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Healthcare Data Migration

Healthcare generates 30% of global data, with an estimated annual growth rate of 6%, and is projected to become the largest data-producing sector by 2025. As the healthcare industry advances its imaging and radiology practices through system upgrades and replacements, the volume and footprint of medical data will continue to rise. This necessitates effective data migration strategies for many organizations to enhance efficiency and capabilities, especially as telehealth and digitalization become more prevalent.

What is Healthcare Data Migration?

Healthcare data migration refers to converting and transferring healthcare data from one system or location to another, often from one format 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 It can involve moving data from outdated legacy systems to modern ones, migrating from one Electronic Health Record (EHR) system to an updated one, or consolidating data from multiple sources into a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: Vitals, lab orders, lab results, allergies, medications, and medical history.
  • Workflow Data: Patient appointments, referrals, and third-party data.
  • Imaging Data: X-rays, MRIs, ultrasounds, CT scans, scanned documents, and reports.
  • Financial Data: Charges, insurance claims, CPT codes, and payments.
  • HR and Payroll Data: Employee details, screening results, and benefits.
  • Demographic Data: Patient registration, insurance coverage, and provider relationships.

Effective imaging data migration accurately transfers all relevant data, supporting clinical decision-making, enhancing patient care, and ensuring regulatory compliance. 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. A critical aspect often overlooked by healthcare data migration companies is the ability to match patients when moving between institutions. It is one of Unifier’s critical capabilities to provide data migration toolsets capable of intelligently integrating, correlating, and matching data.

Imaging Data Types: DICOM and non-DICOM Data and Reports.

Enterprise imaging data can include Digital imaging and communications in medical (DICOM) and non-DICOM image files across various systems and specialties.

Examples of specialty departments and file types include:

  • Cardiology: Echocardiography (echo), catheterization (cath), nuclear studies, ECG, ultrasounds, DICOM files for angiography, CT coronary angiography
  • Dental: Periapical radiographs, panoramic radiographs, cephalometric radiographs, DICOM for cone beam CT
  • Dermatology: Photos (JPEG, PNG), dermatology reports (PDF), dermoscopy images (DICOM)
  • Ear, nose, and throat: MP4 videos, JPG images, otoscope images, DICOM for CT and MRI studies
  • Endoscopy: MP4 videos, JPG images, endoscope images, DICOM for specialized endoscopic procedures
  • Gastroenterology: Endoscopic ultrasound images, capsule endoscopy videos (MP4), ERCP images (DICOM)
  • Mammography: Digital mammography (FFDM), computer-aided detection (CAD), and breast tomosynthesis (3-D) mammography (all in DICOM format)
  • Nuclear Medicine: SPECT scans, PET scans (not limited to oncology), thyroid uptake scans (all in DICOM format)
  • Neurology: Brain MRI reports, Spine MRI reports, Functional MRI (fMRI) reports, DTI (Diffusion Tensor Imaging) reports, DICOM for all neuroimaging studies
  • Obstetrics and Gynecology: Fetal ultrasounds (DICOM), colposcopy images (JPEG)
  • Oncology: Fluorodeoxyglucose (FDG)-positron emission tomography (PET), CT scans, X-rays, and treatment plans (all in DICOM format), reports (PDF)
  • Ophthalmology: Ophthalmology laser images (DICOM), voice dictations (WAV), formatted PDF reports, fundus photographs (JPEG)
  • Orthopedics: MPR, JPG, gait studies, range of motion videos, DICOM for X-rays and CT scans
  • Pathology: Digital pathology slides (SVS format), whole slide imaging (proprietary formats)
  • Pediatric subspecialties: Various imaging types specific to pediatric care (DICOM, JPEG, MP4)
  • Point-of-care ultrasound (POCUS): Audio-video interleave (AVI), jpegs (JPG), and ultrasound reports (PDF), DICOM for some advanced POCUS systems
  • Pulmonology: Bronchoscopy images and videos (MP4, DICOM), pulmonary function test reports (PDF)
  • Radiology: MRIs, CT scans, X-rays, advanced visualization images (all in DICOM format), voice dictation files (WAV), structured reports (DICOM SR)
  • Rheumatology: Joint ultrasounds (DICOM), DEXA scans for bone density (DICOM)
  • Sleep lab: Polysomnograms (EDF format), sleep study videos (MP4), reports (PDF)
  • Surgery: In-department X-rays (DICOM), endoscope videos (MP4), arthroscope images (JPEG), surgery reports (PDF)
  • Wound care: Photographs (JPEG), reports (PDF), 3D wound imaging (proprietary formats)
  • Urology: Cystoscopy images, urodynamic studies, prostate ultrasounds (DICOM)
  • Vascular: Doppler ultrasound images (DICOM), angiograms (DICOM)

Use Cases For Healthcare Data Migration

Healthcare organizations may need to migrate their data for various reasons:

  • 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.
  • 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 and entity separation in healthcare drive significant data migration needs. As organizations restructure, merge, or change ownership, data must be transferred reliably across systems. Healthcare M&A deals reached US $21.5 billion in 2023, highlighting this trend. Divestitures, where organizations split into separate entities, also contribute to a growing demand for efficient data migration solutions.
  • 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.

Key Challenges in Medical Imaging Data Migration

While the use cases for migrating medical imaging data are clear, the process has challenges. Some key obstacles may include:

  1. Data Volume and Complexity: Medical imaging files can be large, often exceeding several gigabytes. The sheer volume of data and its complexity (multiple file formats, metadata, etc.) can make migration cumbersome and time-consuming. The complexity of this data—encompassing various file formats, metadata, and imaging techniques—adds another layer of difficulty to the migration process.
  2. Regulatory Compliance: Healthcare organizations must adhere to strict regulations regarding patient data protection, such as HIPAA in the U.S. Ensuring compliance during the migration process is paramount to avoid legal repercussions​.
  3. System Compatibility: Legacy systems may not be compatible with modern imaging solutions, requiring careful planning and execution to ensure transferred data is without loss or corruption.
  4. Maintaining Data Integrity: It is essential to ensure that the integrity of medical imaging data is maintained during the migration process. Any errors or losses can lead to misdiagnosis or treatment delays.
  5. Training and Change Management: Staff may need training on new systems and workflows, and managing this change can be challenging. Effective communication and support are necessary to facilitate a smooth transition.
  6. Data matching and normalization across the enterprise between different systems: This facilitates a better clinician outcome and helps reduce duplication and unnecessary scanning. It is anticipated that data normalization will address these issues in the future. As part of the migration, having proper tools for data normalization is beneficial.

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, patient match, data normalization and 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.

Migrating Relevant Priors in Healthcare Data

Relevant priors, such as previous mammograms, biopsies, or imaging studies, are vital for interpreting current imaging. These historical records provide context, enabling radiologists to distinguish between new and longstanding findings. Diagnostic mammograms, in particular, rely on prior images to personalize care, reducing false positives and unnecessary interventions.

For example, if a patient has a history of benign cysts, relevant priors allow radiologists to recognize similar cysts in new imaging, avoiding misinterpretation quickly. However, accessing these priors can be challenging, especially when studies from other facilities are on CDs or DVDs. Existing PACS must seamlessly integrate these external studies into the patient’s record.

Migrating relevant priors is critical in healthcare data migration, especially when consolidating EHRs. This data supports accurate diagnoses and reduces Recommendations for Additional Imaging or Interventions (RAIs) by providing a comprehensive history.

Steps for Migrating Relevant Priors:

  1. Identify relevant prior 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.
  7. Patient match and reconciliation: Accurately align patient demographics across systems to ensure all medical images and history are correctly linked, reducing errors and duplicate records.

Post-Migration Considerations

After completing a medical imaging migration, several vital steps ensure the success and integrity of the new system:

  • System Testing and Validation: Conduct thorough testing to verify that all images and data have been accurately transferred and that system functionalities operate as expected.
  • User Training: Train staff on the new system through workshops and hands-on sessions to facilitate practical usage and minimize disruptions.
  • Performance Monitoring: Continuously monitor system performance to ensure it meets operational standards, including image retrieval speeds and integration with other healthcare systems.
  • Data Reconciliation: Perform a final reconciliation of migrated data against the original to confirm completeness and accuracy.
  • Workflow Optimization: Assess and adjust workflows to leverage new system capabilities, enhancing efficiency in clinical processes.
  • Security Audits: Conduct audits to ensure compliance with regulations such as HIPAA and safeguard patient data throughout the migration.
  • Legacy System Decommissioning: Properly decommission legacy systems after confirming successful data migration, ensuring no critical data remains unaccounted for.
  • Ongoing Support: Establish a support framework for users encountering issues or needing assistance with the new system post-migration.

Healthcare Imaging DICOM, Non-DICOM, and Reports Data Migration with Dicom Systems

Dicom Systems offers comprehensive solutions for healthcare organizations to execute complex imaging data migrations. It focuses on DICOM and non-DICOM formats and reports to ensure data integrity, interoperability, and long-term accessibility. The Unifier platform leverages advanced features and data processing capabilities, enabling healthcare organizations to execute complex imaging data migrations, efficiently minimize disruptions to clinical workflows, and maximize the value of their imaging data assets.

Features and Benefits

  • Vendor-neutral archive (VNA) Integration allows seamless data transfer between disparate PACS systems, ensuring format consistency and future-proofing data management.
  • Query/Retrieve Proxy enables efficient retrieval of prior studies across multiple archives, optimizing migration performance and significantly reducing downtime.
  • DICOM Modality Worklist automates the population of patient information, streamlining workflows and minimizing manual entry errors effectively.
  • DicomWeb Integration implements RESTful services for imaging data access, enhancing interoperability and improving clinicians’ accessibility in their daily tasks.
  • DICOM Structured Reporting preserves and migrates structured diagnostic information alongside imaging data, ensuring comprehensive data continuity throughout the migration process.
  • Scalability supports handling petabyte-scale archives through distributed processing, accommodating growing data needs without any performance degradation.
  • Full Compliance adheres to HIPAA, DICOM, and HL7 standards, ensuring regulatory compliance throughout the migration process and safeguarding patient privacy.

Advanced-Data Processing Capabilities

  • Data Normalization: standardizes data formats, tags, and metadata across various source systems, which is crucial for maintaining consistency in multi-vendor environments.
  • De-identification and Re-identification: implement sophisticated algorithms to protect patient privacy during migration while maintaining the ability to re-identify data when necessary.
  • Automated Migration Workflows: Utilizes intelligent routing and prefetching mechanisms to optimize the migration process, reducing downtime and ensuring clinical continuity.
  • Performance Optimization: This technique employs load balancing and parallel processing techniques to accelerate migration speeds and minimize disruptions to clinical operations.
  • Patient Match: Auto-matches patient records with over 95% accuracy by transforming DICOM tags and routing exams to PACS/MIMPS or VNA. For multiple matches, it prompts administrator review; unmatched records go to a QA worklist for MRN assignment.

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