Seeing the bigger picture

PACS is likely to remain part of the medical imaging landscape for many years to come. However, a growing number of healthcare organizations also regard this as an opportunity to assess the merits of a vendor neutral, enterprise-focused strategy, which enables the integration of new imaging formats and technologies that may emerge.

Radiology enterprise imaging platforms and picture archiving and communication systems (PACS) have recently evolved in several ways to help improve workflows. To name a few, streamlined integration of data from various departments into the electronic medical record (EMR) to share images and the addition of Artificial Intelligence (AI) to increase efficiencies.

The evolution of PACS has gone from the integration of separate radiology information systems (RIS) and PACS into one system. This evolution over the past decade has been a trend at large hospitals and healthcare systems from siloed PACS to enterprise imaging systems, enabling a few image-heavy departments, or all departments across the enterprise, to integrate their data and images into one central, vendor neutral location. This makes integration of patient data from numerous sources easier to integrate into the over-arching EMR systems.

Many vendors today are touting enterprise imaging. These systems will likely become the way of the future to enable additional integration of digital health records in one location. However, there is still a robust standalone PACS market for smaller hospitals and clinics.

Fast, easily accessible patient images are crucial in this day and age, as imaging and medical records take on a new meaning during the COVID-19 pandemic. This has put a spotlight on PACS, securing its growth and sustainability in this industry.

PACS imaging technology provides storage and easy access to images from various modalities such as ultrasound, x-ray, magnetic resonance imaging (MRI), nuclear medicine, and computed tomography (CT) and allows hospitals, medical centers, and clinics to easily capture, view, store, and share images both externally and internally.

According to Technavio, the global PACS market is poised to grow by USD 1.17 billion during 2020–2024, progressing at a compound annual growth rate (CAGR) of almost 6 percent. The analysts expect the market to grow by a CAGR of 5.75 percent through 2024.

The increasing demand for mobile PACS—now more than ever—will offer immense growth opportunities as healthcare providers around the world try to navigate through this unprecedented time in history. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments while maintaining their positions in the slow-growing segments. The increasing adoption of PACS by small hospitals and imaging centers as one of the primary reasons driving PACS market growth during the next several years.

The benefits and importance of healthcare IT have been recognized with various governments across the globe and this is also driving technological advancements in the healthcare sector. The usage of PACS for a detailed and accurate evaluation of patient’s health has improved as a result of these initiatives. With increasing government initiatives, the acceptance of connected healthcare solutions, such as PACS, electronic health record, PHR and RIS, have risen with the increase in requirements to improve the overall quality of healthcare services.

Cardiology imaging vendors have adopted digital imaging and communications in medicine (DICOM) standard to develop commercial PACS specifically designed for cardiology. Since cardiology studies are dynamic, large number of images and a vast amount of data are generated per study. Also, the number of cardiology studies performed is increasing by 20 percent per year. Considering the large records, cardiology can expect to benefit substantially from the electronic management of images. This is expected to drive the demand for the PACS in coming years.

Cloud-based PACS segment is expected to be valued at USD 1090.9 million by 2026 and is projected to register a CAGR of 7.1 percent during 2020-2026. Imaging modalities is most demanding component for cloud-based PACS segment across the globe.

Hospitals segment is expected to register a CAGR of 6.3 percent over the next 6 years. Cloud-based PACS segment finds its end-users in hospitals, clinic practices, diagnostic imaging, imaging centers and others primarily for storing, transferring, and archiving patient data and images. Approximately 60 percent of the cloud-based PACS are used by large hospitals and diagnostic centers and organizations.

North America is projected to remain dominant throughout 2020-2026. The presence of several PACS manufacturing companies in North America is expected to play an important role in revenue growth of the cloud-based PACS segment in the North America market. Cloud-based PACS segment is expected to witness robust growth in Western Europe and APEJ markets driven by increasing cloud based solutions demand in the region, as cloud-based PACS are mainly used by large hospitals and diagnostic centers in these regions.

Agfa-Gevaert NV, Apollo Enterprise Imaging, Dell Technologies Inc., Fujifilm Holdings Corp., General Electric Co., Intelerad Medical Systems Inc., Koninklijke Philips NV, Lexmark Healthcare, McKesson Corp., and Siemens Healthineers AG are some of the major market participants.

Utilizing technology to fight COVID-19. In March 2020, PACS provider Image Information Systems launched a diagnostic imaging learning platform for coronavirus cases. Studies have shown that COVID-19 can be detected by chest computed tomography (CT) scans even before the first symptoms begin to show. CT scans are important to monitor the disease and to help predict the outcome. However, this requires a certain level of experience and training that is rarely seen when dealing with a novel virus. A newly created website, www.disAIblecorona.com, helps fill this void by providing the radiology community with scientific medical imaging facts about COVID-19 and the latest in coronavirus-related information. It includes a dedicated learning area with full cases, offering the radiology community a forum to build and share anonymous coronavirus cases from around the world.

In April 2020, Hyland Healthcare launched PACSgear Enterprise — the latest version of the PACSgear server software, which supports the advanced capture and connectivity modules of the PACSgear platform. Accessible through a web-based, thin-client user interface, PACSgear Enterprise supports multiple servers and provides a single platform for all enterprise imaging capture activities, allowing clinicians to quickly capture point-of-care images from anywhere and automatically link them to a patient’s electronic medical record (EMR).

A timely application of PACSgear is the use of PACSgear Image Link and Encounter Workflow. With this module, clinicians can quickly capture point-of-care ultrasound images in the emergency department or other care locations — including temporary locations — as long as there is system connectivity. The images are automatically sent to the PACS or vendor neutral archive (VNA) and linked to the patient’s record in the EMR. This gives radiologists the ability to review the image remotely from the PACS or VNA with an enterprise viewer.

In April 2020, the US Food and Drug Administration (FDA) cleared Intelerad’s InteleConnect EV solution for diagnostic image review on a range of mobile devices. This comes at a critical time for healthcare systems and ensures that radiologists are able to collaborate and leverage resources in more creative ways, even when workstation access is not available. It also is approved for mobile diagnostic image review, and can immediately be used on various iPad and iPhone models, with additional devices on the horizon.

The ability to review images and collaborate with clinical staff through diagnostic radiology is key, especially during this current crisis, to ensuring the best possible patient outcomes with easy transmission of data. With mobile access to diagnostic quality images, radiologists now have the flexibility to collaborate anywhere at any time.

Recent trends in PACS has moved away from standalone systems to enterprise imaging systems that integrate not only radiology, but all departments that generate imaging and image related reports so they can be centralized in one location and integrated into the over-arching electronic medical record (EMR). Related technologies include remote image viewing systems that allow images and reports to be accessed anywhere with a web connection, advanced visualization to reformat and perform measurement quantifications, and archive storage, which in recent years has focused on the use of remote cloud-based vendor neutral archives due to the vast amount of data storage required. Artificial intelligence (AI) is also being incorporated within all areas of PACS to increase efficiency.

Radiology workflow orchestration. The buzzword workflow orchestration is being used by some vendors to describe their systems’ ability to use smart workflows to optimize radiology reading lists.

Orchestration creates order on reading lists where specific exams will be routed to the most qualified radiologist, and it eliminates radiologists cherry-picking the exams they want to review by having the system limit their selections to what actually needs to be read. These systems further prioritize reads and the position on studies in the list based on STAT reads, exam protocol type, and on how long they have been available and are approaching the deadline for reads based on service level agreements (SLAs).

Orchestration can manage distributed workflows so the workload is shared equitably between radiologists and different sites. The systems also can monitor, manage, and analyze radiology workloads to help identify bottlenecks, issues with some types of exams, and offer analytics on the radiologists, technologists, exam protocols, facilities and the ability to meet SLA parameters.

AI integration into PACS and enter­prise imaging platforms. AI has by far been the biggest and most exciting trend in radiology IT systems over the past few years. The discussion at RSNA 2020 has moved away from explaining how AI works, to a general acceptance. If an AI application has regulatory clearance, the main question is how it will help radiologists or hospitals improve their workflow and patient care.

Most of the AI algorithms are being integrated into the backend of PACS or enterprise imaging systems as workflow aids that operate unnoticed. Algorithms developed by Agfa, Philips, and Siemens can identify the imaging protocol used and automatically pull in prior exams and patient history that are relevant to the organ or section of the body being imaged.

Another aspect of AI is diagnostic support. AI algorithms can work in the background to automatically identify regions of interest on images, including FDA-cleared applications to identify pneumothorax, improper placement of endotracheal tubes, if a patient has a stroke and whether it is ischemic or hemorrhagic, the presence of tumors, or to red flag incidental findings that are outside the main area of the exam’s protocol.

Many PACS/enterprise imaging vendors are working on their own AI apps and are partnering with third-party AI vendors to offer a wide variety of AI solutions.

Radiology workflow automation. AI is also being baked into systems to automatically identify anatomy, key image views for reads, set-up of specific radiologists’ hanging protocols, and to automatically make key measurements. AI is also being used to autofill sections of radiology reports with measurements or common paragraphs of text based on findings of what the AI sees or measures in the images. All of these measures help speed workflow and often act as a second set of eyes for the radiologist. The radiologist still maintains control in being able to override and edit anything the AI has added.

Image remote views help radiologists collaborate. Remote viewing systems are increasing in popularity, but their importance has been further propelled by the COVID-19 pandemic. Many radiologists have worked remote when possible, and efforts were made at most hospitals to limit contact between staff. Remote image sharing/viewing systems allow radiologists to send images to referring physicians or to collaborate with peers.

A recent movement in radiology platforms is to allow live, remote collaboration. This can allow discussion on what a surgeon needs from the radiologist while preparing for a procedure, with other radiologists for odd image findings, or for multidisciplinary care team meetings. Some remote viewing systems also enable sharing links to radiologists or doctors outside the hospital, so they can take a quick look at imaging on mobile devices.

Web-based radiology systems. Over the past few years, most PACS and all enterprise imaging systems have migrated to cloud-based solutions. This enables any web-enabled computer to become a PACS workstation, which greatly simplifies user access. Rather than needing to cable connect dedicated PACS workstations across a hospital or enterprise, house and maintain massive servers and require all updates to be done manually on each computer, all users need is internet connection.

This outsourced IT architecture enables software updates to be done in the cloud, rather than individual computers. Data storage is now often accomplished using remote cloud servers. This allows hospitals to get out of the business of maintaining large server rooms.
The technology also allows easier integration of remote viewing systems and the ability to access information and images on mobile devices.

AI has great potential, but to get to the promised land, one need to cross certain barriers. First and foremost, there are technological barriers to overcome. For AI solutions to work, clean, pristine data is required. Far too often, this high-quality data just is not available. In many ways, industry is data rich, yet information poor. This is a notable issue since data quality can dictate the type of outcomes obtained by the applications that depend on it. Therefore, opportunities abound for tech enablers to assist in this capacity.

The second technology barrier experts need to overcome is the process of integrating data from multiple sources to generate deep knowledge, or an algorithm. Additionally, integrating the algorithm within the clinical workflow is a challenge that needs similar attention. So, even if someone has the technology to acquire the right kind of data and build an AI algorithm, they still have to determine the best way to adopt it into the workflow of each appropriate individual.

Lastly, one cannot underestimate the level of change and adaptation needed to make as people, which will help with both the adoption and scaling of AI solutions. A common misconception of AI is that the technology will remove the human element completely from care. This is far from the truth. With the amount of data that is being generated, it is hard for the human mind to process and connect all the information and make the right diagnosis or decision. Additionally, the skill and expertise levels are the not the same across the world. AI and technology can help with accurate diagnosis, leveraging large amounts of data in a consistent fashion.

Looking ahead, AI will undoubtedly play significant roles in diagnostic imaging as the healthcare community continues to pursue more effective, personalized patient care. Radiology groups, teleradiology service providers, and enterprise imaging strategies will face unprecedented challenges in integrating valuable AI applications into their routine workflows. Imaging workflow orchestration solutions will need to continue to evolve to take full advantage of the next era of intelligent patient care delivery.