Three for 2025: What you need to know about agentic AI, cancer informatics, and 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 expertise in healthcare information technology, 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 healthcare executives healthcare systems and other healthcare IT departments. experts. Cybersecurity, cancer informatics and agentic artificial intelligence were the three she chose.
Q. Why do you think it is imperative to emphasize 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 become increasingly interconnected. Organizations that can effectively leverage these data flows while maintaining data security will be best positioned to thrive as healthcare continues to change and become more patient-centric.
The journey to this future requires continued collaboration, innovation and an steadfast commitment to patient safety and data security.
As the healthcare IT sector 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 its data – from personally identifiable information to electronic health records and electronic protected health information.
Healthcare organizations need deep micro-segmentation coverage across applications, server workloads, and users across all asset types.
Q. Cancer informatics is an interesting selection you are making for 2025. Why this area of HIT?
A. There will be an increasing need for cancer informatics as the CDC says the total number of cancer cases is expected to increase by 50% by 2050.
As cancer rates continue to rise, there will be greater 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 pose significant hurdles to 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 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 overstated. As we move forward, 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 and keeping people informed, we can pave the way for a new era in healthcare – one where data-driven decisions pave the way for better patient outcomes and more efficient, accessible healthcare services.
Q. And finally, you suggest that AI will be critical in 2025. What do you mean?
A. For providers and payers, artificial intelligence will be key to minimizing fraud, advancing value-based care, and creating insights for risk assessment and identifying gaps in care. The rise of generative AI expands these capabilities, improving everything from patient interactions to clinical documentation, and even improving the AI algorithms themselves.
In the future, we expect technologies such as agentic AI to play a critical role in increasing efficiency, tailoring treatments and improving patient outcomes.
When adopting AI systems, healthcare organizations should prioritize:
- Setting up a dedicated AI monitoring team
- Developing contingency plans for possible system disruptions
- Providing comprehensive training and support to staff
- Implement timely monitoring and reporting tools
- Setting up a robust data governance policy
- Using predictive analytics to foresee potential problems
As exciting as these developments are, implementing AI in healthcare comes with its own 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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