1. The initial phase
While healthcare organizations across the globe have remained under pressure to deliver cost-effective and high-quality care, the COVID-19 challenge has exposed vulnerabilities in terms of how technology has created certain access and information silos.
The pandemic has also exposed the basic premise on which our health systems have been designed. This initial phase of lack of multispecialty data aggregation and systems interoperability posed significant diagnostic challenges.
While Coronavirus disease was eventually declared a pandemic on March 11, the impact on the diagnostic role of radiology was already being reimagined
2. The pandemic
As COVID-19 spread globally, there was growing interest relating to the role of diagnostic imaging, appropriateness of Chest X-rays and CT scans when it comes to screening, detection and follow up management.
While the appropriateness of Chest X-rays or CT scans to pinpoint COVID-19 pneumonia was being evaluated, a group of experts in Italy had been busy exploring the benefits of bedside ultrasound as one possible alternative for detection of COVID-19 pneumonia.
There were also initial reports that some technology startups were evaluating their machine learning algorithms, and developing new models, to detect COVID-19 specific findings leveraging Chest X-rays and CT scans.
The bottom line is that radiologists within the healthcare system and outside their enterprise were struggling to exchange useful imaging data with their international colleagues in their quest for collaborative learning and understanding of this new disease.
I have already discussed the role of radiology and AI in detail in a recent blog post.
3. The transition
I strongly believe that this phase of transitioning out to the “new normal”, or post pandemic, will provide critical lessons learned in relation to how health systems prepare themselves before returning to business as usual.
Radiology departments, while they have seen significant decline in certain diagnostic procedures due to the focus on COVID-19 related hospital visits, with the need to work remotely and in self isolation, have also mandated the need for secure and modular industrial scale Enterprise PACS solutions.
It was obvious that while COVID-19 was being evaluated from a diagnostic radiology perspective, COVID-19 related AI initiatives would pop up as well. While there have been few initiatives that are still works in progress, one thing’s for sure, the utility of AI within existing PACS systems will require workflow integration, and that’s where I see Agfa HealthCare standing out with its powerful rules-based workflow engine.
At Agfa HealthCare, because we had purposely built our Enterprise Imaging platform for modularity, we have seen several successes during this pandemic, at short notice, fulfilling requests of our customers ranging from at home workstation setups, image exchange and real-time collaboration to 100% remote supported GO LIVES.
4. The new normal
We do not know yet what the new normal would look like, however I think we do agree on the fact that COVID-19 has not only exposed the vulnerabilities in our healthcare system, its operations and infrastructure set up, but also the need for radical change how we deploy secure and modern platform ecosystems.
Clinicians were already overwhelmed with data, it’s now time to implement solutions that automate their workflows, improve productivity, provide analytical intelligence into outcomes measurement, and help offer optimized tasks that machines, and software are better programmed to do.