Readers Write: AI in Imaging: Improving Outcomes Across the Care Continuum

AI in Imaging: Improving Outcomes Across the Care Continuum
By Calum Cunningham

Calum Cunningham, MBA is SVP/GM of healthcare diagnostic imaging for Nuance Communications of Burlington, MA.

The role of AI in medical imaging has been top-of-mind for a number of years, particularly as deep learning algorithms continue to improve to the point they can even identify findings that are not detectable by the human eye. As these solutions gain traction and adoption in clinical practice, more radiologists acknowledge the need for familiarity with the role AI plays in imaging and how to successfully integrate it into radiology workflows. In fact, RSNA recently launched its first-ever certificate program for imaging AI.

While the benefits to radiologists are significant and broad  — enhanced efficiency, greater satisfaction by automation for repetitive and mundane tasks, and freedom to focus on what matters most — AI in imaging, more specifically an integrated imaging network, adds value across the care continuum.

The radiology workflow is at the heart of a patient’s journey, informing healthcare decisions at the point of care, but also traveling both upstream and downstream. Once a patient’s images have been captured, radiologists depend on a range of technological platforms to integrate those images, relevant patient data, and AI services (such as automated 3D visualization solutions) from across the healthcare ecosystem.

When a radiologist reads a mammogram, for example, they need more than just the images from the patient’s most recent study. The patient’s previous images, information from the medical record (e.g., history, demographics, and symptoms), and increasingly, AI-powered diagnostic models are all essential to helping the radiologist accurately, comprehensively, and efficiently prepare the report.

The radiologist’s report likewise becomes part of the integrated imaging network, which breaks down the siloes that traditionally exist between care teams, administrators, payers, and other third parties. Sharing key imaging data with administrative, coding, and billing teams, for example, means supporting the organization’s financial resilience. These teams depend on accurate, comprehensive reports to protect the organization’s revenue cycle. When radiologists harness the power of AI to deliver more content-rich reports, insurance claims may be cleaner, which can result in appropriate reimbursements from payers. Similarly, sharing imaging data in this way can also streamline prior authorizations for treatments, surgeries, and prescriptions.

The downstream impacts don’t end there, however. Health plans and self-insured employers can take advantage of the integrated network to improve coordination and collaboration in ways that can address care quality and cost. Life science companies can make use of these insights to identify candidates for clinical trials.

An integrated imaging network also benefits healthcare provider organizations, which can apply AI-generated insights from diagnostic imaging to support earlier disease diagnosis as well as inform treatment options and planning. Perhaps one of the more exciting aspects of an integrated imaging network is the potential impact on patient follow-up adherence. When a radiologist includes an imaging follow-up recommendation in their report, only about half of those recommendations are adhered to. Not only does this represent a significant risk to the patient in terms of treatment and outcomes, but it can also create liability risks for the providers, not to mention the adverse impact on financial performance for the healthcare organization. The integrated imaging network can help close the loop on these follow-up recommendations, automating certain aspects of provider and patient communication to reduce the risk of delayed diagnosis.

In short, by seamlessly sharing clinical and imaging data and applying AI-generated insights and automation, organizations can maximize the value of existing healthcare IT infrastructure across the care continuum while improving patient outcomes.