Emerging healthcare demands require next-generation imaging infrastructures
For decades, the prevailing platform for managing medical images has been picture archiving and communication systems (PACS). However, this technology has remained relatively unchanged for nearly 30 years and it’s becoming clear that PACS is no longer keeping pace with the rapid changes and emerging trends occurring in healthcare.
For example, PACS is designed to manage images based on the DICOM standard, such as CT scans and MRIs that are common to the traditional imaging centers of radiology and cardiology. However, medical imaging today occurs in several locations outside these departments where images are captured in a variety of non-DICOM formats.
Dermatology, gastroenterology, ophthalmology and even the ED are prime examples of areas within a healthcare system where non-DICOM, visible light images are captured using specialized modalities or even smartphones or digital cameras. These images are often stored as JPEG, TIFF, PNG or GIF files that PACS don’t manage well.
As a result, these images are typically locked away in their own siloed repositories where they remain unintegrated with the EMR and largely unseen by clinicians.
Furthermore, many PACS are still built using proprietary code sets that make sharing images between platforms difficult and costly. This hampers the interoperability, collaboration and continuity of care that are central to optimizing healthcare delivery today.
Finally, PACS are ill-equipped to handle the massive data volumes and information normalization and de-identification requirements necessary to leverage emerging artificial intelligence (AI) and machine learning technologies.
Next-generation imaging
Delivering optimal healthcare today requires more than just PACS. It requires a next-generation imaging infrastructure built around a vendor neutral archive and enterprise viewing. It should allow for the centralized management of all medical image file types, regardless of department of origin, and link these images to the patient record in the EMR for easy access and viewing.
Furthermore, it should allow for the aggregation, normalization and de-identification of images for use in AI initiatives that can have a profound impact on population health.
Recently, I had the pleasure of conducting a webinar with Matt Zawalich, director of Clinical Imaging Technologies at Yale New Haven Health Services (YNHHS). In this web event, we explored how YNHHS reimagined its imaging infrastructure to improve visibility of all medical images throughout the enterprise while enabling AI and machine learning initiatives that support the provider’s academic mission.
You can download this webinar recording here. I encourage you to view it to gain real-world insights on how to strategize and execute a next-generation imaging infrastructure of your own.
