The majority of what payers identify as 'care gaps' are actually 'data gaps'—a lack of information leads to an assumption of missing care. By solving the data acquisition problem first, organizations can distinguish between the two. This dramatically shrinks the problem set, focusing expensive outreach efforts only on patients with true care needs.
Related Insights
Data's role is to reveal reality and identify problems or opportunities (the "what" and "where"). It cannot prescribe the solution. The creative, inventive process of design is still required to determine "how" to solve the problem effectively.
A company can build a significant competitive advantage in healthcare by deliberately *not* touching or seeing Protected Health Information (PHI). Focusing exclusively on metadata reduces regulatory overhead and security risks, allowing the business to solve the critical problem of data orchestration and intelligence, a layer often neglected by data aggregators.
True problem agreement isn't a prospect's excitement; it's their explicit acknowledgment of an issue that matters to the organization. Move beyond sentiment by using data, process audits, or reports to quantify the problem's existence and scale, turning a vague feeling into an undeniable business case.
Data governance is often seen as a cost center. Reframe it as an enabler of revenue by showing how trusted, standardized data reduces the "idea to insight" cycle. This allows executives to make faster, more confident decisions that drive growth and secure buy-in.
Instead of building AI models, a company can create immense value by being 'AI adjacent'. The strategy is to focus on enabling good AI by solving the foundational 'garbage in, garbage out' problem. Providing high-quality, complete, and well-understood data is a critical and defensible niche in the AI value chain.
AI serves as a powerful health advocate by holistically analyzing disparate data like blood work and symptoms. It provides insights and urgency that a specialist-driven system can miss, empowering patients in complex, under-researched areas to seek life-saving care.
The core problem for many small and mid-market businesses isn't a lack of software, but an excess of it, using 7 to 25 different apps. This creates massive data fragmentation. The crucial first step isn't buying more tools, but unifying existing data into a single customer profile to enable smarter, automated marketing.
An effective AI strategy in healthcare is not limited to consumer-facing assistants. A critical focus is building tools to augment the clinicians themselves. An AI 'assistant' for doctors to surface information and guide decisions scales expertise and improves care quality from the inside out.
The primary reason multi-million dollar AI initiatives stall or fail is not the sophistication of the models, but the underlying data layer. Traditional data infrastructure creates delays in moving and duplicating information, preventing the real-time, comprehensive data access required for AI to deliver business value. The focus on algorithms misses this foundational roadblock.
To create transformational enterprise solutions, focus on the core problems of the key buyers, not just the feature requests of technical users. For healthcare payers, this meant solving strategic issues like care management and risk management, which led to stickier, higher-value products than simply delivering another tool.
