As the healthcare market advances in generative AI (GenAI), agentic AI, real-time interactions, and other data-driven innovations, system leaders should not overlook data quality, which is recognized as a crucial factor in expanding AI initiatives and achieving a frictionless patient experience.
Today, when approximately 65 percent of U.S. hospitals report using AI-assisted predictive models, according to a study published in Health Affairs, future-proofing data is no longer optional; it’s strategic. Health system leaders must ask: Is our data complete, accurate, timely, actionable, trusted, and compliant enough to meet tomorrow’s demands?
Health systems collect vast quantities of healthcare consumer data yet fail to make it usable across the enterprise. Most systems place valuable information into data lakes or warehouses, which while well-intentioned, lack curation and context, leading to bottlenecks, misalignment, and dirty data. Data, Marketing, and Patient Experience teams are then left to operate with conflicting versions of truth due to disparate data systems.
To operationalize data intelligence and drive engagement, health systems must prioritize a data readiness foundation to yield individual profiles that are complete, accurate and up to date.
Why Patient Data is So Hard to Use
Systems and their cross-functional teams require data that is readily available to deliver high-value, human-centered experiences. Yet, going beyond usability to consider empathy, context, and the holistic journey of the user requires a wealth of data.
Patient data is especially challenging because it is scattered across EHRs, claims systems, call centers, web interactions, third-party apps, and more. Each source may use different formats, identifiers, and update cycles – making it difficult to unify, interpret, and act on the data in real time. Dirty data leads to poor predictions, irrelevant messaging, operational inefficiencies, and missed revenue opportunities.
The challenge of fragmented data becomes even more apparent when trying to follow a patient’s care journey across the continuum. System leaders have relayed it takes at least 12 months to convert a prospect to bariatric surgery. That means they need to be able to maintain a persistent view of that prospect as they navigate the process. Unlike fairly linear consumer retail journeys, other health events may present along the way, i.e., they might develop a diabetes diagnosis and begin that course of treatment. These parallel or shifting paths make it incredibly difficult to maintain a cohesive view of the patient, let alone understand which interactions influenced their decisions.
This complexity also makes campaign attribution a major challenge. Health systems often struggle to determine which messages, channels, or touchpoints actually move the needle. Without persistent identity resolution and feedback loops, it’s nearly impossible to connect long-term outcomes – ike a completed surgery or improved A1C – to specific marketing or engagement efforts.
The Illusion of Data Readiness in Health Systems
Despite significant investments in data technology, most health systems still struggle with poor data quality, unresolved patient identities, and fragmented care journeys. These issues severely limit the effectiveness of patient engagement campaigns.
A common approach is to tackle these problems within the IT department with solutions like Master Data Management (MDM). However, MDM systems typically focus on static data and often miss the dynamic aspects of patient behavior including transactions, interactions and predicted intent. These behavioral signals are critical for delivering personalized experiences but fall outside the scope of traditional MDM.
Customer Data Platforms (CDPs) were originally designed to bridge this gap by connecting mastered data with behavioral and transactional insights. In practice, though, most CDPs simply aggregate and unify upstream data – flaws and all – and pass it along to marketing teams without addressing the underlying quality issues. As a result, the disconnect between master data and business-ready data persists.
Similarly, storing customer data in a cloud environment is often mistaken for data readiness. But just like many CDPs, cloud storage alone doesn’t solve for identity or data quality. Without these foundational elements, organizations risk misinterpreting patient needs and delivering subpar outcomes.
The Data Readiness Solution
A focus on patient data readiness solves these complex challenges. When data is truly ready for use, it’s not just stored, it’s trusted, connected, and actionable. This means that patient identities are resolved across systems, behavioral and transactional signals are integrated, and predictive insights are accessible in real time. The result is a unified view of the patient that supports better decisions across the organization.
A strong data readiness foundation includes the following critical components.
- Automated Data Quality
In healthcare, where patient data is constantly flowing from different systems, from digital interactions and appointments to lab results and billing, continuous data quality is a necessity.
That’s why data standardization and error correction shouldn’t be one-time events, they must happen continuously and automatically from the moment data enters the system. This ensures that every new record, update, or signal is immediately validated, and cleaned.
This approach prevents errors from compounding downstream, keeps analytics and engagement tools running smoothly, and ensures that every decision is based on reliable, up-to-date information.
- Advanced Identity Resolution
Effective identity resolution is foundational to data readiness. By combining deterministic (exact match) and probabilistic (likelihood-based match) techniques, organizations can tailor match rules to fit specific use cases—whether it’s looser match rules for basic prevention messaging or tighter match rules for communicating sensitive information. It also helps to understand data from anonymous-to-known journeys.
But matching alone isn’t enough. Unlike basic, point-in-time matching, persistent key management enables a longitudinal view of each patient. This means identities are continuously tracked and updated over time, allowing for a richer, more contextual understanding of the patient journey. From initial engagement to ongoing care, this continuity is essential for delivering personalized, coordinated experiences.
Householding — the ability to group individuals who share a household or financial relationship—adds another layer of insight. For example, understanding that two patients are part of the same household can inform outreach strategies. It also helps avoid redundant communications and enables more empathetic, context-aware engagement.
Advanced identity resolution doesn’t just connect data, it connects people to their stories, enabling smarter decisions and better outcomes.
- Contextual Profile Unification
Profile unification allows you to curate customer data into clear and actionable profiles. Too often patient profiles get weighed down by excess detail. Effective unification filters out the noise and surfaces what matters most — validated contact information, meaningful clinical and behavioral signals, and key interactions across the care journey. In this step, profiles are automatically enriched with AI models, calculations and trusted third-party data. This provides deep insights that enables marketers and patient experience teams to communicate with speed, accuracy and empathy.
- Smart Activation
Smart activation is the final step in the data readiness journey, where clean, connected, and contextual patient data becomes a strategic asset. It’s not just about pushing data downstream; it’s about delivering precisely the right data to the right touchpoints, at the right cadence, and in the right context to drive meaningful engagement.
What sets smart activation apart is its reliance on dynamic, real-time segments that evolve as new data flows in. These segments are continuously orchestrated across multiple (digital or physical) engagement channels, ensuring that every interaction is timely, relevant, and personalized. In healthcare, this means the right patients receive reminders, education, and support exactly when they need it.
Data That’s Fit for Purpose – Now and in the Future
The cost of poor data isn’t just inefficiency; it’s lost patients, missed revenue, and diminished trust.
A data readiness solution helps fill the data fragmentation gaps that can arise from varying technology and workflow processes. Data that’s clean, accurate, and ready for business use the moment it’s needed allows health enterprises to understand a patient’s social drivers of health and for marketing and operational teams to act with precision and confidence.
For example, following data readiness principles, one health system was able to personalize outreach across the entire care journey. This hyper-personalization helped to expand scheduled appointments by 50 percent and drive healthcare center expansion.
With a strong data readiness foundation, health systems can finally future-proof their data, turning information into impact across the entire patient journey through data that is fit for purpose—complete, accurate, timely, actionable, trusted, and compliant.
