Data Management Glossary
PACS
What is PACS (Picture Archiving and Communication System)?
Picture Archiving and Communication System (PACS) is the clinical imaging infrastructure that healthcare organizations use to store, retrieve, manage, and distribute digital medical images across their enterprise. PACS replaced physical film archives in hospitals and radiology departments, enabling digital images from CT scanners, MRI machines, X-ray units, ultrasound devices, and other modalities to be captured, stored, and viewed from any workstation on the network. PACS connects acquisition, DICOM storage, diagnostic viewing, RIS worklists, and EHR context into one operational layer, making it the central nervous system of clinical imaging in any modern healthcare organization.
The global medical image management market is expected to grow from USD 5.38 billion in 2025 to USD 7.41 billion by 2029, driven by increasing diagnostic imaging volumes, AI adoption, and the shift from on-premises to cloud-native PACS architectures. Source
Why PACS Creates a Massive Unstructured Data Management Challenge
PACS is the single largest source of unstructured data in most healthcare organizations. The data management challenges it creates are compounding:
- Petabyte-scale growth with no end date — every imaging study adds to the PACS archive permanently; retention requirements of 7 to 10+ years mean the archive only grows; health systems with multiple facilities can accumulate 10 to 50+ petabytes of PACS data
- Cold data on hot storage — the majority of studies in a PACS archive are rarely or never accessed after the initial diagnostic reading, yet they consume the same expensive primary storage as active studies; most health systems have 60 to 80% cold data sitting on their most expensive storage tier
- Storage price crisis — with flash and NAND prices rising 130% by end of 2026 according to Gartner, health systems face an urgent budget problem; buying more PACS storage at current prices is not a viable long-term strategy for organizations where 40% are already operating at a financial loss
- Vendor lock-in — legacy PACS software locks data in proprietary formats; a modern PACS architecture using a VNA core stores patient data in standard DICOM native format, ensuring true data sovereignty; however many health systems remain on legacy PACS with proprietary storage that makes tiering, migration, and AI data preparation extremely difficult Komprise
- AI readiness gap — infrastructure gaps in legacy PACS and non-scalable storage systems cannot support the processing and bandwidth demands of AI-enhanced imaging; preparing PACS data for AI requires a data management layer above the PACS that can find, curate, govern, and deliver specific imaging datasets to AI pipelines
How Modern PACS Architecture Is Evolving
Today, cloud-native PACS separates the data layer, typically a VNA, from the application layer of viewers and workflow tools, enabling institutions to scale storage independently of their software contracts and to access studies from any device over a standard browser connection. This architectural shift creates new opportunities for intelligent data management: Komprise
- The separation of storage from application means cold data can be tiered to lower-cost tiers without disrupting PACS viewing workflows
- Cloud-native PACS architectures make DICOM data accessible to cloud AI services natively, removing the need for secondary data migrations before AI projects can begin
- VNA-based PACS eliminates proprietary format lock-in, enabling storage optimization across any combination of on-premises and cloud storage
How Komprise Optimizes PACS Storage and Enables AI Data Workflows
Komprise works alongside any PACS system as an unstructured data management layer that operates independently of the imaging vendor, providing visibility, cost optimization, AI data preparation, and governance capabilities that PACS systems themselves do not provide:
- Unified visibility across the PACS estate — the Komprise Global Metadatabase indexes all DICOM studies across on-premises PACS, VNA, NAS, and cloud storage, showing study age, modality, access patterns, size, and cost projections across the full imaging estate from a single interface, without requiring any changes to existing PACS configurations
- Transparent tiering of cold studies — Komprise Transparent Move Technology identifies and tiers cold DICOM studies from expensive primary PACS storage to lower-cost cloud or object storage transparently; PACS systems and clinical users access studies from their original location with no workflow disruption and zero rehydration penalty; a major hospital in the southeast is saving $2.5M per year using this approach
- Flash Stretch for PACS cost relief — Komprise Flash Stretch is specifically designed for data-heavy healthcare organizations managing 500TB or more of PACS data; it reclaims 70%+ of primary storage capacity without a hardware refresh, directly addressing the flash price crisis
- Smart Data Workflows for imaging AI — PACS data lifecycle management decides what stays hot, what moves to archive, and what retention rules govern storage; Komprise automates these decisions by policy and extends them to AI data preparation, curating specific imaging datasets for AI training and inferencing from the Global Metadatabase without manual intervention
- PACS migration without disruption — Komprise Elastic Data Migration moves PACS data between PACS vendors, from on-premises to cloud-native PACS, or from legacy to modern VNA-based architectures up to 27x faster than standard tools with full DICOM metadata fidelity, integrity checks, and chain of custody reporting
- Ransomware protection — tiering cold PACS studies to immutable object storage shrinks the active attack surface by up to 80%, providing ransomware defense as a direct byproduct of cost optimization
How PACS Differs from DICOM and VNA
The three terms are often used interchangeably but they describe three distinct layers of healthcare imaging infrastructure:
| DICOM | PACS | VNA | |
|---|---|---|---|
| What it is | File format and protocol standard | Clinical workflow and viewing system | Long-term vendor-neutral archive |
| Primary function | Standardizes how images are formatted and transmitted | Manages active clinical imaging workflows | Stores images in open format for long-term retention |
| Data ownership | N/A — it is a standard | Vendor-specific; often proprietary storage formats | Vendor-neutral; open DICOM format |
| Lock-in risk | None | High with legacy systems | Low by design |
| AI readiness | Provides rich metadata headers | Limited; workflow-focused not data-intelligence-focused | Moderate; stores data accessibly but lacks curation tools |
| Who uses it | All imaging systems | Radiologists, technologists, IT | Enterprise IT, clinical informatics |
PACS manages clinical workflows and viewing; VNA manages long-term, vendor-neutral storage and migration independence; and many enterprises run both. The practical distinction is that PACS is what clinicians interact with daily and VNA is what IT manages for long-term cost control and portability.
How PACS Works with NAS
PACS relies on NAS as its primary storage infrastructure and the architecture of that relationship directly determines storage costs, performance, and flexibility:
- PACS servers write DICOM studies to NAS volumes via NFS or SMB; the NAS appears as a standard file share to the PACS, which manages its own database of study locations and patient metadata separately
- Most PACS systems have a tiered storage model with hot storage for recent studies (all-flash NAS), warm storage for studies within the retention window (hybrid NAS), and cold or archive storage for long-term retention; Komprise automates the movement between these tiers by policy without requiring PACS-specific integration
- The PACS database and the NAS storage are separate systems; this is why PACS data can be managed at the NAS level by Komprise independently of what the PACS application does — Komprise operates on the files directly, not through the PACS API
- As flash and NAND prices rise 130% by end of 2026, the cost of all-flash NAS for PACS hot storage is increasing dramatically; intelligently identifying which studies have moved past their active diagnostic period and tiering them off flash is the fastest path to budget relief for most health systems (Learn more about Intelligent Data Tiering)
Primary PACS Vendors
The PACS market in 2026 is divided between cloud-native and VNA-first vendors and legacy vendors with proprietary storage. The major vendors:
- Fujifilm Synapse — market leader in enterprise imaging; VNA-first strategy with strong pathology and multi-modality support; cloud-native with AWS partnership
- GE HealthCare (Centricity/Genesis) — launched Genesis Cloud Suite in March 2025, an enterprise imaging SaaS portfolio offering edge, storage, VNA, and data migration on AWS; largest installed base by volume
- Sectra — consistently highest KLAS satisfaction scores; strong in radiology and enterprise imaging across multi-site health systems
- Philips (IntelliSpace) — integrated with Philips Clinical Repository VNA; strong in cardiology and radiology
- Siemens Healthineers (teamplay/syngo) — broad modality integration; strong in European and academic medical center markets
- Intelerad (Ambra) — cloud-native; strong in teleradiology and multi-site imaging networks
- Visage — built its reputation on speed through server-side rendering; their Visage 7 platform processes massive datasets on server GPU and streams to the doctor; strong in high-volume academic radiology
- Change Healthcare / Optum — strong in US community hospitals and health system networks
What is PACS and how does it differ from DICOM and VNA?
PACS is the clinical system that stores, retrieves, and distributes medical images across a healthcare organization. DICOM is the standard file format and communication protocol that PACS uses to store and transmit images. VNA is the vendor-neutral archiving layer that stores DICOM images independently of any specific PACS vendor, enabling long-term retention and portability. A PACS system manages image archiving and retrieval; a VNA removes vendor dependency by storing images in open formats, making them accessible regardless of which PACS software generated them. Most modern healthcare organizations run all three together: PACS manages clinical workflows and viewing, DICOM is the universal format, and VNA manages long-term vendor-neutral storage
Why is PACS data so expensive to store and what can healthcare IT do to reduce costs without disrupting radiology workflows?
PACS data is expensive because it combines large file sizes, mandatory long-term retention, and the accumulation of cold studies on expensive primary storage indefinitely. Most PACS archives contain 60 to 80% cold data that has not been accessed since the initial diagnostic reading, yet it is stored, backed up, and managed identically to active studies. Komprise addresses this by analyzing the full PACS estate to identify cold studies, then tiering them transparently to lower-cost cloud or object storage using Transparent Move Technology. Radiologists and PACS systems access cold studies from their original location with no workflow change and no rehydration penalty. The result is 70%+ reclamation of primary PACS storage capacity, with compound savings across storage hardware, backup, and DR costs.
How does Komprise prepare PACS data for AI imaging initiatives?
Komprise prepares PACS data for AI through three integrated capabilities.
- The Global Metadatabase continuously indexes all DICOM studies across PACS, VNA, and cloud storage, building a unified metadata layer enriched with custom attributes extracted from DICOM headers by KAPPA data services.
- Smart Data Workflows and Data Analytics query and tag this metadata to identify exactly the right imaging datasets for a given AI use case — specific modalities, patient cohorts, study dates, and clinical characteristics — filtering out irrelevant, duplicate, and sensitive studies before any data moves.
- Intelligent AI Ingest delivers the curated dataset to any AI stack up to 2x faster than standard transfer tools, with full PHI governance and audit trails throughout. NewYork-Presbyterian used this approach to achieve 10x faster AI data ingestion and 96% cloud cost reduction for its digital pathology AI program.