Embracing AI in Healthcare: Balancing Urgency with Hesitancy
In an era where technology is reshaping industries, healthcare stands at the forefront of this transformation. With more than 230 million individuals turning to ChatGPT weekly for health and wellness inquiries, the integration of artificial intelligence (AI) into healthcare is increasingly evident. Moreover, four out of five doctors are now exploring AI’s potential in their practices, and the introduction of Claude for Healthcare aims to revolutionize medical billing.
Despite these advancements, healthcare leaders face a paradox: an urgent need to adopt AI technologies tempered by a hesitancy to scale them. The emergence of AI provides a unique opportunity to improve access to health information, notably for vulnerable populations. Every week, approximately 600,000 health-related questions are directed to ChatGPT from underserved rural areas, showcasing AI’s potential to bridge gaps in healthcare accessibility.
However, rising capital costs pose significant challenges, limiting strategic investments in AI. As a result, many AI healthcare initiatives struggle to progress beyond the pilot phase. The key to unlocking AI’s potential lies in responsible strategies and a proactive approach to scaling opportunities effectively.
Going Beyond AI Experiments
The rate of AI adoption in healthcare is outpacing other industries, doubling between 2023 and 2025. To fully harness the business value of AI, healthcare organizations must not only deploy the right tools but also scale them efficiently. This requires careful planning to ensure AI initiatives do not overburden an already challenged workforce.
Agent AI, for example, offers significant promise in reducing administrative burdens and costs within healthcare revenue cycles. Nevertheless, a significant barrier to its widespread adoption is the fear among employees of being replaced by AI. Two out of three executives have expressed this concern, which fuels reluctance to embrace agent AI.
As AI use cases in healthcare expand, many organizations, according to KLAS Research, are adopting AI for lower-risk applications. These include ambient AI for creating structured notes from doctor-patient conversations, imaging triage, predictive risk modeling, and patient message responses.
Moving the Needle on AI ROI
With limited resources for AI investment, healthcare leaders must strategically navigate their path to deliver both short-term and long-term value. Here are three crucial considerations in a transformative healthcare environment:
Don’t Just Try the Tool. Track the Tools.
To overcome capital constraints, healthcare organizations should shift focus from experimentation to implementation. Prioritizing scalable solutions from the outset can accelerate deployment, reduce development costs, and enhance data management and workforce adoption. Aligning AI initiatives with the organization’s strategic goals ensures that investments align with the health system’s mission, vision, and business objectives.
Ensure Compatibility with EHR Systems
This cost-effective approach facilitates rapid innovation through seamless integration. By ensuring AI tools are compatible with Electronic Health Records (EHR), healthcare teams can access clean, managed data—turning challenges into viable innovations with high ROI potential. Key opportunities for integrating AI with EHR systems by 2026 include:
- Improving clinical workflows such as documentation, imaging, and diagnostics
- Enhancing patient engagement through virtual assistants, AI-powered symptom checkers, and educational resources
- Optimizing revenue cycle management by transforming unstructured data into actionable information and streamlining processes like billing and coding
Implement Controls for Monitoring AI Value
A recent EY global risk study indicates that organizations with real-time performance monitoring for AI initiatives are 65% more likely to achieve cost savings. This underscores the importance of embedding AI within the broader digital ecosystem and regularly tracking AI efforts with the support of an AI governance committee. The committee should focus on metrics such as operational efficiency, quality improvement, cost reduction, and patient outcomes to ensure AI delivers measurable benefits and aligns with organizational goals.
As AI adoption accelerates in healthcare, developing a roadmap that emphasizes rapid deployment and rewards while mitigating risks will sustain momentum. Building confidence among team members in innovation is critical to long-term AI success.
John Ward is a partner in technology consulting at EY and head of healthcare technology.
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