network Example

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Deep Learning and (Convolutional) Neural Networks

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Searle and the Chinese Room: AI and Consciousness

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AI in Clinical Decision Support: Must It Explain WHY?

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Agenda
• Summary

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The Persuasion of AI Technology

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BEHAVIOR IS A BIG PROBLEM TO SOLVE IN FLATTENING THE
COST CURVE
Source: McGinnis et al, New England Journal of Medicine 2002.
The Digital Blockbuster is Behavior Change

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AUTOMATE THE LABOR INTENSIVE, CLINICALLY VALIDATED
APPROACH
The Diabese Adult The “Healthy” Adult
High Calorie Poorly Balanced Diet
Low Activity
Poor Medical “Compliance”
Calorie Appropriate Diet
Moderate Activity
Reasonable “Compliance”
?
$6,500 / Year $1,000 / Year
Behavior Change Converts A to B

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Intro to lorem ipsum

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DIGITAL THERAPY FOR BEHAVIOR CHANGE: AS GOOD AS
A DRUG?
Omada Health
Prevent™ Program: The Birth of a Digital Therapeutic
Mass Customization for Disease Prevention; AI?

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BEHAVIOR MODIFICATION CAN BE MORE POTENT THAN A
DRUG
31% 58%
Metformin DPP (Behavior Alone)
8% of the population developed diabetes
NNT of 14
5% of the population developed diabetes
NNT of 6
Source: McGinnis et al, New England Journal of Medicine 2002, CDC DPP Data.
Technology and Labor Substitutes & Augmentation

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Agenda
• Summary

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Source: left, “Her” movie website, right, “Minority Report” movie website, all rights reserved to original publishers.
AI and the Automation of Labor for Behavior Change AI/ML & Predictive & Prescriptive Analytics in Real Time
From Scaling to Replacing Labor with AI

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K162513: “The Arterys Software is intended to be used to support qualified
cardiologist, radiologist, or other … practitioners for clinical decision-making...
AI and ML Will First Come of Age As “Clinical
Decision Support” to Make Humans More Efficient
Source: Forbes article 1/20/17 including quote on bottom right. FDA Intended Use Claim from Arterys K162513 %10k clearance in October 2016.

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K161201: “ClearRead CT™ is comprised of computer assisted reading tools designed to
aid the radiologist in the detection of pulmonary nodules during review of CT
examinations of the chest on an asymptomatic population. [Riverain Technologies]
AI and ML Will First Come of Age As “Clinical
Decision Support” to Make Humans More Efficient

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Zebra: AI in Radiology

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MedyMatch: AI in Acute Care Radiology

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System
of Action
Frontline ActivationManagement
Deep Dive, root cause analysis,
Instant performance tracking
Real-time collaboration,
coordination, course correction
Mission Control
Situational Awareness
Qventus acts as a platform for decision-making and action.
It is informed by historic, real-time and predictive data.
Qventus: AI enabled Air Traffic Control for Hospitals

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How Qventus Works

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(AIR) TRAFFIC CONTROL IN PATIENT FLOW USING AI
AND MACHINE LEARNING
Real Time Nudges Reduce LOS, Unnecessary
Tests, and Left Without Being Seen Rates
Source: AnalyticsMD data on actual customer deployments

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Protenus: AI in Healthcare Privacy

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Agenda
• Summary

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LEVELS OF EVIDENCE REVIEW
Level 1 Systematic Reviews & Randomized Controlled Trials
Level 2 Cohort Studies
Level 3 Case-Controlled Studies
Level 4 Case Series
Level 5 Case Based Reasoning or Experts
The Evidence Pyramid: More is Better
And Even Nicer If Randomized and Controlled

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Significant Digital Health Research & AI Is Being
Performed in Randomized Interventional Approach
Source: Analysis of 3,791 records of clinical trials at www.clinicaltrials.gov with “mobile OR app” in content

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DOES CLINICAL DECISION SUPPORT IMPROVE OUTCOMES
• 2016 Clinical Decision Support in the ICU with (near) Real Time Data: 25 articles reviewed in meta-analysis of approaches of CDS in AIMS.
• 2012 Computerized Clinical Decision Support for Diabetes Management: 15 studies, with several at high risk of bias.
• 2012 Machine Learning and AI
Source: (1) Simpao, et al. A systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems. J Clin Monit Comp 2016. (2) Jeffery, R. Diabetic Medicine, 2012.
“Computerized clinical decision support systems in diabetes management may marginally improve clinical
outcomes, but confidence in the evidence is low because of risk of bias, inconsistency and imprecision.”
“ There is strong evidence for the inclusion of near real-time and point-of-care CDS in [Anesthesia Information
Management Systems] to enhance compliance with perioperative antibiotic prophylaxis and clinical documentation…”
Simpao, et al. 2016
Clinical Decision Support Improves Care

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DOES CLINICAL DECISION SUPPORT IMPROVE OUTCOMES
• 2017. Raju et a. Development of a Deep Learning Algorithm for Automatic Diagnosis of Diabetic Retinopathy. Precision Healthcare through Informatics
2017.
• 2017, Esteva, A. Dermatologist – level classification of skin cancer with deep neural netowkrs. Nature.
2017 Bejnordi, B. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA.
Source: (1) Simpao, et al. A systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems. J Clin Monit Comp 2016. (2) Jeffery, R. Diabetic Medicine, 2012.
“In this task, the CNN achieves 72.1 ± 0.9% (mean ± s.d.) overall accuracy (the average of individual inference class
accuracies) and two dermatologists attain 65.56% and 66.0% accuracy.”
“Approximately 35,000 images were used to train the network, which observed a sensitivity of 80.28% and a specificity of
92.29% on the validation dataset of ~53,000 images.”
AI and Diagnosis: Performing Like Doctors?
“The top 5 algorithms had a mean AUC that was comparable with the pathologist interpreting the slides in the absence
of time constraints (mean AUC, 0.960 [range, 0.923-0.994] for the top 5 algorithms vs 0.966 [95% CI, 0.927-0.998] for the
pathologist WOTC)..”

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DOES ‘SMART TEXTING’ / SMS LEAD TO BEHAVIOR CHANGE:
THE EVIDENCE
• 2002: The first text messaging study published in health (1); “Mobile phone text messaging can help young people manage asthma”
• 2014 Househ, et al.: First Meta-analysis of reviews (umbrella): 13 systematic reviews
• 2015: Hall, A. et al.: 15 systematic reviews of 89 unique studies ranging from [10-5,800 patients/study]
• 2015 Hall, A, et al 2015 Included Studies (abridged list of the 89):
• Diabetes: 16 of 16 studies reported statistically significant effects on health outcomes or health behaviors (large variation in size & design)
• Smoking Cessation: 6 of 8 studies demonstrating statistically significant behavior change or outcomes (smoking cessation, self report; 7 RCT)
• Weight Loss/Physical Activity: 11 of 19 had statistically significant effects on weight and/or activity
• Chronic Disease Management: 3 of 4 well designed studies from a group of 16 demonstrated statistical significance in outcome or behavior
• Medication Adherence: 20 of 33 had statistically significant effects on behaviors or outcomes with asthma (3/3) and HIV (5/10)
Source: (1) Neville, R, et al. BMJ 2002. (2) Househ, et al. Health Inform J. 2014 (3) Hall, A, et al. Anna Rev Public Health 2015 Mar 18;
Burke, et al. AHA Scientific Statement: Current Science on Consumer Use of Mobile Health for Cardiovascular Disease Prevention. Circa 2015.
“Our review found that the majority of published text-messaging interventions were effective when addressing diabetes
self-management, weight loss, physical activity, smoking cessation, and medication adherence for antiretroviral therapy”
- Hall, A, et al. 2015 (3)
“…low to moderate research evidence exists on the benefits of SMS interventions for appointment reminders, promoting
health in developing countries and preventive healthcare…” - Househ, et al. 2014 (2)
AI and “Push” Reminders Work In Specific Settings

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AI in Depth in Derm: Malignant vs. Benign Outperforms
Source: Esteva, et al. Nature 2017. Dermatologist-level classification of skin cancer with deep neural networks

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AI in Dermatology: Outperforming Dermatologists

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AI in Dermatology: The Beginning of Automation

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Agenda
• Summary

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AI: The Three Impacts
• Leverage Human Labor to Increase Capacity to Treat by 10x-100x
• Allow Patients to Self Manage Their Conditions With Persuasive Software
• Enable New Interventions, Like Finding Problems in Real Time and
Nudging Providers or Managers with Solutions

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AI in Healthcare
Questions
• Robert Mittendorff MD MBA contact information:
• rmittendorff@nvp.com
• @doctorrem
• https://www.linkedin.com/in/robertmittendorffmd

OPPORTUNITY AND IMPACT OF AI IN MEDICINE AND HEALTH DELIVERY

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