Data Quality in Healthcare Information Systems: A Primer

by | Jul 17, 2023 | Healthcare, Life Sciences, Master Data Management

Concept photo illustrating data quality in healthcare information systems.

Information systems play an increasingly vital role in patient care, making it imperative for all healthcare and life sciences organizations to ensure high data quality. However, recent studies show that 77% of data professionals report that their organizations’ data is unreliable in quality, costing organizations an average of $12.9 million annually. 

Poor data quality in healthcare organizations adversely affects care quality and drains resources. Conversely, high-quality data enhances patient care, improves clinical outcomes, and greatly assists in maintaining regulatory compliance and optimizing resource allocation. This guide covers the significance of data quality in healthcare information systems and outlines actionable steps that organizations can take to improve it.

Key Takeaways:
  • High data quality in healthcare information systems is crucial for improved patient care, clinical outcomes, and regulatory compliance.
  • Good data quality encompasses accuracy, completeness, timeliness, and relevance, enabling informed decisions and minimizing errors.
  • Implementing a data governance framework, standardizing data entry, and employing data quality tools are vital steps for improving data quality.

What is Good Data Quality?

IBM defines good data quality as data that is accurate, complete, timely, and relevant — or fit for purpose. While this definition suits data use cases across industries, it is particularly important for healthcare data to be of high quality to support clinical decision-making effectively. 

  • Accuracy in data ensures healthcare providers base their diagnoses and treatment plans on correct patient information. 
  • Completeness is equally critical, as missing or partial data can lead to misinformed decisions.
  • Timeliness ensures up-to-date data, which is crucial in fast-paced medical environments. 
  • Data relevance ensures that the information used is appropriate for the specific case at hand. 

These attributes collectively enable healthcare providers to make more informed decisions, minimize errors, and improve patient outcomes.

Steps to Improve Data Quality

Healthcare organizations can improve their data quality by following these eight steps.

Step 1: Establish a Data Governance Framework


Data governance framework.
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Establish a data governance framework to uphold high data quality standards in your organization. This framework should define staff roles and responsibilities in data management and include policies and procedures for data handling and standardization. Effective data governance is critical for ensuring data integrity and compliance with healthcare regulatory standards.

Step 2: Invest in Continuous Education and Training

Allocate resources to continuous education and training programs for your staff. Healthcare data is dynamic, and the systems that manage this data evolve constantly. Ongoing training and education are vital for staff to stay apprised of best practices in data quality management. Training should focus on data entry processes, interpretation, and proper use of data in clinical decision-making.

Step 3: Implement Data Quality Tools

Employ data quality tools to monitor and validate the accuracy, completeness, timeliness, and relevance of your healthcare information system data. Today’s data quality tech includes tools that automate the process of identifying and rectifying data errors and inconsistencies. These platforms allow organizations to set their data standards for incoming third-party data, preventing incomplete or improperly formatted data from entering databases and — in certain cases — alerting IT teams about issues. 

Step 4: Standardize Data Entry Processes

Standardize data entry processes across all departments, contracted partners, and healthcare information systems. Consistent data entry practices minimize errors and ensure that all data adheres to a common set of standards. 


Medical data entry error rates.
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Error rates for medical data entry have repeatedly proved difficult to measure precisely, with average rates ranging widely between 2 and 2784 errors per 10,000 fields in audited healthcare organizations. Regardless of where your organization falls on this spectrum, entry standardization will improve patient care and reduce costs. 

Use standardized formats for entering dates and ensure that all staff members follow the same data entry protocol. This consistency enhances the usability and reliability of patient data.

Step 5: Regularly Audit and Review Data

Conduct regular audits and reviews of the data in your healthcare information systems. This systematic examination of data for accuracy and completeness helps identify areas that need improvement. Regular audits help organizations maintain high-quality data, ensuring healthcare professionals can rely on it for decision-making. 

Depending on your organization’s tech stack, you may already have access to data quality auditing features. If not, consider investing in additional IT management tools , including:

Step 6: Encourage a Culture of Data Quality

Create an organizational culture that values data quality. Encourage collaboration among departments to ensure that everyone understands the importance of high-quality data in healthcare. Organizations with a strong data quality culture are more likely to achieve better patient outcomes and operational efficiency. Encourage staff to take ownership and responsibility for the quality of data they handle and create channels for feedback and suggestions on data management practices.

Step 7: Integrate Data from Multiple Sources

Leverage technology to integrate data from multiple sources to ensure completeness and relevance. In the healthcare sector, data often comes from various sources such as Electronic Health Records (EHRs), laboratories, pharmacies, and remote patient monitoring (RPM) devices. Integrated data ensures a more comprehensive view of the patient’s health, leading to better-informed clinical decisions. Use interoperable systems and standardized data formats to facilitate seamless data integration.

Step 8: Implement Robust Security Measures

Data security is integral to maintaining data quality, as breaches compromise data integrity and erode trust in healthcare information systems. Additionally, the Health Insurance Portability and Accountability Act (HIPAA) mandates the protection of patient data, and violations can be costly. Implement industry-standard security measures, such as encryption, access controls, multi-factor authentication (MFA), and regular security audits, to safeguard data from unauthorized access and breaches. 


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Achieving good data quality in information systems is uniquely challenging in the healthcare industry. Coperor’s industry-first healthcare master data management platform equips organizations with the tools to meet these challenges and integrate high-quality master data across complex IT systems and networks of contracted partners.

To learn more, view this demo of Coperor in action.


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