Data Migration in Healthcare: Use Cases, Challenges, Implementation Strategy

Updated October 21st, 2024

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As healthcare organizations adopt new systems, upgrade legacy platforms, or consolidate data sources, they often need to perform data migrations—moving sensitive information from one system to another. See How Atlan Simplifies Data Governance – Start Product Tour

This article will explore the concept of data migration in healthcare, challenges faced, compliance considerations, and best practices to mitigate risks during the migration process.


Table of Contents #

  1. What is data migration in healthcare?
  2. 4 prominent data migration use cases in healthcare
  3. 10 considerations in healthcare data migration
  4. Implementation strategy to tackle critical challenges
  5. How embedded data governance can help
  6. Summing it all up
  7. Related reads

What is data migration in healthcare? #

Data migration in healthcare involves transferring data from one system or storage environment to another. Common use cases include:

  • Transitioning from on-premises solutions to cloud-based systems
  • Switching vendors by moving healthcare data from one warehouse into another
  • Upgrading to a more advanced Electronic Health Records (EHR) platform
  • Consolidating patient records across platforms into a single system – a control plane for all data

Let’s explore each of the data migration use cases further.


4 prominent data migration use cases in healthcare #

1. Transitioning from on-premise to the cloud #


A common use case for healthcare data migration is shifting from on-premises systems to cloud-based infrastructure. Many healthcare organizations make this transition to leverage the cloud’s scalability, cost-effectiveness, and accessibility, which improves operational flexibility and reduces infrastructure costs.

Moving patient data, clinical records, and administrative information to the cloud allows healthcare providers to access real-time data from anywhere, enhancing collaboration and patient care outcomes.

However, this transition comes with significant challenges, particularly around data security and compliance. Given that healthcare data often includes sensitive information protected under regulations like HIPAA, effective data encryption, secure transmission protocols, and access controls are critical.

Furthermore, integrating cloud-based data with existing systems (such as imaging or billing platforms) requires meticulous planning to avoid downtime, interoperability issues, or data loss.

2. Switching warehouses #


As healthcare organizations grow, they may outgrow their current data warehouse, which may no longer support increasing data volumes or new analytical requirements.

For example, an organization using a traditional on-premises data warehouse may need to migrate to a modern, scalable data warehouse that supports advanced analytics, machine learning, and real-time reporting.

Ensuring compatibility with existing systems, tools, and data sources is critical during this migration. You may also need to restructure data assets to fit the new schema and validate the migration process to ensure accuracy.

3. Upgrading the EHR platform #


Healthcare providers often need to upgrade their EHR platforms to systems offering enhanced functionality, such as improved user interfaces, advanced analytics, and extensibility.

EHR upgrades typically involve migrating years’ worth of sensitive data, including patient records, clinical notes, and medical images. A primary challenge is ensuring data consistency and avoiding loss of crucial information during the migration.

4. Setting up a unified control plane as a consistent layer of oversight #


Healthcare data has become increasingly complex, encompassing patient records, research data, medical devices, and wearable technologies.

A unified data control plane acts as the nervous system of this complex environment, integrating and managing disparate tools seamlessly while facilitating interoperability between disparate data sources, such as clinical systems, lab results, and patient portals.

This architecture empowers diverse end-users, such as doctors, researchers, and administrators, to interact with the data effectively. For instance, a doctor might need quick access to real-time patient records, while a medical researcher may require large datasets for population health analysis.

It would also power AI use cases, enabling smarter healthcare operations by leveraging metadata to provide critical context and trust in the data. For instance, AI systems can analyze patient data to identify risk factors, predict disease progression, or suggest treatment plans.


Having understood the concept and potential use case of data migration in healthcare, let’s look at the practical aspects of migrating data – challenges faced, implementation strategy, and best practices for success.

10 considerations in healthcare data migration #

Healthcare data migration projects face unique challenges due to the sensitive and regulated nature of the data. These include:

  1. Volume and complexity of data: Healthcare organizations manage massive volumes of data in various formats. Migrating such data without causing downtime or data loss is a major challenge.
  2. Complexity of legacy systems: Many organizations use outdated, isolated systems that don’t integrate well with modern platforms. Migrating from these systems often requires extensive data transformation and manual intervention.
  3. Data quality: Incomplete or inaccurate data and duplicates in the source system can complicate the migration process, impacting the effectiveness of the new system.
  4. Data security and compliance requirements: Ensuring compliance with regulations like HIPAA or GDPR requires careful attention to data encryption, access control, and audit trail logs.
  5. Downtime and service interruptions: In environments where uninterrupted access to patient records is vital, even a brief period of downtime can impact patient care. Meticulous planning and the use of migration tools that can facilitate a swift transition.
  6. Technical compatibility and integration with existing systems: Differences in database structures, data formats, and platforms between the old and new systems can create technical challenges.
  7. Resource and cost management: Data migration can be resource-intensive, both in terms of human resources and budget. Balancing the cost of migration with its benefits is a key challenge, especially for larger migrations.
  8. Data synchronization: Keeping data synchronized between old and new systems during migration is essential to prevent loss or discrepancies.
  9. Performance optimization post-migration: Ensuring the new system delivers optimal performance, such as quick access to patient records, is key to post-migration success.
  10. Change management and training: Migrating to a new system often involves retraining staff, including clinicians and administrative personnel. Training requires overcoming resistance to change while ensuring a smooth learning and adoption curve.

Data migration in healthcare: Implementation strategy to tackle critical challenges #

To overcome the challenges discussed earlier, a structured implementation strategy is essential. Here’s how healthcare organizations can tackle them:

  1. Pre-migration planning and assessment: Start with a comprehensive assessment of the existing data environment, identifying potential risks, and determining which datasets require transformation.
  2. Data governance framework: Establish a comprehensive data governance framework to address security, privacy, integrity, and compliance concerns. This should include defining roles and responsibilities for data stewards and ensuring compliance with HIPAA and other relevant regulations.
  3. Design a phased migration approach: A phased migration allows for incremental testing and validation, minimizing the impact on day-to-day operations. This approach also reduces the risk of significant downtime.
  4. Prepare for the migration: Clean and organize data before migration to avoid issues with data quality in the new system. Additionally, map data accurately to the target system to ensure consistency.
  5. Test your migration: Validate the data by running tests to ensure that it has been correctly transferred and that there are no discrepancies.
  6. Monitor and optimize post-migration: Continuously monitor the new system to identify performance bottlenecks, security vulnerabilities, or compliance issues.
  7. Train your team and offer continuous support: Provide ongoing training and support for proper adoption while complying with your governance requirements.

Data migration in healthcare: How embedded data governance can help #

Successful healthcare data migration requires embedding data governance at every stage. This approach ensures data quality, security, compliance, and operational efficiency. Key elements of embedded data governance include:

  • Data governance policies for data quality (data accuracy, completeness, consistency), data integration, access, and compliance to ensure that the migrated data remains accurate, complete, and reliable.
  • Data lifecycle management policies—from creation and storage to archival and eventual deletion–to govern data through all of its transformations.
  • Data integration policies to define the standards for how data from various sources, such as EHRs, lab results, and patient portals, should be combined and presented in a unified format.
  • Metadata management by adopting a metadata lake house approach, which will map data across different systems and ensure end-to-end ecosystem enablement.
  • Data access and security controls that can be customized according to user role, data domain, projects.
  • Compliance with regulations throughout the migration process that’s thoroughly documented with automated audit trails, data access logs, etc.

Atlan: Your Partner for Secure Healthcare Data #

Atlan helps healthcare organizations with their data governance by providing a centralized platform that enables better control and understanding of data assets. Atlan automatically captures data lineage, allowing organizations to track data flow and comply with regulations like HIPAA.

Atlan also automates data quality and compliance with automated data profiling and quality checks to ensure the accuracy of PHI. By integrating with communication tools and offering workflow automation capabilities, Atlan promotes collaboration and streamlines data governance processes. Schedule a demo to see how Atlan can help you!


Summing it all up #

Data migration in healthcare is a complex process due to the sensitive and highly regulated nature of the data.

By adopting a structured approach, embedding data governance at every stage, and leveraging modern tools, healthcare organizations can achieve a smooth migration while maintaining compliance and data integrity.



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