As healthcare technology advances rapidly, artificial intelligence (AI) and generative AI (Gen AI) are making significant strides in improving operational efficiencies in both care delivery and financing. From automating routine tasks to enhancing the precision and speed of data analysis, AI and Gen AI are paving the way for a more efficient and patient-centric healthcare system.
However, the success of AI and Gen AI in healthcare is far from guaranteed. It hinges critically on the implementation of strong data collection and integration practices. Without these foundational elements, the potential benefits of AI can be severely undermined. In this blog, we’ll explore the common benefits and challenges of AI-driven analytics, the crucial role of data governance and how Milliman MedInsight can help.
What role do AI & Gen AI play in healthcare analytics?
For many healthcare organizations dealing with data analytics, every minute is crucial. Healthcare analytics professionals are constantly balancing multiple tasks and deadlines to support critical decisions. The draw of AI and Gen AI in healthcare analytics is their potential to significantly increase analytics efficiencies, giving teams more time to focus on strategic business initiatives.
AI encompasses a broad range of technologies and applications, from simple rule-based systems to complex machine learning algorithms and neural networks. While designed to mirror human cognitive abilities, AI can surpass them in certain aspects, particularly in analyzing vast amounts of data to pinpoint patterns, anomalies, and trends.
Gen AI technology, on the other hand, uses deep-learning algorithms to create new content and is particularly adept at extracting objective insights from unstructured data—data without a predefined format that is often difficult to analyze. This capability is especially valuable in healthcare, where unstructured data from sources like clinical notes are common. Gen AI can analyze these data types independently or in conjunction with structured data sets, such as claims data, to identify patterns, generate deeper insights and boost operational efficiency.
As AI gains traction, we are committed to enhancing human capabilities, particularly in industries experiencing workforce shortages. One example is the growing shortage of primary care physicians (PCPs), which presents a major challenge. We believe AI can be a powerful tool to support these industries by assisting doctors with tasks, such as data analysis, patient triage, and personalized treatment recommendations. While AI may lead to job displacement in some sectors, we don’t foresee this in healthcare. Instead, we believe AI will amplify the abilities of healthcare professionals, enabling them to deliver more efficient and effective care. As a trusted data company, we continuously explore ways AI can enhance user experiences and strengthen their ability to draw insights through our products.
Why is data governance & quality important in AI-driven healthcare analytics?
A primary concern with the use of AI-driven healthcare analytics is the sensitivity of the data involved. While these systems can process and generate vast amounts of data, they are not infallible. They can sometimes produce inaccurate or misleading results, especially when dealing with complex and nuanced information.
“AI is undeniably real and holds significant relevance for both me and my organization. Its impact has been gradually realized, and we have witnessed its value in various aspects of our operations. However, the challenge lies in securing the necessary resources, funding, and identifying the most suitable use cases for its implementation.”
-Miro Rakic
Sr. Director, Data Products
AmeriHealth Caritas
Navigating the future of healthcare: AI trends
and predictions for payers and providers
The first step is to ensure organizations are working with high-quality data to minimize the potential for inaccurate outcomes from AI models. Maintaining this data integrity requires robust data governance frameworks that establish clear guidelines for data collection, storage, and usage. These frameworks not only protect sensitive patient information but also ensure that data is used appropriately and ethically within the healthcare context.
Implementing effective governance in AI-driven healthcare analytics involves several key components. First, a comprehensive data governance policy must be established, outlining roles, responsibilities, and procedures for data management. This includes defining who has access to data, under what conditions, and how data should be handled to maintain its integrity. Second, continuous quality data monitoring and validation processes must be in place to identify and rectify any errors or inconsistencies. Regular audits and quality checks ensure that the data feeding into AI models remains accurate and up to date, thereby enhancing the reliability of the analytics.
How is Milliman MedInsight integrating AI into healthcare analytics?
At Milliman MedInsight, we are dedicated to empowering and elevating how users engage with their data through our product offerings. Our approach is designed to empower healthcare professionals with advanced tools and data insights that they can tailor to their specific needs. This enables them to make informed decisions with confidence, leveraging the power of technology while maintaining their critical role in the decision-making process. By integrating AI-driven analytics in this way, we help mitigate the risks associated with full automation, ensuring that technology serves as a support, not a substitute. This balanced approach not only improves the accuracy and reliability of data analysis but also fosters a collaborative environment where technology and human expertise work hand in hand to achieve the best outcomes for patients and healthcare organizations.
A key strength of our organization is our extensive expertise in managing and delivering high-quality data. This expertise uniquely positions Milliman MedInsight as a trusted partner that can help healthcare organizations as they prepare for and implement AI capabilities. Milliman MedInsight provides support through the following:
- Data readiness: Ensuring that data is organized and prepared for AI use is paramount. This involves creating an AI-ready data layer that can be easily consumed by AI models. The Milliman MedInsight platform, for instance, offers robust API support and leverages Azure Databricks to manage data intake, processing, and storage efficiently. Additionally, our Data Confidence Model guarantees that the healthcare data collected is accurate, reliable, and well-understood, which is a critical component of successfully leveraging Gen AI.
- Integration flexibility: It is essential to choose solutions that provide flexibility in data transfer and integration with existing systems. The Milliman MedInsight Health Cloud and platform support a variety of data export formats, including CSV, JSON, and XML, and provides APIs for real-time data exchange. This ensures seamless integration with an organization’s in-house tools or future technologies, making it easier to incorporate AI solutions without disrupting current workflows.
- Access and connectivity: Consider how a potential AI solution will connect with advanced analytics tools. The Milliman MedInsight solution offering includes direct database access and the ability to set up data pipelines that feed into an organization’s analytics tools or data warehouses. This facilitates a smooth and continuous flow of data, enabling us to derive meaningful insights and make data-driven decisions more effectively.
Moreover, Milliman MedInsight not only prepares a data layer ready for AI use, but also ensures it integrates smoothly with your organization’s current or future analytics tools. This integration allows organizations to utilize AI and advanced analytics effectively to gain valuable insights from their data.
Where can I find additional resources about AI-driven analytics?
Schedule a call: Connect with one of our healthcare analytics experts.
Visit our website: Register to attend our booth talks at HIMSS 2025 in Ls Vegas, where we’ll be showcasing the MedInsight Innovation Portal and the MedInsight Knowledge Engine.
Watch the webinar: Hear a panel of industry experts as they delve into the transformative impact of AI.