Radiology

Estimated reading time: 3-4 minutes

What if your systems were so intuitive that your staff got the image right the first time, every time?

We know that AI can aid radiologists in image processing and interpretation. What about its impact at the point of image acquisition?

 

What if smart workflows at image acquisition could contribute to a more confident, efficient and patient-centered imaging experience? AI-enabled1 smart workflows across diagnostic imaging modalities can support radiology technologists, offering greater efficiency and clinical confidence so that they can focus more on the patient and less on the technology.

Technologist helping patient prepare for an imaging exam
At a glance

Challenge


Diagnostic imaging departments face staff shortages and variations in staff experience, putting pressure on imaging teams to find new efficiencies that also allow for quality time with the patient.

Results
 

AI-enabled technologies are significantly aiding workflow in end-to-end solutions, including preparing for the exam, guiding exam set-up, and fast, efficient scanning.

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Radiology staff more burdened than before

 

Radiology staff place great importance on the quality time necessary to focus on patient needs, many feel that they currently can’t devote enough time to patients.2 They feel burdened and at times burned out by increasing system complexity, strained budgets and staff shortages, and they feel a significant part – up to 23% – of their work could be automated.2

Smart workflows support first-time-right imaging


It turns out that workflow efficiency and ease of use contribute more than 25% to getting an image right the first time.2 Think what could happen if you could reduce and simplify the number of radiology workflow steps for a conventional exam, using technology to guide and coach where required, and even automate where possible. Let's take a look at what's happening with workflow advances across diagnostic imaging.

What smart workflows can look like


AI can help at crucial steps in imaging workflow. Guided exam set-up, touchless patient sensing, and fast exam planning, scanning and processing are part of smart automation in end-to-end workflow for MR. CT with AI offers the speed and precision to conquer daily departmental challenges such as fast and accurate patient positioning. Continual quality analysis at the point of X-ray image acquisition can help technicians perform at their best. All of this simplifies the path from image acquisition to diagnosis, helping to enhance the experience for patients and staff.
The whole workflow is easier because inexperienced techs are able to scan confidently without delays caused by missing information."

Julia Ehrsahm

RT, Marien Hospital, Stuttgart

Go deeper

 

Are you interested in learning more about how to address your challenges?

Patient-centered productivity in MR

Video showing Philips SmartWorkflow in action

Quality and efficiency without compromise in MR

Video of MR tech with control room screens, showing how exams can be prepared before patients arrive
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At a glance

Challenge

Diagnostic imaging departments face staff shortages and variations in staff experience, putting pressure on imaging teams to find new efficiencies that also allow for quality time with the patient.

Results

AI-enabled technologies are significantly aiding workflow in end-to-end solutions, including preparing for the exam, guiding exam set-up, and fast, efficient scanning.

    Would you like to know more about systems with intuitive workflows that help your staff get the image right the first time, every time?

     

    Take a deeper look at the solutions that are helping radiology departments successfully address workflow challenges.

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    Results presented are for illustrative purposes only and are not predictive of actual results for your business.

     

    1. According to the definition of AI from the EU High-Level Expert Group.

    2. Radiology Staff in Focus: A Radiology Services Impact and Satisfaction Survey of Technologists and Imaging Directors. Research Report. 2019. Philips.

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