Ryan Sousa is vice president of the data, analytics and artificial intelligence practice at Pivot Point Consulting, a healthcare IT consulting firm. (It ranked first in KLAS for Managed Services and Technical Services in 2024.) His background and expertise in AI and analytics is extensive. And when asked to look ahead to 2025 in healthcare IT, he had a lot to say about these two technologies that are so important to healthcare.
Sousa predicts big things for generative AI, a new way of delivering AI and analytics, and using the two technologies together to drive growth. We interviewed him about next year, and this is what he had to say.
Q. You say genAI will come into its own by 2025, creating the potential for significant savings. How is this going to happen?
A. By 2025, genAI proof-of-concepts and pilot programs will begin to demonstrate positive impact and value for healthcare organizations, which will explore how they can delay new product investments or exit existing product investments by doing it themselves.
Areas such as diagnostics, patient flow optimization, and administrative tasks such as billing and supply chain benefit the most from genAI due to its ability to analyze structured and unstructured data to generate predictive and prescriptive insights.
These early successes will prompt organizations to rethink traditional approaches to technology investments, allowing them to delay purchasing new products and phase out older systems in favor of building custom systems in-house.
This adoption of genAI will not be without challenges. Healthcare organizations will face hurdles such as data privacy and ethics issues, regulatory compliance, integration with existing systems, and the need for staff and patient education.
Addressing these challenges will require robust, scalable data governance policies, investments in cybersecurity measures, strategic planning for technology integration, and extensive training programs to adapt to new tools and workflows.
By leveraging these advanced capabilities, healthcare systems will be able to realize unprecedented gains. Automated coding can significantly reduce errors and processing times in claims management, leading to faster reimbursements and lower administrative costs.
Census forecasting enables better resource allocation and staffing decisions, improving operational efficiency and patient care delivery. As efficiency improves, patients will experience lower costs, shorter wait times, and higher quality care due to more effective use of resources and personnel.
The continued migration to the cloud, with its scalability, data sharing capabilities and computing power, is the cornerstone of this transformation. The cloud infrastructure supports the massive data storage and processing needs of genAI applications, facilitating seamless integration into existing workflows.
However, this transition comes with security concerns, especially when it comes to data breaches and compliance with healthcare regulations such as HIPAA. Organizations will also need to learn how to leverage the vast tools available to innovate with data, while also learning how to excel at fin-ops to do so cost-effectively.
Ask. On another front, you suggest that a new way of analytics and AI will emerge by 2025. What is it and what would it mean for healthcare?
A. Old, centralized, transactional approaches to analytics and AI that are rigid, top-down and project-oriented will give way to a federated and collaborative model. The legacy approach was designed for a more static environment and struggles to adapt to the dynamic needs of today’s healthcare ecosystem.
In contrast, the federated collaboration model allows decentralized teams to make flexible, real-time decisions. This shift is not only a response to technological advances, but also a cultural transformation, emphasizing trust, autonomy and cross-functional collaboration.
By implementing a bottom-up decision-making structure, analytics and AI initiatives are better aligned with the immediate needs of healthcare providers and patients. It enables more customized and context-aware systems, addressing specific challenges in different departments or units.
Such an approach facilitates faster delivery of data products, minimizes bureaucratic delays, and promotes innovation by encouraging structured experimentation at all levels of the organization.
From an operational perspective, federated models can lead to significant productivity gains. Employees who have the power to make decisions and contribute meaningfully to initiatives are more likely to be engaged and satisfied in their roles. This enriched work environment not only increases morale, but also helps attract and retain top talent in an increasingly competitive industry.
This model is not without challenges. Organizations must invest in robust, flexible data management frameworks to ensure consistency, security and compliance across decentralized teams. Furthermore, fostering a culture of collaboration and continuous learning is essential to realize the full potential of this approach.
That said, those who can overcome these challenges will thrive, while those who cannot will struggle to keep up.
Q. Another look ahead to 2025: You say leading organizations will leverage analytics and AI to drive growth. How will they achieve this?
A. With the growth of competition, driven by new players and mergers and acquisitions, there will be significant pressure to leverage analytics and AI to reduce costs and improve profitability by eliminating waste and redundancy from the system.
Leading organizations will balance this relentless focus on cost savings by leveraging analytics and AI to drive growth and greater profitability – improving outcomes and enriching the patient and caregiver experience along the way.
Analytics and AI are not just cost-cutting tools; they are powerful growth engines that contribute to profitability. A good example of this is personalized medicine based on AI. By analyzing vast amounts of patient data, AI can help tailor treatment plans to individual patients, leading to better clinical outcomes and greater patient satisfaction.
For example, healthcare organizations that use AI to optimize cancer treatment pathways can improve patient recovery rates while strengthening their reputations as leaders in advanced care. Similarly, predictive models in revenue cycle management can help organizations identify financial bottlenecks and improve revenue collection, creating new growth opportunities.
Balancing cost reduction with investments in growth initiatives is critical to sustainable success. Leading organizations achieve this balance by reinvesting savings from efficiency gains in innovative projects that strengthen their strategic positioning.
These organizations use analytics to streamline their operations while investing in cutting-edge research and patient-centered care initiatives. This dual focus has driven operational efficiencies and improved patient experiences, helping the organization achieve sustainable growth and profitability.
Looking to the future, several emerging analytics and AI technologies will be critical for healthcare organizations to remain competitive by 2025. Technologies such as generative AI for clinical decision support, real-time predictive analytics for operations management and AI-driven digital twins will become increasingly important. increasingly important.
For example, digital twins allow healthcare organizations to simulate and optimize hospital operations, predict patient flow, and test new care delivery models in a virtual environment. Perhaps the most transformative area of focus will be achieving true interoperability – seamlessly connecting disparate data sources across the healthcare ecosystem.
This allows organizations to generate holistic, actionable insights, ultimately improving care coordination, reducing costs and achieving better patient outcomes.
Healthcare organizations that successfully combine efficiency-driven cost savings with growth-oriented innovation will emerge as leaders. By using analytics and AI strategically, they will improve their financial health and create a more patient-centric and healthcare provider-friendly ecosystem.