This document summarizes Vinod Khosla's views on the future of healthcare presented at a talk at Stanford University in 2012. Khosla believes that within 5 years, most of what doctors know about medicine will be obsolete, with computers and robotics replacing physicians for diagnosis and treatment. He argues that the randomized controlled trial (RCT) has become a barrier to innovation in healthcare, as new technologies and approaches could provide solutions more quickly through alternative studies like smaller feasibility studies, large observational studies, and use of big data analytics and mobile technologies. Khosla believes harnessing new technologies could shorten clinical trials and enable better outcomes at lower costs.

1. The Randomized Controlled Trial is the
Gold Standard for Clinical Evidence
Is it a Barrier to Innovation?
Tim Fayram MS
Vice President of Research
Sunnyvale, CA
November 19, 2013

2. Introduction
Who is Vinod Klosla?
Entrepreneurial i
E t
i l icon i th Sili
in the Silicon
Valley
Co founder
Co-founder of Sun Microsystems and
Daisy Systems
Now leads Khosla Ventures
Views on healthcare at a talk on the
Stanford University Campus in June of
2012

3. Introduction
Healthcare Innovation in the next 5
years …
Approximately 500M people have
access to healthcare
Approximately 6B people don’t have
access to healthcare and want
access
We face a rapidly increasing slope on
the cost versus time graph
How will the healthcare delivery
y
model adapt in the next 5 years?

4. Introduction
Technology is our only hope …
"Almost everything doctors know about
medicine will be obsolete "
obsolete,
Most Physicians and Clinicians as we
know them will be replaced by
Computers and robotics for diagnosis and
treatment
Mobile devices will guide patients in the
management of their disease states
Big Data analysis of huge patient groups will lead
to the development of new treatment strategies
Innovation that has yet to happen
I
ti th t h
tt h
All of this will come at a much lower
treatment cost per patient

5. The Healthcare Landscape: Is the focus on
Outcomes and Comparative Effectiveness?
Medical Device Epidemiology Network
Initiative (MDEpiNet)
5

6. Healthcare Public Policy
A dynamic environment
Implementation of the Affordable Care Act
A conservative regulatory approach seems
to be subsiding
A reimbursement approach that is asking for
additional proof and is paying out less
The rise of the Accountable Care
Organization
Comparative effectiveness research
There are conflicting signals but the
need for cost effectiveness evidence is
stronger than ever before

7. Evidence
New types of clinical evidence will be needed
While scientifically and medically oriented evidence will
always be a requirement
Is the priority shifting from efficacy to economic efficiency?
A new feature or product must demonstrate superiority over
the existing standard of care
f
Pre-clinical evidence
Per subject study costs are rivaling per patient study costs
Translation issues from animal models to human models
FDA’s new emphasis in computational modeling
Probably has more of a role in academic research

8. A Question for Industry
With the dynamic environment that we are currently
in, can we continue to use the existing serial
1.
2.
3.
3
4.
Research
Development
Clinical
Regulatory
approach that we have traditionally used when
there is
demand from new patients entering the system
and an ever growing cost crisis in the existing
healthcare delivery model?

9. The Randomized Controlled Trial (RCT)
Recognized as the gold standard
for conclusive evidence generation
by scientists, clinicians, regulators,
healthcare decision makers
Requires at least two groups (treatment
and control) with randomized
assignments and crossovers
Requires that the clinicians are blinded
to remove bias and deliver objective
results
Requires rigorous statistical analysis
for appropriate design and accurate
analysis of results
Requires significant funding, it takes a
long time to enroll and follow
It has inherent risks to patients, to
clinics, to sponsors

10. The Randomized Controlled Trial (RCT)
Challenge 1: Design
Historical “soft” endpoints in many RCTs have led to a debate on
the meaning of the results
g
Recent regulatory emphasis on increased power that the RCT can
take years to enroll and complete
Reliance on consistent site to site execution of protocols
Challenge 2: Approval and Acceptance
Results are subject peer review by independent panels
Regulatory approval no longer guarantees reimbursement
Providers are now being measured on how much they save and
the public policy environment could change during an extended
RCT
Will the initial requirements in a large RCT be acceptable when it
has been completed?

11. Are there alternatives to the RCT?
A list of alternatives to explore
1.
2.
3.
4.
4
5.
6.
6
7.
Smaller prospective feasibility research studies
Larger observational outcomes studies
Computational modeling as a substitute
The use of “Big Data” analysis in public databases
Big Data
Mobile devices
Patient social networks
Automation

12. Prospective Feasibility Research Studies
What are the opportunities?
Smaller prospective studies (n < 100) take less time to
enroll and complete
Can still have a randomized treatment arm and a control
arm with crossovers
Patients can be followed for extended periods after the
study is completed
The di ti
Th directional results can’t achieve statistical significance
l
lt
’t hi
t ti ti l i ifi
but those results can still have an impact
More investigators are publishing on their early results

13. Observational Outcomes Studies
What are the opportunities?
Very large retrospective studies (n > 10000) take less
time because the data already exists in registries and
industry & government databases
Observational and uncontrolled but very powerful
The results are hypothesis generating
Statistical significance is usually achieved
Explanation for the result (Why?) may not be possible
A commitment to publish the study results regardless of
its outcome

14. Computational Modeling
What are the opportunities?
Computer simulations that can be used to model the
biological interface
Models contain a very large number of extremely small
elements that virtually duplicate the actual interface
Use of complex constraints and relations can differentiate
between healthy and diseased models
FDA h t k an i
has taken
increased i t
d interest i thi methodology
t in this
th d l
and spoken in support of it at various academic and
industry conferences
y
Could save time in the product development cycle by
virtually eliminating the need for pre-clinical studies on
efficacy

15. “Big Data” Analytics
How can technology address
opportunities?
Consider new sources for data that exist
in very large de-identified patient groups
In Cellular Networks
In Government Networks
In Social Networks
Big Data analytics and algorithms could
provide answers about the forest
irrespective of the trees
This concept is a new and growing
opportunity

16. Mobile Devices
How can technology address opportunities?
Proliferation of healthcare smartphone apps
Healthy d i k ti t have access t many apps
H lth and sick patients h
to
This technology could provide a more consistent
execution of clinician trials via mobile apps
This technology could facilitate positive outcomes by
enabling patient awareness
Of their risk profiles
Track their health progress
Increase their compliance
Provide an incentive to stay “healthy”

17. Patient Social Networks
What are the opportunities?
The world of social networking is becoming more popular
in the older patient populations that are more likely to be
sick
It provides a unique opportunity to study patient behavior
and compliance
It could create patient groups that share information for
the good of the group
Connection of genotypes to phenotypes?

18. Automation in the Practice of Medicine
How can automation address opportunities?
Current diagnostic equipment is highly
automated and acute minimally i
t
t d d
t
i i ll invasive
i
equipment is trending towards automation
But not much else is
More automation is on the horizon
Automatic downloading of data with
automatic analysis
Patients enabling minimally invasive
automatic treatments
Leading to computers and robots that
diagnose and treat patients in-clinic

19. The Question
Is the RCT becoming a Barrier to Innovation?
Our Goal in this environment should be to develop
efficacious solutions that reduce treatment costs on
a shorter time-to-market cycle
This Healthcare Environment demands it

20. The Alternatives
The answer may be found in parallel study
strategies as a opposed to traditional and rigorous
serial study strategies
i l t d t t i
1.
1
2.
3.
4.
5.
6.
7.
Smaller prospective feasibility research studies
Larger observational outcomes studies
Computational modeling as a substitute
The use of “Big Data” analysis in public databases
Mobile devices
Patient social networks
Automation

21. The Alternatives
CardioMEMS: The CHAMPION Trial started in
2007 and has yet to be approved
An alternative reality
1. Conduct a prospective feasibility research study for 6
months
2. Receive conditional limited regulatory approval
3. Continue to follow the patients from the feasibility study
4. Add a much larger pool of patients
5. Conduct a retrospective observational study for 1 year
6. Receive full regulatory approval in 2 years

22. Conclusions
The worlds of healthcare and technology are on a
common course
Mobile apps, social networking and Big Data analytics
are on exponential growth curves
Automation algorithms will enable increased clinical
accuracy with a shorter cycle time
Medical computers and robotics could be morphing into
robotic clinicians
Publication of studies using this technology will be used
as evidence by non-physician decision makers and
stakeholders

23. Conclusions
The worlds of healthcare and technology are on a
common course
The availability of new technology could be
harnessed to
shorten our clinical t i l
h t
li i l trials,
enable better outcomes,
save on th cost of treatment for patients that
the
t ft t
tf
ti t th t
receive novel therapy

24. Conclusions
The worlds of healthcare and technology are on a
common course …
The emphasis on economic efficiency may render
the demand f proof of efficacy
th d
d for
f f ffi
Obsolete?
Unnecessary?
Irrelevant?

25. Conclusions
Vinod Khosla bets big on big data
By Michal Lev-Ram, writer November 13, 2013: 5:48 PM ET
The Silicon Valley startup Ayasdi is just the beginning, the
Sun Microsystems co-founder says.
S Mi
f
d

26. "Almost everything doctors know about medicine will be obsolete,"
Khosla told the audience. Earlier this year, the long-time technologist
and co-founder of Sun Microsystems led a $10 million funding round
co founder
in Ayasdi, a newish big data company that utilizes "topological
analysis" to look for patterns in massive, often disparate data points.
(In plain English, Ayasdi turns mountains of linear information, like
what is found in health records, into geometric shapes that people
can interact with.) The Palo Alto, Calif.-based company is one of an
exploding number of big data start-ups. But while most companies'
offerings rely on data scientists to query systems with questions
questions,
Ayasdi says its approach enables companies to glean insights they
didn't know they were looking for. General Electric, Citi and Merck are
among the company's early customers.