Healthcare data is expanding rapidly, generating massive, complex datasets. Data flows continuously from countless touchpoints like electronic health records (EHRs), laboratory information systems, medical imaging platforms, and many more.
With healthcare data projected to grow at a staggering 36% compound annual growth rate (CAGR) this year, effective healthcare data quality management becomes increasingly complex and critically important.
Poor healthcare data quality management is a major concern
Unfortunately, healthcare delivery’s sheer volume and fragmented nature often lead to serious healthcare data quality issues, including inaccuracies, inconsistencies, and gaps.
A single patient’s health record typically spans multiple systems, departments, and providers, creating a patchwork of information that must maintain coherence and accuracy. Without robust data standardization and interoperability protocols, ensuring consistency across a patient’s healthcare journey becomes nearly impossible.
Perhaps most concerning is the prevalence of human error in healthcare data. Frontline clinical staff working under immense pressure, frequent handoffs between care teams, and inconsistent documentation practices all contribute to data entry mistakes. These aren’t merely administrative concerns — errors in allergy information, medication records, or diagnostic codes can directly impact patient safety and treatment outcomes.
For example, in the US, prescription errors affect over seven million people, resulting in costs of roughly $21 billion and approximately 7,000 preventable deaths annually. Although electronic health records (EHRs) have enhanced patient safety, mistakes persist, particularly when flawed or inconsistent patient data leads to misidentified patients or incorrect medication prescriptions.
Understanding the current healthcare data landscape
The inherent complexity of the healthcare sector creates numerous challenges in maintaining healthcare data quality.
Here are a few important ones to know about:
Regulatory requirements
Healthcare organizations operate within a complex web of regulatory requirements. Frameworks like HIPAA in the US and GDPR in Europe demand rigorous data integrity and protection standards. Yet, many healthcare systems continue to operate without sufficiently robust validation and governance frameworks. This regulatory gap creates significant risk, potentially resulting in financial penalties and patient harm.
Decentralized data governance
The increasingly decentralized nature of healthcare data governance creates persistent data silos across the care continuum, leading to fragmented patient records and duplicate entries. These silos make it hard to coordinate ongoing care, track patient outcomes over time, and make informed policy decisions based on data.
Legacy systems
Many healthcare institutions remain bound to legacy systems that aren’t designed for today’s data demands. These outdated technologies create significant barriers to digitization and standardization efforts, as they may not support modern data validation, interoperability, or analytics capabilities.
Data sensitivity
The sensitive nature of personal health information means that health data quality issues carry outsized consequences. Even seemingly minor errors — a misplaced decimal in a medication dosage or an incorrectly recorded allergy — can lead to serious clinical misjudgments. These errors can cascade into missed diagnoses, inappropriate treatments, or adverse drug events.
The role of research
Clinical research and pharmaceutical development depend entirely on high-quality data. From clinical trials to post-market surveillance, poor medical data quality threatens research validity, regulatory approval processes, and continued investment in medical innovation.
Tech sprawl
Most healthcare organizations maintain a complex technology stack comprising multiple systems — some legacy, some specialized, and some focused-on healthcare data management or security. This complexity makes end-to-end data flow challenging to trace, match, and validate. Consequently, quality gaps persist and multiply throughout the ecosystem without centralized governance and visibility.
The drive for innovation
As healthcare increasingly embraces data-driven decision-making and innovation, the consequences of poor data quality become more apparent. Strategic priorities like personalized medicine and digital innovation depend on clean, real-time data. Even the most sophisticated systems and analytics will produce misleading or harmful results when the underlying data is flawed.
10 common healthcare data quality issues
Healthcare organizations face numerous data quality challenges that can directly impact patient outcomes, operational efficiency, and strategic decision-making. The following issues represent the most prevalent threats to healthcare data integrity:
Errors in data entry
Transcription errors or simple typos during patient registration or clinical documentation can cause inaccurate medication prescriptions, inappropriate procedures, or incorrect test orders. These mistakes compromise individual patient care and trigger downstream errors throughout clinical workflows.
Inconsistent data formatting
When healthcare systems store identical data in different formats — for example, addresses recorded as single versus separated fields — it makes patient matching or analytics unreliable without data harmonization.
Missing or incomplete patient records
The absence of medical histories or recent test results can delay critical treatments or lead to inaccurate clinical assumptions, particularly impacting emergency and chronic care management.
Duplicate patient records
Variations in patient names, misspellings, or differences in identification information (such as insurance IDs) often lead to multiple patient records. These duplicated records fragment the patient’s health history, posing risks during treatment decisions.
Outdated information
Out-of-date patient demographics, insurance coverage data, or outdated lab results can result in significant miscommunication, rejected claims, or incorrect clinical interventions.
Errors in terminology and coding practices
The inconsistent use of coding standards, such as ICD or CPT, across providers and departments can distort analytics accuracy, billing, and clinical decision-support tools.
Integration difficulties across systems
Data across laboratories, radiology centers, and healthcare providers may not sync properly, particularly when data exchange standards (e.g., HL7, FHIR) are not consistently followed. This can result in incomplete records or data gaps.
Insufficient data validation
Lack of real-time validation checks may result in systems accepting incorrect or structurally invalid data, creating operational inefficiencies and additional workload in post-entry data correction.
Data silos
Data stored in isolated systems across different departments or care centers inhibits the formation of a unified patient profile, impeding effective long-term patient monitoring, personalized care, and population health assessments.
Unstructured data
Physician notes, scanned files, and PDFs often remain unstructured and underused. Textual ambiguities and lack of metadata complicate extraction and analysis, leaving valuable clinical insights trapped in unstructured formats.
Seven steps to improving healthcare data quality
To effectively resolve data quality issues in healthcare and protect patient safety, healthcare organizations can undertake the following practical remedial steps:
1. Deploy EHR systems with built-in data validation
A solution like the Semarchy Data Platform can integrate seamlessly with existing EHRs to boost data quality, eliminate redundancies, and support better patient care. Real-time validation minimizes human error and accurately captures patient allergies, medications, and clinical history.
2. Adopt industry coding standards and data normalization
Standardizing with ICD, SNOMED CT, and LOINC guarantees uniformity in healthcare datasets, reducing ambiguity when exchanging information and markedly enhancing interoperability.
3. Leverage machine learning and AI for early anomaly detection
Machine learning (ML) and AI algorithms can automatically identify irregular data patterns, such as inconsistent entries or abrupt usage spikes.
4. Automate data cleansing and quality management
Employing automated healthcare data quality management tools helps identify duplicates, resolve common formatting discrepancies, and fill logical gaps based on set rules, increasing record reliability at scale.
5. Establish formal data governance frameworks
Designated data ownership roles and stewardship responsibilities within formal governance programs strengthen long-term data quality monitoring and compliance. Effective Master Data Management (MDM) solutions underpin these data governance frameworks, supporting regulatory compliance with HIPAA, HITECH, and GDPR.
6. Perform regular data audits and profiling
Routine audits identify recurring errors and duplications, while profiling enables accurate assessments based on completeness, uniqueness, and integrity. Such insights drive targeted improvement efforts grounded in real-world performance metrics.
7. Provide data quality training to frontline staff
Clinical and administrative personnel should receive consistent education and practical training on proper data entry and management practices. Consider aligning such training with onboarding or ongoing quality improvement programs.
Case studies in action
Semarchy worked with a global pharmaceutical network to address significant medical data quality issues arising from over 60 different software systems. Previously, the fragmented technology stack made a unified data view impossible, causing frequent errors and extensive manual effort. Implementing a robust data quality strategy reduced error rates and strengthened reporting and decision-making capabilities significantly.
In another instance, French multinational Sanofi struggled with disparate and duplicated data sets across the business from various legacy and new sources following global expansion. They needed “golden data records” for their customers, which often delayed decision-making. Since working with Semarchy, the company has simplified its data management approach and increased its ROI, ensuring regulatory compliance and faster time-to-value.
Similarly, Dublin-based healthcare services provider Uniphar struggled with fragmented data across customers, vendors, suppliers, and products, impairing business visibility. By partnering with Semarchy, Uniphar standardized data governance across more than 16 subsidiaries, enhanced operational efficiency, and significantly improved overall business insights. With Semarchy’s platform providing a strong data-driven foundation, Uniphar is now better positioned to innovate, expand, and effectively respond to evolving business requirements.
Act now to protect healthcare data quality
As the healthcare industry continues to digitize, data quality must become a strategic priority rather than merely an IT challenge. Leaders who emphasize proactive healthcare data quality management will find themselves better positioned to manage regulatory requirements, enhance patient outcomes, and build sustainable, forward-looking operations. Accessible, timely, and interoperable data is valuable at every level of healthcare delivery — from informed bedside decisions to shaping national healthcare policies.
To learn how Semarchy can help you effectively manage healthcare data quality and achieve superior business and patient outcomes, reach out today.
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