12 Aug Healthcare Doesn’t Have a Data Shortage. It Has a Data Usability Problem

Top Takeaways

• Healthcare organizations have access to enormous volumes of data, but much of it still requires significant work before it can support clinical, operational, and strategic decisions.  
• Interoperability has improved access to healthcare data, but exchanging information does not automatically make it standardized, trusted, or usable. 
• Poor data usability creates hidden operational costs through manual reconciliation, duplicate processes, fragmented reporting, and repeated data preparation. 
• A trusted enterprise data foundation can allow the same healthcare data to support quality measurement, population health, analytics, AI, and other use cases. 
• As healthcare organizations accelerate AI and digital transformation, data usability will increasingly determine whether those investments deliver meaningful results. 

Healthcare organizations have access to more data than ever before.

Clinical information sits in electronic health records. Claims data provides another view of the patient. Labs, pharmacies, health information exchanges, registries, remote monitoring technologies, and wearable devices continue adding to the volume of information available across the healthcare ecosystem.

However, much of that information still requires significant work before it can support clinical decisions, quality reporting, analytics, or other day-to-day needs.

A recent Health Data Management article by Dr. Julia Rehman, Executive Fellow of the American College of Health Data Management, makes a related point. Healthcare organizations are investing heavily in AI, predictive analytics, automation, remote monitoring, and other digital technologies while many still operate with fragmented data, inconsistent reporting, disconnected workflows, and outdated governance models.

The article warns that organizations moving too quickly into advanced technologies risk simply digitizing the fragmentation they already have.

That observation gets to the heart of one of healthcare’s biggest data challenges today. Access to information has improved considerably. The next challenge is making that information usable across the organization.

More Data Does Not Automatically Mean Better Information

Healthcare has spent years improving interoperability, and that work has delivered significant progress.

Organizations can exchange clinical information across systems and settings in ways that would have been difficult a decade ago. National initiatives such as TEFCA and standards such as FHIR are pushing that progress even further.

But moving data is only the beginning. Information arriving from multiple systems may use different codes, formats, terminology, patient identifiers, and clinical definitions. Records may be incomplete or duplicated. Important information may exist in unstructured fields.

All of that creates work between receiving healthcare data and actually using it.

For a clinician, that can mean searching through multiple records to understand a patient’s history. For a quality team, it can mean reconciling information before calculating a measure.

For a payer, it can mean bringing together claims and clinical information to develop a more complete picture of a member. The data exists, and its value depends on what happens next.

The Hidden Cost of Healthcare’s Data Usability Problem

Healthcare’s data usability problem does not always appear as a line item on a budget.

Instead, the cost shows up throughout the organization.

Instead, the cost shows up throughout the organization as staff spend time reconciling records, analysts prepare data before they can actually analyze it, and quality teams track down missing information. Different departments may also build separate pipelines to prepare similar data for their own needs, adding complexity and duplicating work across the organization.

Over time, organizations can end up solving essentially the same data problem repeatedly.

The Health Data Management article points to similar consequences, including duplicate reporting, inconsistent definitions, poor data lineage, weak accountability, and reduced confidence in analytics. It also emphasizes the importance of establishing accountability, ownership, quality standards, interoperability requirements, and stewardship across the enterprise.

Data usability therefore becomes an operational concern as much as a technical one.

One Data Foundation, Many Uses

Healthcare organizations have an opportunity to change that model.

Instead of preparing data separately for every new reporting requirement, analytics initiative, or application, organizations can create an enterprise foundation where information is aggregated, normalized, validated, and made available for multiple purposes.

The same trusted clinical and claims data can then support quality measurement, population health, care management, payer and provider collaboration, analytics, and AI.

That approach becomes increasingly important as healthcare moves toward digital quality measurement and value-based care. Organizations cannot afford to rebuild their data infrastructure every time requirements change or a new use case emerges.

They need healthcare data that is ready to use.

AI Makes Data Usability Harder to Ignore

AI has brought new urgency to this issue. Dr. Rehman notes that AI depends on the quality, consistency, completeness, and representativeness of the underlying data. Poor data quality can create problems extending beyond inaccurate reporting to operational risk, bias, patient safety, and loss of trust.

Healthcare organizations therefore need to ask a practical question before launching the next AI initiative: Can our existing data support it?

If considerable manual work is required to reconcile information before people can confidently use it, AI will encounter many of the same limitations.

From Data Availability to Data Usability

Healthcare has made tremendous progress making information available. Now the industry needs to focus just as intensely on what happens after the data arrives.

The real test is whether healthcare data can be trusted and understood consistently across the organization, support multiple use cases without repeated manual preparation, and give clinicians, analysts, payers, and technology platforms the confidence to act on it.

Those questions increasingly define the difference between having healthcare data and generating value from it.

Healthcare does not need another mountain of information. It needs to make better use of the information it already has.

How IMAT Intelligence Helps Make Healthcare Data Usable

IMAT Intelligence helps healthcare organizations aggregate, normalize, validate, and operationalize clinical and claims data from across the enterprise.

By creating a trusted data foundation that can support multiple downstream uses, organizations can reduce fragmentation and make healthcare information more readily available for quality reporting, population health, analytics, AI, and other strategic initiatives. 

Contact IMAT Solutions to learn how IMAT Intelligence can help your organization turn fragmented healthcare data into trusted, usable, and actionable information.

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Additional Insights 

• Healthcare Has Achieved Interoperability. Now Comes the Hard Part: Data Standardization 
• Bulk FHIR and the Next Phase of Data Exchange in Healthcare
• Interoperability in 2026: Progress, Gaps, and What It Means for Closing Care Gaps
•Health IT Answers: Why Data Intelligence Is the Missing Link in Healthcare Modernization