Care Evolution

The healthcare data floodgates are open. Organizations today have unprecedented access to digital patient data streams flowing from EHR vendors, Qualified Health Information Networks (QHINs), Health Information Exchanges (HIEs), and data aggregators. It feels like we should be drowning in insights.

But here’s the uncomfortable truth: access alone doesn’t unlock value. Connectivity without usability is merely an illusion, leaving valuable potential locked away in messy, unrefined data.

This isn’t just a minor inconvenience; it’s a critical barrier hindering progress. Let’s explore the gap between getting data and making it truly actionable, why IT teams are feeling the strain, and how to bridge that divide.

The alluring illusion of connectivity

“What’s your connectivity coverage in this region?” It’s a standard question, and data vendors are quick to respond, often touting deep integrations with Epic, Cerner, and countless other EMRs. They paint a picture of seamless data flow.

But what actually arrives at your digital doorstep? Frequently, it’s a far cry from plug-and-play. You receive data that is:

• Incomplete: Missing critical fields or entire sections.
• Inconsistently coded: A jumble of local codes mixed with partial standard codes.
• Poorly formatted: Requiring significant manipulation before it fits any standard model.

This raw, unrefined data is simply unusable for the very strategic, clinical, or operational decision-making it was intended to support.

Data readiness: The real hurdle after access

Getting the data feed is just step one. The heavy lifting begins when you try to make it ready for use. True data readiness requires navigating a complex landscape, including:

• Cleaning and standardizing formatting: Ensuring consistency across disparate sources.
• Mapping to standard terminologies: Translating various codes into recognized standards like SNOMED CT, LOINC, and RxNorm.
• Harmonizing inconsistent implementations: Dealing with the real-world variations in how standards like FHIR and C-CDA are actually used by different systems.

Suddenly, healthcare IT teams find themselves tasked with normalizing data that was supposed to arrive ready-to-use. They’re often doing this complex work without the specialized tools or bandwidth needed to succeed, turning promised efficiency into a resource drain.

The data expertise workforce gap

Compounding the problem is a shortage of talent. Few professionals possess deep, hands-on experience across the multitude of platforms, data formats, and coding standards encountered in the wild. Even with published specifications, real-world data rarely conforms perfectly.

Parsing, enriching, restructuring, and standardizing this messy data demands a rare skillset. This talent shortage places immense pressure on already stretched IT departments.

The shifted burden: From vendor to you

Historically, data vendors often performed more of the normalization heavy lifting. Today, that operational burden has increasingly shifted onto the buyers – the health plans, health systems, and research organizations. This shift creates significant challenges:

• Complex internal workflows: Teams are forced to build intricate, often brittle, “Rube Goldberg” processes to handle the raw data.
• Increased costs and inefficiencies: Manual effort and custom builds drive up expenses and slow down timelines.
• Misaligned responsibilities: IT teams are burdened with data refinement tasks that fall outside their core competencies and strategic focus.

“CIOs & CTOs are looking for more data while the siloed operators are forced to create inefficient ‘Rube Goldberg’ processes to work with the raw, unrefined patient data.”

Getting started smart: Prove the process before scaling

How do you avoid investing heavily in data pipelines that deliver unusable data? Start small and validate the entire process end-to-end:

1. Request sample data: Get data extracts specifically tied to a known patient population relevant to your business needs.
2. Test your workflow: Run this sample data through your entire planned process: ingestion, patient matching, normalization, and enrichment tools.
3. Validate with stakeholders: Share the results (the cleaned, usable data) with business owners in areas like Stars, risk adjustment, or population health. Do they find it valuable? Can they actually use it?

This approach ensures your investments are based on data that can be operationalized, not just optimistic connectivity claims.

Where many data sourcing efforts go wrong

Getting data access is table stakes. The real make-or-break issues arise after acquisition:

• Broken ingestion workflows: Systems choke on unexpected formats or data volumes.
• Data loss during transformation: Critical information gets dropped during complex mapping or cleaning steps.
• Poor patient matching: Inability to reliably link records for the same patient across different sources.
• Underwhelming data quality: Even after processing, the data isn’t accurate or complete enough.
• Expensive, generic platforms: Using tools not purpose-built for the nuances of healthcare data.

This often leads to what we call “Second Vendor Syndrome.” Many organizations initially invest in general-purpose solutions like cloud data warehouses or generic ETL/integration tools. They successfully build a “data outhouse” – a place to store raw data – but not the sophisticated “data refinery” needed to produce high-octane, usable insights.

It’s like acquiring crude oil but having no way to refine it into gasoline. You can’t pour raw crude into your car and expect it to run. Similarly, raw patient data rarely fuels critical healthcare functions directly.

Symptoms of needing a refinery (not just an outhouse) include:

• Persistent data quality and patient matching headaches.
• Burnt-out IT teams struggling with complex data plumbing.
• Frustrated HEDIS, Risk Adjustment, and Population Health staff unable to get the insights they need.
• Budget overruns and disappointing ROI on data initiatives.

That’s often when organizations realize they need a specialist – and that’s often when CareEvolution® gets the call.

Focusing on usability: The path to value

At CareEvolution, we understand that moving data isn’t enough. The real value lies in cleaning, standardizing, enriching, and upgrading that data. Our UPLIFT data refinery engine is purpose-built for healthcare data complexity, transforming raw feeds into actionable assets that support:

• Real-time data for quality measures: Including Digital Quality Measures (dQMs) and seamless integration with regulatory entity digital content services that certify dQMs for Stars programs.
• Accurate risk adjustment: Ensuring complete and compliant data capture and documentation submission.
• Effective gap closure: Powering EMR-integrated workflows to help providers address care gaps at the point of care.
• Comprehensive data enrichment: Turning non-discrete data (like PDFs and text notes) into structured, coded CDA or FHIR formats.

Conclusion: Demand more than just access

Sourcing digital patient data isn’t a one-time project; it’s a continuous strategic capability. Success demands more than just opening the data tap. It requires:

• Strategic investment: Recognizing that data usability requires dedicated resources.
• Scalable tools: Implementing platforms designed for the volume and complexity of healthcare data.
• Trusted partners: Collaborating with experts who possess deep healthcare data domain knowledge.

Pro Tip: If a potential data partner requires months-long onboarding periods, a large implementation team on your side, and lacks a cloud-native, self-service method for testing their data feeds – be cautious. True readiness often comes with more agility.

Don’t settle for just data access. Choose a platform engineered to deliver value, not just volume.

Ready to turn your raw data feeds into refined, actionable insights?

Let’s talk about building your data refinery.

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