5 lessons from the medical imaging industry
Because the imaging industry has been under a microscope for years, it's had to do more with less. There are many ways in which imaging can serve as a blueprint for other stakeholders as healthcare moves toward value-based care delivery models.
The imaging industry has been under a microscope for many years due to concerns about potential over-utilization, resulting in payment reduction every year for almost a decade. In response to that challenge, medical imaging practices have been doing more with less for longer than most other specialties. In fact, there are many ways in which imaging can serve as a blueprint for other stakeholders as healthcare moves toward value-based care delivery models.
Investing in new technologies that diminish barriers to image access across disparate systems, for instance, is one way imaging practices are attempting to reduce cost and improve patient care. The following are five other lessons that the rest of healthcare can learn from imaging:
Technology standards create efficiency
Imaging practices have long embraced advanced technology and standards as a way to optimize efficiency. Indeed, interoperability standards support and enable the imaging business model.
Digital Imaging and Communications in Medicine (DICOM) and vendor-neutral archives (VNAs), for example, are the two major technology standards that have provided imaging with enormous scale and reach. Standards and interoperability allow imaging centers to simplify the sharing of data, which in turn allows them to optimize service with faster turn-around times and more efficient communication.
Optimizing data access increases clinical value
The imaging industry also has learned to access patient histories quickly and efficiently. Using advanced technology tools such as Computer Aided Detection (CAD), radiologists can provide physicians with relevant data and the appropriate clinical context by identifying changes in an image. CAD tools compare two images and can identify important data points such as tumor size and progression and quickly spot clinical trends or disease progression.
The ability to store and fetch prior images is key to understanding patient history. From a clinical perspective, in many cases a current image has little value without the ability to compare it to the patient’s last image.
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