How to build a foundationin AI to accelerate healthtransformation

Health executives must be strategic about what
to do now, and what groundwork to lay for the
future, to create value through AI.
By Aloha McBrideEY Global Health Sector Leader

In brief• EY interviews with global executives
show the use of AI/GenAI in health carerequires committed leaders, and theright governance, data and skill sets.
• Only 36% of health care CEO
respondents said they had assessed how
to effectively govern the risks unique
to AI, according to the EY CEO Outlook
Pulse Survey.
• As health organizations mature
their governance and foundational
capabilities, they can evolve their use
of AI from back office to care delivery.
• Executives must consider how tomaintain a portfolio of algorithms,
monitor underlying data andsupport testing, training andchange management around each.

Health executives are both intrigued and wary when it comes to AI and generative AI(GenAI) for health, and rightly so, given its potential to improve health care delivery
and the unique challenges around privacy, bias and trust.1 The EY CEO Outlook Pulse
survey found that 66% of global health care CEO respondents say more work is needed toaddress the social, ethical and criminal risks in the new AI-fueled future.2 In our interviews
with health executives and clinicians across the globe, they highlighted the importanceof building out governance and skill sets that will enable the organization to balance therisks and rewards of AI.
An example of the many ways in which AI can provide critical new insights to improvehealth involves the use of GenAI, a form of AI that leverages an unprecedented ability towork with unstructured data to generate new content. If GenAI could be applied in waysthat would signal to care teams what patients need and when, health organizations could
prevent health conditions from escalating to crisis levels and move toward better qualityof care. “Why can’t every single patient who has ever had a diagnosis for diabetes bescreened and analyzed to ensure that every recommendation associated with diabetes
care has been applied to the patient?” asked Dr. C. Matthew Stewart, Associate Chief
Medical Officer at The Johns Hopkins Hospital and core faculty at the Armstrong Institutefor Patient Safety and Quality. “That one intervention alone would have such a profoundeffect on the health of the US population and other countries.”
While GenAI holds much promise, many organizations are starting with strategicallyintegrating it into their back offices to get comfortable with how the technology works
and to institute the proper AI and data governance before moving into the clinical space.
Billing, claims, waste reduction, and scheduling are perceived as safer realms to gain
their footing.

How to build a foundation in AI to accelerate health transformation | 1

With wearables collecting real-time, accurate physiologic andbiometric data from patients, some health systems are adopting
AI technology solutions to go beyond traditional threshold alerts
and monitor for clinically meaningful trends in the patient’s statuslongitudinally to create opportunities for earlier interventions
prior to catastrophic deterioration events. While clinical staff
may be taking temperatures and listening to the heart andlungs every six hours, health systems generally do not have thestaff to effectively monitor that data so they may miss overall
trends, said Dr. James Mault, CEO of BioIntelliSense, a medicalgrade wearable and AI analytics solution for continuous patientmonitoring. “So that’s where AI really is the game changer.
We’re watching for trends and not for spot values, which is a big
difference,” he said. “With AI, you now move from ‘I wonder why
this patient crashed’ to ‘I can see this crash before it happens andintervene appropriately.’”

Figure 1: Both health consumers and clinicians recognize AI’s
potential in health care

52% 0
of respondents to the
EY Global Consumer
Health Survey said they
feel AI will be commonlyused in health care in thenext 10 years.

clinicians interviewed
for EY Global Voices in
Health Care Study said
they have access toanalytic insights about
their patient population
to improve outcomes.

Better outcomes, of course, are the goal for a clinical workforce
that is often mission-driven and frustrated by unsustainable
health care delivery models. Clinicians are clamoring foractionable insights to help them reduce patient suffering.3
For their part, consumers told EY they are coming to expect AI to
be used in health care in the next decade, but they want to makesure their health information is properly protected (see Figure 1).4
As the AI arsenal grows in number of algorithms and in complexity,
it requires a highly mature level of governing, monitoring andquality oversight. So, how can health executives begin taking
advantage of the wealth of opportunities that AI has to offer,
while maintaining a level of assurance that the insights provided
will not put patients in harm’s way?

its capabilities around operating and maintaining a portfolioof algorithms, monitoring the underlying data supporting thealgorithms, supporting testing, training and change management
around each algorithm and how changes in the health careenvironment, such as new data elements, new care models
and procedures, may affect the relative effectiveness of eachalgorithm. One can imagine a future wherein algorithms will
be dependent on other algorithms and, hence, the change
management capabilities, roles and skills across the enterprisemust evolve to appropriately manage such complexities andrisks. As such, executives must consider these future health care
operating model impacts and what steps need to be taken now
to plan, invest, hire, train and protect the health organization.

As health organizations develop their AI and
GenAI strategies, here are five key principles
for health executives to consider:

What you can do now: Start with a quick win — an internalfacing use case that is low risk, low cost and will allow executive
stakeholders to understand the algorithms, governance process
and change management needed before venturing into higher
complexity use cases.

1. Obtain executive commitment to activate and scale
AI and GenAI projects
A holistic AI-at-scale strategy is necessary, so if the organizationneeds to recalibrate during an initial AI project, it does notlead to a loss of organizational buy-in around realizing thepower of GenAI for health. “Adoption of bold new innovations
is often challenging, especially with health care professionals
whose overriding concern is, first and foremost, patient safety.
Nonetheless, strong leadership can overcome these challenges
through proper education and proof points of clear clinical benefit
to patient care,” said Dr. Mault.
More than 40% of health care CEO respondents to the EY CEO
Outlook Pulse survey said they had already established an AI task
force, with a direct line to the C-suite, responsible for the firm’s
vision and strategy (see Figure 2).
First, leadership must bring clarity and strategic vision around thefuture AI/GenAI-infused operating model. Planning for this futurestate, executives must consider how the organization will mature

Focus for the future: Executives should be key players
in championing new projects, and in providing strategic
communications and project oversight.

2. Inspire confidence in your AI strategies throughappropriate governance, legal, and risk management
As AI continues to evolve globally, regulators are scrambling
to navigate this new environment, especially when it comesto integrating the technology into medical devices and clinicalworkflows. To guard against the risks of hallucinations, biased
algorithms or shifting datasets impacting patient care, healthorganizations must be vigilant about performance monitoring
and change management.5
Governance must be the anchor to ensure secure, sustainable,
responsible and transparent AI (see Figure 3).6 “Explainable AI isimportant so that physicians can understand that the information
presented is based on clinically accepted treatment protocols, andHow to build a foundation in AI to accelerate health transformation | 2

Figure 2: Health care CEOs are taking steps to shape their AI strategies

Source: EY CEO Outlook Pulse Survey

can make better informed decisions,” said Femi Ladega, Group
Chief Digital Officer of Dedalus, a global digital health care anddiagnostic solutions company. “An organization’s AI governanceshould be flexible to meet maturing needs of AI models in thehealth care enterprise.”

Figure 3

As health organizations turn to external parties to help with AI/
GenAI, potential danger exists in leaning on dozens of point
solutions, which could become unmanageable and expensive.
Health organizations need policies in place to properly vet andassemble the ecosystem of partners and solutions that they may
choose in this rapidly changing environment.
What you can do now: Harmonize your AI governance with theexisting organizational and data governance. AI must be plugged
into existing processes so that the organization can better
embrace and understand it.
Focus for the future: Establish continuous feedback loops
that monitor for regulation changes, risks, and biases acrossthe AI portfolio. The feedback loop should be part of thegovernance structure to be constantly overseeing and improving
all algorithms.

How to build a foundation in AI to accelerate health transformation | 3

3. Build the right data infrastructure to power
your AI strategy
One of the key obstacles for health organizations with AI is data
infrastructure. In integrating five of its care systems, London’shealth organizations adopted a single health informationexchange infrastructure to securely share records at the point ofcare across all its organizations. This is called the “London Care
Record,” and helps ensure frontline staff have the information
they need about a person when they need it, wherever they are
working in London. Nationally in England, a process is underway
to implement a federated data platform in support of consistent
approaches to local use of data for multiple purposes. To enable
AI now and in the future, health systems must craft a data
infrastructure that can bend and flex to the needs of the healthcare system.
“The London health data strategy was saying actually, we makeall this data available — not just six months’ data, but near toreal-time in a linked way across all our patient contacts andpatient care settings,” said Luke Readman, Director of Digital
Transformation for NHS England, London.
What you can do now: Review your data strategy and data
governance, including existing metadata, data lineage, data
ownership and infrastructure. Leveraging data standards iskey. Ingest data and map the various standards to each other.
Moving from on-premises to the cloud allows for more scalability
and flexibility. Create a semantic layer of data to make theinformation consumable and exposed via APIs. Determine thekey infrastructure components that are necessary for the desired
business outcomes.
Focus for the future: Focus on building a scalable, flexible
infrastructure that can withstand a portfolio of AI algorithms.
Be strategic with procurement decisions when running a suiteof GenAI and AI algorithms at once and at scale enterprise-wide,
as it can become expensive in a very short time.

4. Equip and upskill your workforce with AI training
Health organizations will have employees either starting to
use AI and GenAI on their own, or who are making decisions
based off the outputs, so it is important to think through thetraining needed to help workers recognize bias and understandhow to monitor performance. Working with clinicians when
implementing AI or GenAI tools is integral to successful
integration. Transparency around the data and models that feed
the outputs is necessary from the beginning so that clinicians
know which variables are interacting to produce a recommended
clinical action.
What you can do now: When it comes to AI education in general,many prominent universities are incorporating AI courses toprepare the future workforce. Individuals delivering care should
receive AI literacy training to prepare them for the incorporationof AI and GenAI in their daily lives. Leaders also should evaluateboard member readiness around AI, and the skills it takes to steer
an AI-enabled organization.

Focus for the future: Establish specific roles for more AIliterate or AI-interested health care providers to work with thosedeveloping and maintaining AI algorithms to continually optimize
care delivery and to create experiences that satisfy patients.

5. Prioritize GenAI use cases in alignment withthe maturity of your AI strategy, governance andfoundational capabilities
Health organizations will want to make sure their investment
will pay off in terms of value — whether it be financial value, orto the clinician and patient experience. To produce value, theright type of AI must be applied based on the situation, andwhat is most clinically applicable, cost effective and, importantly,
sustainable. Each use case might not revolve around just GenAI
or one type of AI, but rather a mixture of RPA, machine learning
and GenAI together to be cost effective and sustainable. The key
is architecting these tools together in a way that is manageable
and has the appropriate oversight to produce ethical, valuable
algorithms at scale.
Cost is certainly a driver when it comes to maintaining algorithms
at scale. Data sets and trends in algorithms may change over time,
meaning they will continually need to be optimized. As algorithms
become more complex and grow in scale, the operating modelof how to manage them changes drastically. Further, froman enterprise level, there will be a portfolio of AI algorithms
consistently needing to be overseen, maintained, traced andregulated, which requires specialist skills, data infrastructureand significant oversight.
But value is not just financial, says Dr. Stewart. Value can come in
the form of increased clinician satisfaction through reducing EHR
clicks or taking giant steps forward to improve the world’s health.
“If I could address health care disparity in the application of bestpractices across all people for hypertension, diabetes, congestive
heart failure and breast cancer, and eliminate the health caredisparities for those diseases across all people, then I almost
really wouldn’t care how much it cost,” he said.
Here’s what you can do now: Determine your organization’s
strategic objectives and goals when using AI. Is the goal cost
reduction, revenue growth, operational efficiency, customer
satisfaction or innovation? Start with a low risk, easy to
implement use case that fits your objectives.
Focus for the future: Once your organization has tested its
governance on a few use cases and as the infrastructure and skill
sets mature, it can take on more complex, transformative use
cases. Continually measure the use cases that are implemented
to determine if they are meeting the desired outcomes or if theyneed to be optimized further.
For additional health sector insights, visit ey.com/healthcare.

How to build a foundation in AI to accelerate health transformation | 4

Extra insight: Lessons from the field
A datahub that leads to consensus, better patient experience and reduced care burden
At Erasmus MC in Rotterdam, the Netherlands, a researchteam evaluated intensive care AI models globally andfound less than 2% of the models had been implemented
at the bedside to help the patient, as they were stuck indevelopment and prototyping.7“One of the biggest obstacles is the cross-disciplinary
collaborations needed between data scientists, health care
professionals, and regulatory experts,” said Davy van deSande, PhD candidate, Intensive Care Adults at Erasmus MC.
“Usually when you talk about innovations in a health careorganization, they’re implemented top down, and all other
health care professionals need to use it. And that could leadto misunderstanding between the different stakeholders.”
In January 2022, Erasmus MC created a center called
The Datahub, which aims to foster collaboration among all
stakeholders to develop AI innovations that eventually are
adopted and trusted at the bedside. “Initially, our approach
was to directly engage with our nurses and physicians, asking

them about the challenges in their daily routines that AI
might be able to address,” van de Sande said. He emphasized
the importance of involving health care professionals in
the process, as their input and influence are crucial in theevaluation and integration of AI technologies into clinicalpractices and workflows.
Erasmus MC recently introduced a comprehensive
AI Governance Policy. This policy outlines the process fromconceptualization to business case development, including
prototyping, external validation in other hospitals, andprospective simulation to ensure safety in clinical settings.
The Datahub team has developed a surgical prediction
model that identified a third of patients do not actually needinpatient care past the second day, in contrast to a typical fiveor six-day stay. According to van de Sande, this could result
in up to 250 saved inpatient bed days per year for a single
surgical ward.

References
1. Z. Obermeyer, B. Powers, et. al., “Dissecting racial bias in an algorithm used to manage the health of populations,”
Science, 25 October 2019.
2. EY CEO Outlook Pulse 2023, July 2023.
3. EY Global Voices in Health Care Study 2023.
4. EY Global Consumer Health Survey 2023.
5. ECRI, “Top 10 health technology hazards for 2024,” accessed 6 February 2024.
6. S. Reddy, S. Allan, et al., “A governance model for the application of AI in health care,” Journal of the American Medical Informatics
Association, March 2020.
7. D. van de Sande, M. van Genderen, et al., “Moving from bytes to bedside: a systematic review on the use of artificial intelligence
inthe intensive care unit,” Intensive Care Med, July 2021.

Special thanks to the following individuals who contributed greatly to the development of this point of view:

Sezin Palmer
EY Global Health Sector
AI Leader

Kayla HoranEY Global Smart Health
Analytics Solution Leader

Crystal Yednak
EY Global HealthSenior Analyst

For more EY Smart Health insights, visit ey.com/healthcare
How to build a foundation in AI to accelerate health transformation | 5

Rachel Dunscombe
CEO, OpenEHR

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