When it comes down to it, healthcare is all about solving problems. The goal is to improve patient care by overcoming obstacles. Barriers often arise because of the patient’s condition and diagnosis: What are the symptoms? Why do they occur? How can I treat them? These are the questions clinicians eagerly seek answers to.
There are, however, other barriers to quality care that are harder to overcome: the challenges we face with data, technology, and connectivity. Is our data being used effectively? Do we support our overworked staff? How can technology help us provide better care?
Obstacles, like all others, provide us with opportunities to grow. We cannot overcome them if we don’t understand them. In Albert Einstein’s words, “If I had one hour to save the world, I would spend fifty-five minutes defining the problem, and only five minutes figuring out a solution.” Let’s take a look at some of the technological and data-driven challenges we’re facing, and how we can overcome them.
More data means more variance and more risk
As our imaging technology improves, more data is needed. The trend has grown over time, and now has reached a tipping point. Hospitals produce and store an average of 50 petabytes of data every year. GB stands for gigabyte. As opposed to basic text that requires 5 KB per record, high-resolution imaging now requires more than 200 million KB per genome.
In addition to data complexity, imaging processes themselves are now more detailed than ever. More details mean more variance in results, no matter how skilled the clinician is. It’s important to remember metrics and processes, but it’s also important to take into account the human element. Technology shouldn’t complicate processes; it should help simplify them.
Cyberattacks have also put healthcare systems under scrutiny. 81 percent of healthcare systems have been compromised by one or more cyberattacks in the past year, according to the Institute for Critical Infrastructure Technology (ICIT). The aftermath of a cyberattack can cause medical devices to be offline for up to 10 days, slowing care and raising costs.
Artificial intelligence (and clinical AI) to the rescue
Several of these concerns can be addressed through artificial intelligence, but the most impactful way involves working behind the scenes. There are elements of clinical AI impact you would expect, such as measuring and identifying tumor sizes.
Nevertheless, AI’s greatest impact can be seen through day-to-day operations. By automating manual and administrative tasks, AI systems allow clinicians to focus on making informed decisions and delivering personalised care.
In addition to supporting decision-making, AI can be used to notify the clinician of a condition as well as the severity of it. AI does not make decisions for clinicians but rather alerts them to potential problems and gives them a head start on determining the treatment. AI won’t cut down a tree for you, but it will sharpen the ax.
In many of these advancements, the elephant in the room is security risk. The hospital systems store a lot of digital information, so they need to ensure their systems are secure. Understanding your PACS’s security features is crucial, but healthcare professionals must also take steps to prevent cyberattacks.
Change is the only constant. COVID-19 accelerated the need to support remote and distributed reading workflows as 2020 showed radiology is in a state of flux, just like the rest of the world. The radiology landscape is changing and, even though we throw around the phrase “back to normal,” the reality is that we were already entering a new era.
There are now challenging healthcare professionals-specifically IT teams and radiologists have never had to face before, and they won’t go away anytime soon.
When it comes to imaging in healthcare, what are the walls we might face, and how might we break them down?
Preparing for future obstacles
Healthcare is growing and becoming faster, but it needs to remain agile. Technology comes into play here, and it’s how we need to look to the future. By utilizing cloud-based PACS systems, you can significantly reduce infrastructure costs and future-proof your investment. Similarly, the cloud can be scaled, depending on the size of the service offering and the size of the healthcare system.
Increasing efficiency and improving patient care are hampered by security issues, data size, and workflow challenges. As important, they impede clinicians’ ability to perform and feel their best during one of the most chaotic periods in healthcare history. One way or another, there will undoubtedly be more barriers in the future. With AI and data working for us in the healthcare space, though, we’ll have a head start on being able to see the next barrier at a distance and plan ahead accordingly.
