Enterprise systems rely on vast amounts of information. However, poor healthcare data quality remains a significant issue. According to Gartner’s 2021 research, organizations incur an annual cost of $12.9 million. Fragmented patient records and inconsistent formats affect everything from diagnostics to operational efficiency.
Only 20% of U.S. Integrated Delivery Network leaders fully trust their data—a clear sign of the urgency to improve enterprise healthcare data quality.
This article examines how innovative solutions are enhancing patient outcomes and transforming healthcare delivery. We’ll cover the main data quality challenges, modern technologies, and strategies for implementing effective practices.
At the core is the concept of healthcare data models—frameworks that define how medical data is structured and connected. Our goal is to guide healthcare tech and data leaders in transforming their master data management (MDM) approach and improving patient care.
Highlights:
- Up to 30% of patient records in hospitals are duplicates, leading to delayed or duplicated care.
- Only 20% of U.S. IDN leaders are confident in their data accuracy.
- Healthcare organizations lose an average of $12.9 million annually due to poor data quality.
What Is a Healthcare Data Model?
A healthcare data model is a methodical framework that defines how medical data is arranged, classified, and linked across systems to meet clinical, operational, and analytical purposes. It is a roadmap for standardized data elements—including patient records, diagnosis, drugs, integrations, tests (including raw data), and procedures—to facilitate interoperability and consistent interpretation across healthcare IT environments.
While healthcare data models provide the structure, data quality dimensions ensure the reliability and usefulness of the information within these models. These dimensions are intrinsically linked to the effectiveness of healthcare data models:
Key Dimensions of Healthcare Data Quality for Enterprise
- Accuracy: Clinical data should reflect real patient situations, such as recording the correct medication dosage. Mistakes here can lead to misdiagnosis, billing issues, or even patient harm.
- Completeness: Clinical data should accurately represent actual patient situations, including the precise dosage of medication. Mistakes here can result in misdiagnosis, billing difficulties, and even harm to patients.
- Consistency: Standardized formats and codes, such as ICD-10 for diagnoses and SNOMED CT for findings, guarantee that data is understood consistently across departments. Achieving enterprise healthcare data consistency is essential for smooth coordination, accurate reporting, and reducing clinical errors.
- Timeliness: Data must be available precisely when it is needed. For example, having up-to-date lab results during emergencies can directly affect patient outcomes.
Impact on Healthcare Outcomes
- Clinical decision support: High-quality data reduces misdiagnoses by ensuring providers access complete and up-to-date patient histories.
- Reporting: Data standardization models simplify compliance with regulatory standards (e.g., HIPAA) by structuring data for audit trails and quality metrics.
- Interoperability: Facilitates seamless data exchange between EHRs, payers, and research platforms, reducing redundant tests and administrative costs.
Solutions to Address Common Data Quality Issues in Healthcare Systems
Here are some challenges that can significantly impact patient care, operational efficiency, and compliance. Below are the typical issues:
Duplicate entries
The average duplication rate in healthcare facilities is 10%, with some institutions reporting rates as high as 30%. Another survey conducted by Black Book Market Research found that an average of 18% of patient records were duplicates, amounting to $1,950 per hospital stay. In turn, a Texas hospital found that 22% of its patient records were duplicates.
Duplicate records can lead to fragmented patient histories, delayed treatments, and unnecessary procedures. For example, patients may receive duplicate tests because their records are stored in separate profiles.
Technological solution: EMPI solutions assign unique identifiers to patients and link records across systems, reducing duplication and ensuring accurate patient identification.
Edenlab recently built a national Master Patient Index (MPI) for Ukraine’s E-Health system. It connects each patient to a unique digital profile, helping avoid duplicates and errors. Using machine learning and fintech-level security, we created a secure and accurate system that now supports 36.5M+ patients and over 3B medical records with just a 1% error rate. Learn more about the project.
Cross-platform mismatches
Discrepancies when integrating data from multiple systems with differing formats or standards creates data silos and inconsistencies. For example, mismatched formats between EHRs and lab systems can delay diagnosis and treatment.
Technological solution: Data mappers and ELT solutions facilitate real-time mapping and transformation of healthcare data from legacy into standardized formats, ensuring consistent automated data pipelines between systems.
Inconsistent, invalid, or outdated codes
These issues result from hindered interoperability, errors in billing, compliance issues, revenue loss, and inaccurate clinical reporting due to variations in coding standards, medical terms, or the use of obsolete codes.
Technological solution: Terminology services play a key role in improving health data accuracy by standardizing medical codes, such as SNOMED CT, ICD, and LOINC, ensuring consistency and enabling precise data aggregation for analytics and reporting. We offer our own Kodjin Terminology Service to ensure consistent use of codes and facilitate accurate data aggregation for analytics and reporting.
Our experience in delivering complex healthcare integrations helps organizations connect disparate systems into unified data ecosystems, improving both data quality and interoperability.
Outdated information
Data freshness is crucial for providers. Outdated information in patient records can lead to inappropriate care strategies and potentially harmful decisions.
Technological solution: Real-time validation and data integration pipelines, often powered by data streaming tools like Debezium, Airbyte, Dagster, dbt, and Apache Kafka, ensure data freshness by continuously synchronizing information across systems and alerting providers to critical changes.
Limited data accessibility for analytics
Limited data accessibility for analytics is caused by factors such as free-text overload, legacy systems, and a lack of standardized models, which restrict a healthcare organization’s ability to utilize data for improvement and innovation.
Technological solution: Structuring and normalizing validated healthcare data into a queryable format is key to enabling seamless data access. This often involves loading data into a high-performance analytical database, such as ClickHouse. Data normalization and API-driven integration depend on mapping, extraction, aggregation, and clinical systems data validation processes. While FHIR can serve as a standard for normalization, it is not mandatory. For faster insights, consider Edenlab’s FHIR Analytics.
A modern management system should support the full data lifecycle to strengthen data quality assurance in healthcare and maintain integrity across the organization. The key functions include health data mapping and terminology management to standardize diverse sources, as well as automated data extraction and aggregation for a comprehensive view of the data. Essential processes, such as deduplication, enrichment, semantic normalization, and validation, further refine data to ensure enterprise-wide healthcare data accuracy and reliability.
Edenlab specializes in implementing comprehensive systems to effectively manage healthcare data integrity and quality. These functionalities deliver a single source of truth, minimize error risks, increase data confidence, and establish a dependable foundation for healthcare data analytics accuracy, cross-platform integration, and the advancement of digital medicine through healthcare analytics product development.
To implement any of these solutions, explore our ready-made offerings.
Practical Strategies to Improve Enterprise Data Quality in Healthcare
In the complex landscape of healthcare data stewardship, two primary strategies have emerged as robust solutions for clinical data quality improvement across enterprise systems. At Edenlab, we believe in revolutionizing the healthcare landscape through innovative approaches that address these critical challenges.
Strategy 1: Building a Unified Data Platform
The first hospital data integrity strategy is to establish a centralized data platform that will serve as your healthcare organization’s Single Source of Truth (SSOT), leveraging health data platforms development to ensure scalability, interoperability and analytics readiness. This “data fabric” approach combines information from multiple sources into a single format. It enables seamless interoperability, automated procedures, real-time data access, and comprehensive analytics. These all-in-one platforms provide a comprehensive solution for various company needs, focused on interoperability, real-time access, and advanced data analysis.
Yet your company can select any data format that meets your needs; we strongly advocate for FHIR as the gold standard in healthcare. FHIR is the ideal foundation for modern healthcare data management due to its modular design, RESTful architecture, and widespread adoption within the medical community, which facilitates seamless data integration and exchange. Its built-in healthcare data validation and conformance checks help ensure data follows standardized formats, detecting inconsistencies early and supporting integrity across all systems. Learn more about what FHIR is.
At Edenlab, we’ve developed the Kodjin FHIR Data Platform, a comprehensive solution that addresses all these functions. Kodjin is designed to be a versatile data fabric capable of supporting interoperability, real-time access, and advanced analytics simultaneously.
Explore the Kodjin FHIR Server.
We’ve recently worked on the National Clinical Data Repository. Our solution provides a centralized platform for storing and managing clinical data, equipped with robust quality controls. This enables healthcare organizations to maintain consistent, accurate, and accessible clinical information across their enterprise. Explore this case study.
We also developed a unified registry that serves as a national source of truth for healthcare professional information and a master user’s list for a national e-Health system. The registry now contains information for over 8,000 medical facilities and staff members, with a self-management structure that maintains data quality without additional overhead. Learn more about this case.
Strategy 2: Leveraging Advanced Integration Engines
The second strategy focuses on direct system-to-system integration, utilizing robust integration engines that address data quality issues in real time. This approach is ideal for organizations that prefer not to centralize their data. Modern integration engines offer:
- Real-time updates and high-quality information flow
- Breaking down of data silos
- Seamless communication between disparate systems
Although there are off-the-shelf options like WSO2 and Rhapsody, at Edenlab, we specialize in creating custom integration engines based on our Kodjin products or open-source frameworks. This enables us to modify the solution to fit your unique requirements and infrastructure.
Explore how leading healthcare organizations are transforming their data ecosystems:
Discover Edenlab’s FHIR-driven solutions and case studies to see how high-quality data can elevate your operations.
Improving Enterprise Healthcare Data Quality with Edenlab’s Expertise
Improving data quality in EHRs requires a tailored approach that addresses each healthcare organization’s unique challenges. Edenlab specializes in custom data solutions through thorough business analysis. This approach focuses on creating a centralized, reliable data foundation that adds real value, instead of offering generic products.
Edenlab’s thorough understanding of data processes, metadata governance structures, and interoperability criteria sets us apart as a trusted partner in transforming the healthcare landscape. Whether updating data infrastructure or integrating legacy systems, Edenlab, as a full-service EHR software development company, delivers customized solutions that enable healthcare companies to enhance operations, simplify processes, and drive innovation. Edenlab ensures long-lasting value and a substantial return on investment in data quality initiatives by developing tailored solutions that meet the specific needs of every company.
Conclusion
From diagnosis to operations and regulatory compliance, the healthcare ecosystem depends on trustworthy, uniform data. Yet, many facilities still struggle with isolated systems, old-fashioned formats, and uneven data standards.
By using new frameworks like FHIR and cutting-edge integration engines, healthcare data integration in enterprise systems becomes more powerful, helping providers eliminate data silos and make meaningful changes. A forward-thinking, plan-driven approach to healthcare data quality turns scattered information into one unified source of facts—one that drives analytics, allows systems to work together, and ultimately makes patient care better.
At Edenlab, we bring deep technical expertise and healthcare domain knowledge to develop tailored solutions that address these challenges head-on. Whether modernizing your data infrastructure or implementing a national-scale clinical data repository, our services are designed to meet your unique needs.
FAQs
How can I tell if my healthcare data quality is affecting patient care or reporting?
At Edenlab, we have identified essential indicators of data quality issues that affect patient care and reporting. These include medication errors caused by incorrect records, missed follow-ups due to outdated contact information, and variations in care plans across providers. In reporting, look for inconsistencies in financial data, quality measures, and population health trend analysis. These concerns highlight the need for a robust healthcare data quality framework.
What tools are available to validate and clean healthcare data?
A variety of data quality tools for health IT are available to validate and clean information. For example, the Kodjin FHIR Server offers robust FHIR validation, terminology management, and analytics to ensure data accuracy and compliance while integrating smoothly with existing systems.
Can Edenlab work with my existing EHR and data warehouse tools?
Yes. Edenlab’s Kodjin platform can be smoothly integrated with legacy EHR and a healthcare enterprise data warehouse by providing FHIR-compliant APIs and implementing FHIR facades. This allows your current systems to exchange standardized data without costly replacements, enabling gradual modernization and improved interoperability.
Is FHIR validation useful even if I use older systems?
Absolutely. FHIR validation tools help legacy systems by standardizing data exchange through FHIR facades, improving compatibility with modern applications and regulatory compliance. It enables incremental upgrades without disrupting existing workflows or requiring full system replacement.
How long does a typical data quality improvement project take?
Project timelines depend on the scope. Simple fixes, such as deduplication, take a few weeks, while EHR integration and FHIR validation projects usually require three to six months. Large interoperability initiatives can take six to twelve months. Edenlab’s phased approach focuses on delivering quick wins while progressing toward full data quality improvements.
We dive deep where others skim
Our healthcare software development firm brings the expertise and precision needed to tackle the industry complexities. With a specialized focus on healthcare processes, regulations, and technology, we deliver solutions that address the toughest challenges.
Stay in touch
Subscribe to get insights from FHIR experts, new case studies, articles and announcements
Great!
Our team we’ll be glad to share our expertise with you via email
