Technology in Healthcare Transformation

11:00 am CALNOC
Robert Mittendorff MD MBA
Partner, Norwest Venture Partners
TECHNOLOGY IN HEALTHCARE TRANSFORMATION

WHO AM I?
Partner at Norwest Venture Partners
Norwest Venture Partners is a ﬁrm with assets of $5B+
Focus on venture and growth healthcare investments in:
Digital Health, Consumer Health, and Healthcare IT , Medical
Devices and Diagnostics, Healthcare Services.
VP of Marketing and BD in Medical Robotics Co.
Led concept to commercial launch for Magellan Robot
Led partnership with Philips Healthcare for $100M+
Emergency Physician (Board Certiﬁed)
Emergency Physician, Bay Area hospitals
Residency at Stanford Hospitals
Education
MD Harvard Medical School
MBA Harvard Business School
SM (abt) MIT
BS, Biomedical Engineering, Johns Hopkins

(c) Robert Mittendorff MD
CONFLICT OF INTEREST
Ownership Interest via Norwest Venture Partners in HealthCatalyst, Omada Health, iRhythm,
Telcare, CareCloud, ClearCare, TigerText, Crossover Health, iCardiac, AnalyticsMD
The Views expressed herein are my own and are not attributable to any investment or
employer

Intro to lorem ipsum
TECHNOLOGY TRANSFORMATION AND PREDICTING THE FUTURE
(c) Robert Mittendorff MDRobert Mittendorff MD MBA
…there is signal and there is noise…

SIGNAL AND NOISE
Source: CBInsights Market Report on Digital Health Trends, 2015

ONE OF OUR ROLES AS PATRIOTS: ENTITLEMENTS (WE ARE A COST CENTER)
70%
60%
50%
40%
30%
20%
10%
0%
Source: x

WHY DOESN’T HEALTHCARE (DELIVERY) INNOVATE FASTER
?

"If things seem under control, you are just not going fast enough."
-- Mario Andretti

"If things seem under control, you are just not going fast enough."
-- Mario Andretti
?

"Take calculated risks. That is quite different from being rash."
-- General George Patton
WHY DOESN’T HEALTHCARE (DELIVERY) INNOVATE FASTER: LIFE ON THE LINE

The Forces Changing Healthcare
Innovations Transforming Healthcare
Methods and Evidence
Technology Adoption in Healthcare
Summary
AGENDA

FORCES IN HEALTHCARE: OPPORTUNITY OR PERIL
The Rise of the Consumer Paying for Value
Digital Information
Factories of
Providers as Employees
Alternative Care Models
and Mobility
Analytics, AI, ML and Automation

0%
20%
40%
60%
80%
2001 2003 2005 2007 2009 2011 2013
HITECH ACT FUELED THE FIRST WAVE OF HEALTH INFORMATION DIGITIZATION
First, Digitization and Enumeration…
1. Clinical and operational improvements at the
system level requires digitization and
structuring of data
2. Once information is digitized, analytics
professionals and a culture of data driven
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
Source: CMS data on EHR adoption and summary from KFF

FEWER PROVIDER ORGANIZATIONS OWN MORE PHYSICIANS THAN EVER
Physician Employees
2014
2000
30%
70%
Source: KFF and Leemor Dafny; Healthcare M&A Information Source

THE PURCHASER IS INCREASINGLY BECOMING FEDERAL (OR STATE)
Commercial Federal State & Local
2015
Private
Federal
State & Local
39%
13%
20192010
48%
41%
13%
46%
37%
14%
50%
Source: CMS.gov, state agency public databases for 2015, and CBO.

AT RISK ORGANIZATIONS ARE BEING CREATED (600+) TO CARE FOR 20M+
Source: Muhelstein, et al . Health Affairs 2013.

INDIVIDUALS AND FAMILIES SEE THEIR COSTS DOUBLED WITH SHIFTING TREND
Source: Kaiser Family Foundation 2010 and 2016

MOBILE PHONES ARE A UBIQUITOUS PLATFORM
In 1984, more than half of the world population
lived in a country with less than 1 phone per
100 people, and two thirds had no access to a
phone
In 2014, global mobile phone penetration by
country reached 96% and hit 90% in the
developing world
In 2014, more than 2 trillion text messages
were sent in the US, and more than 8 trillion
globally. (< 1 million in 1984)
Source: (right) www.Marketer.com; 2010/2015. (left) Hall, A, et al. Anna Rev Public Health 2015 Mar 18.

FROM SMS TO VIDEO CHAT: REASONS WHY ALL AGES ADOPT MOBILITY
Source: www.Marketer.com; 2010/2015

FORCES IN HEALTHCARE: OPPORTUNITY OR PERIL
The Rise of the Consumer Paying for Value
Analytics, AI, ML and Automation Digital Information
Factories of
Providers as Employees
Alternative Care Models
and Mobility

WHAT IS ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN A NUTSHELL?
Source: left, “Her” movie website, right, “Minority Report” movie website, all rights reserved to original publishers.

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 and Predictive and Prescriptive Analytics in Real Time

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.
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.
In Healthcare we like evidence to demonstrate capabilities of technology on a “VALIDATION DATASET” that the system has never seen.

IT HAS HEADROOM IN HEALTHCARE ($ OF $100 IN REVENUE SPENT ON IT)
Internet Finance Healthcare Pharma Mfg Construction
$6.70
$6.30 $4.20 $3.20 $1.70 $1.00
Source: Deloitte Touche Tomatsu, 2013, public report on IT spend

$6.70
$6.30 $6.30 $3.20 $1.70 $1.00
$2.10
Source: Deloitte and Touche, 2013, public report on IT spend

$6.70
$6.30 $4.20 $3.20 $1.70 $1.00
Automation
Prescriptive Analytics
Predictive Analytics
Business Intelligence
Customer Rel. Mgmt.
Digitization & Databases
Source: Deloitte and Touche, 2013, public report on IT spend

INNOVATIONS TRANSFORMING HEALTHCARE
Clinical Communications
& Workﬂow Solutions
Real Time Prescriptive Analytics
Digital Therapeutics and
Digital Vaccines
Telemedicine
(Semi Automated)
Healthcare Services
Clinical Decision Support

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

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 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.

Intro to lorem ipsumRobert Mittendorff MD MBA

Intro to lorem ipsumRobert Mittendorff MD MBA
WHAT HAPPENS WHEN YOU CROSS CANDY CRUSH WITH
HEALTH RELATED BEHAVIOR CHANGE PROTOCOLS?

CAN WE USE LESSONS IN ENGAGEMENT TO SCALE BEHAVIOR CHANGE?
+
Source: YMCA (left) and king.com Candy Crush game (right)

DIGITAL THERAPY FOR BEHAVIOR CHANGE: AS GOOD AS A DRUG?
Omada Health
Prevent™ Program

DIGITAL THERAPY FOR BEHAVIOR CHANGE: AS GOOD AS A DRUG?

TELEMEDICINE IS HERE: TECHNOLOGY, PAYMENT, CONSUMERS ALIGN
New Specialties: TeleUrgent Care? TeleGeriatrician?
Source: IHS (various years), and the Journal of Telemedicine and Telecare, June 2012.

GRANDMA SKYPING GRANDDAUGHTER: 3% OF THEM USE MOBILE VIDEO

TELEMEDICINE IS HERE: TECHNOLOGY, PAYMENT, CONSUMERS ALIGN
Source: Forbes 2016

TELEBEHAVIORAL HEALTH COULD BE TELE THERAPEUTIC
Source: TalkSpace Data (internal data; publication forthcoming)

…the inability to balance and steer confronts
students of the flying problem…
…when this one feature has been worked out the
age of flying machines will have arrived, for all
other difficulties are of minor importance…
Source: The Wright Brothers, McCullough, and public historic references.

CARE COORDINATION: SMART SYSTEMS AND INTERVENTION
THE AVIONICS OF POPULATION MANAGEMENT
Source: New York Times, 1947 (left), NASA website (right top and right bottom)

MORE THAN 90 HEALTHCARE AI STARTUPS HAVE BEEN FUNDED
Source: CBInsights 2016

IMPROVING OPERATIONAL PERFORMANCE WITH AI AND MACHINE LEARNING
Real time analytics, predictive & prescriptive, can guide us at the point of care
from describing to forecasting to offering a program of actions within a care team

(AIR) TRAFFIC CONTROL IN PATIENT FLOW USING AI AND MACHINE LEARNING

(AIR) TRAFFIC CONTROL IN PATIENT FLOW USING AI AND MACHINE LEARNING
30% Reduction in Left Without Being Seen Rate (850 families treated instead of leaving)
14% Reduction in ER Length of Stay
20% Reduction in Door to Physician Time
40% Reduction in “Unnecessary“ Tests
Moved from #29 in Patient Satisfaction to #3 in their 30 Hospital ER System within 1 year
Source: AnalyticsMD Case Study

THE NEW PROVIDER IN HEALTHCARE IS TEAMS SURROUNDING PATIENTS

THE RHYTHM ZIO PATCH AND CLOUD BASED ANALYTICS SOLUTION
Source: iRhythm Website

CONNECTED HEALTH IS ABOUT REAL TIME DATA AND OPERATIONAL LEVERAGE
1/28/15, 10:07 AMiRhythm - ZIO Services
The Right Test,
The First Time
The ZIO XT Service is the only long-term,
continuous cardiac monitoring option that is
proven in multiple, peer-reviewed publications
(healthcare-clinical-evidence.php) to produce a
higher diagnostic yield and change patient
management - sooner in the diagnostic
pathway - compared to traditional approaches.
For Reimbursement information: More
®
ABOUT US
Our Mission (About-us-our-
mission.php)
Management Team (About-us-
Management.php)
Board Of Directors (about-us-
board-of-directors.php)
Scientific Advisory Board
(About-Us-Scientific-
Advisory.php)
Investors (about-us-
investors.php)
SUPPORT
Contact Us
Locations (Work-
With-Us-
location.php)
Work with Us
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!
(https://www.facebook.com/i
"
(https://twitter.com/iRhyt
+
(https://plus.google.c
$
(https://www.link
technologies-
inc.)
%
&
1. Automation with MD Supervision Can Increase
Quality
2. Automation Can Make Things Possible That Are
Otherwise Economically Infeasible
3. Technologically Enabling The Offering of a
Healthcare Service (Cardiac Monitoring) Allows You
to Be Best in Class
Source: iRhythm Website

CLINICAL DECISION SUPPORT HAS THE POTENTIAL TO MASS CUSTOMIZE
Mass Customization in Healthcare
Using data, analytics, decision support, and predictive and
prescriptive analytics to: Right/Right/Right…
Treat or diagnose the patient by the right provider using the
right test/treatment with the right equipment at the right time
in the right setting in an efﬁcient and cost effective way with the
right followup and right monitoring using all of the data about
that patient
Source: McKinsey; HealthCatalyst Website

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

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)

IS TELEMEDICINE SAFE, EFFICACIOUS, AND DOES IT REDUCE COSTS?
2012 TeleICU Care: Systematic review of 865 citations with 11 observational studies that met selection criteria.
2016 Telestroke: Systematic review and meta-analysis evaluating 529 records, with 7 studies involving 1,863 patients that met eligibility criteria.
TeleBehavioral Health
2013 Telemental Health: 14 studies met inclusion criteria in comparing telehealth modality with nontelehealth (randomized) for depression.
2016 TeleUrgent Care: CalPERS TelaDoc study of ﬁrst 19 months of experience; 1.3% of enrollees used service, 1 visit/yr avg.
“Our ﬁndings indicated that … tPA delivery through telestroke networks is safe and effective in the 3 hour time
window.“ Kepplinger, et al. 2016 (2)
“Telemedicine was associated with lower ICU & hospital mortality among critically ill patients.” Wilcox, et al. 2012
Source: (1) Wilcox, ME et al. The effect of telemedicine in critically ill patients : systematic review and meta-analysis (2) Kepplinger, et al. Safety
and efficacy of thrombolysis in tele stroke. Neurology 2016. (3) Osenbach, et al. Synchronous Telehealth Technologies in Psychotherapy for
Depression: A Meta-Analysis.. Depression and Anxiety, 2013. (4) Uscher-Pines, et al. Access and Quality of Care in Direct to Consumer
Telemedicine. Telemedicine and e-health 2016.
“Overall, we found no evidence to suggest that the delivery of psychotherapy via … telehealth…is less effective
than nontelehealth means in reducing depression symptoms…” Osenbach, et al. 2013. (3)
“Teladoc providers were less likely to order diagnostic testing and had poorer performance on appropriate
prescribing for bronchitis…[and patients] were not preferentially located in underserved communities…” (4)

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.
20XX 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

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
$0.0 M
$10.0 M
$20.0 M
$30.0 M
$40.0 M
$50.0 M
1900 1910 1920 1930 1940 1950
Horse
Auto
Technology Substitution: US Autos and Horses

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, Frigidaire historical photos, website.

AMZN: NEW DISTRIBUTION MODELS LEADS TO THE SHUTTERING OF OLD
In just five months, new Kindle replaces ‘Harry Potter and the
Deathly Hallows’ as best-selling product in Amazon’s history
SEATTLE, Dec 27, 2010 (BUSINESS WIRE) –
(NASDAQ: AMZN)–Amazon.com today announced that the third-generation Kindle is now the bestselling product in
Amazon’s history, eclipsing “Harry Potter and the Deathly Hallows (Book 7).” The company also announced that on its
peak day, Nov. 29, customers ordered more than 13.7 million items worldwide across all product categories, which is a
record-breaking 158 items per second.
Source: The Wall Street Journal and Business Wire (top and bottom respectively), and images from Borders and Amazon websites.

TECHNOLOGY ADOPTION ALSO LEADS TO OBSOLESCENCE (PCI VS CABG)
Holmes J S et al. Health Aff 2007;26:169-177
CABG born: 1960, Goetz et al., New York
PTCA born: 1977 Gruentzig et al., Zurich
PCI-BMS born: 1986 Puel, Sigwart, et al. (1994 Palmaz-Schatz stent approved)
PCI-DES born in US: 2003 JNJ-CYPHER approved in US.

"Do not be too timid and squeamish about your actions. All life is an
experiment. The more experiments you make the better."
-- Ralph Waldo Emerson

“…the rocket worked perfectly except for landing on the wrong planet…”
(presumed) Wernher von Braun
Source: “Wernher von Braun - The Man Who Sold the Moon.”, et al.

TBD
X

FROM PILOTS TO CREATING “BUSINESS AS USUAL”
Real value is created by moving from compelling logic to evidence in outcomes or cost
Clinician and patient engagement is required; think passive data collection & simple workflow
The commercial model should ultimately pay for the cost of adoption

ADOPTION AND BUSINESS MODELS FOR HEALTHTECH
Perpetual License
(+) Aligns selling and
implementation costs with revenue
(+) Can demonstrate rapid revenue
ramp
(-) Lumpy and unpredictable sales
(-) Requires maintenance re-up
annually
Per Click or Per Procedure
(+) Aligns use with revenues
(+) Rapid D2C on-boarding
possible
(-) Requires conversion of customer
each click unless “habit” results
(-) Requires training and
implementation investment
without known return for B2B2C
Software as a Service and PMPM
(+) Aligns use with revenues and
cloud
(+) Aligns with user or patient
onboarding
(-) Requires careful on boarding
resourcing
(-) Requires more capital
commitment into company

Pilots are here:
Pilots are becoming a mainstay in healthcare transformation with technology
Pilots are useful if designed properly, with short duration, clear goals, and managed costs
Pilots allow rational assessment of the risks, benefits, and operational challenges of innovation
Pilots fail both the provider organization and the innovative company:
If the product or service doesn’t work
If there is no executive buy-in, a learning organization mentality, and provider/user buy-in
If there are not clearly defined goals as to the duration, scope/size, and go-no go decisions for rollout
If the executive sponsor cannot drive larger rollout from a successful pilot
PILOT VIGOROUSLY, BUT DON’T FORGET TO SET YOUR SIGHTS TO TAKE OFF

AGENDA
The Forces Changing Healthcare
How Innovation Capital (VC) Works
Themes for Innovation In Healthcare
Business Models
Summary

SUMMARY
Take Calculated Risks and Experiment
Administrative and Technologic Forces Will Drive Change or Extinction
Use Data to Drive Decisions on Adoption and Look For Early Fast Wins
Be Mindful of the Limited Resources (but incredible technology and teams) of Innovative Companies
Labor (semi) Automation Is Coming In the Form of Analytics and AI/ML

Technology in Healthcare Transformation

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