Ryan Sousa is vice president of the data, analytics and artificial intelligence practice at Pivot Point Consulting, a health IT consulting firm. (It was ranked #1 in KLAS for Managed Services and Technical Services in 2024..) His experience and expertise in AI and analytics is extensive. And when we ask him to look ahead to 2025 in terms of health IT, he has a lot to say about these two technologies that are so important for health.
Sousa predicts big things for generative AI, a new way of delivering AI and analytics, and using both technologies in tandem to drive growth. We interviewed him about next year, and here’s what he had to say.
Q. You say that in 2025, genAI will come into its own, creating the potential for significant savings. How will this happen?
A. In 2025, genAI proofs of concept and pilot programs will begin to demonstrate positive impact and value for healthcare organizations, who will begin to explore how they can defer new investments in or discontinue existing products by doing it themselves.
Areas such as diagnostics, patient flow optimization and administrative tasks such as billing and supply chain Get the most out of genAI due to its ability to analyze structured and unstructured data to generate predictive and prescriptive insights.
These early successes will encourage organizations to rethink their traditional approaches to technology investment, allowing them to defer the acquisition of new products and gradually abandon existing systems in favor of creating custom systems in-house.
This adoption of genAI will not come without challenges. Healthcare organizations will face obstacles 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 and scalable data governance policies, investments in cybersecurity measures, strategic planning for technology integration, and comprehensive training programs to adapt to new tools and workflows.
By leveraging these advanced capabilities, health systems will unlock unprecedented gains. Automated coding can significantly reduce errors and processing times in claims management, leading to faster reimbursements and reduced administrative costs.
Census forecasting enables better resource allocations and staffing decisions, thereby improving operational efficiency and patient care delivery. As efficiency improves, patients will experience reduced costs, shorter wait times and higher quality care through more efficient use of resources and staff.
The ongoing migration to the cloud, with its scalability, data sharing capabilities and computing power, is the cornerstone of this transformation. Cloud infrastructure support the vast data storage and processing needs of genAI applications, facilitating seamless integration into existing workflows.
However, this transition raises security concerns, particularly around data breaches and compliance with healthcare regulations like HIPAA. Additionally, organizations will need to learn how to leverage the vast tools available to innovate with data, while also learning how to excel in financial operations to do so profitably.
Q. On another front, you suggest that a new way of delivering analytics and AI will take hold in 2025. What is it and what would it mean for healthcare health?
A. Old centralized, transactional approaches to AI analysis and delivery that were rigid, top-down, and project-driven will give way to a federated, collaborative model. The traditional approach is designed for a more static environment and struggles to adapt to the dynamic needs of today’s healthcare ecosystem.
In contrast, the federated collaborative model allows decentralized teams to make agile decisions in real time. This shift is not only a response to technological advancements, but also a cultural transformation, emphasizing trust, autonomy and cross-functional collaboration.
Adopting a bottom-up decision-making structure aligns analytics and AI Initiatives more closely to the immediate needs of healthcare providers and patients. It allows you to create more personalized and contextual systems, responding to specific challenges in different departments or units.
Such an approach facilitates faster delivery of data products, minimizes bureaucratic delays, and fosters innovation by encouraging structured experimentation at all levels of the organization.
From an operational point of view, federated models can lead to significant productivity gains. Employees empowered to make decisions and contribute meaningfully to initiatives are more likely to be engaged and satisfied in their role. This enriched work environment not only improves 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 and flexible data governance frameworks to ensure consistency, security and compliance across decentralized teams. Additionally, fostering a culture of collaboration and continuous learning is essential to realizing the full potential of this approach.
That said, those who manage to overcome these challenges will prosper, while those who fail to do so will struggle to keep up.
Q. One final look at 2025: You say large companies will leverage analytics and AI to drive growth. How will they achieve this?
A. With intensifying 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 system redundancy.
Leading organizations will balance this unforgiving priority of cost reduction by leveraging analytics and AI to promote growth and greater profitability, thereby improving outcomes and enriching the patient and provider experience as they continue. road.
Analytics and AI are not just cost-cutting tools: they are powerful growth engines that drive profitability. A great example lies in AI-based personalized medicine. By analyzing large amounts of patient data, AI can help tailor treatment plans for each patient, leading to better clinical outcomes and increased patient satisfaction.
For example, healthcare organizations that use AI to optimize cancer treatment pathways can improve patient cure rates while strengthening their reputation as a leader in advanced care. Similarly, predictive modeling in revenue cycle management allows organizations to identify financial bottlenecks and improve revenue collection, thereby creating new growth opportunities.
Balancing cost reduction with investments in growth initiatives is essential for sustained success. Leading organizations achieve this balance by reinvesting savings from efficiency gains into innovative projects that improve their strategic positioning.
These organizations are leveraging analytics to streamline operations while simultaneously investing in cutting-edge research and patient-centered care initiatives. This dual focus has driven operational efficiency and improved patient experience, enabling the organization to achieve sustainable growth and profitability.
Looking ahead, several emerging analytics and AI technologies will be crucial for healthcare organizations to remain competitive by 2025. Technologies such as generative AI for clinical decision support, analytics real-time predictive for operational management and AI-driven digital twins will become increasingly important.
Digital twins, for example, allow healthcare organizations to simulate and optimize hospital operations, predict patient flow, and test new care delivery models in a virtual environment. However, perhaps the most transformative area of focus will be achieving true interoperability – seamlessly connecting disparate data sources across the healthcare ecosystem.
This will enable organizations to generate holistic and actionable insights, thereby improving care coordination, reducing costs and improving patient outcomes.
Healthcare organizations that successfully balance efficiency-driven cost reductions and growth-driven innovation will become leaders. By strategically leveraging analytics and AI, they will improve their financial health and create a more patient-centric and provider-friendly healthcare ecosystem.
