Healthcare Data Migration

Healthcare generates about 30% of global data and, with an estimated annual growth rate of 6%, is projected to become the largest data-producing sector by 2025. The healthcare industry continually advances its imaging and radiology practices by upgrading and replacing systems to enhance efficiency and capabilities. As telehealth and digitalization become more prevalent in healthcare, the volume of medical data will continue to rise, necessitating data migration for many organizations. To prepare for efficient data migration, healthcare organizations should consider how to manage the storage, data accessibility, and security of the collected data.

What is Healthcare Data Migration?

Healthcare data migration refers to converting and transferring healthcare data from one system or location to another. It can involve moving data from outdated legacy systems to modern ones, migrating from one Electronic Health Record (EHR) system to an updated one, or consolidating data from multiple sources into a single system. With 96% of U.S. hospitals using electronic health records, that often means moving patient forms to a new EHR platform.

Healthcare data may include the following.
  • Clinical data examples include vitals, lab orders, lab results, allergies, medications, diagnosis, procedures, medical, family, and surgical history.
  • Workflow data includes patient appointments, referrals, and 3rd party data.
  • Images may involve identification and insurance cards, X-rays, ultrasounds, and scanned documents.
  • Financial: Charges, CPT codes, payments, refunds, insurance claims, balances.
  • HR and Payroll: Employee details, screening, and benefits.
  • Demographics include patient registration, insurance coverage, account notes, preferences, provider relationships, and authorizations.

As healthcare organizations continue to adopt evolving technologies, systems, and processes, the integrity and availability of patient data are becoming increasingly critical throughout the data migration process. Healthcare data migration requires careful planning, data validation, quality assurance, and adherence to the Health Insurance Portability and Accountability Act (HIPAA) to ensure confidentiality, integrity, and patient data availability throughout the migration process. This process involves extracting data from the source system, transforming it into a format compatible with the destination system, and populating it into the target system. During this process, it is essential that patient history and demographics, and clinical information, are accurately and securely transferred.

Use Cases For Healthcare Data Migration

There are several reasons why a hospital, diagnostic center, or research organization may need to migrate its data from one system to another:
  • Replacement of a legacy system or system upgrade occurs when healthcare facilities upgrade or replace their current healthcare information (HIT) systems through a system upgrade or replacement of a legacy system. The process involves transferring patient data, clinical records, and administrative information from the old system to the new one.
  • Increasing storage capacity happens when hospitals and clinics migrate their data to another system or the cloud to increase storage capacity or ensure that the new storage can quickly scale for future growth due to the rapid development of medical technology and devices.
  • Migration to the cloud when healthcare organizations move data from on-premise data centers to cloud-based systems to improve scalability and flexibility and reduce infrastructure costs. Many hospitals and healthcare organizations switch to cloud storage to avoid clutter and ensure convenient document workflow.
  • Complying with regulations under HIPAA rules, business associates and covered entities must store healthcare records for at least six years from their creation date or when it was last in effect, whichever is later. The regulations cover what should be stored and how to store it, but the legacy storage systems may still need to implement a HIPAA-compliant technical base.
  • Consolidation across business entities When healthcare organizations change owners and structure, merge into one joint entity, or undergo other changes, the mergers and acquisitions (M&A) processes take place, requiring data from system to system. In 2022 alone saw 2,852 healthcare M&A deals globally.
  • Ensuring system interoperability: An organization may have multiple disparate systems leading to inefficiencies and data inconsistencies, even without M&A activity. Legacy systems may lack interoperability, resulting in delays and inefficiencies. Migrating data to a unified approach can improve operational efficiency and patient care.
  • Vendor changes: switching to a new EHR vendor, upgrading, or a business need requires data migration.
  • Data center migration transpires when healthcare organizations must move their data from one location to another for various reasons, like consolidating healthcare systems, disaster recovery planning, or cost optimization. Consolidation within a business entity or due to vendor changes may also require data migration.
Successful data migration can improve efficiency, accessibility, cost savings, scalability, and patient outcomes. However, it also poses challenges such as data integrity, interoperability, system compatibility, and potential disruption to clinical workflows. Whatever the specifics of the migration, healthcare data migration involves copying or moving potentially sensitive information, which introduces considerable risk. Therefore, careful planning, thorough testing, and adherence to best practices are crucial for successful healthcare data migration.

Planning Considerations for Healthcare Data Migration

While data migration is a daunting initiative for any hospital, imaging center, clinic, or research facility, identifying and aligning the right resources, strategies, and technology will streamline the process. Before starting the process, key considerations include the following:
  • Digital Imaging and Communications in Medicine (DICOM) versus Non-DICOM data (text-based clinical documents, audio files, or video files).
    While industry standards, DICOM, DICOMweb, HL7, and FHIR are gaining wider adoption, there are still many use cases for non-DICOM data. One example could be photos of a patient after a car accident, which can provide additional context for the practitioner evaluating the neck injury. The conversion of data formats, the extraction of metadata, the validation of data, and the mapping of data are the considerations to maintain seamless migration and interoperability between DICOM and non-DICOM data within the target system.
  • Infrastructure
    Scalability, compatibility, security, performance, backup and recovery, and data governance are all infrastructure considerations for data migration to ensure the target system has the necessary resources, environment, and capabilities to support the migrated data effectively. When looking for a solution, ensure it is scalable and reliable enough to handle larger data volumes while adhering to your organization’s security requirements. Similarly, review any data migration tools to make sure they’re secure and work well with the kinds of data moved.
  • Inputs
    Data mapping, transformation, validation, and cleansing tasks are essential to ensure the accuracy and reliability of data migrated from diverse input sources into the target system. Properly managing these inputs allows a successful and error-free data migration process.
  • Interface Planning
    Designing and implementing interfaces for data transfer, mapping, and transformation is part of interface planning. It includes checking the interface compatibility, security, performance, and data mapping or image transformation challenges. In addition, effective interface planning ensures smooth data migration and integration between systems, facilitating efficient data exchange during and after for a successful outcome.
  • Internal Resources
    Ensuring the organization has internal resources, such as skilled personnel, hardware, software, and infrastructure, effectively supporting data migration involves allocating resources appropriately. This process includes hiring personnel with expertise in data migration, offering necessary training and resources, ensuring adequate hardware and software resources for data migration tasks, and coordinating with internal IT departments or other stakeholders to ensure smooth data migration.
  • Legacy Data Requirements
    Data formats, data structures, and incorporation of any specific requirements of the legacy data migrated.
  • Non-Imaging Clinical Documents
    The migration of non-imaging clinical documents includes patient records, lab results, and other textual or multimedia files. Some legacy data requirements for these documents may involve extraction, transformation, validation, and mapping. These considerations ensure seamless migration and integration of secondary documents into the target system while adhering to data security and privacy measures.
  • Tag Management
    Involves handling and managing metadata tags during data migration, extracting, translating, and mapping metadata tags for accurate data transfer. Effective tag management, or tag morphing, refers to changing values in one or more DICOM attributes and is essential for connecting disparate healthcare systems. It ensures data integrity, consistency, and interoperability during the migration process.
  • Validation
    Data validation ensures accurate, complete, and consistent data during migration through checks, assessments, and verifications to mitigate the risk of errors and inconsistencies in the target system.

Stages of the healthcare data migration process

Some stages of the healthcare data migration process include the following:
  1. Planning: This aspect involves identifying the scope of the migration, defining the goals and objectives of the project, and creating a detailed plan that outlines the tasks and timelines for each stage of the migration.
  2. Data Analysis: In this stage, the healthcare organization performs a thorough analysis of the data that needs to migrate, including the format, structure, and quality of the data. This analysis helps to identify any potential issues or challenges that may arise during the migration process.
  3. Data Mapping: This step includes developing a plan for mapping the data from the source to the target system and creating a document specifying the data fields, their format, and their relationships between the source and target systems.
  4. Data Cleansing: During this portion, errors or inconsistencies are identified and corrected, a critical component to ensuring the migrated data is accurate and reliable.
  5. Testing: Once the data has migrated, it is vital to perform thorough testing to ensure that it has migrated correctly and that the target system functions as expected.
  6. Training: This segment involves training the end-users working with the migrated data to ensure they are familiar with the new system and can use it effectively.
  7. Go-Live: The final stage of the migration process involves transitioning from the old system to the new system. This stage typically involves a last data migration, system testing, and a cutover plan to ensure a smooth transition to the new system.

Migrating Relevant Priors

Migrating relevant priors data is fundamental to healthcare data migration, particularly when relocating electronic health records (EHRs). Relevant priors data refers to historical patient data, such as previous diagnoses, medical procedures, and medication history. Many patients have relevant prior scans and data that serve as a helpful tool for making differential diagnoses and can help avoid additional imaging or interventions (RAIs) through a review of the information. By incorporating relevant priors into the interpretation, radiologists can more accurately distinguish between benign and suspicious findings, reducing false positives and unnecessary biopsies to improve accuracy and specificity.

Here are some steps to follow when migrating relevant priors data:
  1. Identify relevant priors data: Locate the data pertinent to the patient’s current health condition, for example, lab results, imaging studies, and previous medical history.
  2. Extract data from the source system: Use appropriate tools to extract the data in a format that can easily migrate to the target system.
  3. Transform the data: Transform or map to match the data format and structure of the target system and involve mapping data fields, standardizing codes, or converting data formats.
  4. Load the data into the target system: Once transformed, load data from the target system to the new system using data migration tools or APIs.
  5. Verify the data: Check the accuracy of the migrated data by comparing it to the source data and ensuring that all relevant priors data migrate successfully.
  6. Validate the data with the patient: Ensure the patient’s medical history is accurately displayed in the new system to help avoid errors or omissions that could impact patient care.

How to Migrate PACS Data

PACS (Picture Archiving and Communication System) data migration involves the transfer of medical images, such as X-rays, CT scans, and MRIs, from one PACS system to another. Here are some steps to follow when migrating PACS data:

  1. First, define the scope: Some questions to help determine the project scope.
    • How many different types of images will be migrated?
    • What is the average size and volume of medical images and DICOM data?
    • How many patient records are involved?
    • What is the timeline?
    • What are the internal resources required for this project?
    • Any potential risks associated with this project?
  2. Prepare the data: Before migrating PACS data, ensure images are appropriately labeled, sorted, and organized and can involve reviewing metadata and ensuring that images are correctly indexed.
  3. Choose a migration method: Several methods exist for migrating PACS data, including manual migration, third-party migration tools, and vendor-supported migration. The preferred method will depend on factors such as the dataset’s size, the migration’s complexity, and the resources available.
  4. Test the migration: Before performing the migration, conducting a thorough testing phase by running tests on a subset of the data to identify potential issues helps ensure the data migrates accurately and efficiently.
  5. Perform the migration: The migration can begin once testing is complete by transferring the data from the source PACS system to the target system using the chosen migration method.
  6. Validate the data: Once the migration is complete, it is crucial to validate the data to ensure that all images have migrated correctly and that there are no missing or corrupted images.
  7. Train users: Provide training sessions, user manuals, and other resources to help users get up to speed on the new system.

What are the best practices for healthcare data migration?

There are several best practices to follow when performing healthcare data migration to ensure the process is efficient, accurate, and secure. Here are some of the most critical best practices for healthcare data migration:
  1. Develop a comprehensive migration plan: include the scope, goals, timelines, and milestones of the migration project and identify the resources required and the potential risks and challenges.
  2. Ensure data quality: Perform data quality checks and cleansing to ensure the data is accurate, complete, and consistent to help to prevent errors, omissions, and duplication of data.
  3. Backup data: Before starting the migration, backup the data to ensure it is not lost or damaged during the process.
  4. Use a secure data transfer method: Transfer data securely between systems to prevent unauthorized access or data breaches using secure data transfer protocols such as SSL or SSH.
  5. Test thoroughly: Perform thorough testing to ensure the data migrates correctly and the new system functions as expected.
  6. Provide user training and support: Users should be provided with training and support to ensure that they are familiar with the new system and can use it effectively to help reduce the risk of errors and improve the system’s overall effectiveness.
  7. Follow regulatory requirements: Healthcare data is subject to regulatory requirements such as HIPAA and GDPR to avoid legal or regulatory issues.

Dicom Systems Data Migration

Data migration offers several benefits for healthcare organizations, including enhanced efficiency, Increased data accessibility, cost savings, scalability, and improved patient outcomes.

Dicom Systems provides tools that help healthcare facilities migrate and store their data in a neutral standard format to minimize or avoid future migrations. It is necessary to pull priors, vendor-neutral archives (VNAs), Query Retrieve Proxy, DICOM Modality Worklists, DicomWeb integration, DICOM structured reporting, and other modules needed to automate the migration process. The normalization of data and some de-identification and re-identification of data can be part of the migration process, making the Unifier platform an essential tool for any data processing project requiring data normalization.