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Allegheny Health Network Selects New Medical Imaging Solution

Allegheny Health Network has selected M*Modal Fluency for Imaging to improve radiology reporting to better integrate medical imaging into their EHR solution.

Allegheny Health Network (AHN) has selected M*Modal Fluency for Imaging as their multi-facility radiology reporting and workflow management solution, the company announced midweek.

Radiology imaging in healthcare

AHN, part of Highmark Health, sought better quality reports and faster response times to improve workflow.

"The M*Modal technology helps us deliver high-quality reports, faster and more efficiently," said Dr. Marc Wallace, Director of Imaging Informatics at Allegheny Health Network. "The accuracy and response time of the M*Modal solution has improved our workflow considerably."  

M*Modal has allowed AHN to implement a cohesive documentation strategy operating in conjunction AHN’s Epic electronic health record (EHR) system. M*Modal Fluency for Imaging has assisted AHN in delivering approximately one million high quality radiology exams this year and has incorporated M*Modal’s speech recognition and natural language technologies into their EHR solution.

M*Modal’s built-in computer assisted physician documentation (CAPD), Assist, improves clinician workflow by providing real-time automated feedback to radiologists which improves the completeness of radiology reports.

"M*Modal CAPD is a unique technology that embeds contextually-relevant information directly in our current reporting workflows in an effective but non-disruptive manner. We expect this will have a huge impact on patient care, report standardization and even reimbursement," said Dr. Wallace.

Healthcare organizations are seeking better technology to interpret medical images and improve diagnostics with machine learning and artificial intelligence.

GE Healthcare is currently working with radiologists at Boston Children’s Hospital to develop advanced imaging technology. The collaboration aims to develop a new decision support platform to help distinguish the large variability in brain MRI scans. The system will provide doctors with scans of children of all ages with different conditions to use as reference points for reading scans.

Brain scans can be difficult to read even for the most seasoned radiologists and professionals at Boston Children’s are glad to assist GE Healthcare lending their expertise to the development process. The machine learning technology will use the knowledge provided by radiologists at Boston Children’s along with archived brain scans to detect small inconsistencies and patterns.

Using these patterns, the technology will make connections between scans that are too complex for humans to make and create a database for clinicians to use as a reference resource to diagnose conditions more accurately and sooner.

The current advancements of artificial intelligence in healthcare aims to eliminate human error in patient diagnosis by making use of the large volumes of data collected by clinicians, specialists, and medical devices. With a wide database of information that considers every aspect of a patient’s lifestyle, demographic, genetics, etc., and compares that data to thousands of other patients with the same health conditions and diseases, connections can be made that may not have ever been discovered due to the lack of understanding of the data along with the massive amounts of unstructured data stored in organization data centers.

A computer can recognize and react to the patterns and pixels that make up a digital image by using an algorithm to calculate and measure the data in the images. The algorithm can take the image produced by a scan of a patient’s brain, heart, spine, etc., measure the image and compare it to other measured images in the stored data base with confirmed diagnoses.

M*Modal Fluency for Imaging is one of many solutions healthcare organizations are implementing to better process images to provide better patient care and more accurate diagnosis.

Similar to all high tech medical initiatives, the success of AI and machine learning deployments depends on the strength of an organization’s health IT infrastructure. As this technology grows, IT infrastructure technology will scale alongside it to support better patient care.

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