Radiology Without Walls: Overcoming PACS Challenges to Brighten the Future of Healthcare

The only constant is change. 2020 showed us that radiology, much like the rest of the world, is in a state of flux as COVD-19 accelerated the need to support remote and distributed reading workflows. The radiology landscape is changing, and although we throw around the term “back to normal”, the reality is that a new era was already imminent anyway.

We’re now looking at challenges that healthcare professionals—specifically healthcare IT teams and radiologists—have never had to face before, and they aren’t going away anytime soon. And not just challenges, but new opportunities to support remote work and empower clinicians are raising questions about how our systems and processes can evolve. So what walls are we facing when it comes to imaging in healthcare, and how will we scale them or remove them altogether in order to provide the needed flexibility in the future?

More data and higher risk

The better the technology in our imaging gets, the more data it requires. This has been a growing trend over time, and now has reached somewhat of a breaking point. On average, hospitals now produce and store 50 petabytes of data per year. That’s 50 million gigabytes. That can hamstring proper communication simply because of file size. If anyone has ever tried to send an email with a large attachment, large amounts of data aren’t easy to send or share.

The current PACS systems also require a great deal of IT support, from software upgrades to security monitoring. This not only involves precious time from IT teams, but also large capital investments and specified skillsets to manage the day-to-day requirements. And, if the systems aren’t all connected, the software and hardware updates need to be done multiple times, almost constantly.

In addition to the data complexity, the imaging processes themselves are more detailed. With more details come more variance in the results, no matter how skilled the clinician. Radiologists are already stretched thin, and with each new variable, it becomes more difficult to be consistent.

Expanding out to the healthcare system itself, imaging has come under scrutiny in the past because of the risk of a cyberattack. In fact, according to the Institute for Critical Infrastructure Technology (ICIT), 81 percent of healthcare systems have been compromised by one or more cyberattacks in the last year. Dealing with the aftermath of a cyberattack can lead to up to 10 days of downtime for medical devices, slowing care and raising costs.

In other words, there are many more cars on the road, but we need to make sure they move faster, more efficiently and with improved safety.

Using AI to streamline and empower

Artificial intelligence can address many of these concerns, and often in a different way than many might think. There’s an element of clinical AI that you would expect, such as measuring and identifying the size of a tumor. However, the larger impact is made through the day-to-day operational AI. AI systems can automate manual tasks—including software and application updates—which frees up time for the radiologist to focus on informed decision-making and personalized care and ensures that equipment and systems always operating correctly.

AI can also support the determination of “next steps” in care, with the ability to notify the clinician of a condition as well as the severity. AI does not make the decision for the clinician, but rather draws their attention to potential problems and gives them a head start on determining the treatment. This support for the radiologists has been proven to reduce burnout and improve care by enhancing reading speed, helping reduce errors, improving accuracy and enabling more confident diagnoses.

The elephant in the room with many of these advancements is security risk. With so much digital data stored in hospital systems, organizations need to take the proper measures to make sure that their systems are secure. Understanding the security features of your PACS system is a key element, but healthcare professionals also need to do their part to prevent cyberattacks. With the help of AI and advanced monitoring capabilities, PACS systems will be able to automatically make software and security updates, keeping them on the forefront of protection against cyberattacks.

Planning for the future

The industry is getting bigger and faster, but it needs to remain agile. That’s where technology comes in, and it’s how we need to look towards the future. Agility also means being able to access information from various locations. Radiologists want to have the flexibility to review imaging remotely, and cloud-based platforms can deliver that.

As far as cost goes, investing in cloud-based PACS systems can save you a lot on both up-front initial investment costs, infrastructure costs, and future-proof your investment. The cloud is also scalable, both with the size of the service offering, as well as the size of the healthcare system.

As it relates to AI, a cloud-based system provides more data for the AI features to gather information and distribute it where it needs to go. The more scans it sees, the more patterns it can detect, offering better support for clinicians.

Security, data size, workflow challenges…these are all barriers to efficiency and improved patient care in the healthcare industry. Perhaps just as importantly, they’re barriers to empowering clinicians to perform and feel their best in one of the most chaotic times in the history of modern healthcare. And even as the COVID-19 pandemic continues to fade in some ways, the challenges remain.

As Albert Einstein said, “If I had only one hour to save the world, I would spend fifty-five minutes defining the problem, and only five minutes finding the solution.” We are keenly aware of the problems, and there are cloud-based solutions on the horizon that can help equip us to scale the walls into the “new normal” of healthcare.