How to Manage Data Quality in Healthcare

These are interesting times for data leaders in highly regulated industries—especially healthcare—because for the longest time, we’ve seen only the tip of the iceberg regarding the power and promise of data at scale. Now, more of the iceberg is revealing itself as AI and machine learning (ML) provide more and more evidence of what’s possible when leaders prioritize the importance of data quality.

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That said, data leaders in healthcare can’t throw caution to the wind by moving fast and breaking things, like their peers in other industries. In healthcare, innovation means nothing if it compromises patient outcomes, safety, patient information, and privacy. 

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So how can these leaders help healthcare professionals and patients avoid falling behind in this exciting new era of data utilization? First, it’s important to focus on what factors make healthcare data quality so important. Second, leaders need to appreciate the specific challenges regarding data quality management in healthcare. And third, leaders who are looking to improve and maintain healthcare data quality in their own organizations should rely on best practices to ensure a solid foundation. 

Why is healthcare data quality becoming much more important?

What in the healthcare industry is driving this additional reliance on data quality? 

Like in many industries, it’s complicated. But five trends in particular that are unique to the industry itself are worth noting here:

Data-driven healthcare transformation

Healthcare has always depended on high-quality information to ensure effective diagnoses and treatments. Now, digital transformation is rapidly increasing both the volume of healthcare data and the diversity of its sources.

This shift is revolutionary. High-quality data enables medical practitioners to form accurate diagnoses, create effective treatment plans, and maximize time with patients. Data analysis then helps healthcare organizations improve operations—and many leverage AI and machine learning to drive greater efficiencies.

However, poor data quality leads to inaccurate health information, resulting in misdiagnoses, treatment errors, and inefficiencies that compromise patient safety and increase costs.

AI and advanced analytics

The success of AI and ML in healthcare depends not only on clean, structured, and reliable data but also on transparency and explainability. Poor data quality can distort model outputs, yes—but in healthcare, the greater concern is often AI systems’ black box nature. Without visibility into how models reach conclusions, healthcare providers can’t fully trust or verify their outputs.

Given the massive scale of data that is involved in training and refining these tools, even minor issues can lead to suboptimal—or dangerous—outcomes. As such, ensuring data quality must go hand-in-hand with implementing models that are interpretable and auditable and align with clinical standards.

Improved unstructured data processing

While already substantial, the overall volume of data in healthcare shows no near-term signs of slowing. However, one interesting shift taking place today, amid all the data itself, involves industry data practitioners working to significantly improve their abilities and processes to make sense of unstructured healthcare data.

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This is because, from clinical notes in electronic health records (EHRs) to radiology scans and data from wearable devices, a significant amount of healthcare's most valuable information now increasingly exists outside structured tables and schemas.

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Recent advances in AI, natural language processing, and image recognition are unlocking new insights from this previously underutilized data. But with this opportunity comes greater responsibility: data leaders must ensure the quality, consistency, and context of unstructured inputs to prevent misleading outputs and avoid patient harm.

Interoperability and standardization

In addition to managing more data overall, data leaders must also ensure that their organizational data seamlessly integrates with any healthcare system.This is because interoperability allows healthcare providers to access comprehensive and accurate patient data, which leads to better diagnoses, treatment plans, and real-time care coordination. This means healthcare data quality directly correlates with improved patient outcomes. 

What’s more, data leaders who enable seamless data exchange and interoperability can reduce administrative burdens, minimize errors, and streamline workflows. On the whole, these efficiencies then translate into cost savings and allow healthcare providers to focus more on patient care.

Regulatory compliance

Finally, as healthcare looks more toward data to modernize, strict regulations like the Health Insurance Portability and Accountability Act (HIPAA) act as a counterbalance that requires healthcare organizations to keep the data they use both accurate and secure. This makes poor data quality a compliance issue—and rightly so. Subpar or poorly secured data can lead to data breaches and substantial financial penalties.

Ultimately, though, security breaches erode trust, both in healthcare professionals and their patients. Moreover, considering the fact that data-related incidents are on the rise in the healthcare industry, this trend alone makes a strong case for data quality to be a priority for data leaders in the healthcare space.

Common data quality challenges and their unique healthcare implications

Data leaders in healthcare can’t claim that the data quality challenges they face are unique to health data—but the implications (and impacts) they lead to certainly are since they affect the lives of millions of people in the United States alone. By understanding each of the below challenges, however, they can work to mitigate the risks each can pose:

Fragmented data and data silos

Data fragmentation and silos are constant threats to healthcare data quality. While some compartmentalization is necessary—data cannot flow unchecked through EHRs, insurance claims systems, or third-party databases like AWS Data Exchange, for example—fragmentation must not jeopardize patient health.

Overall, preventing silos is an ongoing challenge for data leaders. Interoperability initiatives play a key role, but these complementary strategies are also critical:

• Implementing robust data governance frameworks
• Adopting universal standards like HL7 FHIR for consistent formatting and terminology
• Using advanced data quality tools and patient identity matching, where applicable

Data entry and human error

Again, the impacts of data entry and human error on data quality aren’t unique to the healthcare industry. But while their related impacts can be innocuous in other industries, the misinformation they can create in patient records, data elements, and data analytics in healthcare are in no way acceptable.

The relative amount of unstructured and complex data in the healthcare industry further exacerbates these related challenges. Physician notes and narratives, imaging reports, genomic data, and biosignal data—in addition to social media and patient feedback—create ample opportunities for human error to occur during processing. The challenges of manual processes, miscommunication, and outdated methods can also lead to everything from scheduling and billing errors to misdiagnoses and incorrect treatments.

Navigating regulatory healthcare frameworks

While HIPAA often dominates discussions about data compliance in healthcare, it’s just one part of a broader regulatory landscape that governs data quality and security. 

Other key regulations include the following:

• The Health Information Technology for Economic and Clinical Health Act (HITECH): HITECH complements HIPAA by promoting EHR adoption and mandating strong security and privacy measures for electronic healthcare data.
• The California Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA): CCPA and CPRA expand privacy protections for California residents’ personal data, including some health-related information. However, data that HIPAA already governs is exempt from CCPA and CPRA provisions, meaning these laws primarily apply to non-HIPAA-covered entities or data types outside HIPAA’s scope.
• The Washington My Health My Data Act: This act expands protections for sensitive health information that apps, websites, and businesses collect outside HIPAA’s scope.
• The Payment Card Industry Data Security Standard (PCI DSS): PCI DSS sets security requirements for healthcare providers that process payment data.
• The General Data Protection Regulation (GDPR): GDPR imposes stringent data protection standards for healthcare organizations that handle data for individuals located within the EU at the time of collection, regardless of citizenship.

7 best practices for ensuring optimal healthcare data quality

Just as in any industry, you can never avoid healthcare data quality challenges entirely. You can, however, manage—if not mitigate—them by consistently using key best practices. 

While best practices vary across healthcare organizations, the following form a quality-forward foundation to build from:

1. Maintain a robust data governance framework

Data teams, with the buy-in and support of leadership, should clearly outline and document all roles, responsibilities, and accountabilities for data quality management across every department in the organization. As a whole, this documentation then stands as the organization’s comprehensive data governance framework. 

But this is only the first step in implementing robust data governance, not the last. As such, data leaders must designate and empower individuals or teams to develop and enforce these data quality principles over time—and then update and enhance this documentation as business and regulatory needs evolve. 

Data quality within organizations is increasingly becoming everyone’s responsibility. However, by establishing specific stewards of data governance, organizations can ensure accountability, standardize processes, and create a culture of continuous improvement in healthcare data management.

2. Ensure optimal privacy and security

Privacy and security are the operational yin to governance’s yang. Because of this, leaders must enact exceptional protocols and protections to safeguard healthcare data and integrate comprehensive privacy measures like encryption and access controls into data quality efforts to comply with regulations such as HIPAA.

Moreover, external sources of health data, like wearable devices and remote monitoring tools, provide both a growing source of data and additional security concerns. As such, additional patient safeguards should validate healthcare data quality from such sources before use.

3. Conduct regular data audits and monitoring

Data leaders should regularly audit datasets to identify and resolve inconsistencies, errors, and redundancies that naturally arise.

Audit leaders should also base their work on key performance indicators that reflect the auditing process (like data entry error rates, audit frequency, and issue resolution time), overall data quality (accuracy, completeness, reliability, and timeliness), and healthcare-specific metrics (such as duplicate patient records, name accuracy, and contact information consistency).

Additionally, data leaders should use dashboards to generate real-time reports, detect anomalies, and address issues as quickly as possible.

4. Standardize data formats and processes

Data leaders should oversee the implementation of consistent health data formats, coding systems, and validation protocols to foster and maintain cross-system uniformity. This standardization enables cross-industry interoperability, which is now mission-critical in healthcare since it facilitates seamless sharing and data comparison across a growing network of healthcare organizations.

The smooth flow of data also offers additional benefits, like improving data accuracy and reducing errors—both of which are essential for ethical, impactful patient care and research.

Additionally, data leaders should streamline data capture processes by providing clear guidelines and training for all staff involved in data entry.

5. Invest in data quality tools and automation

As with data leadership positions in any industry, it’s important to strategically use advanced tools for real-time data validation, automated cleansing, deduplication, and integration of multiple data sources. 

In healthcare, this is especially important because healthcare data doesn’t just flow to different organizations, departments, and facilities. It also spans disparate systems, including EHRs, wearable devices, lab systems, and billing platforms.

As the number of these healthcare-related systems continues to grow, data teams need to adopt and integrate assistive technologies like ML that enable them to predict and prevent potential data quality issues before they occur.

6. Address the root causes of data quality issues

Despite advancements in data management and monitoring, some problems still evade detection and correction until after they occur. As a result, data leaders must perform root-cause analysis on every data quality–related error to identify its origin.

Even if lives and patient health weren’t at stake, data errors could still lead to serious consequences, such as regulatory penalties and a loss of organizational trust. However, approaching healthcare data quality management as an ongoing process, with regular updates for new insights and technology, ensures that data teams remain adaptable and up-to-date.

7. Foster a culture of data quality

Finally, processes and procedures that build resilient data teams form the foundation for fostering a culture that embraces data as a product across the organization. Data leaders may even have an advantage here compared to their peers in other industries.

Most professionals inherently recognize that accurate, complete, and timely data is essential for patient safety, diagnosis, and treatment. This awareness makes it easier for data leaders to advocate for and implement data quality initiatives.

But even with these cultural advantages, supporting a data-driven mindset still requires ongoing education, training, and resources. Regular training sessions, workshops, communication campaigns, and feedback loops foster awareness and empower everyone, regardless of role, to uphold data quality.

Healthspan vs. lifespan: A final thought on healthcare data quality

The spirit of healthcare remains rooted in a Hippocratic standard: the idea that professionals in the industry should prioritize quality of care over quantity. This echoes work in the data world, where even in a post “big data” world—as data continues to grow ever more vast and complex—data quality matters most of all. 

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But in both worlds, the pressure of growth and the increasing need for efficiency and innovation make it far too easy to lose track of what should matter most. 

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These pressures are precisely why, in both healthcare and data work, technology leaders must place a selective emphasis on leveraging tools and technologies that help them understand, access, and work with data, not on tools that simply help them collect and store more of it. 

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That’s where Gable.ai helps data leaders stay ahead with proactive data quality management through data contracts. As industries become increasingly data-driven, reliable and accurate data are more essential than ever for making better decisions and achieving more effective outcomes.

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Sign up for the product waitlist today to build a foundation of high-quality data that drives meaningful results.

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