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 Eon 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.

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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 Eon 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. 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.

Why is this important for YOU?

Reason 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.

Reason 2: Hospital systems across the U.S. are looking for areas from which to drive INCREASED REVENUE.

Each IPN captured by Eon constitutes a finding from which revenue can be derived. After performing a retrospective analysis, a large IDN client-partner found that each IPN located represented about $3,491 in new 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 IPNs as well as the longitudinal patient tracking features to ensure compliance and follow-up means EPM as a technology platform can be a valuable partner for your facility as you look to the future.

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Now more than ever, hospitals are leaning on state-of-the-art software to make the work they do every day more accurate and more efficient. The right software can improve patient experience and treatment quality while dramatically saving hospitals time and money.

In this guide, we’ll explain how Eon’s lung cancer screening software helps you do more with your EHR. Plus, we’ll walk through our recommended review methods and integration advice.

The Review Committee

Purchasing and implementing an organization-wide software can affect a lot of different areas of your hospital. That’s why it’s important to establish a diverse review committee representing a range of departments. Here are a few things for your review committee to consider when evaluating any software solution:

Domain Expertise 
Look for a solution designed by providers, not engineers. Providers understand provider workflows and clinical pathways, and know how to minimize data entry and optimize the effort of the staff. Ultimately, a well-designed application should adjust to your workflow—not the other way around.

When choosing a software provider, look for someone who has executed in multiple environments, including integrating with multiple EHR’s in complex environments. The right partner will provide resources from every customer, not just one or two hand-picked customers.

Look for a company that is agile. This means understanding where the market is going and how to be a good vendor partner to meet your unique environment’s needs.

Today’s needs are one thing, tomorrow’s needs are another. When choosing a partner, look for a company who can iterate quickly and meet the future needs of healthcare providers. Avoid “orphan apps” that don’t address multiple disease states.

Why pay for a large company’s overhead costs when technology is increasingly becoming cheaper and easier to build? Look for a company that will have a cost ceiling and not upcharge you every chance they get.

What to look for in patient management and lung cancer screening software

Cloud based
Cloud-based software solutions increase efficiency and flexibility, while dramatically reducing cost. With Eon’s cloud-based software, there are zero upgrade fees, no infrastructure costs, endless scalability, and automation for otherwise laborious tasks.

Patient Registration
Traditional registries can come with silos and barriers. This makes registration difficult, especially when your patients source from many different locations and systems. By allowing registration to span different environments, Eon creates increased flexibility and makes it easier than ever to increase patient volume over time.

Longitudinal Tracking
Workflows should be adaptable to your environment. That’s why Eon’s lung cancer screening software uses Robotic Process Automation (RPA) to automate repetitive lung cancer screening tasks so all of your expected procedures and exams are automatically populated. That means 90% of your patients’ next expected exams are already there, so you instantly know what’s already been scheduled and what needs to be scheduled. This enables FTEs to spend more time on at-risk patients and value-added tasks that drive downstream revenue.

Follow-up Listener
Eon also lets you know what patients actually received their exams or procedures, and which ones are at risk for being lost to follow-up. Eon’s proprietary Follow-Up Listener technology listens for expected exams to become scheduled in EPM and matches pertinent performed exams to scheduled exams. The software also predicts patient no-shows and creates patient leakage information, enabling hospitals to make strategic interventions if necessary.

AI Technology
Technologies like Computer Aided Detection (CAD) and Natural Language Processing (NLP) can disrupt radiology workflow. Eon uses the most advanced form of AI to create layered Computational Linguistics (CL) data science models. CL understands text and the linguistic structure of English, similar to how the human brain reads, eliminating the need to require any changes to radiology workflow. This approach helps Eon achieve 98.95% accuracy and 98.66% recall—reducing the number of false positives while increasing patient retention. In addition, Eon’s software identifies incidental pulmonary nodules at a rate ten times greater than standard identification methods. 

Zero Data Entry
Eon’s lung cancer screening software makes data entry easier than ever by allowing you to enhance, refine, and improve raw data with a robust data-enrichment toolset. Our computer models are integrated with traditional data engines, allowing for never-before-seen patient data automation. Plus, with Eon’s ACR submission software, you get real-time flags on required data fields for LCSR submission, CMD, NRDR, IELCAP, or research and clinical trial requirements—so you never have to guess if you’re in compliance.

Real-time Analytics
It’s crucial to be able to see a clear, concise overview of your key results from lung cancer screening, incidental nodule tracking, and clinical trial activity in order to track trends and measure growth. But overviews aren’t enough. With complex patient management data, it’s important to create visualizations in order to bring your data to life. With Eon’s state-of-the-art analytics tools, you can create and export custom reports that help you analyze and visualize your results more easily than ever. Plus, Eon’s lung cancer screening software takes the busywork out of incidental nodule tracking by allowing you to assign data fields to specific users and get access to all required data fields—with just one click.

One Click LCSR Submission
With Eon’s ACR submission software, you can automatically audit and submit to the National Radiology Data Registry, RedCap, and other clinical trial registries with one click. Plus, the software includes NRDR confirmation of accepted data—you never have to guess if your CMS-required LCSR submission was accepted.
Patient and Provider Communication
Eon’s communication portal lets you automate, document, and track all of your communication, all in one place. You can create custom letters with automated content associated with a specific patient and their specific results, organized in a way that makes sense to you. Then you can send, fax, batch, print or email with a single click.

Implementation Expectation

There’s a lot that goes into carefully selecting the software that will best fit the current and future needs of your hospital. But that’s not all you need to consider. Ensuring a quick and seamless implementation of the software is crucial in realizing a return on your investment as quickly as possible. That’s why the topic of implementation must be a part of any vendor evaluation. Here are a few things we recommend considering:
Consider each vendor’s implementation experience. Specifically, ask vendors to provide references for their fastest implementation and their longest implementation. You don’t need a vendor to learn on your dime. Choose a vendor that is proven and experienced.

Do vendors rely heavily on your IT team to create solutions? Or, are they experienced and able to provide solutions for you without dragging in additional resources?

No more than 8 weeks 
No one wants to wait. While each implementation scenario may be unique, 8 weeks or less should be the standard for technical implementation of any organizational-wide software. This ensures that you start realizing a return on your investment as quickly as possible. Eon offers a 7-day implementation of its lung cancer screening software.

Do more with your EHR, with Eon.
Eon provides complex patient management software for today’s hospital. We believe data is the lifeblood of healthcare. That’s why we’re using state-of-the-art technology to revolutionize everything from incidental nodule tracking and complex LCSR submissions to patient communication and follow-up.

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Lung cancer is the second-most commonly diagnosed cancer and is the leading cause of cancer-related deaths, accounting for more cancer deaths than breast, prostate, and colon cancers combined. Smoking remains the largest risk factor for lung cancer, accounting for about 90% of all lung cancer cases. Increasing age is also a known risk factor, with the median age at diagnosis being 70 years old. Lung cancer is associated with a poor prognosis due to its asymptomatic features and predominant late stage diagnosis, leading to an overall 5-year survival rate of only 20.5%. When diagnosed in its earliest stages before symptoms develop, lung cancer has a significantly better 5-year survival rate, upwards of 81–92%.

In 2011, results were published from the National Lung Screening Trial (NLST) which showed a 20% reduction in lung cancer mortality when utilizing Low-Dose CT (LDCT) for lung cancer screening. Following these results, the United States Preventive Services Task Force (USPSTF) announced a level-B recommendation in December 2013 for LDCT for lung cancer screening in current or former smokers aged 55–80 years old, with a 30 pack-year history, who have quit no longer than 15 years earlier. Following a systematic review of the accuracy of LDCT screening for lung cancer and associated risks versus benefits, the USPSTF has now updated the 2013 recommendation. On March 9, 2021, the USPSTF announced a level-B recommendation for annual screening for lung cancer with LDCT for adults aged 50–80 years old who have a minimum 20 pack-year smoking history and are current smokers or former smokers who have quit within the past 15 years. 
Please note, these new USPSTF recommendations have not yet been formally adopted and the existing lung cancer screening eligibility criteria listed below will continue to be enforced until further notice from CMS and other healthcare payors.

USPSTF Lung Cancer Screening Recommendation
  • Age 55–80
  • 30 pack-year smoking history
  • Current or former smokers who have quit within past 15 years
  • Age 50–80
  • 20 pack-year smoking history
  • Current or former smokers who have quit within past 15 years

Once adopted, this expanded screening criteria is estimated to nearly double the number of people who will qualify to receive LDCT for lung cancer screening. As new evidence continues to be evaluated, there is proof that the benefits of screening earlier and including lighter smoking history can provide real benefits. 

LDCT programs continue to be implemented in medical centers across the United States due to the results of the NLST and subsequent USPSTF recommendations and adoption of coverage by the Centers for Medicare and Medicaid Services (CMS). Despite solid data demonstrating the clinical value of lung cancer screening, establishing a successful LDCT program can often take time and resources not easily available to medical centers. Successful implementation of an LDCT program requires a great amount of organization, collaboration with stakeholders, and strict adherence to recommendations, along with continuous quality control to ensure proper patient adherence to recommended follow-up. With the existing USPSTF lung cancer screening recommendations, it was estimated that 9 million Americans were eligible for an annual LDCT. With these updated recommendations, that number has the potential to double. 

Eon’s lung cancer screening software was developed to help facilities with both existing and new screening programs. Eon automates routine LDCT management tasks—considering that 90% of lung cancer screening patients only need an annual LDCT, the time saved from eliminating FTE manual data entry is considerable. On average, FTEs using Eon save up to 90% of their time. FTEs can spend more time with patients who need true care coordination, driving downstream revenue and better patient outcomes. 

Learn more at: Lung Cancer Screening Software


Final Recommendation Statement. (2021, Mar 9). Lung Cancer: Screening, Retrieved from https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/lung-cancer-screening#fullrecommendationstart

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Referral for surgery varies slightly on patient condition but in general it is about 5 cm for women and 5.4cm for men.  If the aneurysm has grown 1cm or more over the past year, this is another indication.

Pre-operative imaging should evaluate and document key aneurysm morphology, access vessel size, and patency including:

  • Aortic tortuosity
  • The proximal and distal landing zones. Ideally, these should be a minimum of 2 to 3 cm apart [6] to ensure an adequate seal and decreased rates of endoleaks, aneurysmal degeneration, and device migration.
  • The proximal and distal landing dimensions as well as the aortic diameters over the graft (to determine endograft sizing).
  • In the abdomen, care must be taken to assess for possible stent coverage of major branch vessels.  If abdominal visceral branches are involved, and there is potential for the celiac trunk or superior mesenteric artery to be covered by the stent graft, then the presence of collateral vessels must be documented. In the absence of collaterals, an open surgical or hybrid approach may be necessary to avoid visceral ischemia [6].
  • When the distal landing zone is located within one or both of the common iliac arteries, the diameter and extent must be documented.
  • For conventional endovascular repair to have an adequate proximal graft seal, an aneurysm neck size of >10 to 15 mm in length and <30 mm in diameter.  Over 50% of patients have aneurysm morphology unsuitable for conventional endovascular repair.  Unfavorable neck anatomy, based on its diameter, length, angulation, morphology, and presence of calcification, is the most frequent cause of exclusion.
  • Mural thrombus and atherosclerotic calcification covering more than 90° of the circumference of the aortic diameter in the proximal neck is associated with a higher endoleak and stent-graft migration risk.
  • It is also necessary to evaluate the access path from the femoral artery through the iliofemoral vasculature [6]:
    • The minimal external iliac artery intraluminal diameter should be ≥7 mm to safely accept AAA delivery sheaths.
    • Since thoracic aorta endografts are larger than their abdominal counterparts, their insertion sheaths can have outer diameters up to 27 French and require a minimum vessel diameter of at least 8-9 mm.

Increased vessel depth, degree of femoral artery calcification, and iliofemoral tortuosity are negative predictors of percutaneous repair success.

Aortic dissection may be classified according to either the Stanford and DeBakey systems. Stanford is more widely used for TAAs; it classifies dissections into those that involve the ascending aorta as type A, and all others distal to the left subclavian artery as type B.  The DeBakey system may be used for both TAAs and AAA;  this system classifies dissection based on the site of origin and is divided into types I, II, IIIa, and IIIb.

The majority of dissections arise from ascending aorta – either first few cm or just distal to the origin of left subclavian.  On pre-operative imaging, in addition to identifying the start and end locations, the following morphology should be described:

  • Aortic dilatation
  • Dissection flap (linear mobile structure within the aorta which moves more than the aortic wall)

As well as flow (Color Doppler or multi-phase imaging) characteristics:

  • Flow in true and false lumens will be different and may be able to localize entry and exit points.
  • There may be thrombus in false lumen (partial or no flow)

And finally, look for complications of dissection:

  • Aortic rupture

EPM AAA Solution
Eon offers the most powerful solution to identify incidental AAAs, automate screening for eligible populations, and longitudinally track patients who need serial surveillance.

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Eon + Epic

It’s not Eon vs. Epic, it is Eon + Epic, and Eon pairs well with Epic to make Eon Patient Management seamless, efficient and effective. While Epic does some things really well, it does not have a focus on data science. So, when identification of patients based on free-text is needed, Eon excels. Eon also excels at imbedding guidelines and automation for longitudinally tracking patients, thereby ensuring no patient is overlooked or does not receive evidence-based care. Conversely, Epic relies on the radiologists appropriately flagging these patients. At Eon, data science is our lifeblood, and our mission is to make patients healthier and healthcare affordable.

Eon is a powerful supplement to Epic. Eon’s sophisticated implementation team works closely with your hospital’s IT group to build the most proficient EPM solution available. Depending on the scope of the project, the internal technical IT capabilities, and required clinical behavior, Eon can provide a customized solution that exceeds expectations. The Eon solution incorporates high precision linguistics and guideline based automation to streamline clinical workflow and achieve documented patient outcomes.

Here is why the Eon + Epic integration solution is vastly superior to an Epic-only build:

  1. Identification of Abnormalities

    Best in class Computational Linguistics (CL) that does not require radiologist workflow disruption or behavior modification Specifically, the Eon CL solution does not require use of macros or structured radiology reports to identify patients with abnormalities found on imaging. CL is a superior form of Natural Language Processing (NLP) because it is contextual (see Figure 1). Eon CL achieves a higher Positive Predictive Value (PPV) by understanding complex medical ontologies and identifies abnormalities with high precision and accuracy.

    Epic-only solution requires radiologists to utilize Macros and Structured reports that are used as flags. These trigger phrases are required because Epic is not focused on data science and has not created data science models for this space. Because of this, an Epic-only solution is highly dependent on Radiologist Adherence, creating the potential for false negatives and patients slipping through the cracks.

    Epic-only solution is labor-intensive because Epic creates a list of patients for manual Coordinator curation of actionable patients versus patients with previous or known findings.


    • Computational Linguistics extracts pertinent data to inform the Coordinator about the finding so they can make quick decisions without looking in disparate systems to collate information. This data also helps to determine next steps for the patient’s care pathway, as defined below in #2. Please see Exhibit A for all CL Models and Data Extraction.
    • Computational Linguistics also extracts Lung Cancer Screening Registry specific data from radiology free-text reports. Between this and supplemental EMR data, subsequent submission to the registry can be fully automated.

    Figure 1: Computational Linguistics (CL) is a superior form of Natural Language Processing (NLP) because it is contextual. Contextual information integration is shown below.

  2. Automation of Care Pathways

    Eon is able to remove approximately 80% of the Coordinator’s repetitive tasks and manual tracking using advanced business logic. The logic is based on YOUR clinical requirements, not the care plan or guidelines we require. This logic automates and streamlines clinical workflow by 80%, allowing your resources to focus on the patients most at risk and who need immediate care coordination. EPM is not rigid and can customize care pathways based on how you treat patients. Epic does not utilize business logic to automate redundant tasks and requires manual efforts for data collation, expected next steps, and care pathway decisions.


    • In EPM, patients are automatically triaged based on pertinent clinical information extracted from the CL models. Based on the collation of this information, patients are risk stratified, the clinically appropriate next steps (chosen and approved by you) are auto-populated and the patients are placed on the appropriate worklist.
    • Low-risk patients have the expected next steps automatically entered. In addition, your requirements for provider and patient communication are automated including pre-populated letters, faxes and texts. These documents can be batch created and sent, as well as forwarded to Epic (level of integration will depend on routing configured by your IT team). In addition, any phone calls documented in Eon can be forwarded to Epic as phone encounters (again configuration by your IT team).
    • High-risk and indeterminate patients can be sent directly to a Subject Matter Expert (SME) worklist for review and expected next step entry. Upon completion, the patient is sent back to the Coordinator for care coordination and communication to provider and patient.
  3. Longitudinal Tracking

    No more spreadsheets, no patients left behind. Eon’s cloud-based dashboard allows for multiple stakeholders to view patients at different times in the patients care pathway. Once Patient Identification and Care Pathway Automations have occurred, Eon ensures insight and oversight of patient processes — ensuring follow-up exams occur and guidelines are adhered to (as approved by you).While Epic is the main hub for ordering and scheduling, Epic does not create carepath-specific worklists and provide automated expected next steps for patient care. This means Epic cannot provide an overview of patient adherence. For example, Epic will not know when a carepath-specific event was supposed to be scheduled and wasn’t, and does not identify patients who were scheduled to receive a care pathway exam but did not show up for it.


    • EPM knows exactly what event should happen next for every patient and when. EPM is constantly listening for that order to be scheduled and completed. If an order or scheduled exam does not occur, the Coordinator is notified to intervene. If an exam is scheduled, the Coordinator knows and does not require further action.
    • If the patient misses a specific care pathway event, with Eon, the Coordinator will be notified and the patient auto-added to the Overdue Worklist.
    • Patients move between worklists depending on their care plan and what is required next.
    • Discrete Deactivation Reasons for Care Pathways ending are always captured in Eon, allowing for real-time program review and analysis.
  4. Real Time Analytics

    Eon offers powerful reporting and data analysis that does not require IT analyst data extraction or take weeks, or even months, to produce. All reporting is real time as well as date-range based.

    • In-dash data export
    • Canned reporting
    • Custom reporting

  5. Fast Implementation

    Eon is able to implement within complex Hospital Networks quickly and efficiently. Epic sites can be some of Eon’s best integrations. While Eon can implement as fast as seven days, complex implementation requiring bi-directional feeds and multiple patient cohorts will require 12-16 weeks. Typically client IT timelines control the implementation process and determine time to completion. Eon always provides a Project Manager during the implementation and requires a minimal amount of Hospital Resources to achieve success. In contrast, when a facility decides to build a solution within Epic, an Epic build can require nine or more resources on multiple project calls for a minimum of six months and take up to 17 months before the project can be live. See the requirements for an internal Epic solution build:

    Internal Epic Solution Build Requirements

    • Staffing: Your system will need to staff at a minimum of 9 resources
      • Project Lead
      • Project Manager
      • Clinical Informatics Specialist;
      • Clinical Operations SMEs x2 minimum
      • IT staff including Epic Clarity Analyst (reporting), Epic Clindoc analyst (Hospital & ED build), Epic Ambulatory analyst (Outpatient build);
      • Quality Assurance staff;
      • Training staff
      • And, potentially other technical resources.
    • Project Manager Time Allocation: A Project manager will take 2-3 months working with Informatics Specialist and Clinical SME’s to design the desired workflow before activating IT services.
    • Project Prioritization: Build requirements as evaluated by IT staff for feasibility and scheduling
      • Because this is typically an operational project – NOT CAPITAL – the IT staff will schedule the changes with other priorities and build may not start for at least 6 months…
    • Clarity/Reporting Analysts: These will be needed to build an Epic Registry to identify patients, and Metrics to capture critical observations for clinical staff
      • Typically > 6 month project for any new registry to make it to production in Epic. The majority of this time is building logic that Eon already has
      • Epic Report analysts are paid ~$100k and will be required for post project maintenance
    • Supporting Team: Additional Epic analysts will be required to configure workflows, this is a different skill set than the report analyst.
      • Workflow build can happen in parallel, but will require the same change time frame > 6 months for release
      • This requires a hospital build analyst and ambulatory analyst as the workflow will cross these areas
      • Workflow will use patient workqueues (lists) to guide users in follow up requirements
        • These are often exported from epic and addressed on spreadsheets.
    • Once the build is done by Reporting and Application analysts, QA and Training can start.
      • Additional changes may be needed after QA extending the timeline
      • QA will take at a minimum one month, training another month
        • Additionally this build must be evaluated with every Epic upgrade that happens, which is quarterly for most organizations.

    Exhibit A – Computational Linguistics Available Models and Data Extraction

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Did you know that Radon is the leading cause of lung cancer in non-smokers and the second leading cause of all lung cancers in the United States, contributing to over 20,000 lung cancer deaths per year?

Radon is a radioactive gas released from the normal decay of naturally occurring elements found right beneath our feet. This invisible, odorless, tasteless gas seeps up through the ground into the air. The level of radon varies greatly in different parts of the United States depending on the characteristics of the rock and soil in the area. Radon gas usually exists at very low levels outdoors, but can be found at dangerous levels indoors as the gas given off by rock or soil can enter buildings through cracks or gaps in the floors and walls. These levels of radon are often highest in basements or crawl spaces. 

According to the Environmental Protection Agency (EPA), the average level of radon outdoors is about 0.4 picocuries per liter (pCi/L) compared to average indoor levels of 1.3 pCi/L. The EPA recommends taking action to reduce radon in homes that are at  or above 4.0 pCi/L. It is estimated that about 1 in 15 homes are at or above this EPA action level. Scientists estimate that lung cancer mortality could be reduced by up to 4% by lowering radon levels in homes at or above 4.0 pCi/L.

The best thing you can do today to reduce your risk of harms associated with radon is to test your home. EPA and the U.S. Surgeon General recommends all homes in the United States be tested for radon. Winter is a good time to test your home when windows and doors are sealed tightly which can cause radon levels inside your home to rise. Contact your state radon office for information on how to obtain a test kit. Some states offer free or discounted test kits to the public. You can also visit http://www.sosradon.org to order test kits and obtain information.

If you think you have been exposed to high levels of radon over an extended period of time, talk with your doctor. Regular health checkups can look for possible signs of lung cancer including shortness of breath, a new or worsening cough, hoarseness, or trouble swallowing. If you are a current or former smoker, you may be eligible to receive a Low-Dose CT for Lung Cancer Screening.

EPM Lung Cancer Screening Solution

Eon’s lung cancer screening software empowers FTEs to focus on patients with significant Lung-RADS scores by triaging patients by risk and automating the longitudinal care and communication with patients at low risk. This dramatically improves patient outcomes and boosts screening programs’ bandwidth with a fully compliant lung cancer screening solution.

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Eon Care Management is the first and only market-available product to manage every aspect of a lung cancer screening and incidental pulmonary nodule program—so you don’t have to.

With Eon Care Management, you can:

Offload resource-intensive tasks to Eon’s team of highly skilled providers
Capture more patients and ensure patients don’t fall through the cracks
Improve outcomes by focusing on care over manual administrative tasks

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Empowering facilities to capture more incidental pulmonary nodules and identify lung cancer earlier is at the heart of what Eon does every day. That’s why Eon expanded its data science models to now identify incidental pulmonary nodules on Magnetic Resonance (MR) and  X-Ray radiology reports in addition to CT. 

Eon Patient Management platform uses Computational Linguistics to identify incidental pulmonary nodules on computed tomography (CT) reports with 98.95% accuracy, and 97% accuracy on MR and X-Ray radiology reports. This game-changing update allows facilities to capture approximately 25% more incidental pulmonary nodules and empowers providers to identify lung cancer earlier when treatment is most effective.

“Any imaging that covers a lung field can identify an unexpected pulmonary finding, such as an IPN. Hundreds of thousands of IPNs each year are identified on CT and MR exams, often of anatomy other than the chest. Suspicious or concerning areas of abnormal density on radiographs are also common. Unfortunately, these nodules and abnormal regions are frequently lost to follow-up or inappropriately followed ,” said Dr. Erika Schneider, Chief Science Officer at Eon. “Our goal is to create technology that identifies disease before symptoms present, at its earliest and most treatable stages. By expanding our linguistics model, we now offer the most sophisticated solution on the market for early detection of lung cancer.”  

Eon uses Computational Linguistics, a data science discipline that interprets text similar to how the human brain does, to engineer the most advanced models on the market today. This approach allows providers to positively identify and track incidental pulmonary nodules with more accuracy than other forms of artificial intelligence like Natural Language Processing (NLP) and Computer Aided Detection (CAD). The technology is developed by a team of physicians and data scientists to provide incidental patient identification and management solutions with embedded evidence that decrease administrative burden and improve patient adherence to follow-up exams.

With Eon’s proprietary Computational Linguistics data science model, EPM also extracts clinically relevant findings from radiology reports. The IPN model documents nodule location and characteristics like density, shape, edge, and calcification and automatically populates the information into the EPM dashboard. This allows evidence-based Fleischner Society guidelines to be automatically applied to create an actionable worklist. This approach helps providers by removing excess noise (false positives, low-risk nodules), ensures appropriate patient tracking, and automates complex follow-up.

“By expanding our linguistics model, we now offer the most sophisticated solution on the market for early detection of lung cancer.”

Schneider adds, “Computational Linguistics is the gold standard for language understanding, in particular for lung nodule identification and characteristics extraction. By embedding evidence, the nodule characteristics focus providers’ attention on patients with a high probability of having lung cancer. The high accuracy and reproducibility of our model reduces false positives and does not require radiologists to use a structured report. This approach, along with the embedded risk prediction and automation, should enable providers to prioritize patients and improve their outcomes.” 

Eon continues to be at the forefront of raising the bar on incidental disease identification and management solutions. And, expanding its data science models to multiple radiology modalities is another way to identify catastrophic disease earlier and improve patient outcomes. 

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