Cleaning Up Healthcare’s Data Diet: From Junk to Quality Insights

By Vanessa Candelora, Senior Consultant, Gravity Project Program Manager

Just as nutritionists say, "You are what you eat," the same principle applies to health data systems: Poor data quality leads to poor outcomes. Feeding systems with incomplete or inaccurate data results in subpar or even useless insights. While healthcare has improved the "plumbing" for data exchange, much of what flows through those pipes is junk. The phrase "trash data" has become a common refrain among stakeholders frustrated by low-quality information. A recent LinkedIn post on the Trusted Exchange Framework and Common Agreement (TEFCA) highlighted the problem: Interoperability advances have made moving data easier, but if the data are riddled with errors, what’s the point? Instead of improving care, we’re often left drowning in unusable information.

The problem is pervasive and multifaceted. Nonsearchable images, duplicate or conflicting records, and data dumps that force clinicians or administrators to sift through irrelevant information all contribute to an overwhelming mess. Poor data quality isn’t just an annoyance—it creates real risks. For example, when a provider receives a massive file instead of just the necessary, distinct data elements, they might miss a critical detail buried in the flood of information. If something is overlooked, the provider could be held liable for failing to act on data it technically possessed but couldn’t efficiently use.

So, what do we mean by “quality” data? A systematic literature review conducted in 2023 identified six key dimensions of digital healthcare data quality:

1. Accessibility–Can users extract the data they need in a timely manner?
2. Accuracy–Do the data truthfully represent the event being described?
3. Completeness–Are there missing data points now or over time?
4. Consistency–Do different systems reflect the same information for the same entity?
5. Contextual Validity–Are the data fit for their intended use?
6. Currency–Are the data up to date?

The study found that consistency–ensuring data are uniform across systems–was the most influential factor, affecting all other dimensions. Without consistency, accessibility and accuracy crumble and incomplete or outdated information can lead to costly errors.

The urgency to fix this issue is mounting. Regulatory requirements such as the Centers for Medicare and Medicaid Services’ (CMS) Interoperability and Prior Authorization final rule (CMS-0057-F), and TEFCA are pushing organizations to improve data sharing. But improved interoperability doesn’t automatically mean useful data. These regulations aim to facilitate better data access and exchange but without addressing the underlying issue of data governance and quality, organizations may simply be moving bad data faster.

Data governance plays a critical role in addressing this challenge. Governance is the framework organizations use to ensure data integrity, security and usability across different systems and stakeholders. Strong governance practices help organizations establish the policies, processes and accountability needed to improve data quality consistently. Without governance, efforts to improve data quality are fragmented, and the same issues of inconsistency and inaccuracy persist across the healthcare system.

The hard work of cleaning up healthcare data is long overdue, but who is responsible? How can organizations improve data quality in a way that supports multiple use cases? And what are the real risks of failing to act? In the following sections, we’ll explore barriers to high-quality data, the impact of poor data on different stakeholders and strategies for improving data governance in a sustainable, scalable way.

Barriers to Establishing Effective Data Governance and Improving Data Quality

Establishing governance and improving data quality across healthcare organizations, especially between payers and providers, is challenging due to the complexity of clinical data, varying standards, and trust issues. Key barriers include:

• Complex Clinical Data Use: Clinical data are repurposed for multiple functions, such as quality reporting, risk adjustment, prior authorization, population health and digital health programs. Poor data quality hinders these efforts, creating inefficiencies and risks.
• Lack of Standardized Governance: Policies and practices vary widely across organizations, leading to inconsistent data management and compromised interoperability.
• Trust Issues: Concerns about accuracy, privacy and security complicate data sharing. Frameworks like TEFCA aim to improve trust but widespread adoption remains a challenge.
• Unstructured Data: A significant portion of healthcare data exists in unstructured formats like PDFs and scanned documents, limiting automation and usability.
• Data Provenance: Without clear tracking of data origin and integrity, stakeholders struggle to trust and reuse shared information.
• Expanding Payer Responsibilities: Payers now manage data from providers and other payers, often without adequate governance frameworks, increasing the risk of inconsistent stewardship.

These barriers underscore the need for clear policies, standardized practices and stronger governance frameworks to ensure high-quality, usable healthcare data.

The Cost of Doing Nothing: Impact of Poor Data Quality by Stakeholder Group

Failure to address poor data quality leads to inefficiencies, risks and missed opportunities across the healthcare system. Below, we break down the impact of the stakeholder group.

Providers

• Degraded Patient Care: Incomplete or inaccurate patient records can lead to delays in diagnosis, unnecessary or duplicative tests and misinformed treatment decisions.
• Increased Liability: Erroneous, missing or overly voluminous data can expose providers to legal risks if adverse patient outcomes result from incorrect information.
• Operational Inefficiencies: Poor data quality requires manual reconciliation of records, consuming valuable staff time and increasing administrative burdens.
• Interoperability Failures: Providers need clean, standardized data to efficiently share patient records across care settings. Poor data quality hinders care coordination and leads to fragmented patient histories.

Payers (Health Plans)

• Administrative Burden: Poor data governance across departments results in duplicate data requests for different purposes, creating inefficiencies for both payers and providers.
• Regulatory & Compliance Risks: Inaccurate data increases the risk of failing audits, resulting in penalties, reputational harm, and operational disruptions.
• Inconsistent Data Stewardship: Payers are responsible for managing and sharing external data but often struggle with governance frameworks that may result in continued siloed data. The risk is that different departments having partial data, may come to different conclusions & results of analysis. 
• Challenges for Risk Adjustment & Quality Programs:  incomplete or stale data along with difficulty with electronic provenance leads to manual and expensive processes in these critical business areas.

Patients

• Misdiagnoses & Treatment Delays: Errors in patient records can lead to incorrect diagnoses, inappropriate treatments or delays in critical interventions.
• Frustration & Lack of Trust: Patients frequently encounter outdated or incorrect data in their medical records, undermining confidence in the healthcare system.
• Privacy Risks: Poor data governance increases the likelihood of data breaches, jeopardizing patient confidentiality.

Life Sciences & Research

• Slower Drug Development: Clinical trials and real-world evidence studies depend on high-quality data. Inaccurate or incomplete datasets delay trial execution and regulatory approvals.
• Bias in AI & Machine Learning Models: Research driven by artificial intelligence (AI) relies on clean data for training models. Garbage data leads to garbage insights, reducing the effectiveness of predictive analytics.
• Missed Opportunities for Personalized Medicine: Poor data quality limits the ability to match patients with targeted therapies, reducing effectiveness in precision medicine.

Pharmacy

• Medication Errors: Inaccurate data on patient allergies, diagnoses or medication histories increase the risk of dispensing errors.
• Supply Chain Inefficiencies: Poor data quality can result in incorrect demand forecasting, leading to medication shortages or overstocking.
• Prior Authorization Challenges: Pharmacies often struggle with incomplete or conflicting data when processing medication prior authorizations, leading to delays in patient access to critical therapies.

Addressing the Challenge: Considerations and Approaches for Improving Data Quality

Improving healthcare data quality requires strong governance frameworks and actionable steps to address trust issues, inconsistent practices and interoperability challenges. This work can be overwhelming and complex, but we’ve put together some critical strategies to help you do the hardest part, which is taking the first step.

Audit and Inventory Data

Organizations must start by identifying all data sources, including clinical, claims and external datasets, while pinpointing gaps in critical data. A thorough assessment of data quality across dimensions like accessibility, accuracy, completeness, consistency, contextual validity and currency is essential to prioritize improvements and fill gaps effectively.

Develop and Enforce Policies

Establishing clear governance policies is crucial to standardizing practices. Assigning roles such as data stewards and analysts ensures accountability for maintaining data quality. Leveraging tools like Fast Healthcare Interoperability Resources (FHIR) application programming interfaces (FHIR APIs) can enable exchange of only necessary data, reducing inefficiencies and risks. Governance frameworks should align with interoperability requirements like TEFCA and CMS-0057-F to support compliance and consistency.

Implement Processes, Roles and Technology

Standardized workflows are needed to ensure data consistency during transitions, such as from providers to payers, and to produce concise, consumable summaries rather than overwhelming datasets. Key roles, including a chief data officer supported by stewards and analysts, provide strategic direction and continuous monitoring of data quality. Advanced tools for data validation, nonduplication and traceability, along with FHIR APIs and metadata management solutions, are critical for implementing these processes effectively.

Measure and Monitor Data Quality Efforts

Quantifying the financial impact of poor data quality and return on investment from governance initiatives helps organizations prioritize and track improvements. Establishing key performance indicators ensures progress is monitored and tied to such broader business goals as reduced administrative burden, better care coordination, and improved compliance.

Clarify and Manage Data Types

Differentiating consistent data, such as patient demographics and chronic conditions, from transient data, like situational clinical information, allows organizations to manage each type appropriately. Policies should ensure consistent data are maintained with integrity while transient data are responsibly managed throughout their lifecycle.

Streamline Data Handoffs

To improve transitions between stakeholders such as providers, payers and patients, organizations should adopt standardized, easily consumable summaries that highlight relevant information. Ensuring clear data provenance and aligning terminologies across systems fosters trust, reduces errors and improves interoperability.

By adopting these strategies, healthcare organizations can address barriers, meet regulatory requirements and leverage high-quality data to drive better outcomes, efficiency and decision-making.

Emerging Efforts: Advancing Data Quality with the PIQI Framework

One promising initiative driving progress in data quality is the National Collaborative for Innovation in Quality Measurement (NCINQ), which leverages Patient Information Quality Improvement (PIQI) Framework. The PIQI Framework, developed to promote consistency and efficiency in quality measure implementation, provides a structured approach to improving data quality by focusing on clarity, consistency, and feasibility of digital measures.

NCINQ, a collaborative effort supported by NCQA, aims to tackle challenges in digital quality measurement, such as data completeness and alignment across stakeholders. Their work highlights the critical role of high-quality data in advancing value-based care and improving patient outcomes.

This initiative not only exemplifies the industry's momentum toward better data quality but also offers opportunities for stakeholders to engage, learn, and contribute. Those interested can explore the PIQI Framework at piqiframework.org and follow NCINQ’s progress via the NCQA website and listen to related Informonster Podcast episodes

Call to Action: Driving Progress on Data Governance and Quality

The time to act on improving data quality and governance is now. Stakeholders across the healthcare system have an opportunity – and responsibility – to contribute to industry efforts promoting better standards, processes and outcomes. Collaborative action is essential to address the systemic challenges of poor data quality and fragmented governance. Here are some actionable ways you can get involved:

• Participate in Standards Development: Join focused FHIR accelerators or participate in workgroups to shape standards that address specific use cases. These efforts are critical to creating scalable, interoperable frameworks that ensure high-quality data exchange.
• Engage with the Sequoia Project's Data Usability Workgroup: This initiative focuses on improving data usability to ensure data exchanged are actionable and meet user needs. Learn more or join the effort at sequoiaproject.org/interoperability-matters/data-usability-workgroup.
• Monitor and Contribute to Other Industry Collaboratives: Keep an eye on initiatives like the Massachusetts Health Data Consortium Data Governance Collaborative, which provides a forum for organizations to align governance frameworks and respond to regulatory developments. More details are available at mahealthdata.org/dgc.
• Engage POCP as a Strategic Partner: If your organization is ready to take actionable steps, Point-of-Care Partners (POCP) can help. We bring objective insights, free from office politics or internal biases, to conduct audits, assess governance and data quality practices and guide you through recommended processes for improvement. Our clients value our extensive industry expertise and pragmatic approach to tackling data challenges.

By taking these steps, you can play an active role in transforming healthcare data to a strategic asset that drives better outcomes for patients, reduces inefficiencies and ensures compliance with evolving regulatory requirements. Whether through industry collaboration or by engaging with an experienced partner like POCP, your efforts can contribute to a stronger, more effective healthcare system.

Ready to get started? Contact POCP to learn how we can help your organization navigate these challenges and build a foundation for long-term success.