Low angle view of female African American healthcare worker using digital tablet outside of a hospital building.

How to build a foundation in AI to accelerate health transformation

Health executives must be strategic about what to do now, and what groundwork to lay for the future, to create value through AI.

In brief

  • EY interviews with global executives show the use of AI and GenAI in health care requires committed leaders, and the right 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. 

This article was originally published on LinkedIn.

Health executives are both intrigued and wary when it comes to artificial intelligence (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 to address 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 importance of building out governance and skill sets that will enable the organization to balance the risks and rewards of AI.

An example of the many ways in which AI can provide critical new insights to improve health involves the use of GenAI, a form of AI that leverages an unprecedented ability to work with unstructured data to generate new content. If GenAI could be applied in ways that 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 quality of care.

“Why can’t every single patient who has ever had a diagnosis for diabetes be screened 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 Institute for Patient Safety and Quality. “That one intervention alone would have such a profound effect on the health of the US population and other countries.”

While GenAI holds much promise, many organizations are starting with strategically integrating it into their back office 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.

With wearables collecting real-time, accurate biometric data from patients, some health systems are going further to apply AI clinically to monitor for statistically meaningful trends in the patients’ status and act much earlier.

While clinical staff may be taking temperatures and listening to the heart and lungs every 6 hours, health systems generally do not have the staff to effectively monitor that data so they may miss overall trends, said Dr. James Mault, CEO of BioIntelliSense, a medical-grade wearable and AI analytics solution for continuous patient monitoring. “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 and intervene appropriately.’”

AI really is the game changer… With AI, you now move from ‘I wonder why this patient crashed’ to ‘I can see this crash before it happens and intervene appropriately.

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 clamouring for actionable insights to help them reduce patient suffering.³ For their part, consumers told EY they are coming to expect emerging technologies to be used in health care in the next decade, but they want to make sure their health information is properly protected.⁴ As the AI arsenal grows in number of algorithms and in complexity, it requires a highly mature level of governing, monitoring and quality oversight. 

So how can health executives begin taking advantage of the wealth of opportunities AI has to offer while maintaining a level of assurance that the insights provided won’t put patients in harm’s way?

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

A total of
of respondents to the EY Global Consumer Health Survey said they feel AI will be commonly used in health care in the next 10 years
In the EY Global Voices in Health Care Study
clinicians interviewed said that they have access to analytic insights about their patient population to improve outcomes

As health organizations develop their AI and GenAI strategies, there are five key considerations for health executives. Explore them in the five chapters below.

Hospital corridor.

Chapter 1

Obtain executive commitment to activate and scale AI and GenAI

Leaders should strive for a comprehensive vision and not abandon AI strategies entirely if a pilot fails.

A holistic AI-at-scale strategy is necessary, so if the organization needs to recalibrate during an initial AI project, it doesn’t lead to a loss of organizational buy-in around realizing the power 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 of BioIntelliSense.

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 direct line to the C-suite, responsible for the firm’s vision and strategy.

First, leadership must bring clarity and strategic vision around the future AI- and GenAI-infused operating model. Planning for this future state, executives must consider how the organization will mature its capabilities around operating and maintaining a portfolio of algorithms. They must plan for how they will monitor underlying data supporting the algorithms, support testing, training and change management around each algorithm and watch for changes in data elements, care models or procedures that may affect the relative effectiveness of each algorithm. One can imagine a future wherein algorithms will be dependent on other algorithms and hence the change management capabilities, roles and skills across the enterprise must evolve to appropriately manage such complexities and risks. 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.

Health care CEOs are taking steps to shape their AI strategies

What you can do now: start with a quick win — an internal-facing 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.

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

Business people working on a laptop computer in a modern office. There is paperwork on the table with charts and graphs on it. There is a screen behind them with financial charts and graphs on it.

Chapter 2

Build confidence in your AI strategy with appropriate governance

As demographics shift and new treatments and technologies emerge, data governance and AI performance management is critical.

As AI continues to evolve globally, regulators are scrambling to navigate this new environment, especially when it comes to integrating the technology into medical devices and clinical workflows. To guard against the risks of biased algorithms and shifting datasets impacting patient care, health organizations must be vigilant about performance monitoring and change management.⁵

Governance must be the anchor to ensure secure, sustainable, responsible and transparent AI.⁶ “Explainable AI is important so that physicians can understand that the information presented is based on clinically accepted treatment protocols, and can make better informed decisions,” said Femi Ladega, Group Chief Digital Officer of Dedalus, a global digital health care and diagnostic solutions company. “An organization’s AI governance should be flexible to meet maturing needs of AI models in the health care enterprise.”

An infographic showing the relationship between the maturity of a health care organization’s governance programs and AI use case complexity

In the excitement over AI and GenAI, as health organizations turn to external parties to help, danger exists in leaning on dozens of point solutions, which could become unmanageable and expensive. Health organizations need policies in place to properly vet and assemble 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 existing 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 across the AI portfolio. The feedback loop should be part of the governance structure to be constantly overseeing and improving all algorithms.

A businesswoman explaining series of graphs and data sets displayed on some large, wall mounted monitors in the office.

Chapter 3

Build the right data infrastructure to power your AI strategy

Craft a data infrastructure that is rooted in standards and can flex to the needs of the health care system in the future.

One of the key obstacles for health organizations with AI is data infrastructure. In integrating five of its care systems, London’s health organizations adopted a single health information exchange infrastructure to securely share records at the point of care 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 the city. 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 future needs.

“The London health data strategy was saying actually, we make all this data available — not just six months data, but near to real-time in a linked way across all our patient contacts and patient 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 is key. 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 the information consumable and exposed via APIs. Determine the key 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 as it can become costly quickly when running a suite of GenAI and AI algorithms at once at scale enterprise-wide.

Mature woman giving presentation during business conference. Female entrepreneur presenting her ideas during a seminar.

Chapter 4

Equip and upskill your workforce with AI training

Health organizations should build support for the entire workforce – from the AI-averse to GenAI leaders.

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’s important to think through the training needed to help workers recognize bias and understand how 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 exactly how these new tools might impact their future state.

What you can do now: when it comes to AI education in general, many prominent universities are incorporating AI courses to prepare the future workforce. Individuals delivering care should receive AI literacy training to prepare them for the incorporation of AI and GenAI in their daily lives.

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

The mid adult female physician takes a break from her busy work day to meet with her mentor in the lobby.

Chapter 5

Prioritize use cases that match your AI maturity

As organizations build the needed AI governance and capabilities, they can unlock the potential of AI to transform health care.

Health organizations will want to make sure their investment will pay off in terms of value equation – whether it be financial value, or to the clinician and patient experience. To produce value, the right type of AI must be applied based on the situation, what 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 robotic process automation (RPA), machine learning and GenAI together in order 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 model of how to manage them changes drastically. Further, from an enterprise level, there will be a portfolio of AI algorithms consistently needing to be overseen, maintained, traced and regulated, which requires specialist skills, data infrastructure and significant oversight.

But value is not just financial, says Dr. Stewart of Johns Hopkins Medicine. 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 best practices across all people for hypertension, diabetes, congestive heart failure and breast cancer, and eliminate the health care disparities 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 case. Continually measure the use cases that are implemented in order to determine if they are meeting the desired outcomes or if they need to be optimized further.

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 Horan, EY Global Smart Health Analytics Solution Leader

Crystal Yednak, EY Global Health Senior Analyst

Rachel Dunscombe, CEO, OpenEHR

Smart Health

Smart Health puts the patient at the heart of health services design, and is reimagining personalized health at scale.


In order to build a foundation for AI in health care that both accelerates transformation and adds future value, executives must strategize current work while architecting for the future.

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