Picture Archiving and Communication Systems (PACS) is a digital medical imaging system that is used to acquire, store, and transfer images. In particular, PACS storage systems allow for the archival and retrieval of medical images while PACS image viewers allow for the visualization of radiography images on a computer display station. Currently, PACS is regarded as the hub of the medical enterprise as it plays a critical role in the digital image workflow in institutions.
PACS has played a critical role in meeting the increasing demand for medical imaging services. With the continued growth in imaging volume, the adoption of PACS is regarded as the most cost-effective strategy for the storage of medical images. PACS allows for permanent archival of these images. Different countries have established policies highlighting specific timeframes during which images have to be retained in the organization. In the USA the images should be available for 5-10 years while in Germany X-rays ought to be stored for 30 years. Therefore, without an elaborate storage platform, it would be a challenge to store large numbers of medical images while ensuring easy retrieval.
As the demand for imaging procedures has surged, leading to a substantial increase in data volume. Medical imaging is currently among the most expensive and fastest-growing procedures in healthcare. The growing volume of data in imaging is significantly larger than that of other clinical data. Moreover, with improved capabilities of medical imaging modalities, the average size of an image has increased along with the volume of study. 3D imaging generates large files and substantial volumes of imaging data that require efficient storage, transmission, and display. As such there is a significant need for a scalable storage system that can cater to the growing demand for radiological images.
Another significant challenge in the handling of medical imaging data is accessibility. Imaging data ought to be easily accessible and retrieved for clinical routine practices. Without efficient systems, data retrieval time increases significantly, thus impacting the efficiency of healthcare delivery.
Therefore, in order to meet these challenges, it is imperative to have a system that can not only store huge amounts of imaging information but also allow seamless retrieval and access. RamSoft’s cloud-based imaging solutions, powered by Microsoft Azure, provide a highly scalable, secure, and interoperable platform designed to streamline medical imaging workflows. With fast, reliable access to imaging data and industry-leading compliance, healthcare providers can enhance efficiency while delivering exceptional patient care.
Scalability entails the ability of a system to handle increasing volumes of images as your practice grows. On the other hand, flexibility entails whether the PACS can be tailored to your workflow. An innovative PACS storage solution should be expandable and adaptable as the radiology practice’s needs change.
Cloud technologies such as Microsoft Azure provides scalable and flexible systems for PACS storage solutions. Leveraging Microsoft Azure for Picture Archiving and Communication System (PACS) storage solutions offers significant benefits. Azure provides scalable and flexible storage capabilities, allowing healthcare providers to efficiently manage the increasing volumes of medical imaging data without substantial upfront hardware investments.
Cloud storage alone isn’t enough—true imaging efficiency requires a powerful, purpose-built software solution. That’s where OmegaAI, RamSoft’s cloud-native, zero-footprint RIS/PACS/VNA platform, takes imaging to the next level. While Microsoft Azure provides the scalable infrastructure, OmegaAI transforms it into a fully integrated, AI-driven imaging ecosystem that enhances workflow automation, diagnostic efficiency, and patient-centric care.
With OmegaAI’s built-in Vendor-Neutral Archive (VNA), healthcare providers aren’t just storing data—they’re mobilizing it. OmegaAI seamlessly consolidates imaging records from multiple sources, eliminates data silos, and ensures instant, secure access across facilities and specialties. Its serverless architecture, AI-powered automation, and intuitive design enable radiology departments to accelerate workflows and enhance interoperability.
For healthcare enterprises looking for more than just a storage solution, OmegaAI on Microsoft Azure delivers the complete imaging experience—scalable, secure, and built for the future of diagnostic imaging.
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The Health Insurance Portability and Accountability Act (HIPAA) sets the standards for the protection of sensitive patient data protection. Since PACS is used to acquire, archive and transmit protected health information, it ought to adhere to and comply with the HIPAA standards.
PACS must address the confidentiality and integrity of patient information. Confidentiality entails safeguarding patient information with only authorized personnel accessing it. This is achieved through the use of access controls where access to the system is achieved through the use of appropriate user ID and password. Additionally, PACS should contain audit controls that log every event pertaining to image access. This ensures that the organization at any specific time knows who has accessed the patient information
Integrity entails the completeness and correctness of patient information. HIPAA standards require that controls be implemented to ensure that medical information is not altered or destroyed improperly. In the transmission of patient information appropriate measures such as the use of secure networks should be established to ensure the integrity of patient information in transit and prevent unauthorized destruction.
OmegaAI ensures the highest levels of cybersecurity and compliance, including HIPAA, SOC 2, and ISO 27001. By leveraging Microsoft’s global cloud infrastructure, along with OmegaAI’s AI-driven automation and vendor-neutral accessibility, healthcare providers can achieve a seamless, secure, and future-proof imaging ecosystem that enhances both operational efficiency and patient outcomes.
Retrieval of imaging data presents increasing challenges due to the growing volume of data. Some studies can reach several gigabytes in size, further exacerbating access latency issues in PACS and cloud-PACS environments. This latency, particularly in cloud-based PACS solutions, is a major drawback as remote access is significantly slower than intranet connections. Consequently, large data volumes contribute to longer retrieval times, affecting system performance and clinical workflow efficiency.
Optimizing data access in medical imaging requires advanced strategies like local caching and intelligent prefetching, which help reduce latency and improve radiologists’ workflow efficiency. Progressive Loading is a breakthrough solution that eliminates traditional delays by intelligently prefetching and caching imaging data. Instead of waiting for an entire study to load, OmegaAI instantly delivers the first images in under a second, while additional images continue loading in the background, ensuring a seamless, uninterrupted diagnostic experience.
This cloud-native approach enables radiologists to interact with high-volume imaging studies in real time without the need for high-speed networks or local servers. By leveraging predictive caching and AI-driven prefetching, OmegaAI ensures that relevant imaging data is instantly accessible, optimizing bandwidth and storage resources.
Historically, image viewing entailed the production of a physical copy of the radiographic films which was then viewed on a lightbox. However, digital radiography (DR) and computed radiography (CR) have replaced this type of radiography. Computed radiography generates digital images indirectly. First, an image plate captures the X-ray which is then processed to convert the captured information to a digital image. On the other hand, DR generates images directly. A digital detector directly captures the X-ray exposure and converts it into a digital image. The raw image produced is typically preprocessed to correct for factors such as noise, contrast and grayscale mapping before it is displayed to the radiologist.
After the processing, the digital image is wrapped in the Digital Imaging and Communications in Medicine (DICOM) standard format. During this step, the system embeds metadata such as patient information, study details, and equipment settings into the DICOM file's header along with the pixel data of the image. Compliance with this standard allows for interoperability for the transfer of images and associated information.
The management of digital DICOM-based images is based on PACS. As such, PACS image viewers utilize several features to view and manipulate DICOM images. These features include:
Traditionally, radiologists and clinicians needed dedicated PACS workstations to access and interpret medical imaging. However, with enterprise web-based viewers like OmegaAI PACS, imaging studies can now be accessed securely and instantly from any device with a web browser. OmegaAI eliminates the need for software installations, local maintenance, or high-performance hardware, providing a zero-footprint solution that ensures seamless access to imaging data without IT overhead.
Advanced viewers increase the accessibility of imaging data by clinicians and radiologists. This streamlines their work since they do not have to get to a PACS station in order to handle reporting. Additionally, advanced viewers contain worklists that allow for optimized assignments, where studies are assigned to the most appropriate radiologist, such as by subspecialization. This ensures that complex cases are reviewed by specialists with the relevant expertise, improving diagnostic accuracy and efficiency Some even contain reminders that can show radiologists the number of studies that they have not yet reported. Overall, advanced viewers play a critical role in optimizing workflows.
Viewer customization is a crucial feature of advanced features to help cater to the needs of the organization. There are various ways through which image viewers can be customized to increase the efficiency and effectiveness of PACS. They include:
PACS is characterized by a high degree of innovation driven by the integration of advanced technologies such as AI and machine learning. However, in recent years the increasing demands for universalization of clinical and medical image storage have led to the increased adoption of cloud technology in the storage and transmission of medical images. Compared to traditional PACS, cloud-based PACS offers several advantages. They include:
Hybrid storage solutions combine the advantages of traditional PACS storage and Cloud-based PACS storage. In the hybrid model of PACS, the server is placed both onsite and offsite.
All of the facility's images are stored on the offsite server. However, there also exists a locally installed server that copies most of the recent images. The solution provides redundancy such that the facility does not have to worry about loss of revenue when PACS is down. Even so, cloud storage providers typically guarantee 99.9% uptime or higher in SLA agreements, but this can vary based on the provider (e.g., AWS, Azure, Google Cloud). This makes the use of hybrid storage solutions inessential.
Moreover, hybrid systems are significantly more expensive compared to cloud-based PACS due to their high overhead costs, particularly in IT support and management. Additionally, they add to IT complexity, requiring more resources for maintenance and integration. The redundancy of images further increases storage costs, making hybrid systems a costly solution.
Data migration to the cloud is one of the key benefits of adopting a cloud-based PACS solution, as it eliminates the challenges associated with transitioning between vendor-specific PACS storage systems in the future. Unlike traditional on-premises solutions, where migrating data to a new PACS vendor often involves complex and costly transfers, cloud-based PACS ensures that imaging data remains accessible and interoperable regardless of vendor changes.
However, while cloud migration offers long-term flexibility and security, the process itself requires careful planning to ensure a smooth transition. Factors such as data integrity, compliance with regulatory standards, security protocols, and minimal downtime must be considered. Proper strategies, including phased migration, data validation, and backup redundancies, help safeguard imaging archives during the transition. By choosing a vendor like RamSoft, which provides seamless data migration, 99.9% uptime, and 24/7 support, imaging facilities can ensure a hassle-free migration experience while maintaining continuous access to critical patient data.
Taking all these considerations into account can be challenging and overwhelming. However, with the right partner, you will be able to adhere to all these requirements and make the migration easier. Therefore, it is imperative to choose a vendor that will help successfully manage the migration to the cloud.
Technology advancements coupled with improved capabilities of imaging modalities have resulted in an increase in study sizes to about 100MB. Today, digital medical imaging makes up approximately 70% of all clinical data stored worldwide. Managing this data places an increasingly huge burden on organizations.
One of the most important strategies to manage large volumes of imaging data entails lossless compression of images. Formats such as JPEG2k compression allow for lossless compression. Brain CT scan images average 67MB in size and are reduced to 13MB by lossless compression with image quality being maintained. This is an 80% reduction in size which means that a PACS server can now store 80% more compressed images.
Another significant strategy in the management of imaging data is policy deletion. Most organization store their data indefinitely. However, in line with policies and mandates from the government, some data can be purged from the archives. This will aid in significant savings by avoiding future migration costs as storage can be freed to be used for newer data.
Data retention is a crucial aspect of managing medical imaging data. However, the likelihood of these images being retrieved after 60 days is less than 10% yet they still need to be available in just seconds by the hospital's PACS. Retention requirements require that imaging organizations and hospitals retain imaging studies for an average of 7 years. As such it is imperative to understand the clinical and medicolegal requirements for imaging retention and how to use new storage technologies to effectively manage your imaging data.
HIPAA requires organizations to establish and test a data backup plan as part of their contingency planning to protect electronic Protected Health Information. This can be achieved through operations planning which entails focusing on long-term management of records including the migration of data from one system to another. This could be as a result of hardware/software systems becoming obsolete or disaster preparedness.
In choosing a system architecture for PACS, it is crucial to consider the ease of future transition. Migration policies should be set well before they are needed as part of the data maintenance protocol. Long-term management of data is therefore part of the strategic planning in the adoption and use of PACS.
Cloud storage has become a game changer in regard to the management of medical images. On-site server storage poses significant challenges with regard to costs and resources. Primarily with the exponential increase in data, scaling onsite storage to meet this demand is very expensive. However, cloud-based storage solutions allow for easy scalability without huge investments.
Additionally, other costs such as data recovery, IT personnel and miscellaneous costs such as utilities, maintenance and security are foregone when using PACS. As a result, one can focus on improving other crucial aspects of PACS such as image viewing. This may entail investing in better image viewer systems, implementing user-friendly interfaces and optimizing retrieval times.
Artificial Intelligence (AI) models utilize machine learning algorithms to analyze data and develop appropriate solutions. In radiology, AI and machine learning are utilized to analyze large amounts of data to assist in anomaly detection which aids in more accurate diagnosis and improved workflow.
Apart from the diagnostic value role of AI and machine learning, can play a crucial role in storage optimization. One way through which AI can be leveraged to optimize storage is through elaborate compression practices. Through adaptive compression, AI can dynamically adjust compression rates based on the image type, modality or expected usage. This allows for high efficiency which preserves critical details for diagnostic purposes.
Another way through which AI can aid in optimizing storage is through deduplication and data cleansing. AI is able to identify and remove redundant images stored across different locations. Additionally, AI can scan and purge storage systems of corrupted or incomplete files ensuring that storage spaces are not wasted on unusable data. These strategies will help save significant costs associated with media storage.
Finally, AI can be used in intelligent tiering. This entails analyzing access patterns and moving imaging data to the appropriate storage tier. For instance, the hot tier entails images that are frequently accessed such as recent scans. The cold tier entails infrequently accessed images while the archive tier entails data storage in line with data retention policies and for future references.
Discover how AI can optimize your imaging facility’s efficiency, streamline workflows, and reduce storage costs. Book a demo with us today!
The imaging industry has seen image data become more shareable across different systems. However, there still exists a lack of compatibility between data storage systems from different vendors. One of the major factors contributing to this is that different vendors add numerous tags to medical images during the storage process. This makes the images incompatible with different vendor systems. This lack of interoperability is a critical barrier to the digital transformation of healthcare.
Various interventions have been suggested to combat data interoperability. They include:
These integrations are crucial for the radiological workflow. Therefore, it is important to ensure that your vendor provides you with a system that entails all these integrations. Otherwise, your practice may be crippled because of the systems' inability to communicate with other systems.
There are several key features to consider when selecting the right PACS storage solution for your practice. These features include:
A modern PACS should be fully cloud-native, eliminating the need for on-premise infrastructure while ensuring seamless access to imaging data from any location. Cloud-based solutions offer improved flexibility, security, and cost savings compared to traditional setups.
RamSoft’s OmegaAI is a fully cloud-native, zero-footprint PACS that allows radiologists and clinicians to access, view, and report on studies from anywhere, eliminating the need for on-site servers and complex IT maintenance.
Fast image retrieval is critical for efficient diagnostics. PACS platforms must deliver near-instant access to imaging studies, regardless of file size or network conditions.
With OmegaAI’s Progressive Loading technology, high-resolution imaging studies, such as 3,000-slice CT scans, load in under one second. This ensures a seamless reading experience, even over standard internet connections.
Advanced automation tools help optimize radiology workflows, reducing administrative burdens and enhancing efficiency. Automating tasks like study prioritization, critical result notifications, and prior study retrieval minimizes delays and ensures a smooth operation.
OmegaAI features AI-driven automation that streamlines reporting, intelligent case assignments, and automated prior study retrieval, ensuring radiologists can focus on clinical analysis rather than manual tasks.
Artificial intelligence plays an increasing role in radiology, from automated image analysis, voice recognition and report generation. AI-powered PACS solutions improve diagnostic accuracy, reduce reporting time, and enhance clinical decision-making.
OmegaAI integrates AI-driven imaging tools, including PET/CT fusion, volumetric analysis, and voice recognition-powered structured reporting, allowing radiologists to generate reports faster and with greater accuracy.
Depending on your needs and requirements there are various factors that can be used to evaluate PACS providers. These factors ensure that you select vendors who are able to cater to your needs. They include:
RamSoft offers flexible pricing models tailored to the size and needs of your imaging center. Talk to us today for a custom pricing option designed to help you scale without unnecessary costs.
PACS software is evolving continuously to meet the challenging demands of healthcare professionals. With the advancements in technology, there are bound to be better ways to store images through PACS in line with confidentiality, reliability and accessibility standards. Moreover, elaborate image viewer systems are being developed with features entailing real-time 3D post-processing, MPR and volumetric comparisons embedded in PACS no longer requiring a separate post processing workstation.
Altogether, with the evolving nature of PACS, it is crucial to understand the needs of your organization currently and strive for storage and viewing solutions that cater to those needs. Moreover, you should ensure that PACS storage and image viewer systems are in line with the mandates from the federal government or organizations such as HIPAA.
PACS storage often entails long-term planning. Therefore, in making your choice of storage solution you should have a prospective outlook of your organization. By focusing on the current and future needs of your organization, you will be able to make appropriate decisions that will serve you in the long term.