www.mercuryds.com Open in urlscan Pro
52.49.198.28  Public Scan

Form analysis
1 forms found in the DOM

Name: wf-form-newsletter-signup — GET

<form id="wf-form-newsletter-signup" name="wf-form-newsletter-signup" data-name="newsletter-signup" method="get" class="side-bar-form" aria-label="newsletter-signup"><input type="text" class="side-bar-form-field w-input" maxlength="256"
    name="First-Name-2" data-name="First Name 2" placeholder="First Name" id="First-Name-2"><input type="text" class="side-bar-form-field w-input" maxlength="256" name="Surname-2" data-name="Surname 2" placeholder="Surname" id="Surname-2"><input
    type="email" class="side-bar-form-field w-input" maxlength="256" name="email-2" data-name="Email 2" placeholder="Email" id="email-2" required=""><input type="submit" value="Submit" data-wait="Please wait..." class="button is-dark-blue w-button">
</form>

Text Content

SERVICES
Work

Life Sciences & Healthcare

Tech
PLATFORMS

ERGO

VIVO
Insights

Blog

AI Resources
About

About us

Team
Contact
Newsletter Signup
Close
x
Stay in Touch
Enter your information below and receive our monthly newsletters direct to your
inbox
You're Signed Up!
Oops! Something went wrong while submitting the form.


INSIGHTS


BLOG




AI/ML FOR HEALTH TECH AND MEDICAL DEVICES: INVEST EARLY IN DATA SCIENCE
INFRASTRUCTURE TO CREATE COMPETITIVE ADVANTAGE

Harnessing the power of AI/ML has the potential to transform health tech and
medical device companies by providing better outcomes, better patient and
provider satisfaction and engagement, and better insight into product
performance and real world evidence.


Increasingly health tech and medical device companies are building AI/ML-driven
applications. Medical devices are being “connected” to build combination
products. Digital health is expanding into regulated applications that qualify
as Software as a Medical Device (SaMD). Harnessing the power of AI/ML has the
potential to transform health tech and medical device companies by providing
better outcomes, better patient and provider satisfaction and engagement, and
better insight into product performance and real world evidence.  


Health tech and medical device companies starting on this journey often hire a
data scientist to build a proof-of-concept model. Once the company commits to
this path, we believe that it pays substantial dividends to plan and implement a
scalable data science infrastructure early in the process.  Unfortunately, most
health tech and medical device companies and their data science hires don’t
start with the cloud, DevOps, or software architecture background to do this
easily. Failure to do so can lead to substantial technical debt (backtracking)
later and serious costs and inefficiencies in a company’s data science mission
in both the short and long term.


AI/ML REQUIRES MORE ONGOING ENGINEERING THAN YOU MIGHT THINK

Like the quality assurance aphorism: “Quality is a journey not a destination”,
an investment into AI/ML or even simple analytics is more than just producing a
model, like some final equation to be written into code and forgotten.

Model performance can unexpectedly degrade with exposure to unplanned patient
populations, shifting treatment paradigms, changed use cases, or underlying
shifts in data streams spanning EHR information, patient inputs, sensors on a
cell phone, etc.  On the other hand, AI/ML model performance is often increased
over time as a company develops a larger data set.  The lifecycle of build,
train, test, deploy, monitor is continuous and, if infrastructure is not well
designed, a company loses time and spends more just to keep that cycle going.



WHAT DOES A WELL-DESIGNED DATA SCIENCE INFRASTRUCTURE LOOK LIKE?


It should:


 * Abstract away as much of the data engineering complexity as possible (while
   still providing the future capabilities that will be needed as the AI/ML
   initiative grows)
 * Automate data pipelines for cleaning, feature extraction and AI/ML
 * Track data transformations, model changes, and experiments so that data
   scientists can collaborate and understand how any given model was developed
 * Run on any cloud and keep computation costs low as the company scales
 * Allow AI/ML models to be developed, tested, deployed, and maintained
   efficiently
 * Provide for future needs like model monitoring, feature stores, etc.

There is more (there is always more) but just thinking through these features
will lead to a better design.


WHAT DOES A WELL-DESIGNED DATA SCIENCE INFRASTRUCTURE GET YOU?


Our view is that putting the right infrastructure in place early in the game
results in:


 1. Lower Cost of Cloud/Data/ML Engineering
    The biggest mistake we see with companies in the process of data science
    transformation is to have the data scientists build the infrastructure for
    deployment and maintenance. Very few data scientists are trained to build
    scalable, maintainable infrastructure and once your company is committed to
    a set of tools, it is very hard to back up and start over. Technical debt
    slows down innovation and often means more spend on engineering staff in the
    future.
    ‍
 2. Alignment with the Regulatory Process
    For regulated applications, traceability of data and model versions allow
    model testing and improvement and aligns with the FDA-mandated design
    process for testing, verification, and record keeping.
    ‍
 3. Faster Innovation
    As a consulting firm, we use our own “click to deploy” architecture to
    build, test and deploy models. It allows rapid creation of an environment
    and automation of many processes to be shared by the entire data science
    team without engineering overhead. That, along with the benefits of
    collaboration and retention of knowledge, allow a level of excellence that
    you can’t get with an ad hoc data science infrastructure.
    ‍
 4. Collaboration and coordination
    If the data science team grows in the future, a well planned infrastructure
    can mean more efficiently getting the best results into production.
    ‍
 5. Retention of institutional knowledge
    For the same reasons that collaboration is easier, new data scientists can
    come up to speed faster. This is critical given the fight for data science
    talent; we have seen life sciences companies lose their entire data science
    team all at once. Worse, we have seen companies that have lost critical data
    because the infrastructure wasn’t well defined and secure.
    

“HOW MANY DATA ENGINEERS DOES IT TAKE TO SUPPORT A DATA SCIENTIST?”


The tech industry consensus is that a business needs 2-5 data engineers per data
scientist, to maintain a deployed model and to maintain the automated
infrastructure that makes your data science efforts efficient.  We don’t think
this is always the case, but our experience suggests that data science
initiatives always require more engineering work in the long run than companies
anticipate at the outset of their AI/ML initiatives.


Data engineers (or ML Engineers or Cloud Engineers) are not cheap – think at
least $250,000 per year each after overhead, recruiting fees, misfires (hiring
the wrong person). Design your system correctly upfront and you reduce the risk
of future technical debt and will need fewer engineering resources to maintain
your competitive edge.  


WHAT IF YOUR COMPANY IS JUST DOING “ANALYTICS” AND NOT REALLY “AI”?


Anything beyond very simple data analytics could benefit from many of the same
processes to build and maintain as complex machine learning models so it’s worth
thinking about future needs.  Once there is a commitment to be a data forward
company, both the amount of data and the demand for increasingly sophisticated
solutions only seem to grow.


BOTTOM LINE


We encourage CEOs to look at the benefits of a well designed data science
infrastructure early on in their AI/ML journey.  We believe that investors and
customers reward companies that are committed to building better products using
data science and that a well-designed data science infrastructure leads to a
real competitive advantage.


CONNECT WITH US

With expertise in NLP, Machine Learning, Bioinformatics, and Video/Voice
Analytics and a passion for cutting-edge data science, our team is always
looking for ways to enhance discoveries and accelerate your potential. If you
have an AI/ML-related question or would like to discuss your AI strategy, we’d
love to hear from you! Reach out at inquire@mercuryds.com or on LinkedIn.


Written by:

DAN WATKINS

CEO

ANGELA HOLMES

COO

MICHAEL BELL

PRINCIPAL DATA SCIENTIST

PUBLISHED ON:

FEBRUARY 8, 2022

Shares
Share
Tweet
Share
Email
Share


THIS IS A HEADING 1


THIS IS A HEADING 2


THIS IS A HEADING 3

THIS IS A HEADING 4

THIS IS A HEADING 5

THIS IS A HEADING 6

A rich text element can be used with static or dynamic content. For static
content, just drop it into any page and begin editing. For dynamic content, add
a rich text field to any collection and then connect a rich text element to that
field in the settings panel. Voila!

 * This is a test
 * This is a test
 * This is a test

 1. This is a test
 2. This is a test
 3. This is a test

Below is an image


Back to All Blog Posts

VIEW MORE RECENT BLOG POSTS


DESIGNING NOVEL PROTEINS WITH DEEP HALLUCINATION

Learn More


A BETTER FACIAL EMOTION RECOGNITION MODEL

Learn More


ELIMINATING RACIAL BIAS IN AI/ML: SOLVING THE TRAINING DATA PROBLEM

Learn More

We are a mission driven team of data science experts partnering with our clients
to build AI solutions to improve outcomes, enhance discoveries, and enable
data-driven decisions.

P:
832.304.0281
E:
inquire@mercuryds.com

3737 Buffalo Speedway #1750, Houston, TX 77098, USA

HOmeSERVICESWorkPlatformsInsightsAboutCareersContact

Copyright © 2022 Mercury Data Science
All rights Reserved
Privacy Policy
Site by Studio Forum