Three for 2025: What you need to know about agentic AI, cancer informatics, and the imperatives of data security
Vijayashree Natarajan is senior vice president and chief technology officer at Omega Healthcare, which produces financial, administrative and clinical systems for healthcare organizations.
Given her extensive experience in healthcare IT, we recently asked her to look ahead to 2025 and describe three key trends and imperatives she will be watching that will also be of great interest to health system executives and other health IT professionals. Experts. Cybersecurity, cancer informatics, and agentic artificial intelligence were the three she chose.
Q: Why do you feel it is necessary to focus on data security in 2025?
A. As healthcare continues to digitally transform, we will see an increasing interconnection between clinical data, revenue cycle processes, and patient care. Organizations that can effectively harness these data flows while maintaining data security will be better positioned to thrive as healthcare continues to evolve and become more patient-centric.
The journey toward this future will require continued collaboration, innovation, and an unwavering commitment to patient safety and data security.
As the health IT industry increasingly embraces artificial intelligence and other digital technologies, the importance of strong cybersecurity measures cannot be overstated. The healthcare industry faces unique challenges due to the sensitivity of data – from personally identifiable information to electronic health records to electronic protected health information.
Healthcare organizations need broad, accurate segmentation coverage of applications, server workloads, and users across all asset types.
Q: Cancer informatics is an interesting choice for 2025. Why this field of HIT?
A. There will be an increased need for cancer informatics as the CDC says the total number of cancer cases is expected to rise by 50% by 2050.
As cancer rates continue to rise, there will be increasing 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 represent major obstacles to management. Data come 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 critical 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 move forward, the focus will be on developing solutions that not only simplify data operations, but also unlock new insights that drive clinical and operational excellence.
By combining innovative technologies with deep industry expertise, and keeping humans informed, we can pave the way for a new era in healthcare – one in which data-driven decisions pave the way for improved patient outcomes and more efficient and accessible healthcare services.
Q: Finally, you suggest agentic AI will be key in 2025. How so?
A. For providers and payers, AI has become a key component in reducing fraud, enhancing value-based care, and generating insights to assess risk and identify care gaps. The emergence of generative AI is expanding these capabilities, enhancing everything from patient interactions to physician documentation, and even improving AI algorithms themselves.
Going forward, we expect technologies such as AI to play a critical role in enhancing efficiency, designing treatments, and improving patient outcomes.
When adopting AI systems, healthcare organizations should prioritize the following:
- Establish a dedicated AI oversight team
- – Develop contingency plans for any potential disturbances in the system
- Providing comprehensive training and support to employees
- Implement timely monitoring and reporting tools
- Establish strong data management policies
- Use predictive analytics to predict potential problems
As exciting as these developments are, implementing AI in healthcare comes with its own set of challenges and considerations. Organizations must carefully manage the risks associated with data privacy and security and integrate AI into existing workflows.
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