Healthcare That Overcomes Obstacles

Updated on November 19, 2021

By Timothy Rose—Vice President, Product Management, Radiology, GE Healthcare

When you boil it down, healthcare is all about solving problems. It’s about overcoming obstacles to create better care for the patient. Oftentimes, the barriers relate to the patient’s condition and the diagnosis: What are the symptoms? What are the causes of the symptoms? What’s the most effective treatment? Those are the barriers that clinicians approach eagerly. 

But there’s a different set of barriers to quality care that are sometimes harder to overcome: The challenges we’re facing with data, technology, and connectivity. Are we using our data well? Are we supporting our overworked staff? Can technology help us provide better care?

These obstacles, like all others, offer us an opportunity to grow. And if we don’t understand them, we can’t overcome them. 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.” Let’s take a look at some of the technological and data-driven challenges we’re facing, and how we can succeed in spite of them.

More data, more variance, more 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 annually. That’s 50 million gigabytes. Where basic text 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 more detailed now than ever before. With more details comes more variance in the results, no matter how skilled the clinician. There are many metrics and processes to remember and, with staff stretched thin, the human element needs to be considered. After all, technology should help make processes more simple, not more difficult. 

Healthcare systems have also come under scrutiny because of the growing risk of cyberattacks. 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.

Operational (and clinical) AI to the rescue

Artificial intelligence can help address many of these concerns, but the most impactful way is actually behind the scenes. There are elements of clinical AI impact you would expect, such as measuring and identifying the size of a tumor. However, the larger impact is made through day-to-day operational AI. AI systems can automate manual and administrative tasks, which frees up time for the clinician to focus on informed decision-making and personalized care.

Speaking of decision-making, AI can also support the clinician in their 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. To think of it another way, AI isn’t going to chop a tree down for you, but it’ll sharpen the ax.

The elephant in the room with many of these advancements, again, is security risk. With so much digital data stored in hospital systems, they need to take the proper measures to ensure 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.

The only constant is change. 2020 showed us that radiology, much like the rest of the world, is in a state of flux as COVID-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. So what walls are we facing when it comes to imaging in healthcare, and how will we scale them or knock them down altogether?

Planning for future obstacles

The healthcare 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 into the future. Investing in cloud-based PACS systems can save a significant amount on infrastructure costs, and future-proof your investment. The cloud is also scalable, both with the size of the service offering and the size of the healthcare system. 

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, one way or another, there will no doubt be more barriers in the future. But, by harnessing AI and data to work for us in the healthcare space, we’ll have a head start to be able to be better prepared to see the next barrier in the distance and plan for a way to scale it.

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