Radiology practices and departments are facing a significant challenge. Demographic change is becoming more acute, while the shortage of specialists and number of patients with complex diseases are increasing. One solution is workflow and process optimization with the help of various software solutions.
The starting position is clear: something has to change in radiology! But what? It must be possible to cope with the shortage of radiologists and the constantly increasing number of examinations.
At first glance, a promising solution is to streamline workflow and processes to increase efficiency. Technical software solutions already exist in abundance.
Quantum leap through AI
At second glance, however, things are no longer that simple. In retrospect, the widespread introduction of RIS and PACS took almost 20 years. Today, finally, communication between HIS, RIS, and PACS runs smoothly almost everywhere.
And it is clear to everyone: digital radiography is worth the money. Who still wants to run to the archive to look for preliminary images? Who can still remember meetings on the auto-alternator? Who still wants to hold the X-ray film toward the window at the patient’s bedside?
With AI, a new wave of technology is just around the corner and promises a quantum leap. And again, radiology faces the same question as 20 years ago: Who are these new companies? Why don’t modality manufacturers have simple solutions to offer? Even then, with RIS and PACS, they struggled and entered into numerous more or less successful cooperations. That may be why it took so long to introduce these tools.
The benefits of the new AI-based workflow optimization, diagnostic support, and automated reporting are undisputed. Many of the solutions are mature. Functional interfaces are more important than ever.
Starting with online registration, the digital completion of the medical history and information forms, the seamless workflow leads to sending the patient data to the control console of the examination device. The technical settings for the modality are already preselected.
After the examination, images go directly to the PACS and feed automatically to the corresponding AI algorithm, which sends its annotations back to the RIS or PACS within a few seconds. The AI system highlights essential findings and prioritizes them in the worklist. The AI readings are immediately transferred to the diagnostic solution if an automated diagnostic system is used, and the radiologist creates the finished findings report with just a few clicks.
Another challenge is sharing the images and findings across disciplines and communicating closely with the other disciplines involved in diagnostics and therapy.
Benefits with deep integration
However, the number of interfaces increases with each additional software system and department involved. Workflow and process optimization is therefore not only about RIS and PACS, online registration, digital anamnesis and clarification forms, AI algorithms and their integration via a digital platform and a diagnostic solution, and all the data protection issues. It’s also about interdisciplinary communication with the software solutions of other departments.
Getting to grips with all the different products and technologies takes a lot of effort. In radiology alone, the questions are: Who are these many new AI companies anyway? Can they be trusted? Can the results of the algorithms be trusted? Who is going to pay for all of this?
These are, of course, many questions. But there is probably little alternative because the technology leap will come, and it is best to deal with it now. Of course, change comes with a cost. But after a short time, no one will miss the “old days”. All questions can be answered according to statements from so-called early adopters, i.e., healthcare providers that already use AI systems.
The important thing here is that it’s about searching for solutions and not finding problems. You will notice the efficient workflows once you have worked with a deeply integrated AI solution.