Like many healthcare organizations, yours may be embarking on a clinical data migration project to enhance efficiency, modernize your healthcare organization, and leverage the data in new ways.
Unfortunately, moving healthcare data from one system to another isn’t as simple as pressing a button. It requires a multi-step process that prioritizes data accuracy, reliability, and security. You may undermine your efforts if you fail to ensure data integrity.
Before we explore data integrity’s importance, let's clarify some related and often-confused terms.
Data Quality vs. Data Integrity
Data integrity is often used synonymously with data quality, even though the two terms are technically distinct concepts.
Data quality considers whether the data is accurate, comprehensive, consistent, well-defined, relevant, and timely. It's the assurance that the data retains its value to whoever uses it, and it’s widely considered a subset of data integrity.
Data integrity is the measure of data’s reliability and trustworthiness. It ensures that the data hasn’t been corrupted or changed from its original state.
Data protection in healthcare directly impacts data integrity by guarding against threats like hackers accessing and altering the information. When information is private and secure, its integrity is less likely to be compromised throughout the data lifecycle, including during data migration.
Why Data Integrity Is Essential in Healthcare
Data integrity is important because it impacts every aspect of healthcare provision in the digital health era, starting with clinical decisions and care delivery. Data of poor integrity might lead providers to make false assumptions or draw incorrect conclusions, resulting in unfavorable outcomes or an exacerbation of health disparities.
Data integrity also affects care continuity. Data of high integrity promotes safe care transitions, effective care coordination, and evidence-based decision-making. Additionally, the integrity of data is critical for ensuring regulatory compliance. Healthcare organizations must maintain accurate and reliable data to avoid lawsuits and penalties.
Finally, data integrity helps build trust and enhance reputation management. Data of high integrity is information everyone—including patients, providers, and others—can rely on to foster safe and effective care.
Challenges With Maintaining Data Integrity in Healthcare
The busy and fragmented nature of health system and hospital operations, massive amounts of data captured in healthcare, and understaffed or technology-deficient departments all contribute to an environment in which data integrity can be difficult to sustain. Providers may document tests and diagnosis differently, creating data quality issues. Manual data entry may cause errors and delays, while data silos and an overly complex data infrastructure can undermine the organization’s ability to put data to practical use. Moreover, legacy systems are vulnerable to cyberattacks, which frequently compromise integrity.
System integration plays an important role in data integrity. For example, a provider may collect high-quality clinical data during an encounter but unintentionally compromise its integrity if the system doesn’t integrate with other software to provide a holistic view of the patient’s history and inform accurate decision-making.
This issue sometimes occurs when organizations implement a new electronic health record (EHR) while maintaining a legacy system that doesn’t integrate with the new EHR instead of archiving the data for easier, safer access. As such, preserving access to integrated archived data is critical for ensuring data integrity.
How to Ensure Data Integrity in Healthcare
When considering how to ensure data integrity in your organization, you may implement various strategies before, during, and after data migration. For example, before migration, you could:
- Discontinue using legacy systems to store and manage data. Instead, use a data archiving solution that integrates with your EHR to support the transition.
- Establish a governance framework. Decide how your organization will collect, retain, use, access, and share data. This framework may include policies, procedures, standards, ownership, decision rights, roles and responsibilities, and accountability related to data management. You may want to think about including guiding principles as part of this framework, such as, “Individuals who create or acquire data are accountable for the quality of that data” or “Data is a strategic asset that has value and risk.”
- Train employees on ensuring data integrity. Provide training on data entry procedures to reduce errors. Also consider establishing and enforcing data entry policies and documentation best practices. Help employees understand differences in data quality vs. data integrity.
- Secure the data. Implement strict access controls, adopt comprehensive security measures (e.g., advanced encryption, firewalls, multi-factor authentication, and secure data transfer protocols), and perform frequent vulnerability scans. Establish a comprehensive security policy and provide regular employee training on data security best practices. Finally, partner with data archiving and migration vendors that can help you prioritize data security and comply with all data security standards.
Implement these strategies during data migration to ensure data integrity:
- Perform extensive testing. Test the system to ensure data transfers correctly without errors and omissions.
- Use secure data transfer protocols. Implement robust security measures to protect data from unauthorized access that can compromise data integrity during migration.
- Clean the data. Transform the data into a format compatible with the new system. Cleaning the data may require reorganizing folder structures, renaming files, extracting specific data points, and digitizing information.
After data migration is complete, implement the following strategies:
- Use third-party data to add context to existing datasets. Integrate data from different sources to gain a unified and consistent view of the information.
- Properly integrate and configure all analytics tools. Ensure your tools interpret and process data consistently.
- Perform ongoing data audits. Use various controls, processes, and technologies to maintain data integrity throughout the data lifecycle. Regular data audits can help you identify and rectify issues before they escalate.
- Maintain audit trails. Periodically review who alters data and why to ensure integrity.
Augment Data Quality and Integrity During Data Migration
Data integrity is critical during data migration. Organizations prioritizing data quality, integrity, and data protection in healthcare will reap the rewards of highly actionable information on which leaders can rely to make accurate decisions.
Olah offers a safe, secure, and fast archiving solution to help promote data integrity during data migration. Contact us to see how we can help simplify data migration with a system that integrates with your new software.
