Unlocking New Possibilities in Healthcare with Generative AI

From automating patient records to improving treatment outcomes, data has fueled an invaluable digital transformation in health care. And with generative AI, data’s role in improving health care is about to expand. GenAI promises to make every aspect of patient care more effective and personalized. But in parallel with the excitement, there are a lot of questions on how to get started on this journey.

A recent EY survey found that over 60% of hospital CIOs plan to adopt GenAI within the next 6 to 12 months, building on the 25% who have already started. The biggest areas of opportunity are patient experience and overall operations' efficiency. But benefits can go beyond clinical uses into areas that improve interactions with patients, like AI-assisted chat support to personalized messaging.

GenAI thrives on data, which is where health care professionals can obtain the most gains. The best way to obtain value from this technology is to scan areas with a lot of unstructured data. This means information that doesn’t follow a fixed format, like emails, images, videos, or doctor’s notes. GenAI can automate tasks associated with this data, like connecting doctor’s notes to emails and images related to the same patient. Here are my key considerations on the opportunity ahead and how to have a strategy in place to get started.

Define Clear Objectives.

Start by outlining specific goals tied to GenAI adoption — business problems that you're trying to solve. These should align closely with strategic health care priorities like improving patient outcomes, streamlining processes or enhancing operational efficiency. Identify pain points where traditional methods fall short and consider how GenAI will bridge these gaps. Examples include personalized patient care, automated medical documentation and virtual health agents for patient inquiries.

McKinsey's survey suggested that most health care providers are beginning their GenAI adoption in clinically adjacent areas — processes like documentation, resource management and patient outreach. By easing GenAI into operational workflows, organizations can gain foundational experience while building trust in the technology.

Develop the Required Data Infrastructure.

Data quality is a top priority to avoid bottlenecks that can lead to AI project failures. Health care data is often fragmented and stored across different systems, so you need to clean up your data before moving forward with any projects. The recommendation here is to build a robust data infrastructure to support GenAI systems. This includes data pipelines, secure storage and tools to integrate data from diverse sources such as patient records, imaging systems and clinical notes.

Deploy solutions that can grow based on the volume of your data — at the core, edge and cloud. This isn't just about planning: the right infrastructure saves money from your budget and allows you to invest in priority projects.

Frame Governance From Day One

Organizations need clear rules to make sure teams are using AI in a responsible way.  Establish a governance structure that includes guidelines about using patient data, regulatory compliance and ethical decision-making. Cross-departmental collaboration between IT, clinicians and legal teams is essential here.

Address compliance with health care and technology regulations such as HIPAA, GDPR or FDA guidelines. Prioritize ethical considerations like fairness, accuracy and patient consent if you're training and deploying GenAI models. Collaborate with legal and compliance teams to create a risk management framework, making sure you're aligned with health care standards.

Foster a culture of innovation and learning.

Build a culture that embraces innovation and experimentation. Pair IT teams with clinical leaders to codesign solutions that support what's possible from a technical perspective but benefit clinical practices. Provide resources, such as internal GenAI-enabled tools or support systems, to empower teams. Ensure that all stakeholders understand the ethical and operational boundaries of using AI in health care.

Break down silos between IT, clinical teams and data scientists. A simple and effective action is to start a training program that educates health care professionals on how to use GenAI tools effectively in their workflows.

Measure Impact and Scale Up

Start small, measure impact and expand adoption based on proven successes. For example, if GenAI reduces a significant percentage of administrative errors in one department, scaling that solution horizontally could give you enterprise-wide impact. Continuous evaluation will ensure that the solutions you adopt meet their original value propositions.

Track progress through measurable KPIs like efficiency gains, cost savings and patient satisfaction levels. Use feedback to refine data models, address gaps in workflows, and identify new opportunities for deployment. Once successful, you can expand to additional departments or use cases.

The Next Frontier at Health care

It’s clear that for health care, GenAI offers far more than incremental improvements. From shrinking operational inefficiencies to helping clinicians in their practice, technology is once again at the center of an improved health care experience.

If you’re a tech innovator ready to take the first step, here’s where to begin. Develop a GenAI strategy that includes governance processes and clear frameworks to anticipate and manage challenges. The leaders in health care today are the ones asking proactive questions like, “What will the patient experience look like five years from now — and how can AI power that vision?”

The health care landscape is rapidly evolving, and GenAI isn’t just an option for staying competitive. It’s the compass guiding you to the industry’s future.

Views are personal. Author is President & Managing Director, Dell Technologies India.