Enablement, Optimization, Transformation: How to Implement New Technologies in Healthcare

Marc Helberg
Office Managing Vice President, Philadelphia Office

The hype surrounding artificial intelligence in healthcare is significant. The market is projected to reach a value of about $36.1 billion by 2025 — yep, you read that right — with a compound annual growth rate of over 50%. 

That number is a reflection of transformational potential: Practitioners have an easy time imagining the hazy yet exciting future of AI-enhanced healthcare. Adoption, however, is still a significant barrier.

In practice, there seems to be a gap between theories surrounding digital transformation in healthcare and its applications in addressing day-to-day patient needs.

Starting Small and Implementing New Technology

As opposed to other sectors where AI adoption has been more swift, healthcare is comparatively hampered by operational challenges, regulatory friction, and a lack of follow-through in digital transformation. Case in point: A cohesive data strategy is the first step in implementing analytics (and should be an early step in any digital transformation), but 56% of hospitals don’t even have a data-governance strategy.

When we hear about the potential scope of digital transformation in the healthcare industry, the image can be so vast that it’s difficult to see the entire journey at once. Consequently, spotting the first step to implementation can be difficult for providers.

The solution is to adopt new technology and AI in smaller increments. If providers can start with incremental changes in their digital lives — using analytics to predict scheduling, for example — then doctors will become used to working with this technology in a safe environment. Eventually, this will empower them to make better care decisions informed by new digital tools.

First, though, providers need to trust AI.

AI’s Future Applications in Healthcare

Right now, there’s often a fear of completely trusting data insights. In one notable example, a Harvard professor compared machine learning in healthcare to asbestos during a recent precision medicine conference. Whether the claim is founded, it gives us a good idea of just how much some people mistrust emerging technologies.

If a computer suggests that a patient has a high likelihood of relapsing, for example, the doctor should be able to view that suggestion with confidence and make care decisions accordingly. Without that trust, all the advanced technology in the world can’t actually improve outcomes.

It’s helpful to recognize and reinforce that AI in healthcare should focus on support and augmentation rather than replacing doctors. In fact, AI innovators tell providers that their priority is automating the mundane. They’re not trying to fly healthcare to the moon — they want to make small yet meaningful impacts to aid treatment.

Building on that trust, doctors must be able to understand data on a human level. If data from a monitoring device shows that a patient’s blood pressure spiked the last three times she left the hospital and returned to work, the doctor can confidently suggest that she needs to wait a little longer before returning to the office.

To reach that point, doctors need to be able to integrate expertise regarding their patients (and those patients’ habits) with data that makes that expertise more effective in driving outcomes. The more complete and integrated predictive data is, the more doctors will be able to bring increased clarity and calm to the patient’s experience

When implementing new technology in healthcare, providers must start by thinking small. Trust data to support medical staff in direct, tangible ways, and from there, build up to more transformational deployments — no hype necessary.

Marc Helberg

About the Author

Marc Helberg Office Managing Vice President, Philadelphia Office

Marc is an energetic people developer and problem solver, focused on ensuring that the right size solution is brought to enable overall strategic progress.  He has expertise in large program and project management across complex and large organizations.  Marc is the Office Managing Vice President for our Philadelphia office. 

More Perspectives

Perspective
by Kent Norman and Kevin Moormann
I’ll be Home in Five Story-points
Perspective
by Kerry Stover
I Know You Believe You Understood What You Think I Said...
Perspective
by Adrian Kosiak
Lessons from Dior on Becoming a Premium Brand
Perspective
by Margaret Rogers
Failing Fast Is Fine — As Long As You’re Failing Well, Too
Perspective
by Bruce Ballengee
Leadership and Company Culture
Perspective
by David Watson
Work Life Balance
Perspective
by Sean McCall
4 Ways Sports Can Benefit Careers
Perspective
by Sean McCall
Forget Coffee: Energize Your Work Morning
Perspective
by Russell Clarkson
Stand Up for Good Presentations
Perspective
by Scott Hajer
The Importance of Making Space to Think
Perspective
By Alexandria Johnson
The Hottest Thing at SXSW You Learned it in Kindergarten
Perspective
by Bruce Ballengee
Developing the Individual
Perspective
by Lori Dipprey
Why Performance Review are Here to Stay at Pariveda
Perspective
by Mike Strange
4 Reasons to Leverage the Power of Small Teams
Perspective
by David Watson
The Benefits of Working in Teams
Perspective
by Sean McCall
The Architecture of a Selfless Team
Perspective
by Nathan Hanks
What it Means to be in the People Business
Perspective
by Bruce Ballengee
Unleashing the Power of Humility
Perspective
by Russell Clarkson
Mark Your Exits
Perspective
by Bruce Ballengee
Teaching Roots Run Deep
Perspective
by Dimitrios Vilaetis
Business Capabilities: a Spotlight for Strategic Decision Making
Perspective
by Samantha Nottingham
Brandsparency: Who Builds Brands These Days?
Perspective
by Russell Clarkson
Is your Ecosystem a pipeline or a platform?
Perspective
by Derrick Bowen
Stop Complaining About Changing Requirements
Perspective
by Brian Duperrouzel
Hippos and Cows are Stopping Your Machine Learning Projects
Perspective
by Jack Warner
Building Smart Deployment Pipeline
Perspective
by Marc Helberg
3 Ways You Can Begin to Take Patient Experience More Seriously
Perspective
by Ryan Gross
What Does It Really Mean to Operationalize a Predictive Model
Perspective
by Tom Cunningham
The Sound of the Future: Predictive Analytics in the Music Industry
Perspective
by Kent Norman
Limit, to Win It - How Putting Limits on Your Team Will Allow Them to Do More
Perspective
by Sophie Zugnoni
Did You Catch Machine Learning Fever?
Perspective
by Susan Paul
Capabilities as Building Blocks
Perspective
by Susan Paul
When Corporate Culture Undermines IT Success
Perspective
by Margaret Rogers
Identifying the Value of Nonprofit Customer Experience
Perspective
by Margaret Rogers
Why an Agile Mindset is at the Root of an Excellent Guest Experience
Perspective
by Collins DeLoach
What does Cloud Transformation mean to IT?
Perspective
by Mike Strange
Untangling and Understanding DevOps
Perspective
by Clayton Rothschild
Blockchain in an Enterprise Setting
Perspective
by Mike Strange
DevOps: A Practical Framework for Technology Process and Organizational Change
Perspective
by Julio Santos
Context as Currency
Perspective
by Oussama Housini
Why DevOps?
Perspective
by Dave Winterhalter
Data in the Dugout
Perspective
by Mike Strange
Can We Predict the Future?
Perspective
by Julio Santos and Jon Landers
How Customer Context and Smarter Algorithms will Power the Next Level of Experiences and Engagement
Perspective
by Victor Diaz
6 Things to Consider when Choosing a Data Visualization Library
Perspective
<p>by Brian Duperrouzel</p>
Post Cloud and the Lonely CIO
Perspective
by Marc Helberg
How AI Will Affect Your Patient’s Experience
Perspective
by Phillip Manwaring
Let Serverless Solve the Tech Problems You Don't Have
Perspective
by Mike Strange
Bigger is Not Necessarily Better
Perspective
by Margaret Rogers
How to Tell the Hype from the Digital Reality
Load More