Each year, medical errors in the US alone claim 100,000 lives. This alarming statistic highlights the importance of maintaining data quality in healthcare. As healthcare data volume increases, ensuring the accuracy and completeness of the information obtained has become a challenge. Any compromise in data quality can lead to inaccurate diagnoses, ineffective treatments, and faulty billing, which can have significant consequences on patient safety, financial performance, and regulatory compliance.
To ensure data quality in healthcare, it’s crucial to identify the sources of data quality issues, comply with regulations, implement proper procedures, and invest in the right data integration tools.
In this blog, we’ll explore the importance of data quality management in healthcare, the causes of poor-quality data, and the best practices for ensuring improved data quality.
Importance of Data Quality Management in Healthcare
Good data quality is essential in for extracting actionable insights that benefit patients, healthcare providers, and payers alike.
We will be assessing the quality of health data on six dimensions, including:
- Accuracy: Healthcare data accurately reflects the patient’s health status, including test results, diagnoses, and treatment plans.
- Completeness: All necessary information is included in the data, such as patient demographics, clinical assessments, drug allergies, and medical history.
- Consistency: The data is shared and exchanged across different healthcare systems over time in a standardized, uniform, and reliable format.
- Accessibility: Healthcare professionals can access the data they need to make informed decisions and provide quality care while maintaining patient confidentiality and security.
- Relevance: The data is applicable for the intended purpose, whether it’s for clinical decision-making or research and development.
- Timeliness: The data is up-to-date and available on time to inform clinical decisions and support patient care.
Causes of Poor Data Quality in Healthcare
Good quality healthcare data is essential, but many healthcare organizations struggle to maintain it due to a range of factors, including:
- Lack of Standardization
Healthcare data is generated by a variety of sources, including electronic health records (EHRs), medical devices, and patient self-reports. Without standardized data formats, definitions, and protocols, comparing and integrating data from different sources can be difficult. This can result in data inconsistencies, reduced efficiency, and limited data sharing.
- Lack of Data Validation
Human error is inevitable during the manual data entry process. Data omission, duplication or recording incomplete and inaccurate data can lead to potential issues with patient care, reimbursement, and reporting. An absence of proper validation checks leaves these errors undetected which can result in inaccurate diagnosis, delayed treatments and improper care.
- Integrating Data Across Multiple Systems
Healthcare providers often use multiple systems to manage patient data, including EHRs, medical imaging, and billing systems. Integrating data from these disparate systems can be challenging and may result in errors, duplication, and inconsistencies. Duplicate data can lead to a waste of resources and negatively impact the quality of care.
Explore how CCHP Improved Data Quality using Astera Centerprise
CCHP was facing challenges with claims processing, as their data analysts were spending an average of 3 hours manually sifting through each record, identifying errors, and ensuring compliance with Wisconsin Department of Health Services’ standards. In an effort to automate the process and achieve higher approval rates for maximum claim reimbursements, CCHP sought a solution.
CCHP chose Astera EDIConnect to build an end-to-end EDI correction system that could correct errors in claims using a code-free interface with just a few clicks. As a result, CCHP’s data approval and submission rates have improved, now reaching an impressive 99%.
By using Astera EDIConnect, CCHP has been able to streamline its claims processing, ensuring that errors are corrected, and compliance is achieved with greater efficiency and accuracy.
Best Practices to Improve Data Quality in Healthcare
Improving data quality in healthcare is a continuous process that requires a multifaceted approach. Here are some best practices for how to improve data quality in healthcare that organizations can implement:
- Add Data Validation Checks
Implementing rigorous validation checks and procedures can significantly improve data quality. The data processing software must only process records that follow predetermined rules, and promptly flag any errors or outliers that do not meet acceptable ranges.
By doing so, healthcare providers can ensure that the data they have is reliable, accurate, and complete. As a result, they can avoid potentially life-threatening mistakes and ensure that the right treatments are administered at the right time.
- Convert Data into a Standardized Format
Implementing standardized terminologies, codes, and abbreviations is a great way to ensure consistency in healthcare data. Healthcare providers can achieve data standardization by adopting well-established frameworks, such as SNOMED-CT, FHIR, or ICD-10, allowing seamless data exchange among internal and external systems.
Following these standards promote transparency and openness in data-sharing. Additionally, the use of standard file formats, forms, and templates helps promote uniformity in data entry across different systems facilitating efficient collaboration efforts among healthcare providers.
- Perform Routine Data Audits
Routine data audits allow healthcare providers to ensure that their records are accurate and up-to-date. Reviewing data on a regular basis help identify discrepancies or errors and take corrective action before they lead to negative outcomes.
Moreover, routine audits allow healthcare facilities to identify areas for improvement, resulting in better patient outcomes. This approach can also help minimize the risk of system vulnerabilities, which is critical to protect patient confidentiality and privacy. These data audits also ensure that healthcare providers are in compliance with HIPAA and GDPR regulations.
- Upskill Healthcare Team Members
Developing digital literacy programs and encouraging data ownership can help improve data quality. When team members feel ownership of the data they collect and analyze, they’re more likely to take responsibility for ensuring its accuracy and completeness. This approach also fosters a culture of data quality.
Along with digital literacy, staff should receive HIPAA training to ensure compliance with regulations for handling sensitive data. Implementing such training programs can equip healthcare organizations with knowledgeable and skilled staff, helping to maintain accurate and reliable data.
- Invest in Data Quality Management Solutions
Healthcare providers should invest in data quality management solutions with capabilities. Modern organizations prefer end-to-end in healthcare with advanced data validation features that automate data quality processes. Using these solutions leads to faster data processing and greater performance.
A good practice is to invest in a no-code as it helps ensure the accuracy and completeness of their data without requiring extensive programming knowledge. As a result, your business users can also take ownership of the data quality.
Ensure Data Quality with Astera Centerprise
Astera Centerprise is an enterprise-grade, end-to-end data integration solution that allows healthcare organizations to combine data from disparate sources to create a centralized data repository. Our on-code solutions have built-in data quality and profiling features to ensure data quality.
With Centerprise, you can:
- Cleanse your raw data by accounting for whitespaces, duplicates, and missing values to present it in a refined, structured, and standardized way.
- Validate incoming records based on predefined conditions ensuring that only data which falls within the acceptable criteria is stored.
- Automate the data validation process by leveraging an industrial-strength engine to ensure data is automatically screened and deemed fit for use.
- Track the source of the data using data lineage functionality to identify the root cause of the error.
- Integrate multiple systems, file formats, and applications to consolidate data into a single source of truth and prevent .
To experience the benefits of Centerprise and access high-quality health data, download our 14-day trial now.