The Impact of FDA Digital Health Guidance
on CDS, Medical Software, and Machine Learning
Carolyn Wong Simpkins, MD, PhD
& Daniel Orenstein, General Counsel

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FDA Health Guidance for the 21st Century
The FDA recently released guidance
documents on select provisions of the 21st
Century Cures Act (the Cures Act).
Digital health products and services providers
are most concerned about guidance on the
use of clinical decision support (CDS) and
medical software.
For healthcare organizations working
these fast-changing technologies and constantly pushing digital innovation,
federal guidance that hints at changing the regulation of their pioneering
efforts may send up warning flags.

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FDA Health Guidance for the 21st Century
There is little cause for alarm. The FDA regularly
issues clarification on how it is going to enforce
statutes and regulations to provide useful
interpretive information to the industry.
The FDA digital health guidance documents are
not regulation, they are non-binding,
informational communications.
Digital health executives and developers can still
use the FDA’s guidance to understand the
current approach it is taking toward regulation,
and how this approach impacts CDS and
medical software development, as well as
provider workflows.

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FDA Guidance on CDS
Healthcare organizations use CDS to reduce
errors and improve efficiency, standardization,
and cost savings.
The FDA guidance on CDS (the CDS
Guidance) describes what types of software
tools the FDA considers to be CDS, and
which CDS is excluded from FDA
regulation as a medical device.

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FDA Guidance on CDS
The CDS Guidance states that software functions must meet the following
three criteria to be considered CDS:
1. Not intended to acquire, process, or analyze a medical
image or a signal from an in vitro diagnostic device or
a pattern or signal from a signal acquisition system;
2. Intended for the purpose of displaying, analyzing, or
printing medical information about a patient or other
medical information (such as peer-reviewed clinical
studies and clinical practice guidelines);
3. Intended for the purpose of supporting or providing
recommendations to a health care professional about
prevention, diagnosis, or treatment of a disease
or condition.

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FDA Guidance on CDS
Having met these three criteria, CDS must then meet a fourth criterion to fall
outside the FDA’s definition of a medical device and be exempt from
regulation:
4. Intended for the purpose of enabling such health care
professional to independently review the basis for such
recommendations that such software presents so that
it is not the intent that such health care professional
rely primarily on any of such recommendations to
make a clinical diagnosis or treatment decision
regarding an individual patient.

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FDA Guidance on CDS
The CDS Guidance provides examples of CDS software functions that do
and do not meet these criteria. Two key elements are provided in the
guidance to further define the criteria for exemption:
1. The healthcare practitioner must be able to independently
evaluate the basis of the CDS recommendations.
2. The CDS recommendations must be based on publicly
available clinical guidelines (i.e., studies, published
literature, clinical practice guidelines).
These two elements beg the question:
What about using algorithms and machine
learning as the basis for CDS recommendations?

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Machine Learning as a Basis for CDS Recommendations
Healthcare machine learning models produce
information that improves patient care.
This information may not always be based
on published literature, but its reliability
is correlated to significant historical
patient data and actual outcomes.
Providers will exercise clinical
judgment before taking any
action on the output of machine
learning, which is often in the
form of recommendations, alerts,
and risk prioritizations.

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Machine Learning as a Basis for CDS Recommendations
What’s concerning is that an algorithm could operate
on unspecified health data within a “black box” and
deliver a result, the basis of which a provider might
not understand.
The FDA is appropriately addressing this issue by
requiring disclosure of the inputs used in order for
software to be excluded from regulation.
Health Catalyst agrees with the principle that any
applications using algorithms should have
functionality that allows users to view the input
features and variables, including the clinical
guidelines or findings that helped shape the
predictive model.

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Machine Learning as a Basis for CDS Recommendations
It is also important to note that, as discussed in
an earlier article, a clinical intermediary will
always be part of machine learning-enabled CDS
to impose judgement and connect any
recommendations (produced by the machine
learning model) to relevant clinical guidelines.
Health Catalyst and other organizations will seek
clarification from the FDA on the use of algorithms
and machine learning as the basis for providing
CDS recommendations, in lieu of, or in addition
to, publicly available clinical guidelines.

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Enforcement Discretion
The CDS Guidance provides informative examples of CDS functionality that
fall into the following three categories:
1. CDS functionality that does not constitute a medical device—these
are CDS functions that do not meet the definition of a medical device;
2. CDS functionality that may meet the definition of a medical device but
for which the FDA does not intend to enforce compliance; and
3. CDS functionality that the FDA intends to regulate as a medical device

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Enforcement Discretion
Where the FDA “does not intend to enforce
compliance” means that the decision to not
regulate non-medical-device software is
based on the FDA’s discretion.
In the future, the FDA could modify its policy,
which could result in a more significant
impact on vendors that use CDS with
machine learning functionality.
While the FDA’s approach introduces
uncertainty, it is our opinion that, because
there have been enough policies issued over
a sustained period, radical shifts in
enforcement policy are unlikely.

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Patient Decision Support
The CDS Guidance also addresses patient decision
support (PDS) software, regarding functionality for
use by patients rather than clinicians.
This guidance has similar criteria to CDS and
indicates that PDS will not be regulated as a device.
The FDA appears to be drawing the distinction
because PDS likely applies to a different audience
(patients).
For example, PDS guidance applies to patient-
accessed modules and functions within Health
Catalyst’s Care Coordination application in
the Care Management suite.

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FDA Guidance on Medical Software
The FDA guidance on changes to existing medical software policies (the
Medical Software Guidance) provides clarification on policy changes made
by the Cures Act.
The Medical Software Guidance builds upon several previous FDA guidance
documents and clarifies that software functions classified into any of four
areas are exempt from regulation as a medical device:
1. Software function intended for administrative support of a healthcare facility.
2. Software function intended for maintaining or encouraging a healthy lifestyle.
3. Software function intended to serve as electronic patient records.
4. Software function intended for transferring, storing, converting formats,
or displaying data and results.

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Additional Ramifications of the FDA
Digital Health Guidance
As long as software vendors comply with the
FDA guidance, any cost impact should be
minimal, provided that their software does not
seem to fall within a category that the FDA
indicates is a medical device.
If vendors aren’t already providing transparency
through their applications, then workflow
processes could be impacted as they move
from a black box approach to one in which data
inputs used by algorithms are disclosed to users.

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Additional Ramifications of the FDA
Digital Health Guidance
Providing these disclosures could potentially
involve more time and effort by providers
because it could add an input review phase
with an internal clinician panel, although this
would be discretionary.
The CDS Guidance and Medical Software
Guidance were issued in draft form to allow
the FDA to consider public comments.
While possible, it is not likely that the
exemptions will change significantly in
the final documents.

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Additional Ramifications of the FDA
Digital Health Guidance
Vendors might expect to see clarification on how to
disclose the basis for recommendations or
conclusions derived by CDS, and where machine
learning fits into these disclosures.
The FDA will also likely include a few more
examples of what functionality it considers to be
exempt from, or subject to, regulation when it
issues its final guidance later in 2018.

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Good News for Digital Health Innovators
There are no major surprises with the new FDA
guidance documents in terms of what will be
regulated or not.
The CDS Guidance reassures practitioners that
CDS functions that meet the non-device criteria
will either be excluded from regulation or the FDA
will not enforce regulation.
This approach can help promote innovation by
ensuring a quicker time to market—and value—
for developers and CDS system users.

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Good News for Digital Health Innovators
In its own words, the FDA’s intent is to foster,
not inhibit, innovation, and to encourage the
development of tools that can help people be
more informed about their health.
Though these documents don’t establish rules,
they should be taken seriously because they
contain useful information about how the FDA
will enforce its rules, and how it interprets the
Cures Act.

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Good News for Digital Health Innovators
The industry response to these new guidance
documents should be positive.
The policies provide appropriate oversight,
promote software transparency, and let digital
health developers “know where they stand
relative to the FDA’s regulatory framework.”
Most importantly, the guidance supports the
success of vendors and healthcare organizations
as they continue working to improve the
features and functionality of their CDS systems
and medical software.

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For more information:
“This book is a fantastic piece of work”
– Robert Lindeman MD, FAAP, Chief Physician Quality Officer

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Carolyn Wong Simpkins is a physician, health technology executive, and health system
transformation leader. She joined the leadership team at Health Catalyst as Chief Medical
Informatics Officer to lead the development of medical content and accelerate the infusion of
clinical insight its next-generation suite of products, and is also helping to shape machine
learning algorithms so they can best be used to influence important care decisions. She brings to this role
insights from her experiences practicing medicine in diverse settings, from academic medical centers to
critical access hospitals and community health centers, combined with a keen understanding of federal
health policy and its systemic implications, gleaned from her time on the staff of the U.S. House of
Representatives Ways and Means Health subcommittee and her observations from working globally on
health system transformation programs and solutions for the UK based British Medical Journal. She is
passionate about data, technology, design and disrupting healthcare paradigms to improve health
outcomes for all. Carolyn is a Fellow of the second class of the Liberty Fellowship and a member of the
Aspen Global Leadership Network.
Carolyn Wong Simpkins, MD, PhD

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Prior to joining Health Catalyst Dan Orenstein was at athenahealth, Inc. (NASDAQ: ATHN) for 10
years, the last 7 as General Counsel and Secretary. Prior to that, Dan practiced in the areas of
corporate, intellectual property, and healthcare law with law firms in Boston and Washington, D.C.
He has written and presented on health care law and IT topics throughout his career. He also
served as a leader of the American Health Lawyers Association’s Health IT practice group.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Daniel Orenstein