What You Should Know: 

– Rad AI, a leader in generative AI for healthcare, announced today it has raised $50M in Series B financing, bridging its total capital raised to over $80M. 

– The round was led by Khosla Ventures, with participation from prominent investors including WiL (World Innovation Lab), ARTIS Ventures, OCV Partners, Kickstart Fund, and Gradient Ventures (Google’s AI-focused fund).

Revolutionizing Radiology Reporting

Rad AI’s solutions are already making waves in the healthcare industry. Used by over a third of US health systems and 9 of the 10 largest radiology practices, their technology saves physicians significant time and improves patient care.

Pioneering generative AI in healthcare since 2018, Rad AI automates report generation, allowing radiologists to customize reports with their own language and style. This frees them from the burden of dictation, a task that consumes 75% of their time, often for reports on over 100 patients daily.

Leading the Way in AI-powered Reporting

Today, Rad AI offers cutting-edge solutions like Rad AI Reporting, the industry’s leading AI-powered tool for radiology reporting workflow. Additionally, Rad AI Continuity simplifies patient follow-up, with adoption by many of the nation’s largest health systems. Combined, these solutions positively impact nearly 50 million patients annually.

Reduced Burnout, Improved Care

Rad AI’s AI models take over repetitive tasks like dictation and follow-up management. This frees up valuable time for physicians, reduces burnout, and allows them to focus on what matters most – providing exceptional patient care. Hospitals using Rad AI’s solutions have seen:

• A dramatic increase (from 30% to over 85%) in patient follow-up rates for critical findings, ensuring timely diagnoses and treatment, especially for new cancers.
• Doubled report creation speed, often with a 90% reduction in dictated words, leading to less fatigue and burnout for radiologists.
• A near 50% reduction in report errors for complex cases, ultimately improving the quality of care provided.

“At Rad AI, we’ve built the most widely adopted generative AI solutions in healthcare, saving physicians time and improving patient care. Rad AI has become a mission-critical part of health system workflows over the past five years. This strategic funding round further cements our position as the leading AI-driven workflow platform in healthcare.”-Doktor Gurson, co-founder and CEO of Rad AI

What You Should Know:

• OXOS Medical, the MedTech innovator developing simple and safe X-ray solutions, announced a $23 million Series A funding from Parkway Venture Capital and Intel Capital, bringing its total funding to $45 million.
• OXOS continues to build on its traction across outpatient clinics, the military, the Veterans Administration, sports teams, hospitals, imaging centers, and bioskills labs. To help accelerate the company’s growth, Gregg Hill, Parkway Venture Capital co-founder and managing partner, and Eric King, Intel Capital health and life science investment director, will join the OXOS board of directors.

Leading the Way Forward in Delivering and Distributing Medical Imaging

Founded in 2016 by Dr. Greg Kolovich, a Harvard-trained, board-certified orthopedic surgeon, and serial entrepreneur Evan Ruff, OXOS Medical launched its first FDA-approved device, the Micro C®, in 2022. The device has won many orthopedic customers, spanning the VA, urgent care facilities, NFL teams, and medical education customers. Under its Cooperative Research and Development Agreement (CRADA) with the VA, the OXOS team works closely with radiologist Dr. Beth Ripley, Deputy Chief, Office of Healthcare Innovation and Learning at the U.S. Department of Veterans Affairs. Dr. Ripley and OXOS are increasing access to radiographic care by deploying OXOS solutions to multiple care settings for U.S. veterans. 

“At OXOS, we have made it our mission to close the growing healthcare divide – the inaccessibility of radiographic technology is impairing patient outcomes in every corner of the globe,” shared Evan Ruff, co-founder and CEO of OXOS. “OXOS’ smart and safe technology addresses the growing needs of orthopedic and radiographic professionals across all care scenarios, expanding access well beyond established sectors. We aim to put safe, powerful, and simple X-ray technology in the hands of urgent care centers, sports teams, home care, and international mission operations, where a real-time diagnosis is critical to saving human lives.”

Growing Shortage of Radiology Resources Across the Globe

With over $47 billion spent annually on medical imaging by 2030, OXOS is uniquely positioned to capture a significant portion of this growing market. OXOS technology overcomes the significant challenges associated with expensive and complex X-ray technologies while addressing the increasing global shortage of radiology resources.  

The demand for radiology services has steadily risen since 2013. Nearly half of the radiologists in the U.S. are of retirement age, and fewer residents are waiting to fill these soon-to-be-vacant positions, leaving the 2025 projected shortage in the tens of thousands. The staffing shortage is even more acute among radiation technologists. In the U.K., 97% of radiology departments cannot meet diagnostic reporting requirements, leaving patients waiting a month or more for their imaging results. OXOS addresses this hospital-driven bottleneck by deploying technology that can be used in diverse environments by users with varying levels of training.  

OXOS Smart and Safe Radiation Technology

Traditional radiology solutions, such as C-Arms, the machines used for fluoroscopic imaging during procedures, are well known for their limitations in image quality and high radiation exposure. With an incredibly low radiation profile, OXOS devices change the status quo, providing exceptional image sharpness and clarity in the smallest form factor, making X-ray more accessible.

OXOS combines advanced image processing, computer vision, X-ray detector improvements, easy ergonomics, and innovative X-ray tube architecture to deliver safer devices, while significantly reducing radiation exposure. OXOS developed the only devices on the market that can provide both static images (digital radiography) and live imaging (dynamic digital radiography), for both diagnostic and surgical radiography, in any setting.

OXOS devices connect directly to the OXOS Cloud Platform,  allowing physicians to instantly access radiographic studies from anywhere on any device. The platform redefines expectations for seamless care and delivers improved access and efficiency for patients and providers. Fully HIPAA-compliant, the OXOS cloud platform seamlessly integrates with existing radiology infrastructure or can leverage OXOS’ nationwide teleradiology service to provide study interpretation in a fraction of the time.

Today, cancer is the second leading cause of death in the United States. Sadly, cancer disparities exist, with racial/ethnic minority, low-income, and uninsured populations suffering the greatest burden. That’s why routine cancer screening is critical to addressing cancer disparities as they have the potential to greatly reduce both incidence and mortality rates. To address this, Federally qualified health centers (FQHCs) are funded by the Health Resources and Services Administration to provide preventive and primary healthcare services, including cancer screening, to the nation’s most vulnerable populations 

We recently sat down with Oatmeal Health’s Co-Founder and CEO Dr. Ty Vachon to learn more about the company’s mission and how its AI-powered solution is working with FQHCs, health plans, and employers to help underserved patients get screened for chronic disease and cancer. 

What is Oatmeal Health?

Dr. Ty Vachon, Co-Founder and CEO of Oatmeal Health: Oatmeal Health is a virtual nodule clinic and patient engagement service.  We leverage technology to help FQHCs and health plans improve the standard of care by identifying and preventatively screening vulnerable populations for chronic disease and cancer year after year, starting with lung cancer.

What’s the reasoning behind the name Oatmeal Health?

Dr. Vachon: We chose Oatmeal as a name for our company for a few very simple reasons, it’s a very unique name that coincides with a healthy lifestyle and it prompts the question that we want everyone to ask us, “Why is the company named Oatmeal?”

This question opens a line of conversation around the problem America is facing with chronic disease, health equity, and preventative screenings for underserved patients.

When were you founded?

Dr. Vachon: My co-founder Jonathan Govette and I started Oatmeal Health in 2022

Where did you get the idea for this company?

Dr. Vachon: During my time in the Navy as a primary care doctor and radiologist, I observed the challenges of preventative care and the tendency for doctors to focus on being reactive rather than proactive in their approach to cancer screening.

Without regular, yearly scans to monitor changes over time, radiologists are unable to provide the best treatment options. This gap in care also becomes an education and access problem, particularly for underserved patients who may not have the resources or knowledge to navigate the complex healthcare system. This is an issue that primary care is struggling with, compounded by the lack of time to properly see and assess patients during the day.

On a personal note, the inspiration for Oatmeal Health came from a personal place for both Jonathan Govette and me. Sadly, we both lost family members to lung cancer. In 2013, 

My mother would have met the screening criteria to get a CT but she did not get scanned and in September of that year, her stage 4 lung cancer was identified and she died shortly after. My co-founder Jonathan lost both his grandparents to lung cancer, and recently his mother passed away from another debilitating chronic disease.

We both decided it was time to change the status quo and make changes to how healthcare preventatively screens our loved ones so no one else has to die needlessly.

What problem are you trying to solve?

Dr. Vachon: Our goal at Oatmeal Health is to help the sickest generation in history by partnering with Federally Qualified Health Centers (FQHCs) and health plans to save their members’ lives. With 80% of seniors living with chronic conditions and chronic disease being responsible for 7 out of every 10 deaths in America, we recognize the urgent need for preventative care.

We are starting with lung cancer, which affects 16 million Americans living with a smoking-related illness. 1 in 17 Americans will be diagnosed with lung cancer in their lifetime, with more people dying from lung cancer than from breast, colon, and prostate cancers combined.

However, many clinics are not equipped to preventatively screen for cancer, including lung. They are facing significant challenges due to being overworked and understaffed, which leaves little time for managing preventative lung cancer screenings. On average, clinics have only 13 minutes per patient visit, making it difficult to provide thorough care.

Additionally, these clinics often lack the resources and staff expertise to develop their own tech-enabled solutions or to purchase and manage additional software programs to streamline operations and patient care.  They do not want to do “one more thing” or buy any more software.

As a result, many seniors wait until their condition is exacerbated and use the Emergency Room for care. This episodic care approach is costly for Medicare, with the average doctor visit costing $50 and a hospitalization costing $45K, while lung cancer treatment costs an average of $282K (lifetime).

Oatmeal Health aims to address this gap in care by partnering with FQHCs and health plans to provide preventative screenings for lung cancer and other chronic conditions, ultimately reducing costs and improving patient outcomes.

What is the key driver behind lung cancer screening? 

Dr. Vachon: When compared with breast, colorectal and cervical cancer, where ~70% of eligible people are screened, only ~4% of patients are screened for lung cancer.

– In 2021, more Americans were recommended to get screened and in 2022 reimbursement was approved to allow the 14.5 million eligible Americans to receive these scans at no cost to them.

– It was also determined that the new guidelines in 2021 address the typically underserved communities that were well represented in the 2013 guidelines. 

– Through an award by the American Cancer Society and the American Lung Association, NCQA has begun development of a HEDIS^® quality measure for lung cancer screening which should be live in the next few years.

So lung cancer screening will save more lives, more efficiently, in the communities that need it most.

Who are some of your clients? 

Dr. Vachon: Oatmeal Health has recently entered into a partnership with El Rio Health, an FQHC serving 125,000 patients in Arizona. We plan to expand its services to Oklahoma, Texas, California, Michigan, and Illinois once it completes its funding round.

What differentiates Oatmeal Health from your competitors?

Dr. Vachon: Oatmeal Health is different from traditional technology companies because it provides a comprehensive, technology-enabled concierge service that eases the burden of screening patients for cancer from primary care providers due to a ‌lack of time, resources, or expertise. This allows ‌primary care facilities to concentrate on their patients’ immediate needs while Oatmeal Health proactively manages the complete patient journey including staffing and technology without burdening the FQHC or the patient with additional tasks.

Current technologies often focus on only one aspect of the complex problem of chronic illness detection and management, resulting in a lack of a holistic approach. Our solution addresses all five crucial steps in the process: identifying hard-to-find patients with disease prediction software, determining their eligibility, facilitating scans at imaging centers, utilizing AI to interpret the results and determine malignancy, and providing ongoing education and support to ensure patients return for regular follow-up scans. 

By addressing all of these steps in harmony, our hybrid technology and clinical model ensure compliance and lead to better outcomes for patients.  In the future, they plan to expand services into breast, colorectal, and other chronic disease screening services. 

For more information about Oatmeal Health, visit https://oatmealhealth.com/

Lung cancer is the leading cause of cancer deaths worldwide, with approximately 1.8 million people dying from this disease each year. Most patients are diagnosed after symptoms have appeared and the disease has progressed to an advanced stage (Stage III or IV), which explains the current worldwide five-year survival rate of just 20 percent. In contrast, the survival rate for small lung tumors that are treated at Stage 1A is as high as 90 percent. This significant difference highlights a critical need for the diagnosis and treatment of lung cancer at the earliest possible stage.

One of the best opportunities to diagnose more small, pre-symptomatic lung cancers earlier is presented by the two million patients in the United States every year who have a lung nodule identified incidentally during chest CT scans ordered for other reasons, such as during an ER visit or after a cardiac event.

Current care guidelines mandate follow-up over one to two years to determine whether a nodule is cancerous. However, more than 60 percent of these patients do not receive guideline-recommended follow-up, severely limiting opportunities for early intervention and treatment. Patients who do receive recommended follow-up often require multiple imaging scans and biopsies, and sometimes unnecessary invasive procedures such as surgical biopsies and lung resections, before arriving at a definite diagnosis.

Several factors contribute to this situation:

1. Broken care workflows. As noted earlier, a patient may receive a chest CT scan for any number of reasons unrelated to a lung issue. During their review of the scan, the radiologist notes that there is a lung nodule present and recommends follow-up by the patient’s primary care physician (PCP). However, at that moment, this is a secondary and non-urgent issue for this patient, so therefore the care team may not alert the appropriate care team for nodule management. Also possible: the PCP assesses the radiology report as non-critical and does not inform the patient. It’s important to note that there are Standard of Care and legal liability issues associated with both scenarios.

2. Incidental screening diagnoses may not receive the attention they merit. Regionally, doctors may be aware that a significant percentage of the local population has completely benign lung nodules. And it’s true: 95 percent of these modules will stay benign. So, when the patient’s PCP is informed of an incidental diagnosis, they can be hesitant to prescribe a care path that involves a course of six-month CT scans—which are expensive and may unnecessarily alarm the patient.

3. The high cost of chasing down a definitive diagnosis. It is widely accepted that nearly one-third of all CT scans that include part of the lungs describe an incidentally detected lung nodule. Managing these nodule patients can present enormous resource challenges in scheduling appropriate follow-up care. The larger the healthcare network, the greater the challenge.

4. Low ROI. Implementing a workflow without automation to properly manage incidental lung nodule alerts is costly and has a low ROI. Most hospitals are therefore reluctant to implement a program to diligently review path lab notes of all scans. Clinical teams are already very busy, so allocating resources to track benign nodules with conventional manual processes that require additional full-time employees is a low priority.

Scale up: the cost-benefit equation is changing

Recent advances in artificial intelligence (AI) are changing the calculus of these decisions. For example, an AI-powered platform applies natural-language processing (NLP) automation to instantly read and grade any radiology report, and then identify and track patients who should be assigned special care. Additionally, the system assigns a Lung Cancer Prediction score to the nodules of interest, which stratifies patients and assists with an accurate diagnosis. This, in turn, supports better clinical decision-making.

The potent combination of NLP and AI-assisted diagnostic tools represents a viable solution for many healthcare systems, enabling the treatment of more early-stage lung cancers without increasing the workload of clinical teams. And, by arriving at the right diagnosis sooner, hospitals can also minimize unnecessary invasive biopsies.

Is AI-assisted lung cancer diagnosis right for your hospital? 

Given the importance of early diagnosis, hospitals should implement a plan for tracking and managing incidental lung nodules—to avoid reputational risk and save the lives of more patients. As you assess your course of action, your clinical teams should ask these questions:

1. Last year, how many nodules were identified incidentally at your healthcare system? 

 2. Were they all tracked and what procedures are in place to recommend a care pathway?

3. How many patients were lost to follow-up? 

4. In 2023, if we were to track and treat significantly more nodules appropriately, could we do this without adding resources and staff?

If you cannot find any of the above information easily, it’s time to re-evaluate your approach. It’s quite likely you have a serious issue that needs to be addressed.

About Ryan Hennen

Ryan Hennen is VP of US sales at Optellum. He has over 20 years of experience consulting with large healthcare IDNs while helping deliver enterprise-wide solutions to healthcare. Ryan has experience with imaging, oncology, value-based care, population health, clinical decision support, AI, NLP, and machine learning. Reach Ryan at ryan.hennen@optellum.com and LinkedIn.

References

1. Optellum projections based on Gould MK, Tang T, Liu IL, Lee J, Zheng C, Danforth KN, Kosco AE, Di Fiore JL, Suh DE. “Recent Trends in the Identification of Incidental Pulmonary Nodules,” American Journal of Respiratory and Critical Care Medicine, 2015 Nov 15;192(10):1208-14

2.  Pyenson BS, Bazell CM, Bellanich MJ, Caplen MA, Zulueta JJ. “No Apparent Workup for Most New Indeterminate Pulmonary Nodules in US Commercially Insured Patients,” Journal of Health Economics and Outcomes Research, 2019;6(3):118-129.

For many years, natural language processing (NLP) has held the promise of dramatically increasing the ability of healthcare organizations to quickly and accurately understand unstructured medical text in clinical notes. Using medical NLP, healthcare providers, clinical researchers, and payers would uncover meaningful insights hidden in unstructured text faster, with fewer errors, and at less cost than manual data review and analysis. This high-quality medical-grade data in turn would drive advances in understanding disease progression, assessing treatment efficacy, and detecting population health trends and other use cases.

Things haven’t quite worked out that way.  While single-institution, single-document-type NLP projects have proven viable, dealing with the complexity of language across multiple institutions and document types has eluded accurate NLP. 

One mistake healthcare organizations commonly make is they assume the medical NLP software they purchased is adequate for their use case. Yet these tools simply are not accurate enough to provide clinical-quality NLP because they are not fluent in the language of medicine. Medical vernacular is full of inherent complexities such as significant ambiguity, a special lexicon, and heavy use of localized medical shorthand. Add in the diversity of medical specialties and dearth of standards for the structure of medical documents, and it is clear that healthcare organizations require highly specialized medical NLP that leverages advanced technologies such as artificial intelligence (AI) and deep learning.

Fortunately, AI models have improved drastically with the advent of deep learning. Still, without any sort of medical expertise guiding the development of these deep learning models, users end up with results that basically say, “We found a whole bunch of things. Now you go figure out what is important.” That’s not exactly clinical-grade information.

What’s needed for a quality medical NLP platform is a combination of technology and medical expertise. By infusing deep learning models with specialized medical expertise, modern medical NLP software can help providers, payers, pharmaceutical companies, and clinical researchers get the most value from the data.

Limits of traditional NLP in medicine

While NLP undoubtedly has proved useful to researchers, the process involved can be labor-intensive and time-consuming. Let’s say researchers wanted to use NLP to find all patients in a target population who had appendicitis last month, with the data to be used in a white paper. Low-precision traditional NLP may identify 1,000 patients – but the NLP can be frequently wrong.   As a result, a researcher must go through the data and confirm it all. Granted, that’s still better than the researcher laboring over manual chart reviews, but that still falls well short of an efficient and effective solution. 

For other healthcare use cases – such as understanding human speech, computer-assisted coding (CAC), and clinical decision support – even “mostly accurate” software is nowhere close to acceptable. Healthcare organizations that implement a medical NLP platform that is extremely accurate will be able to apply their data to uses cases beyond research.

Choosing the right medical-grade NLP platform can be difficult for healthcare organizations that may not know precisely what features or functionality will work for them. Here are three things to look for in a clinical-grade NLP platform:

Accuracy

Does the platform provide enough accuracy for your organization’s purposes? For example, some platforms may have an algorithm for negation detection, the process of determining the presence or absence of conditions or diseases such as cancer or diabetes. However, the accuracy of these algorithms can vary depending on their ability to contextualize language in medical notes. 

The ability of an NLP platform to accurately identify common medical terms, including slang, must be a priority to guarantee high levels of accuracy. Annotation software can perform the work of physicians who traditionally would annotate thousands of medical reports – and do it much faster – but the medical NLP solution must be able to keep pace in speed and accuracy. 

Features

Healthcare organizations must know the specific functionalities of a medical NLP platform. Which ontologies does it support (SNOMED, RadLex, MEDCIN, ICD-10, etc.)? Does it identify questions or uncertainty? Can it extract insights from the unstructured text of clinical, diagnostic, and semi-structured reports?

Another important feature involves the platform’s ability to identify relations within data. Does it identify measurements? Or dates? If the platform is analyzing a report about a patient with a history of appendicitis, does the algorithm understand that appendicitis happened in the past? Or does it just say that the patient has appendicitis now?  If the report contains a statement that the patient’s mother has breast cancer, does it attribute breast cancer to the patient, or can it accurately identify the experience?

Deployment location

Many medical NLP vendors offer only cloud-based services, but not all healthcare organizations are eager to send their patient data to the cloud. Today’s focus on information safety makes cloud-based solutions in this space less attractive. For those organizations, on-premises medical NLP platform deployments are essential. 

Conclusion

Relative to the requirements of provider and payer organizations, medical NLP for too long has left much to be desired in accuracy and flexibility. Recent advances in AI now make it possible for medical NLP to help healthcare organizations leverage highly accurate data for clinical work, research and drug development. Healthcare organizations should ensure a medical NLP platform is accurate enough and includes the features they need to get the most from their data.

About Dr. Tim O’Connell 

Dr. Tim O’Connell is the founder and CEO of emtelligent, a Vancouver-based medical NLP technology solution. He is also a practicing radiologist, and the vice-chair of clinical informatics at the University of British Columbia.

4.7bn diagnostic imaging procedures were performed globally in 2021, representing a strong recovery over 2020. Healthy demand for teleradiology services resulted in the global penetration of teleradiology reads into diagnostic imaging procedures bouncing back to 1.9%, and the overall teleradiology reading services and IT market revenue elevating past its pre-pandemic level to USD $1.7bn. With a projection for the market to reach USD $3.7bn in 2026, here I explore five drivers fueling this period of robust growth.

1. Radiologist shortage leading to improved compensation

The accelerated post-pandemic demand for teleradiology services has posed a new set of challenges for vendors, such as ensuring adequate resourcing capacity. This is a task made tougher by an unprecedented radiologist shortage, particularly in the US, driven by burnout and wage inflation. This has caused considerably longer turnaround times for non-urgent diagnostic examinations. To attract and retain radiologists, teleradiology market leader vRad set the tone 12 months ago, announcing a substantial radiologist compensation increase of up to 25%, beginning in January 2022. Staff increases ranging from between 10-30% have also been common across many US-based teleradiology providers this year.

Teleradiologist wage inflation and subsequent higher teleradiology reading service fees (+12% y-o-y global revenue per read projection in 2022) could be viewed as a barrier and deter some healthcare providers from outsourcing their diagnostic imaging workload. However, the reality is that most healthcare providers are in desperate need for external support and view teleradiology as a necessity to ensure efficient reading turnaround times and provide high-quality patient care. In addition, more than 50% of radiologists are over the age of 55, meaning that many experienced radiologists will be entering retirement over the next decade. With radiology demand (reading hours) rising at a faster pace than the supply of experienced radiologists, the shortfall between supply and demand is set to accelerate further.

2. Increased diagnostic procedures being performed

Prior to COVID-19, the volume of diagnostic imaging examinations had been growing annually by >3% and hit 5.0bn in 2019. Although volumes dipped in 2020, the strong recovery witnessed in 2021 is set to continue with volumes reaching >6bn by 2026. The number of examinations is being fueled by several well-known factors, including an aging population, rising demand for early disease diagnosis among patients and physicians, improved government funding towards chronic disorders, and increases in disposable income/middle-class populations, particularly in less developed geographies.

3. Rising demand for specialist imaging procedures

The utilization of CT and MRI is forecast to grow from a 21% share of overall US diagnostic imaging procedures in 2021 to 23% in 2026. In absolute terms, this translates to volume growth of 6% (CAGR), significantly outpacing the growth of other modalities, especially X-ray (+2% CAGR). As demand for specialized modalities increases, so does the requirement for specialist radiologists capable of interpreting more complex examinations. Where access to sub-specialty expertise can be limited in-house (e.g. in smaller hospitals), teleradiologist expertise provides an ideal solution.

4. Increased healthcare provider focus on operational efficiency, partly owing to reimbursement cuts

The Centers for Medicare and Medicaid Services (CMS) announcement (December 2020) to drastically cut diagnostic radiology reimbursement by 10% in 2021, and proposed 2022/2023 Medicare Physician Fee Schedule (MPFS) changes will impact the diagnostic imaging market. To some extent, reduced reimbursement rates may act as a market barrier and reduce the attractiveness of setting up a teleradiology reading service. However, reimbursement cuts are expected to place further pressure on US healthcare providers to reduce costs (e.g. downsize their internal workforce) and push to more outsourcing from hospitals, with an increased focus on operational efficiency; this is expected to drive additional demand for imaging IT/AI products and tools that enrich a provider or radiologist’s efficiency and productivity, whether that is increasing patient throughput or faster reading and reporting via teleradiology services.

5. AI Developments

Three of the most important factors that heavily influence the success of teleradiology reading service providers are the speed that teleradiologists perform and report on their reads; the accuracy of the reports produced by teleradiologists; and the workflow and decision support processes that service providers put in place to ensure urgent reads are prioritized and reported on quickly. Over time, AI can be used to support and improve all key ingredients for a successful teleradiology service.

Although AI usage in teleradiology image/clinical analysis remains relatively limited, most instances of AI adoption to-date have been in relation to improving teleradiology workflow efficiency. In the short-to-medium term, AI workflow optimization is expected to move towards wider teleradiology market adoption and support improvements in turnaround times.

Outlook for 2022 and Beyond

As displayed in the figure below, these market drivers are projected to result in teleradiology reading service revenues increasing by 30% in 2022 and 16% per annum (CAGR 2021-2026). Teleradiology reading volumes are on course for slightly lower double-digit growth over this period.

About Arun Gill, Senior Analyst at Signify Research

Arun Gil is a Senior Market Analyst at Signify Research, a UK-based market research firm focusing on health IT, digital health, and medical imaging. Arun joined Signify Research in 2019 as part of the Digital Health team focusing on EHR/EMR, integrated care technology, and telehealth. He brings with him 10 years of experience as a Senior Market Analyst covering the consumer tech and imaging industry with Futuresource Consulting and NetGrowth Consultants.

Much has already been written about attendance at RSNA, but from a first-hand perspective, RSNA 2022 could be regarded as a step back toward “normal”. Amongst mostly bustling halls, vendors reported very positive feedback on the volume and quality of customer meetings, with one prominent global imaging vendor citing “We’re signing deals here at the show again – things are looking up”. 

Against this positive backdrop, however, the stark challenges facing radiology in a post-COVID world were very evident. These boil down to three substantial issues: 

– A global shortage of radiologists, technicians, and imaging service-line personnel  

– Increasing demand for imaging services, especially outside of the hospital 

– Inefficient and siloed informatics systems   

While RSNA provided a hint of the cutting-edge future of radiology, in reality, new advanced imaging modality and informatics products exclusive to the 2022 show were relatively thin on the ground.  

The “heady” days of extravagant modality and IT launches may therefore have run their course – above all, and perhaps rightly, reflecting the current tide of economic stressors facing healthcare providers, RSNA was wholly concerned with frugality in 2022.  

Trimmed down vendor booths, a greater focus on efficiency and cost-of-ownership across new product releases were evidently woven amongst the biggest booths. The AI zone had also featured a “refreshed” AI exhibitor group, featuring a mix of increasingly established and assured market front-runners, a myriad of new platforms, plus a few expectant fresh logos hoping to capitalize where others had already failed. Positively, post-COVID market forces had also been at work, shedding previously conspicuous exhibitors (well at least in the case of DeepRadiology) and asking more searching questions about the clinical and economic validity of market participants. Combined, this all pointed to the commercial radiology sector getting a little more “real” about the challenges ahead.    

To be frank, this was not a great surprise, given the sentiment and feedback we have received from the many research discussions conducted prior to the show in 2022.

As all good analysts should tell you, predictions are valuable, but even more so when retrospectively assessed for validity; below, we, therefore, assess how the realities of RSNA 2022 shaped up against our predictions.     

Modality Predictions  

Prediction 1: RSNA 2022 will be a subdued show for major modality vendors  

Correct: Overall, new product announcements were scarce, and for those that were released, many were targeted at filling gaps in portfolios/addressing particular customer segments, as opposed to portfolio-shaping new platforms. This was unsurprising given the COVID-19 distraction and disruption to the innovation cycle, with many vendor discussions pointing to more “disruptive” product releases on the near horizon – the hype for RSNA 2023 is already building.   

Prediction 2: Vendors’ exhibitions will focus on clinical workflows  

Partly Correct: While this was evident on some booths (most notably Canon’s novel booth layout) broadly most vendors have done little to move away from presenting products “in category” e.g., by modality type; while this makes for easy visibility and being directed around the largest booths, the apparently limited focus on care pathways and specific clinical workflows will have disappointed some customers. Moreover, as integrated, multidisciplinary care pathways are increasingly adopted, the industry will need to more acutely demonstrate the lynchpin role of imaging within wider diagnostics and therapy. This is, in part, also down to the slowly changing evolution of business models – until commercial contracting models are more complementary to how care is delivered, and providers are convinced of the value of moving to more progressive models of financing equipment purchasing (managed services, outcome-based risk sharing) this is unlikely to change quickly.      

Prediction 3: Vendors will be keen to highlight total cost of ownership and sustainability  

Correct: For modalities, this was particularly evident across new launches and existing products; from the ongoing “helium-free” debate in MRI, the use of edge AI to reduce the need for contrast agents, smart subscriptions, the potential role of “extremity” imaging leveraging lower-cost imaging devices, to more tangible focus on leveraging remote diagnostics, smarter fleet management service contracts and remote acquisition services. Doing more with the same or less shone through as the leading trend of the show. While this reflects the current focus of the customer base facing a tough period ahead, it was also clear that vendors still have some way to go in ensuring the “solution” offering they promise will deliver substantial long-term cost savings. While many touted substantial savings within bullish marketing, more customer-led and independent long-term evidence is required to fully convince buyers that these savings are realistic.      

Prediction 4: Vendors will be focused on advanced imaging systems  

Partly Correct: broadly the leading imaging modality vendors doubled down on “big iron” as their leading lights at RSNA 2022 yet given the influence of COVID-19 on innovation cycles and the fact many have record order books yet to be delivered for CT and MR, there was understandably fewer headliner grabbers in advanced modalities. What was apparent from vendor discussions, however, was the incoming tide of new CT platforms featuring photon-counting technology in the next two years, spectral CT in the near term, and the increasingly important role AI will play in image reconstruction. Combined, these trends will resonate throughout the rest of the decade for big iron modalities, forming a new era equivalent, but more subtle, to the CT “slice wars” period decades before. It should not be overlooked, however, that innovation was also apparent at lower-price tiers; new mobile X-ray systems, C-arms, ultrasound and even some new modalities (see 4DMedical) were all on show. Ultrasound was the most underserved modality at the show – given its expansion into tens of new clinical applications across the care continuum, the radiology-centric nature of RSNA has led to some dedicated point-of-care vendors and clinical specialists opting to spend their marketing budget at more targeted shows.      

Prediction 5: There will be an emphasis on digitization 

Partly Correct: while undertones of digital trended throughout the imaging modality showcase, as highlighted in prediction 3, many of the software offerings on show were disjointed or still nascent in their availability. Across the categories of pre-acquisition operations, acquisition (and virtual acquisition support), post-acquisition operations and fleet management, new and novel digital technology have received substantial focus and R&D investment, a positive development to support imaging service providers in solving their biggest challenges. However, the integration of these tools and interfacing them into wider imaging informatics and health IT platforms have some way to go, a potential hindrance for adoption. Reassuringly, the growing focus on the use of AI and advanced analytics to support predictive demand forecasting tools and smart scheduling of scans should also be applauded. Given that this technology will not be hamstrung by regulatory barriers, this operational application offers far more tangible benefits to healthcare providers short-term in comparison to some of the venture-funded AI image analysis tools attempting to come to market.        

Imaging IT and AI  

Prediction 1: A busy show for imaging IT and AI vendors  

Partly Correct: at face value, many vendors in AI and imaging IT reported satisfaction with the quantity and quality of engagements with customers and prospects. However, reading between the lines, the growing centralization of decision-making for imaging IT in larger acute hospital networks towards CIOs and CMIOs, both personas which were absent amongst the attendees, is having an impact on the importance of RSNA as a deal-making opportunity. Instead, imaging IT vendors are seeing the most value in keeping existing customers informed on new product developments, while also receiving valuable feedback from front-line users (radiologists) on their recent innovations.  

For AI vendors, “busyness” is a poisoned chalice at RSNA; while many are dependent on RSNA as a showcase for brand-building and greater visibility, a more educated and cynical attendee audience is asking ever-increasingly probing questions on the clinical and economic value of their products. This is no bad thing for a still nascent market sector that has for too long failed to address well the real challenges of integration and commercial viability – with external funding drying up and becoming more selective post-COVID, it will soon be all too evident which vendors have products that will meet the increasingly pressing needs of imaging service providers today, and those that will need drastic strategic change or cease to exist near-term.  

Prediction 2: Efficiency and optimization will be key  

Correct: Similarly, to the theme amongst imaging modality vendors, efficiency and optimization was prominent theme amongst the imaging informatics announcements. Echoing the sentiment of imaging modality prediction 5 above, more frustrating for some users was the apparent disjointed nature of service line provision of these tools from pre- to post-acquisitions. This will come in time, but also perhaps reflects the slow interoperability programs of leading modality vendors that are leading in this segment. Dedicated imaging IT platforms were more progressive around radiologist workflow tools, with new generations of case-load balancing, user-friendly interfaces supporting radiologists managing workloads (and avoiding burn-out) and further progress on viewer and reporting interface consolidation. The same sentiment is echoed across AI, where integrated triage tools continue to gain traction and are becoming better interfaced with reporting workflow and care pathways. Reporting tools also took a further step towards more robust structured reporting, though in practice there appears to be a long road ahead for widespread adoption, despite the obvious future benefits for patient quality and care.    

Prediction 3: Platforms, platforms, platforms  

Correct: specifically targeted to AI, this prediction was a continued theme from previous shows, with vendors from a range of PACS, AV, AI, modality, reporting and workflow all tussling to demonstrate their prowess in AI integration. RSNA 2022 was no different, with a plethora of new and re-booted AI platforms on show and a growing list of partnerships for more mature platforms. In theory, user “choice” is commonly seen as a positive for customers, yet perhaps the inverse is the case in medical imaging AI; too much choice is leading to market inertia and confusion, at a time when imaging service providers have limited resources to assess varied approaches.   

While a clear and standardized route to AI access remains unclear, image analysis tools specifically will be influenced by some wider common market “truths”: the power of imaging IT (e.g. PACS) incumbency in customer road-mapping should not be underestimated; customers really do want tightly integrated AI tools (.pdf results outputs help no-one); the presence of big technology AI service offerings for imaging at the show (Google, Amazon, Microsoft, NVIDIA) is going to promote more self-development of AI by providers, requiring a more flexible platform integration strategy; reimbursable AI image analysis tools remain the primary draw for customers. Viewing the explosion of platforms for AI through this lens suggests many of the newly launched platforms will be short-lived. Moreover, with market forces driving impending consolidation of AI algorithm developers’ mid-term, convincing potential customers to look beyond their existing PACS-based platform or one of a few established leading independent platform vendors will be a hard sell.         

Prediction 4: The consolidation of data  

Wrong: Surprisingly with many AI vendors facing impending funding challenges and many providers tackling resource and operational challenges, discussions surrounding the potential value of imaging data were scarce at RSNA 2022. While VNA 2.0 has been touted for some time (and discussed at length in our research) the practical realities of data consolidation have remained subdued. There was progress on show for imaging IT vendors working on common data consolidation challenges across DICOM-based applications, but wider data ingestion and management of unstructured and interfacing with EMR-based clinical data was nascent. The slow progress of structured reporting is in part to blame, yet fundamentally, many of the new integrated care models and demands for more personal, predictive medicine are doomed to fail without more focus on robust data management technology and breaking down data siloes within healthcare networks. 

In context, this is also a missed opportunity for vendors and providers alike; while still nascent, the growing value of well-structured diagnostic imaging data within the pre-clinical arena is obvious, with cash-rich pharma vendors post-COVID well underway with massive investment in research digitalization and overhaul of drug discovery and clinical trial processes. Providers, not looking beyond near-term operational challenges, have either been blind to this opportunity or looking to capitalize on the “goldmine” of data they hold but are unable to execute on commoditizing the value they hold. For imaging IT vendors, this is a significant, yet undervalued opportunity. At its simplest, robust data management platforms are a good starting point to embed deeper with a customer organization in supporting future data commoditization. The savvier should already be exploring the provision of research-grade “sandbox” toolsets to support data aggregation, cleaning, and de-identification; the bravest will also take this a step further, brokering data deals between providers and pre-clinical consumers.            

Prediction 5: AI beyond radiology  

Mostly Correct: Despite some AI vendors still hanging on to the premise that AI-based image analysis tools offering small iterative improvements in radiology reading efficiency and quality are enough to convince providers to pay substantial sums per scan for use, most of the market has realised AI’s biggest potential value mid-term is probably “downstream”. This view has been buoyed further by substantial reimbursement announced in the US for triage tools in stroke care and the success of vendors such as HeartFlow and Cleerly in cardiac care.   

However, measuring downstream “value” in care pathways is fraught with challenges, as pathways are often complex, and health providers are notoriously poor at measuring economic value, let alone clinical value. Reassuringly, leading vendors in the market are investing heavily in producing real-world clinical evidence to support the case for AI (and to help move payers toward reimbursement); this will also gain greater traction as the inevitable market consolidation drives more comprehensive care or body area solution availability (as smaller AI vendors partner up to remain competitive).  

More disappointingly, the downstream value of AI as a predictive indicator for future disease or care intervention was more subdued at the show, or at least subtler behind the mass of platform announcements and focus on basic results integration from many exhibitors and leading imaging IT vendor platforms. This focus on use of incidental findings in screening or non-emergency imaging again seems obvious if the less well-trodden path for AI, given the potential benefits for payers, providers, and patients. While this may only be a temporary impact given many AI vendors have their backs against the wall financially and the road to commercial viability for population health-based AI-analysis tools is a more challenging route, this may have been an opportunity for some to stand out from the crowd at the show this year.         

About Steve Holloway 

Steve Holloway is the Director at Signify Research, an independent supplier of market intelligence and consultancy to the global healthcare technology industry. Steve has 9 years of experience in healthcare technology market intelligence, having served as Senior Analyst at InMedica (part of IMS Research) and Associate Director for IHS Inc.’s Healthcare Technology practice. Steve’s areas of expertise include healthcare IT and medical Imaging.

As the healthcare sector embraces value-based care, the patient – not the procedure – is the central focus for providers. But the move to patient-centered care requires several significant deviations from the status quo. 

For example, more personalized treatment is required through information sharing and collaborative decision-making among providers, patients and their families. There is also a greater need to focus on overall physical, mental and emotional well-being of patients, as well as more opportunities for self-service,  including access to their records, making payments and locating additional information.

But what exactly will it take to turn these goals into reality? For starters, providers must rethink their daily workflows to improve and expand the ways they interact with patients, other healthcare providers and payers. Technology must also be applied to support every appropriate stakeholder in order for patient-centered care to be a reality. 

The Digital Transformation Healthcare Movement

For many years, healthcare organizations relied on paper-based files and film images, hesitant to move to onsite software systems and electronic health records (EHR) solutions. Organizations were slow to adopt newer technologies for managing images and patient records over concerns about security, legal compliance, and the risk of downtime. While other industries managing critical data, such as banking, manufacturing and logistics, embraced digital transformations early – and reaped the benefits of improved customer experience and business performance – healthcare organizations have typically moved more cautiously when adopting EHRs and other digital tools that have the potential to improve efficiency, performance and patient outcomes.

Fast-forward to 2020 when the COVID-19 pandemic struck. In crisis mode, healthcare providers swept aside the self-imposed technological constraints and undertook a significant and swift digital transformation. They needed new methods and processes to prevent interruptions in care while maintaining safety for patients and providers. 

At the heart of these changes was the adoption of cloud-based solutions. The use of the cloud has opened up incredible opportunities for patients and providers alike. Virtual appointments and online messaging provide new avenues to access care in a timely manner. Primary care doctors and specialists are now able to collaborate online to improve quality of care. And with EHRs online, patients can access their records to see their latest test results, treatment plans and other information, giving them more control over their outcomes.

Driving an Increase in Patient-Centered Care

Spurred on by events over the last two years, cloud adoption is seeing a significant uptick in the healthcare space. What was a $26 billion global market in 2020 is poised to nearly triple in size to $76.8 billion by 2026. The U.S. alone accounts for more than half of the market, with an estimated $16.9 billion share in 2021. 

The cloud will continue to play a foundational role in a variety of healthcare applications, but perhaps one of the biggest impacts will be in supporting the delivery of patient-centered care.  

Cloud solutions are helping to:
1. Improve access to patient data – Whether it’s the patient’s ability to access their own records online or providers’ ability to collaborate, the cloud is making it easier to get information in a timely and more efficient manner. For example, patients no longer have to wait for a radiologist to burn a CD and then take it to a specialist for review. With the cloud, images and related reports can be read and be shared immediately, regardless of where the clinicians are located. And the chance of a lost CD is eliminated since the cloud offers a secure, reliable way to transmit images.

2. Offer greater transparency – One of the keys to delivering patient-centered care is gaining a full understanding of the individual and actively engaging them in their outcomes. But for this to become a reality, patients and their providers need access to medical records and open lines of communication matched to patient preferences. The cloud enables centralized access to patient records, giving healthcare providers a full view of the tests, diagnoses, treatments and other patient information. The addition of artificial intelligence (AI) in cloud-based solutions is enhancing care by analyzing vast amounts of patient data and offering insights that aid in clinical decision-making, which can result in faster time-to-treatment and better outcomes.

3. Enhance communications – In the past, connecting with physicians outside of appointments often turned into a game of phone tag and resulted in multiple voicemails. But with the cloud supporting patient portals, video chat and text messaging, patients and their providers have more ways to communicate. They can quickly exchange information or ask and answer questions electronically at their convenience, and refer back to those messages should they forget something. And the addition of video appointments allows patients to connect from the comfort and convenience of their own homes for brief follow-ups that do not warrant a full office visit.

The strong focus on value-based care, combined with the successful use of technology during the pandemic, has driven a significant digital transformation in the healthcare space. At the center of these technological changes is the cloud, which allows for an unprecedented degree of collaboration, communication and transparency. By combining these three facets and delivering a more holistic view of the individual, providers can effectively achieve true patient-centered care. 

About Morris Panner

Morris Panner is the President of Intelerad Medical Systems, leading the company on delivering better care through improved technology. Morris served as CEO of Ambra Health from 2011 until its acquisition by Intelerad in 2021. Morris is an active voice in the cloud and enterprise software arena, focused on the services and healthcare verticals. He is a frequent contributor to business, healthcare, and technology publications. Previously, Morris built and sold an industry-leading business-process software company, OpenAir, to NetSuite (NYSE:N). 

What You Should Know:

– Prenuvo, an Redwood City, CA-based advanced, radiation-free whole body imaging for early detection of cancer and other diseases raises $70M in Series A equity and debt funding led by Felicis, with participation from existing investors including Tony Fadell, NYT bestseller author and founder of Nest; Dr. Timothy A. Springer, Lasker award recipient; Anne Wojcicki, CEO of 23&Me; Steel Perlot, with Eric Schmidt as chairman; entrepreneur Rande Gerber; and wellness investor, supermodel, and actress, Cindy Crawford.

– The funding will be used to invest in the company’s artificial intelligence (AI) team, new radiology tools, custom advanced MRI builds, and national expansion.

In-Depth Whole Body Screening

With Prenuvo, people and their doctors can reveal health problems early, if there are any, and gain peace of mind, if there are not. The company offers radiation-free, full-body MRI scans that are faster, less costly, and more intelligent than traditional methods, which can be prohibitively expensive and take three to four hours to complete. Using cutting-edge software and proprietary AI technology, a single Prenuvo scan covers 26 regions and organs of the body in under one hour. With high clinical diagnostic quality, it can screen for and diagnose more than 500 conditions – including most solid tumors at Stage 1 – at a fraction of the cost of traditional MRI screenings.

“The typical physical hasn’t evolved in decades and lacks predictive power. Prenuvo gives patients unparalleled clarity and control,” added Dr. Raj Attariwala, co-founder and radiologist at Prenuvo. “Not only can patients make better-informed decisions about their health, but doctors can also make more accurate treatment or monitoring plans with technology that can measure changes that are simply invisible to the naked eye. We’re making powerful health insights more readily available to anyone at any time.”

Prenuvo clinics redefine the imaging experience. Scans are spacious and allow patients to relax, watch a show on flatscreen TVs or listen to music for the duration. Trained radiologists read the images and results are delivered to patients and their doctors – along with next steps – through the user-friendly Prenuvo web and mobile app.

What You Should Know:

– Oncoustics, San Francisco, CA-based ultrasound-based tissue characterization solutions announces the initial close of a $5 million+ seed round of funding to advance its SaMD (software as a medical device) technology for the low-cost assessment of structural diseases at the point of care.

– Oncoustics’ first products will focus on liver disease, one of the fastest-growing causes of morbidity and mortality in the world. The round is co-led by Creative Ventures and Saltagen Ventures, and further includes NorthSpring Capital Partners, Fraser Kearney Capital Corp., Pallasite Ventures, and Dr. Chen Fong, a renowned radiologist, entrepreneur/investor and inductee into the Order of Canada for his contributions to medical technology innovation and philanthropy.

Acoustic Data Meets Ultrasound Imagery

Oncoustics’ patented approach utilizes both the ultrasound images as well as the acoustic data derived from the raw sound signals to automatically differentiate tissue types. Every different type of tissue in the body bounces back a unique acoustic signature and Oncoustics mines these signals to differentiate healthy versus diseased tissues. Oncoustics has been collecting ultrasound signal datasets and has amassed the largest RF signal data set in the world. This hardware-agnostic approach works on any ultrasound system, including new low-cost point-of-care ultrasound systems, making this an affordable and accessible diagnostic tool.

James Wang, partner at Creative Ventures who leads AI investments, leveraging his background in computer science specializing in AI/ML, shared, “Oncoustics’ approach is unique. They mine the raw data that is typically thrown away and can use this to go beyond what can be seen by the human eye. They’re basically creating a ‘virtual biopsy’ that has vast applications in healthcare. Innovations like Oncoustics are what will help change the trajectory of our currently unsustainable healthcare system and costs.”

Leveraging the rise of new point-of-care ultrasound systems, Oncoustics takes advantage of all the benefits of these systems, including their low cost, portability and ease of use, and builds on this by guiding the data acquisition and providing easy-to-read results via a smartphone app.

Oncoustics’ Liver Assessment Solution

Oncoustics’ first product, the OnX liver assessment solution, is focused on detecting structural liver diseases including fibrosis and steatosis (fat) that can occur in all types of chronic liver disease (CLD). CLD today affects more than 2 billion people globally and is rising dramatically, driven by a condition called Non-Alcoholic Fatty Liver Disease (NAFLD).

“There’s a tsunami of need around detecting these types of liver diseases and our ultimate goal is to decrease or eliminate the need for high-end imaging or painful and invasive biopsies,” said Beth Rogozinski, CEO, Oncoustics. “With this new round of funding, we will accelerate our liver solutions and enable low-cost diagnostics for earlier interventions and better patient care.”

Milestones

The Oncoustics platform promises a whole new level of access to care with the benefits of ease of use, accessibility, affordability, and optimizing clinical workflow. Oncoustics applies AI to raw ultrasound signals to do tissue characterization at point of care for low-cost, noninvasive surveillance, diagnostics, and treatment monitoring of diseases with high unmet clinical need. The Oncousticssolutions for ultrasound will be submitted for regulatory approval in the United States (FDA 510(k)), Canada (Health Canada medical device license) and the European Union (CE Mark). The OnX Liver Assessment Solution has not been cleared for clinical use and is For Investigational Use Only.

As the healthcare system continues to evolve to adopt a more patient-centric approach, surprise billing has become a topic discussed by consumers and policymakers. Surprise billing can occur when a patient unknowingly receives care from providers that are outside their network. This can result in balance billing, the practice of billing a patient the difference between what their health plan covers and what the provider charges. Unfortunately, these bills are often the result of care provided in an emergency situation or when a person is unaware treatment is being given by an out-of-network provider. 

According to the Journal of the American Medical Association, one in five insured adults had a surprise medical bill from an out-of-network provider. Overall, two-thirds of adults are worried about their ability to afford unexpected medical bills.

In a survey conducted by the Harris Poll on behalf of the American Heart Association, 49% of U.S. adults reported that worrying about an unexpected medical bill keeps them from seeking care, and 44% said if they received an unexpected medical bill for $1,000 they could not afford it. Two-thirds of U.S. adults with private health insurance have received an unexpected medical bill, and of those, one in three were not able to pay the bill with money immediately available to them.

Surprise Billing Causes

Surprise billing most often occurs when patients are treated by out-of-network providers in an in-network facility. For example, a patient could be treated by an anesthesiologist or radiologist that is not covered by their insurer in a hospital that is within their network. A recent JAMA study of one large health plan found that 20% of patients receiving elective surgery from in-network surgeons received an out-of-network bill. Anesthesiologists and surgical assistants were responsible for 37% of these bills, with an average out-of-network bill for anesthesiologists of $1,219 and surgical assistants of $2,633. 

The second most likely cause of surprise billing is during an emergency situation. A person may be transported to a hospital that is out of network or may become ill when they are out of state or away from home. In an emergency when a patient is transported to a hospital via air ambulance, the transport costs are often out-of-network. 

Finally, surprise billing can occur when a provider directory is not kept up to date. Patients rely on these directories, hosted by health plans, to ensure that they select a provider that is in-network. When these directories do not contain the correct information, a patient can unknowingly schedule an appointment with a provider that is no longer within their network. 

To address these issues, Congress passed the No Surprises Act, which took effect on Jan. 1, 2022. The act was a move towards patient cost and treatment transparency. With the passage of the No Surprises Act, a large book of new regulations and requirements for healthcare providers and payers was handed down in an effort to protect patients.

While the legislation does not address every concern regarding surprise billing, it is a good first step in protecting patients and providing a better overall patient experience. Providers and payers must adhere to the regulations or risk being fined. The Centers for Medicare and Medicaid Services (CMS) can impose penalties on health plans of up to $25,000 per beneficiary for errors in Medicare Advantage plan directories and up to $100 per beneficiary for errors in plans on the federal insurance exchange. States also have their own regulations. California, in particular, has fined plans for posting incorrect provider directory information. 

What Payers Can Do

Under the No Surprises Act, payers are required to keep an accurate and up-to-date provider directory. Updates must be made within two business days of receiving information from providers, removing those who have not been verified. Although an updated directory may not solve every issue that comes with surprise billing, it will give patients access to accurate information about providers that are in-network. Armed with that knowledge, patients can make better decisions about where to get treatment. 

While the process of updating a directory sounds simple, it is anything but. Payers receive an enormous amount of data from providers each day. The data must be cleaned up and entered into the plan’s system. Under the new regulations, payers have 48 hours to make sense of the data and update their directories. This manual process certainly takes longer than that, and there is substantial room for human error. In fact, when CMS reviewed Medicare Advantage directories, they found 52% had at least one error. 

Managing the data, especially in real-time, can be a complex and time-consuming challenge. Without automation, it places a burden on staff to be in constant communication with providers in order to obtain accurate information. Automating this process through a cloud-based system can reduce the likelihood of errors and help insurers keep their directories within regulations, in real-time. 

Health plans should consider an automated process that:

– Consolidates multiple provider profile data from multiple internal and external services

– Allows bi-directional access from any system

– Queries, views and syncs provider profiles

– Configures ranking logic and data accuracy logic

– Offers built-in OIG exclusion checks

The goal is to reduce manual input, improve efficiency and increase margins with automated workflow while keeping the provider director as accurate as possible. Cloud-based solutions that are scalable will help health plans stay within regulations and better serve patients.

About Michael Gardner 
With over 25 years of experience in healthcare, Michael Gardner serves as the Chief Strategy Officer at Virsys12. Combining his experience across payers, large health systems, clinically integrated networks, and ACOs within the healthcare industry with his creativity and problem-solving ability, Gardner provides industry leadership and knowledge for Virsys12’s growth and market expansions including strategic partnerships and customer relations.

What You Should Know:

According to data from Intelerad, patient trust is not a barrier to AI adoption by medical imaging professionals in the U.S. 64% of respondents either trust or are neutral about a diagnosis solely from AI.

The Intelerad study of over 1,000 healthcare consumers across the U.S. uncovers the impact of healthcare’s digital transformation on the healthcare consumer in a post-pandemic world. The results unveil new insights into patient attitudes toward AI.

Patient Attitudes Toward AI Medical Images Adoption

When asked to rate their level of trust in a diagnosis by a radiologist assisted by an AI application, a whopping 79% of respondents reported they trust or are neutral about it. Respondents aged 55+ are much more likely to trust diagnoses assisted by AI as opposed to solely relying on it (59% compared to 22%). Not surprisingly, trust in AI correlates with age: the younger an individual, the more likely they are to trust it.

Other key findings include:

– AI is highly trusted for making appointments and organizing a radiologist’s workload. When it comes to specific activities, 88% of respondents trust or are neutral about AI’s role in making appointments. Additionally, 86% trust or are neutral about AI organizing a radiologist’s workload by flagging questionable abnormalities. 

– Education is key: Patients do not know when their radiology services are supported by AI. Only 19% of respondents believed they received care supported by AI, while 24% did not know, and 58% believed they had not. The younger an individual, the more likely they were to believe that AI has played a role in their care, with only 4% of 55+ believing so. 

– Healthcare consumers believe AI will play a major role in medical imaging in the future. The majority of respondents (60%) think that AI will perform over half of radiology services in five years, with that number increasing to 75% of respondents in the next 20 years. Furthermore, 8% of individuals think AI will account for 100% of services in the next five years, with that number increasing to 19% of respondents by 2042.

– Sentiments among healthcare consumers diverge from current uptake of AI across the radiology field. Approximately 30% of radiologists are currently using AI as part of their practice and among those not currently deploying AI, 20% plan to purchase AI tools in the next one to five years, according to research from the ACR Data Institute.  

“There has been significant research about how AI is transforming radiological services, yet little has been done to gather insight and preferences from the perspective of the healthcare consumer. Our latest study provides a new dimension to understanding how AI is impacting medical imaging by asking the patient their thoughts on the emerging technology in the field. This insight can help physicians with their decision-making around when and how to implement AI services,” said Morris Panner, President of Intelerad. 

A century ago, X-rays transformed medicine forever. For the first time, doctors could see inside the human body, without invasive surgeries. The technology was so revolutionary that in the last 100 years, radiology departments have become a staple of modern hospitals, routinely used across medical disciplines.

Today, new technology is once again radically reshaping medicine: artificial intelligence (AI). Like the X-ray before it, AI gives clinicians the ability to see the unseen and has transformative applications across medical disciplines. As its impact grows clear, it’s time for health systems to establish departments dedicated to clinical AI, much as they did for radiology 100 years ago.

Radiology, in fact, was one of the earliest use cases for AI in medicine today. Machine learning algorithms trained on medical images can learn to detect tumors and other malignancies that are, in many cases, too subtle for even a trained radiologist to perceive. That’s not to suggest that AI will replace radiologists, but rather that it can be a powerful tool for aiding them in the detection of potential illness — much like an X-ray or a CT scan. 

AI’s potential is not limited to radiology, however. Depending on the data it is trained on, AI can predict a wide range of medical outcomes, from sepsis and heart failure to depression and opioid abuse. As more of patients’ medical data is stored in the EHR, and as these EHR systems become more interconnected across health systems, AI will only become more sensitive and accurate at predicting a patient’s risk of deteriorating.

However, AI is even more powerful as a predictive tool when it looks beyond the clinical data in the EHR. In fact, research suggests that clinical care factors contribute to only 16% of health outcomes. The other 84% are determined by socioeconomic factors, health behaviors, and the physical environment. To account for these external factors, clinical AI needs external data. 

Fortunately, data on social determinants of health (SDOH) is widely available. Government agencies including the Census Bureau, EPA, HUD, DOT and USDA keep detailed data on relevant risk factors at the level of individual US Census tracts. For example, this data can show which patients may have difficulty accessing transportation to their appointments, which patients live in a food desert, or which patients are exposed to high levels of air pollution. 

These external risk factors can be connected to individual patients using only their address. With a more comprehensive picture of patient risk, Clinical AI can make more accurate predictions of patient outcomes. In fact, a recent study found that a machine learning model could accurately predict inpatient and emergency department utilization using only SDOH data.

Doctors rarely have insight on these external forces. More often than not, physicians are with patients for under 15 minutes at a time, and patients may not realize their external circumstances are relevant to their health. But, like medical imaging, AI has the power to make the invisible visible for doctors, surfacing external risk factors they would otherwise miss. 

But AI can do more than predict risk. With a complete view of patient risk factors, prescriptive AI tools can recommend interventions that address these risk factors, tapping the latest clinical research. This sets AI apart from traditional predictive analytics, which leaves clinicians with the burden of determining how to reduce a patient’s risk. Ultimately, the doctor is still responsible for setting the care plan, but AI can suggest actions they may not otherwise have considered.

By reducing the cognitive load on clinicians, AI can address another major problem in healthcare: burnout. Among professions, physicians have one of the highest suicide rates, and by 2025, the U.S. The Department of Health and Human Services predicts that there will be a shortage of nearly 90,000 physicians across the nation, driven by burnout. The problem is real, and the pandemic has only worsened its impact. 

Implementing clinical AI can play an essential role in reducing burnout within hospitals. Studies show burnout is largely attributed to bureaucratic tasks and EHRs combined, and that physicians spend twice as much time on EHRs and desk work than with patients. Clinical AI can ease the burden of these administrative tasks so physicians can spend more time face-to-face with their patients.

For all its promise, it’s important to recognize that AI is as complex a tool as any radiological instrument. Healthcare organizations can’t just install the software and expect results. There are several implementation considerations that, if poorly executed, can doom AI’s success. This is where clinical AI departments can and should play a role. 

The first area where clinical AI departments should focus on is the data. AI is only as good as the data that goes into it. Ultimately, the data used to train machine learning models should be relevant and representative of the patient population it serves. Failing to do so can limit AI’s accuracy and usefulness, or worse, introduce bias. Any bias in the training data, including pre-existing disparities in health outcomes, will be reflected in the output of the AI. 

Every hospital’s use of clinical AI will be different, and hospitals will need to deeply consider their patient population and make sure that they have the resources to tailor vendor solutions accordingly. Without the right resources and organizational strategies, clinical AI adoption will come with the same frustration and disillusionment that has come to be associated with EHRs. 

Misconceptions about AI are a common hurdle that can foster resistance and misuse. No matter what science fiction tells us, AI will never replace a clinician’s judgment. Rather, AI should be seen as a clinical decision support tool, much like radiology or laboratory tests. For a successful AI implementation, it’s important to have internal champions who can build trust and train staff on proper use. Clinical AI departments can play an outsized role in leading this cultural shift.  

Finally, coordination is the bedrock of quality care, and AI is no exception. Clinical AI departments can foster collaboration across departments to action AI insights and treat the whole patient. Doing so can promote a shift from reactive to preventive care, mobilizing ambulatory, and community health resources to prevent avoidable hospitalizations.

With the promise of new vaccines, the end of the pandemic is in sight. Hospitals will soon face a historic opportunity to reshape their practices to recover from the pandemic’s financial devastation and deliver better care in the future. Clinical AI will be a powerful tool through this transition, helping hospitals to get ahead of avoidable utilization, streamline workflows, and improve the quality of care. 

A century ago, few would have guessed that X-rays would be the basis for an essential department within hospitals. Today, AI is leading a new revolution in medicine, and hospitals would be remiss to be left behind.

About  John Frownfelter, MD, FACP

John is an internist and physician executive in Health Information Technology and is currently leading Jvion’s clinical strategy as their Chief Medical Information Officer. With 20 years’ leadership experience he has a broad range of expertise in systems management, care transformation and health information systems. Dr. Frownfelter has held a number of medical and medical informatics leadership positions over nearly two decades, highlighted by his role as Chief Medical Information Officer for Inpatient services at Henry Ford Health System and Chief Medical Information Officer for UnityPoint Health where he led clinical IT strategy and launched the analytics programs. 

Since 2015, Dr. Frownfelter has been bringing his expertise to healthcare through health IT advising to both industry and health systems. His work with Jvion has enhanced their clinical offering and their implementation effectiveness. Dr. Frownfelter has also held professorships at St. George’s University and Wayne State schools of medicine, and the University of Detroit Mercy Physician Assistant School. Dr. Frownfelter received his MD from Wayne State University School of Medicine.

What You Should Know:

– GE Healthcare announced a new artificial intelligence (AI) algorithm to help clinicians assess Endotracheal Tube (ETT) placements, a necessary and important step when ventilating critically ill COVID-19 patients.

– The AI solution is one of five included in GE Healthcare’s Critical Care Suite 2.0, an industry-first collection of AI algorithms embedded on a mobile x-ray device for automated measurements, case prioritization, and quality control.

GE Healthcare today announced a new artificial intelligence (AI) algorithm to help clinicians assess Endotracheal Tube (ETT) placements, a necessary and important step when ventilating critically ill COVID-19 patients. The AI solution is one of five included in GE Healthcare’s Critical Care Suite 2.0, an industry-first collection of AI algorithms embedded on a mobile x-ray device for automated measurements, case prioritization, and quality control. GE Healthcare and UC San Francisco co-developed Critical Care Suite 2.0 using GE Healthcare’s Edison platform, which helps deploy AI algorithms quickly and securely. Critical Care Suite 2.0 is available on the company’s AMX 240 mobile x-ray system.

The on-device AI offers several benefits to radiologists and technologists, including:

– ETT positioning and critical findings: GE Healthcare’s algorithms are a fast and reliable way to ensure AI results are generated within seconds of image acquisition, without any dependency on connectivity or transfer speeds to produce the AI results.

– Eliminating processing delays: Results are then sent to the radiologist while the device sends the original diagnostic image, ensuring no additional processing delay.

– Ensuring quality: The AI suite also includes several quality-focused AI algorithms to analyze and flag protocol and field of view errors, as well as auto, rotate the images on-device. By automatically running these quality checks on-device, it integrates them into the technologist’s standard workflow and enables technologist actions – such as rejections or reprocessing – to occur at the patient’s bedside and before the images are sent to PACS.

Impact of ETTs

Up to 45% of ICU patients, including severe COVID-19 cases, receive ETT intubation for ventilation. While proper ETT placement can be difficult, Critical Care Suite 2.0 uses AI to automatically detect ETTs in chest x-ray images and provides an accurate and automated measurement of ETT positioning to clinicians within seconds of image acquisition, right on the monitor of the x-ray system. In 94% of cases, the ET Tube tip-to-Carina distance calculation is accurate to within 1.0 cm. With these measurements, clinicians can determine if the ETT is placed correctly or if additional attention is required for proper placement. The AI-generated measurements – along with an image overlay – are then made accessible in a picture archiving and communication system (PACS).

Improper positioning of the ETT during intubation can lead to various complications, including a pneumothorax, a type of collapsed lung. While the chest x-ray images of a suspected pneumothorax patient are often marked “STAT,” they can sit waiting for up to eight hours for a radiologist’s review. However, when a patient is scanned on a device with Critical Care Suite 2.0, the system automatically analyzes images and sends an alert for cases with a suspected pneumothorax – along with the original chest x-ray – to the radiologist for review via PACS. The technologist also receives a subsequent on-device notification to provide awareness of the prioritized cases.

“Seconds and minutes matter when dealing with a collapsed lung or assessing endotracheal tube positioning in a critically ill patient,” explains Dr. Amit Gupta, Modality Director of Diagnostic Radiography at University Hospital Cleveland Medical Center and Assistant Professor of Radiology at Case Western Reserve University, Cleveland. “In several COVID-19 patient cases, the pneumothorax AI algorithm has proved prophetic – accurately identifying pneumothoraces/barotrauma in intubated COVID-19 patients, flagging them to radiologist and radiology residents, and enabling expedited patient treatment. Altogether, this technology is a game-changer, helping us operate more efficiently as a practice, without compromising diagnostic precision. We soon will evaluate the new ETT placement AI algorithm, which we hope will be equally valuable tool as we continue caring for critically ill COVID-19 patients.”

Research shows that up to 25 percent of patients intubated outside of the operating room have misplaced ETTs on chest x-rays, which can lead to severe complications for patients, including hyperinflation, pneumothorax, cardiac arrest and death. Moreover, as COVID-19 cases climb, with more than 50 million confirmed worldwide, anywhere from 5-15 percent require intensive care surveillance and intubation for ventilatory support.

What You Should Know:

– RADLogics today announced that it has secured 510(k) clearance from the FDA for the company’s novel AI-Powered chest X-ray pneumothorax application, which identifies and prioritizes chest X-ray scans that appear to contain a pneumothorax, a collapsed lung, for urgent radiologist review.

– RADLogics’ FDA cleared CT and X-ray solutions – including this application – are available to hospitals and healthcare systems throughout the U.S. for patient triage and management. All of these applications are available immediately through the Nuance AI Marketplace for Diagnostic Imaging.

RADLogics, a healthcare company developing AI-Powered solutions that support image analysis to improve radiologists’ productivity while enhancing patient outcomes, today announced it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its novel AI-Powered chest X-ray pneumothorax application. The clearance paves the way for healthcare providers, hospital networks, and clinicians to have access to artificial intelligence (AI) software that is trained via pattern recognition to process chest X-rays and immediately flag scans with a suspected pneumothorax, a collapsed lung, for urgent radiologist review.

How RADLogics AI-Powered Solution Works

Operating in parallel with existing workflow, RADLogics’ chest X-ray solution uses an artificial intelligence algorithm to analyze images for features suggestive of pneumothorax. It then makes case-level output available to a PACS workstation for worklist prioritization or triage. The AI-Powered solution integrates into a comprehensive, seamless, and secure workflow to augment acute care teams with deep clinical insight and actionable data in minutes. The application was validated and trained by a multi-center study for detecting pneumothorax in hundreds of chest X-rays.

In accordance with FDA guidance for imaging systems and software to address the COVID-19 public health emergency, RADLogics has made its FDA cleared X-ray and CT solutions available to hospitals and healthcare systems throughout the U.S. for patient triage and management. Designed for easy integration and installation both on-premise and via the cloud – RADLogics’ algorithms are supported by the company’s patented workflow software platform that enables rapid deployment at multiple hospitals, and seamless integration with existing workflows.

“We are very pleased to receive FDA 510(k) clearance for our AI-Powered chest X-ray pneumothorax application, which adds to our array of AI-Powered solutions for CT and X-ray that are designed to improve efficiency and reduce burnout for radiologists that are under greater pressure than ever before,” said Moshe Becker, CEO and Co-Founder of RADLogics. “From extreme pressure on capacity and resources to a major financial strain due to the ongoing COVID-19 pandemic, there is no question that the healthcare system is in need for new solutions such as AI to augment caregivers to alleviate the increased burden and support better outcomes.”

Availability

The application is available immediately through the Nuance AI Marketplace for Diagnostic Imaging.