Vijayashree Natarajan is senior vice president and head of technology at Omega Healthcare, which produces financial, administrative and clinical systems for healthcare organizations.

Given her well-rounded expertise in healthcare information technology, we asked her recently to look ahead to 2025 and describe three key trends and imperatives she'll be watching that will also be of keen interest to health system C-suite executives and other health IT experts. Cybersecurity, cancer informatics and agentic artificial intelligence were the three she chose.

Q. Why do you feel it is imperative to stress data security in 2025?

A. As healthcare continues its digital transformation, we will see the convergence of clinical data, revenue cycle operations and patient care becoming increasingly interconnected. Organizations that can effectively harness these data streams while maintaining data security will be best positioned to thrive as healthcare continues morphing and becoming more patient-centric.

The journey toward this future will require ongoing collaboration, innovation, and a steadfast commitment to patient safety and data security.

As the health IT industry increasingly embraces AI and other digital technologies, the importance of robust cybersecurity measures cannot be overstated. The healthcare industry faces unique challenges due to the sensitivity of the data – from personally identifiable information to electronic health records to electronic protected health information.

Healthcare organizations need pervasive micro-segmentation coverage for applications, server workloads and users across all asset types.

Q. Cancer informatics is an interesting selection you make for 2025. Why this area of HIT?

A. There will be an escalating need for cancer informatics as the CDC says the total number of cancer cases is projected to increase by 50% by 2050.

As cancer rates continue to climb, there will be a heightened emphasis on the need for high-quality data, or "cancer informatics," to support cancer-related public health initiatives.

However, the exponential rise and increasing complexity of cancer data present considerable hurdles for management. Data comes from a variety of sources, including clinical records, pathology reports, imaging studies and genomic data.

Skilled professionals must take a comprehensive approach to accurately integrate these different data sets and extract valuable information. This information then influences crucial downstream activities such as precision medicine techniques, public health surveillance, new treatment guidelines and policy recommendations, clinical trial enrollment, and clinical research ideas.

The increasing importance of robust clinical data cannot be emphasized enough. As we advance, the focus will be on developing solutions that not only streamline data processes but also unlock new insights that drive clinical and operational excellence.

By integrating innovative technologies with deep industry expertise, keeping humans in the loop, we can set the stage for a new era in healthcare – one where data-driven decisions pave the way for enhanced patient outcomes and more efficient, accessible healthcare services.

Q. And finally you suggest agentic AI will be key in 2025. How so?

A. For providers and payers, artificial intelligence is becoming key to minimizing fraud, advancing value-based care, and creating insights for risk assessment and identifying care gaps. The rise of generative AI is expanding these capabilities, enhancing everything from patient interactions to clinician documentation, and even improving the AI algorithms themselves.

Moving forward, we anticipate technologies like agentic AI to play a crucial role in boosting efficiency, tailoring treatments and enhancing patient results.

When adopting AI systems, healthcare organizations should prioritize:

• Creating a dedicated AI oversight team
• Developing contingency plans for potential system disruptions
• Providing comprehensive staff training and support
• Implementing timely monitoring and reporting tools
• Establishing robust data governance policies
• Using predictive analytics to foresee potential issues

As exciting as these developments are, implementing AI in healthcare comes with its own set of challenges and considerations. Organizations must carefully manage risks associated with data privacy, security and the integration of AI into existing workflows.

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