Incidental Abnormalities in Radiology

Have you noticed that incidental abnormalities seem to be on the rise? This is largely due to advancements in imaging technology and improved patient care—when you order cross-sectional imaging for one problem, you are more likely to discover abnormalities that are unrelated to the primary problem. In fact, they’re showing up in about one-third of CT scans today.


This is causing a significant shift in patient care. Before, the radiology community didn’t have a standardized approach on how to handle incidental findings, also called incidentalomas. The lack of direction and confidence about what to do would often compromise patient care. In some cases, radiologists may have not even reported incidental findings to patients or their providers. As technology has improved and incidental abnormalities are being discovered more often, the medical community has improved its approach.


Radiologists are answering the call


Radiologists—and providers in general—are focusing more on incidental abnormalities than ever before. Recently, institutions have published evidence-based recommendations for incidental findings in multiple disease states. Currently, these include The Fleischner Society guidelines for incidental pulmonary nodules, Lung Reporting & Data System (Lung-RADS) for lung cancer, Thyroid Imaging Reporting & Data System (TI-RADS) for thyroid nodules, and USPSTF Society For Vascular Surgery guidelines for abdominal aortic aneurysms. Radiologists who identify incidentalomas should follow these recommendations to recommend next steps for patient care pathways and follow-up. Adherence to these guidelines depends on radiologists, providers and facilities—and ultimately the patients themselves. Some patients may need more immediate action, while some only require serial surveillance. Regardless, all incidental abnormalities need to be properly evaluated, communicated and followed up on.


Even with accepted guidelines, there is vast room for improvement. For example, 95% of lung nodules are discovered incidentally, but less than 30% of those patients will receive follow-up care based on recommended guidelines. In the case of abdominal aortic aneurysms (AAA), 90% of incidental patients require only serial surveillance to watch for progression, but that follow-up can be life-saving—the annual survival rate for a ruptured AAA is only 20%. Because patient adherence needs to improve and the process can be time-consuming, healthtech companies are developing software to help facilities manage patients with incidental findings. Facilities need a comprehensive solution based on the recommended guidelines for specific disease states, which starts with artificial intelligence (AI) that interprets radiology reports and identifies incidental abnormalities.


Eon’s incidental findings solution


Eon offers just such a platform with Essential Patient Management (EPM) solutions for multiple disease states, including lung cancer screening, incidental pulmonary nodules, abdominal aortic aneurysms, thyroid, pancreas, adrenal, renal and liver. EPM utilizes proprietary Computational Linguistics (CL), a form of AI that understands text and linguistic structure to interpret imaging reports and identify incidentals at up to 98% accuracy. EPM also offers end-to-end patient management and longitudinal tracking, including automated communication and follow-up that saves FTE time. Radiologists and providers can have confidence in the longitudinal tracking of identified incidentaloma patients. Administrators can be satisfied in the ROI from increased patient adherence, optimized workflow and increased downstream revenue. A partner like Eon helps health care providers go all the way to give patients the best care possible, even through the unexpected.

Eon’s EPM Incidental Pulmonary Nodule Solution Improves Patient Outcomes and Increases Revenue

According to the CDC, cancer is the second leading cause of death in the United States. Lung Cancer specifically is America’s deadliest, killing just shy of 146,000 Americans each year (CDC, 2017). It is particularly destructive once symptomatic spread takes place. According to the 2020 NIH SEER Cancer Review only 5% of patients survive the 5-year mark once lung cancer spreads to other organs. Conversely, survival rates skyrocket to 57% if the cancer is detected early and contained locally. Unfortunately, at present, only 16% of cases are able to be diagnosed this early and the grim fact remains that more than ½ of all people with lung cancer currently die within a year of diagnosis. Given this background, to positively influence community health, an important goal for healthcare professionals is to find and diagnose lung cancer at an early stage, when it is more likely to be successfully treatable and survivable.


To that end, health technology company Eon has created a cloud-based platform designed to accurately identify and assess incidental pulmonary nodules (IPN’s) located by radiology in imaging exams.


Eon’s Incidental Pulmonary Nodules solution uses an advanced form of artificial intelligence called Computational Linguistics (CL) developed to understand and interpret the structure of written English. When used in Eon’s proprietary Essential Patient Management (EPM) platform, CL is able to locate and assess IPN’s at a 98.3% accuracy rate by pulling raw data directly from facility radiology reports as they are created. Once identified, EPM is able to assess nodule specifics such as size, shape, location and morphology, and then uses embedded Fleischner Society best practices guidelines to stratify risk in order to suggest a next course of treatment for the patient.


EPM then uses sophisticated RPA tools to automate routine management tasks, customized differently for each patient risk category. This allows a program to focus important resources on high-risk patients, while using RPA to automate many tasks for the longitudinal care and follow-up communication for low-risk patients. Eon EPM automates scheduling reminders for recommended next steps of care for all patients, including reminders for all follow-up care. This frees your patient navigators and staff to focus on the 20% of patients who are MOST AT RISK of cancer to ensure best use of resources and to coordinate follow-up procedures for improved patient health.


What does this mean for YOU?  Why is this important? – Two Reasons:


  1. Eon’s priority is to find and treat lung cancer at its earliest stage, when it is localized and has the best survival rates. Approximately 1.5 million IPN’s are identified by radiology every year but at present less than 30% of these get follow-up care according to recommended guidelines. Eon’s CL accuracy rate of 98.3% and precision rate of 98.1% ensures that no nodule located by radiology goes unnoticed in your facility. The open-structured CL platform runs constantly in the background, so your radiologists never have to modify workflow for data to be captured. The efficiency at which Eon locates, assesses and prioritizes nodule treatment means a program can effectively deal with a large number of patients safely and efficiently while freeing navigators or nurse managers to personally follow up on those high-risk patients who need the most attention to ensure treatment protocols are met.
  2. Hospital systems across the U.S. are looking for areas from which to drive INCREASED REVENUE. Each IPN located by Eon constitutes a finding from which revenue can be derived. One internal Eon review of an early product adopter found that each IPN located represented about $3,491 in revenue over the treatment span of the patient. Further data shows that for every 1,000 chest CTs and 100 LDCT scans, yearly revenue increases to $1,178,213 for a facility. Additionally, the longitudinal tracking ability of EPM ensures that patients stay with your facility for the duration of care, maximizing imaging and procedure revenue on a per-patient basis. Finally, Eon has expanded its software solution to additional disease states, identifying and tracking AAAs, pancreatic cysts, thyroid nodules and more. Like pulmonary nodules, each new finding represents an opportunity to improve patient health through procedural follow-up, driving new revenue for your facility.


Lung cancer progression can be very rapid, and any delay in treatment can mean the difference between multiple-stage progression, and potentially life and death for those people with unidentified pulmonary nodules. If your facility goals are to improve community health while increasing system revenue, the 98.3% accuracy of CL in locating and assessing IPN’s as well as the longitudinal patient tracking features to ensure compliance and follow-up means Eon EPM as a technology platform can be a valuable partner for your facility as you look to the future.