As the healthcare industry continues to embrace digital transformation, data quality is more important than ever. With rising expectations from quality measurement activities and the growing need for actionable insights, quality data is the key to advancing healthcare performance. As the technology to streamline and optimize digital data exchange grows, the quality of that data is more critical than ever. Poor quality data—whether fragmented, incomplete, or inaccurate—can impact quality reporting, undermine insights, and hinder patient outcomes. 

What does this really mean, and how do healthcare leaders go beyond just “managing” data to truly optimizing it for data exchange while increasing its quality? 

When Fragmented Data is a Barrier to Healthcare Efficiency

With more data comes more challenges around data fragmentation, interoperability, and validation. Take a patient with diabetes who switches health plans multiple times in a year. Each new provider and payer holds only fragments of their health history, missing critical details such as lab results, medication adherence, or social determinants of health (SDOH). Without a complete picture, care decisions may be compromised, putting patient outcomes at risk.

At an organizational level, fragmented data disrupts performance reporting and makes compliance with value-based care models harder. Key data points, like lab test results, are typically captured in structured formats, but other essential information, like SDOH, may be recorded as unstructured text. Without access to comprehensive and accurate health records, payers, providers, and care delivery systems struggle to deliver optimal care or meet the quality benchmarks that drive reimbursement and performance under VBC models.

Solving Fragmentation with Interoperability and Data Validation

The solution to fragmented data lies in improving interoperability and enhancing data validation practices. Fast Healthcare Interoperability Resources (FHIR) APIs and other frameworks have already begun transforming how healthcare data is shared. However, the biggest improvements happen when interoperability is paired with robust data validation, ensuring that the data exchanged between systems is accurate, complete, and reliable.

Centralizing data aggregation and validating data at the point of entry is a powerful way to further reduce fragmentation. By creating a unified data repository and verifying data—whether from electronic health records (EHRs), lab systems, or unstructured sources, healthcare organizations can significantly reduce errors and inconsistencies. This approach ensures that data flowing between systems remains trustworthy and ready for real-time use in clinical decision-making.

However, technology alone isn’t enough. To fully realize the potential of digital quality, healthcare organizations must foster stronger collaboration between payers, providers, and technology vendors. Breaking down both technical and operational silos is key to ensuring data flows smoothly and accurately across the healthcare ecosystem.

Why Data Quality is Essential for Healthcare

Digital quality represents more than an upgrade in technology—it marks a fundamental shift in how healthcare is delivered and measured. With automated data validation and exchange, healthcare organizations can reduce administrative burdens, improve reporting accuracy, and enable care teams to act on timely insights that drive better patient outcomes.

For example, after addressing data quality issues, healthcare organizations have seen significant gains, including a 25% increase in adult BMI reporting and a 40% improvement in childhood immunizations. These improvements highlight the impact that accurate, validated data can have on population health management and care coordination.

Digital quality also supports more comprehensive reporting. By integrating data from various sources, including unstructured data like clinical notes and SDOH, healthcare organizations can gain a fuller understanding of patient care, track performance across multiple metrics, and improve their ability to meet quality goals.

Practical Solutions to Overcome Data Challenges

Addressing the challenges of fragmented data and interoperability requires a combination of technical solutions and strategic collaboration. Below are actionable strategies that can help healthcare organizations succeed:

• Adopting Standardized Data Formats: Implementing standards such as FHIR ensures data can be shared seamlessly across systems and organizations. This reduces friction caused by proprietary systems and enhances the ability to exchange structured and unstructured data.
• Centralizing Data Aggregation: A centralized repository for patient data minimizes fragmentation and ensures consistency. Centralized aggregation also enables the inclusion of unstructured data, such as clinical notes, in quality reporting and care coordination.
• Strengthening Collaboration Across the Ecosystem: Strong relationships between payers, providers, and technology vendors are essential for improving data-sharing capabilities and fostering trust. These partnerships are critical to meeting the demands of value-based care models.
• Investing in Interoperable Health IT Systems: Healthcare organizations must ensure their health IT systems can communicate effectively with others. Interoperable systems provide timely access to patient data, ensuring accurate information is available at the point of care.



As healthcare continues to evolve in an increasingly digital world, quality data will play a pivotal role in shaping its future. The ability to harness clean, validated, and interoperable data is key to more efficient, patient-centered care. By addressing the challenges of fragmentation and poor data quality, healthcare organizations can not only enhance their operational efficiencies but also drive meaningful improvements in patient outcomes.