Radiology is an essential part of the healthcare delivery system. Its presence in the clinical setting is steadily increasing year over year as overall healthcare demand increases, new technology is developed, and radiology needs continue to expand. Nationally, radiology has seen consistent annual growth over the last 50 years. This national growth is reflected in local departments, including those I have personally overseen. Additionally, most experts agree that radiology is expected to increase in demand over the next thirty years. With expected radiology growth, maintaining an efficient clinical workflow is imperative to proper operations and patient satisfaction. In my role of overseeing a very large outpatient imaging facility, I was living these struggles. Our department, specifically our MRI modality, struggled with in-efficiencies and throughput issues that reduced volume and sub-par patient satisfaction scores. We sought out on a mission to improve clinical workflows, efficiency, and throughput. But how can anyone achieve efficient clinical workflow in a high volume, hectic and unpredictable environment like ours? For us, leveraging technology was the answer.

We embarked on a journey to find a technology we could use to improve throughput and clinical workflow without any negative impact on safety and quality. This was not an easy task, but something we felt was imperative to the success of our facility. The first task was evaluating what technologies were available in the marketplace and which could work for our needs. Although this part of the process is often undervalued, we understood the importance of the selection process to ensure we chose the technology that would best fit our requirements and set us up for the best possible chance of success. We took our time to evaluate different vendors, perform demos, and talk to our peers to ensure we made the best decision. We settled on a technology that most imaging vendors offer: image acquisition accelerating software for our MRI scanners. Our MRI department was our most utilized modality and had significant challenges with clinical workflows and efficiencies. Therefore, MRI was the perfect place to implement this technology to have the biggest impact on our patients. Implementing this in our MRI department would allow us to perform MRI scans faster with no impact on image quality, ensuring we continue to provide the highest quality images efficiently.

Better is possible. It does not take genius. It takes diligence. It takes moral clarity. It takes ingenuity. And above all, it takes a willingness to try. – Atul Gawande

Once we had identified the technology and appropriate vendor, it was time to purchase and implement.  Proper implementation is critical to success. We created a multi-disciplinary approach to implementation inclusive of clinical (technologist, radiologist, and referring physicians), support staff, local IT staff and vendors. The implementation was methodical to ensure our end users, including technologists and radiologists, were not only trained in proper usage of the technology, but trained in full optimization of the technology. This ensured that staff were confident in the software and systemic in its use.  

Once implemented, we looked at data analytics to monitor the success or evaluate opportunities to improve our process. We utilized key data metrics to see exactly what scans, by specific patient types and specific times where impacting clinical workflows. We then utilized the same metrics post-implementation to validate the value of the technology and its impact on our patients and workflow.  The data was astounding. The data showed that by leveraging technology, we were able to increase our capacity by 28 percent, adding an additional 1,177 MRI studies annually. This increased capacity significantly improved our clinical workflow and efficiency. We were able to increase outpatient satisfaction scores by 40 percent largely due to the significant reduction in wait times created by the improved efficiencies. Lastly, the increased volume added an additional $1 Million annually to our bottom line. With that additional revenue, the return on investment (ROI) was less than a year, making this a true success for the department, healthcare system and, most importantly, the patients we serve.

Leveraging technology to improve key metrics that drive success in your departments makes sense. With the increased use of Artificial Intelligence (AI), we now have a new plethora of options to evaluate and utilize. AI will impact all sectors of life, particularly healthcare. Radiology has been using AI for over thirty years and therefore, can be considered the healthcare trailblazer when it comes to AI. As this technology continues to develop and be utilized, we can better evaluate the beneficial impact on our patients.

The journey to improve our clinical workflow, efficiencies and throughput in a large outpatient imaging center started with a thorough evaluation. Evaluating technologies that work best for our needs and positively impact our patients. Once identified, the systematic implementation was critical in ensuring confident and effective use of the technology. Using data analytics to either validate success or evaluate the opportunity to pivot and adjust after implementation allowed for the flexibility that is indispensable when introducing a new technology. Additionally, with the incremental development and adoption of AI, the technological options available to leverage have increased dramatically. Most importantly, it’s fundamental to ensure that whatever tool you utilize to improve processes within your departments truly impacts the patients and communities we serve. After all, our purpose is to improve the health of our communities and the patients we serve.

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Healthcare is constantly changing at a rapid pace. One of the key medical disciplines promoting this change and advancement in medicine is radiology—aka diagnostic imaging. Specifically— this change is based on radiology’s advances in technological capabilities. Radiology is the discipline within medicine that uses imaging technology to diagnose and in some cases treat disease. Generally speaking, one can divide radiology into two large categories that include diagnostic radiology and interventional radiology. Interventional radiology is a therapeutic and diagnostic specialty that comprises a wide range of minimally invasive image-guided therapeutic procedures as well as invasive diagnostic imaging. 

So where is all the advancement in radiology occurring? A significant focus of development is within the space of Artificial Intelligence or AI. Artificial intelligence in radiology has several goals that include improving quality, productivity, and automation. So what is AI and how can it be used in radiology? Artificial intelligence used within the space can be seen as a self-learning software that assists in identifying potential abnormal findings on images. This assistance has the ability to improve how quickly a study is interpreted by potentially identifying areas on the image which the radiologist (physician) can review to determine if the disease is present. The optimal end result is AI would assist in the quality of the interpretation and how quickly it can be interpreted—a win-win situation.

Advances in technology have long been a catalyst for change in radiology, this has facilitated the pace by which healthcare in general is evolving.

Nuclear imaging has also seen a significant resurgence within the specialty—with particular emphasis within the PET/CT space. Nuclear medicine is the subspecialty within radiology which uses radioactive material (radioisotopes or tracers) inside the body to see how organs or tissue are functioning or in some cases to target and destroy diseased tissue such as cancer. PET imaging or Positron Emission Tomography falls into this subspecialty of radiology. It is a scan that measures the physiological function of tissues by looking at blood flow, metabolism, neurotransmitters, and radiolabeled drugs. New radioisotopes which have recently been released have specific targeting capabilities to help diagnose, stage and monitor common diseases such as alzheimer’s and prostate cancer, both major illnesses impacting the lives of thousands of people.   

As many readers will know, COVID-19 has had a significant impact on the business of healthcare—that includes radiology functions. With the help of the extreme measures early in the pandemic, alternative approaches to providing care while also social distancing needed to be implemented or augmented. Long before the pandemic, diagnostic radiology was already a pioneer in the telemedicine/radiology space with the ability for a radiologist to work remotely and  read (interpret) diagnostic images via PACS (Picture Archiving and Communication systems). These systems receive diagnostic images from all imaging modalities such as Computed Tomography, Magnetic Resonance Imaging, X-Ray and so on. These images are sent via a secure network to the database for the images to be uploaded and then ultimately transferred. Radiologists view these images on a workstation and will often use software tools at their disposal to enhance their interpretation. The radiologists dictate a report with his or her findings and they are sent to the appropriate referring clinician. The data which comprise the images are ultimately stored and archived for future review or comparison if necessary. 

Why is Teleradiology a hot topic if it has existed for many years now? When you combine the rise in imaging procedure requests, the diagnostic radiologist shortage occurring nationally and the reliability and efficiency in which teleradiology offers, you have a recipe for growth. This growth is accelerated with the specific rise in high-tech imaging such as CT, MRI or PET and allows for expanded access to subspecialty radiologists after normal operating hours for medical centers, private centers, and even rural centers. There is no shortage of vendors offering these platforms. One needs to wade through the abundant number of players in the marketplace to see if their platform meets your needs. 

Advances in technology have long been a catalyst for change in radiology, this has facilitated the pace by which healthcare in general is evolving. Out of all the emerging and advancing technologies discussed, AI seems to be where the current spotlight is on. One can expect AI to continue its growth in the discipline as healthcare evolves to value-based arrangements and there is a continued emphasis on productivity, quality and superior outcomes.

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In the 1987 film “Wall Street” Michael Douglas’s character, Gordon Gecko, stated “greed, for the lack of a better word, is good”. He made this claim to support the idea that our advances as a country were directly attributable to the greed of mankind. Just look at the amazing technology available in healthcare to see that, at least at some point in time, Gordon’s statement rang true for us as well. It’s incredible to think that we’ve only had MRI machines for 42 years and CT scans for just 50 years. And all the amazing breakthroughs in medicine that were developed because of what these technologies can see. The creation of these machines was not 100% altruistic and the advances made to them over time to make them more hi-tech and faster weren’t done just to improve outcomes.

In this instance, regardless of your political views on capitalism, we could all agree that greed just might have done some good. Greed in healthcare is prevalent on all sides, vendors, administrators, staff, the government, insurance companies, the legal system, etc. and they all want their piece of the pie. And when someone finds a way to get a larger slice, you better believe they will; that drives more innovation. At some point, our primary consumer will say, ‘enough is enough’ and the spending will have to stop. Coming out of the COVID pandemic, we may have reached this point. This past year we have seen a massive staff exodus, decreased revenues, increased costs of care, and we see that continuing to operate the business of healthcare the way we have always done, is illogical. So how can we leverage technology from the world outside of healthcare to improve the patient experience?  

Healthcare systems need to develop solutions designed to address the shortfall in human capital without allowing startup costs to deter them.

True, greed is already present in healthcare. While COVID has led to much needed pay increases to frontline staff who have gone years without a raise that matches the annual cost of living increases; it took staff departures, closures, and negative impacts on patient care before that happened. And at the other end? Why does anyone in healthcare need to earn seven figures a year or more? We collectively seem to have forgotten that everything is paid for by the patient. Everything. We owe it to them to start exploring new ways to do our work and leveraging the tools created for capitalist purposes for the improvement of healthcare.

Healthcare systems need to develop solutions designed to address the shortfall in human capital without allowing startup costs to deter them. We need to look at who is doing what work, day in and day out. Who is placing IVs? Who is taking vitals? Who is doing procedures? And on and on. Once we know what the work is and who is doing it, we need to figure out what work can be improved by technology. One easy solution: self-scheduling platforms for appointments. It’s ridiculous that you can book travel on your own to anywhere in the world, but you can’t book a CT by yourself.

There are plenty of solutions outside of healthcare that could be applied today. Artificial Intelligence, Deep Learning, etc., should be looked at for all aspects of our healthcare systems. We could have AI screening patient records and results to triage our schedules and ensure no information is missed. AI to pre-read our images, reports, specimens, etc., to reduce defects and mistakes. Intuitive systems take a provider’s report or results typically filled with medical jargon and translate it into information that is understandable to the patient and their family, and in a tech format that is accessible to them. And we need to mandate the connectivity of all electronic medical records, PACS, etc., requiring full and on-demand access to any previous records regardless of where it was done, whenever it’s needed. I can search Google, Yahoo, etc. for any information and at the same time, I can’t get results from a year old blood test without signing a release and communicating directly with the facility.   

What if we went all in? Fully leveraging the technologies that made the creators of Google, Facebook, Instagram, etc., insanely rich? In our world today, a large majority of the population live their lives entirely for the world to see, we only have to pull up any number of applications or social media platforms and we can learn what someone had for lunch, whether they are a cat or dog person, what music they like, even down to how warm they prefer their home. A simple algorithm could pull all this data, prior to a patient’s arrival, checking them in for all their appointments, alerting the caregivers’ topics to avoid, important dates, room color, background music, and setting room temperatures. An AI could screen them at the beginning of the process by asking a few questions, getting vitals from smartwatches, etc., to make the care teams apprised of potential illnesses. Geofencing technology would allow these changes to precede them for all of their appointments while at the facility. Smart sensors can detect their medication compliance so that it can be discussed during visits. All their lives that they have shared online can be picked through for keys to what they can do to improve their own health. And when they are done, their car can be notified so it will be ready when the patient’s visits are completed. A 100% automatically customized experience based on all their smart devices, posts, likes, and dislikes. Surveys would be a thing of the past because the care team would know as soon as the patient posted about their experience. We would finally know our patients and how they wish to be known on a much deeper level. 

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It is without a doubt that medical imaging has seen exponential growth in advancements in technology. We are technologically far moved from where we stood 20 years ago.

Merely 15 years ago, we were seeing a 64 slice CT scanner advertised as “the fastest scanner on the market.” Many outpatient facilities were racing to buy a relatively expensive open MRI to capture claustrophobic patients. Ultrasound was beginning to offer 3D imaging, which seemed futuristic. There were stirs of conversations of general radiography going completely filmless and to most seemed preposterous. Now as we reflect on our advancements, these “upgrades” are laughable to most in the imaging world.

As of today, it is not unheard of to see a facility with multiple dual source CT scanners that can perform 300 or more slices and scan a single body part (including cardiacs) in under one second. MRIs have almost made it a standard that the bore size and bore length are comparable to the size of a CT scanner. These types of bores have almost completely eliminated open MRI scanners. In ultrasound, 3D and even 4D come standard on most pieces. Conventional X-ray almost unknowingly skipped right past the computed radiography phase of advancement and jumped straight to digital exposure with images being revealed instantly.

Given these advancements, are we utilizing the updated technologies to their full extent? Conversely, are we making these advancements too accessible? This is a discussion that seems to cause reflection on how we treat these technologies to analyze, care, and diagnose our patients.

Are we utilizing these technologies to their fullest extent?

In short, we are engaging most of these advancements correctly in clinical settings. However, there are opportunities as technologists and administrators where we could better fulfill patient care with our imaging technologies.

As improvements are made with each modality, more opportunities arise to use technology and engineering to decrease patient dose, exposure time, scan time and improve image quality. Cognitively evaluate the crossing point of cost and use of equipment with patient outcomes. For example, some dual source CT scanners offer flash or quick scan modes that can be used on examinations. These modes DRASTICALLY decrease dose and scan time. However, these are hefty price tags that many community hospitals cannot afford. There are other advanced scanners available to greatly improve image quality and patient experience. These offer more affordability for the locations that do not support the volume.

The options are almost endless and can easily conquer the decision-making skill of even the keenest administrator. Extensive research, observation, knowledgeable managers and experienced staff can be resourceful in making those decisions. Medical imaging is advancing what seems to be daily. Decisions would need aid to conclude on how to use your money wisely, where to place the equipment to get the most utilization and which technologies are needed to ensure you are serving your patient population.

Are we making our technological advancements too accessible?

Healthcare has become extremely reliant on medical technologies and is becoming more prevalent in the clinical setting. As previously mentioned, our radiological advancements have given us opportunities to improve patient outcome.

Most often, we see a rush to image in the Emergency Department. Time is of the essence with true emergency patients, but this also does not open the doors to rush to imaging. There are numerous examples that plague advanced imaging modalities. In addition to our equipment advancing, so has the criteria and research. The newest technology used to ensure proper imaging has been mandated by the CMS to utilize Appropriate Use Criteria (AUC). The AUC is an intelligent technological advancement developed on indications with scores ranging from 1-10. A score of one being inappropriate use of imaging and a ten being completely appropriate. These diagnoses are taken through a clinical decision support mechanism backed by evidence-based research.

Even though AUC scores an examination as a one, providers continue to image patients. We are utilizing the technologies supplied and bypassing just to rely on the speed of imaging. Some providers have openly admitted that the advancements have allowed them to lean heavily on the results from imaging.

With updated picture archiving systems and dictation systems, radiologists are able to have extraordinary turnaround times. Again, due to the advancements, providers are relying on and pushing radiologists to have quick turnaround times.

The issues does not reflect provider only reliance. There is much to be said about technologists’ performance given these advancements. Undoubtedly, a technologist’s role is crucial and a technologist will face a multitude of issues throughout a shift. Technological advancement has made their duties accelerate rapidly. Contrarily, it has caused contentment with suboptimal imaging and quality. Digital radiography has changed the process of portable examinations. Digital radiography has completely eliminated “the walk of shame” back to the floor to re-shoot an image. Instead, the complacency with attention to detail has given the option of an immediate fix and repeat without the pressure of returning to the floor to re-image. As with most discussions, there are always nuances to how a subject is perceived. However, it is not debatable that advancement in radiological technologies has improved patients almost universally for their visits. Each modality has seen their fair share of improvements in technology. Some modality advancements were quick and ever-changing while some divisions saw only little change. Nonetheless, it has been favorable for the field. It should be taken with great observation how we use our radiological technologies in the clinical setting. Advancements will continue to grow and we will continue to see enhanced treatments, diagnoses and most importantly, patient outcomes.

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Every radiology modality should strive to follow ALARA – which stands for As low as reasonably achievable. This sole guiding principle of radiation safety is one of the most important practices in medical imaging field in past, present and future generations. Not all imaging modalities use radiation, but for the ones that do, such as CT scan or diagnostic radiology, time, distance and shielding help protect that patient and achieve the concept of ALARA.

Time refers to the amount of time spent close to the radioactive center. The tech should only do what needs to be done, minimizing the time required to complete the job. There is no need to spend more time around that area once the specific case or exam is finished. This practice applies to the tech doing the exam, the patient and everyone involved. The perfect expression here would be the classic phrase, time is of the essence. Every hospital wants to protect its employees and patients from harm. Ensure someone monitors the time in and out for cases and documentation of fluoroscopic times of radiation output is critically important. HCA Healthcare has a designated radiation safety officer who focuses on ensuring the hospital follows up-to-date standards and policies.

If you stand next to a heater, you will feel the heat piercing through your skin, making you feel very hot and uncomfortable. This is why distance is another best practice. Ensure techs and other colleagues are at a safe distance from the radioactive source. When we X-ray pediatric patients, parents come with the child into our radiology room to provide extra comfort for the child. Ensure they stand behind a lead-lined wall or far away from radiation. If you increase your distance, you decrease your dose.

HCA provides PPE to every staff and ensures protocols and policies followed to keep both patients and employees safe. HCA’s motto is “Above all else, we are committed to the care and improvement of human life.” In addition to PPE, proper shielding is another best practice while dealing with radiologic procedures. Just because it is available, doesn’t mean everyone always shields themselves or provides education as to why shielding is so important around radiation. To care for human life, we must protect and educate our future. HCA goes above and beyond providing training and courses on understanding radiation safety and shielding. There needs to be something between you and the radiation source, especially if you are in a procedure that requires a heavy output of fluoroscopic radiation.

The radiologic techs at HCA practice this to ensure patients are not overexposed in surgery cases requiring a C-arm. Depending on various procedures in the OR, some may require quite a bit of fluoroscopic radiation for the surgeon to operate. In this case, it is extremely important that radiology staff take a leadership stance on ensuring everyone in the room is aware of the radiation risks and provides shielding. Other staff members may not fully understand the risk of long-term radiation, but as a radiologic tech, the education of a 2-year program gives us proper insight on this information.

HCA incorporates ALARA through continuous employee education and training to keep the staff up-to-date with the latest technological advancements and best practices. The focus is to find key methods to help implement the concept of ALARA and share it with everyone. When best practices are found, it is important to implement and ensure it works. Once all the technical aspects are sorted out, another key concept is sharing that best practice with other hospitals. Because of how large HCA Healthcare is, the benefit gets shared and best practices are taught everywhere. If you want to make a difference in radiologic healthcare, radiation safety would be the first greatest factor to solve and overcome. To keep advancing in the area of radiation safety, lowering radiation dose but still receiving optimal quality imaging should be at the heart of every imaging technologist.

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It was not long ago that the COVID-19 pandemic-related shutdowns in China led to a worldwide iodinated contrast shortage. Hospitals, health systems, government organizations, and radiology societies developed policies and initiatives to ration the available contrastand reduce overall consumption. Alternative tests and procedures were offered; however, many life-saving minimally invasive procedures performed by interventional cardiologists, vascular surgeons, and interventional radiologists still required iodinated contrast. Except, what if they didn’t?

Intravascular ultrasound has been around for many years, but has recently started to gain more prominence in minimally invasive image-guided interventions. Traditionally, ultrasound is thought of as an external beam that penetrates through the soft tissues, with a reflection of the sound waves allowing physicians to determine different structures and clinical conditions. Using the same technology, an intravascular ultrasound probe is placed within the blood vessels to evaluate various vascular conditions and assess surrounding structures. Previous research has shown a clear benefit of using intravascular ultrasound in the coronary space for characterizing plaque, assessing potential dissections, and ensuring optimal stent placement. Intravascular ultrasound is also being increasingly utilized for peripheral interventions in both the arterial and venous spaces. Similar to coronary utilization, peripheral arterial disease patients benefit from being able to visualize pathologies and ensure optimal treatment modality and resolutions from said treatment. In the venous space, a recent study in the Journal of Vascular and Interventional Radiology, showed over 70% utilization by operators placing venous stents to demonstrate optimal wall apposition and placement. Studies have previously demonstrated the value of intravascular ultrasound in assessing treatment response to procedures designed to remove thrombus from the venous system. During endovascular aortic repair, interventional physicians are able to use intravascular ultrasound to distinguish the true aortic lumen from the false one in order to prevent complications from malpositioned grafts.

For intravascular ultrasound and other technologies, more education can only help grow the practice of medicine and improve patient outcomes.

As it is most commonly being used, intravascular ultrasound is used as an adjunctive tool to assist procedural physicians during their procedure in combination with contrast injections with fluoroscopy. While there are some case reports in the literature detailing examples of procedures being performed solely with intravascular ultrasound, these instances are far from commonplace for most providers. There are likely multiple reasons for this: operator discomfort with not using fluoroscopy and iodinated contrast, established physicians being reluctant to change their practice so wildly, clinical scenarios where sole use of intravascular ultrasound would not be appropriate, and lack of appropriate education on how to interpret the imaging. Most of those reasons are likely unchangeable; however, education about using the device and understanding the images it provides is the best way to increase utilization of this growing technology.

Under the current educational environment, most education for intravascular ultrasound is either provided by attending physicians who have adopted the technology to their trainees and partners, or by the device manufacturing companies who provide seminars, workshops, and online resources to bridge the knowledge gap with potential novice users; however, there are inherent limitations to both of these models. Training programs in these highly subspecialized fields have a wide spectrum of disease states and interventions to cover in a relatively short time, and spending significant time focussing on one technology, which is not mandated by any of the societies that regulate these residencies or fellowships, would likely detract from perfecting the more common tasks and situations these physicians will encounter throughout their lifetimes. Device manufacturers have a large amount of resources to educate physicians on proper use of their product, but these come with obvious biases.

At the core of this dilemma is how does the medical education system functionally address the lifelong learning aspect of being physicians as new technologies arise and are adopted optimally for the provider and the patient. This is not a new challenge for medicine, but one that will become increasingly more relevant as the years progress when artificial intelligence (AI) becomes more integrated into the healthcare system, and all the opportunity for growth and potential for harm that this will bring. When new technologies are developed and adopted by practicing physicians, physicians attend workshops, have supervised cases by experienced operators, receive support from company representatives, and then go out and practice with little verification of being able to use their product effectively. Patients need to trust that they are not being used as test subjects for novel technologies, of which potential complications are not fully known.

It is the responsibility of the medical community to come up with rigorous ways to train not just the next generation of physicians, but also those out in practice in the community. Most physicians do not practice in an academic setting are not necessarily being exposed to new technologies in the best ways to serve their patients. Academic centers, industry, medical societies, and thought leaders in various medical fields provide a multitude of materials to educate their colleagues, employees, and trainees, and this focus must not diminish as the medical field advances into the future. For intravascular ultrasound and other technologies, more education can only help grow the practice of medicine and improve patient outcomes.

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There is no doubt that medical imaging is the most frequently used diagnostic tool in medicine. Since it is being mostly utilized to get rapid results for both acute and chronic ailments, without the use of invasive measures, it should come as no surprise that imaging is the largest source of outpatient revenue for many hospitals, bringing in about 35% of the bottom line on average. Having such a high percentage and bearing such great responsibility and pressure in the scope of our healthcare institution, the understanding and management of that operation are faced with many challenges that require both careful and strategic planning in order to overcome these obstacles.

The introduction of COVID-19 hit our healthcare system in a way that no one could have predicted. As a radiology administrator, the biggest challenge that many of our institutions faced at that time was employee coverage, or rather employee shortages. It’s no secret that before blood tests, rapid tests and nasal swabs, the first ever created method for COVID-19 diagnosis was through a Chest CT, looking for that distinctive “ground glass” opacity appearance on patient images. During the initial onset, many patients came through the ER with uncertain diagnosis of what appeared to be flu and bronchitis symptoms. At the time, they were positive for COVID-19, but no one knew. We had no idea what we were dealing with or how to handle it. We obviously couldn’t turn patients away, so we had to arm ourselves and do what we signed up for when we chose this profession. Only this time, we were on the front lines fighting an enemy that we knew nothing about.

Radiological technologists are one of the many unsung heroes during the emergence of COVID-19. When isolation was at the precipice of our daily lives and many americans were at home or out of jobs, rad techs were working overtime hours at a rate that would be considered inhumane compared to current labor standards. After excessive hours and continuous exposure, rad techs were getting sick and were diagnosed with COVID-19. Available staff members were decreasing by the week. As a result, there were staff shortages. I can recall a time when staffing was so bad that I was being offered $10,000 per week to come to NY during their COVID-19 crisis. All expenses and accommodations were covered as long as I would agree to at least a four week contract. As good as that might sound, I still had work and family obligations to consider before respectfully declining the offer.

My advice to anyone starting and learning how to navigate this field would be to pick a modality that you love and then pursue a 2nd certification to help you become more marketable.

Dealing with a rising number in patient sickness’ and a growing concern for inadequate staff coverage, we turned to agency and contract staffing to help. Patient care needs were being met and we were able to ensure continuity of all radiology services for the foreseeable future. Even though this seemed like a great plan at the time, there were still a few setbacks. Agency staffing takes quite a bit of time to be approved, as they needed the appropriate clearance checks before being onboarded in our system and it comes at a high cost to the bottom line. I’m sure many administrators can relate to the exuberant costs that were being paid at the time. A large percentage of revenue was lost due to COVID-19, and then having to turn around and pay a high expense for additional staffing hurt our financials in a way that many outside the healthcare field will never understand. It was a balancing act that needed to ensure that our current staff members were being paid their full salaries, without the fear of losing their jobs or the reduction of hours, while simultaneously maintaining a reserve to ensure that additional staffing was a possibility when necessary. I’ll give you an example. If I needed an additional CT tech from an agency, I would have to endure the cost of paying about $200 per hour. What many technologists are not aware of is that a lot of these agencies will sign them up for a contract anywhere from 4 to 16 weeks and pay them at a rate between $60 to $80 per hour or more, which is significantly more than the normal Full-time or PRN rate and consume the rest of the money for their administrative and overhead cost. The trend that was happening during COVID-19 was that many radiological technologists were leaving behind their secure full-time jobs to hop on a bandwagon that seemed to pay out large sums of money. Yes, they would acquire a larger sum of money in a short period of time, but there were no benefits, long-term commitments or guarantees of sustainability. 

Leaving a secure position for an agency is something I strongly advise against. No matter how promising it seems at the moment, I find that the current trend in agency or contract staffing requests are declining as we begin to create ways of normalizing our standard practices in a newly recognized COVID-19 world. I’ve noticed that many technologists that once left their secure roles are trying to find once again a home base to settle down in. The problem is, when they’ve knocked on those employment opportunity doors that were once widely opened, it is now closed. Those positions are now secured by those that chose to stay behind and remain in a secure full-time position. 

My advice to anyone starting and learning how to navigate this field would be to pick a modality that you love and then pursue a 2nd certification to help you become more marketable. Nowadays, directors or hiring managers are looking for team members that can do multi-modality operations. If you have more than one trick up your sleeve, your chance of being hired grows tremendously. Lastly, find a place with a leader that respects and values your contribution to their team. This kind of place will ensure your happiness, give you a sense of purpose and nurture you in your career growth. A great working environment and a positive culture will always beat out a higher paycheck from an unsustainable source.

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In the last several years, my healthcare system has implemented several pieces of AI software in the radiology departments, both across the system and at individual hospitals. We have 13 wholly owned hospitals and six joint venture hospitals with three radiology physician groups reading across the enterprise. Recently, companies and vendors that develop and market AI for radiology have brought many, many software solutions to market. AI software for radiology has been developed for multiple modalities (i.e., CT, MRI, and X-ray, for example) and is able to assess images from specific areas of the body to look for and identify disease states. This is to assist and enhance what a radiology physician would be identifying during their formal read of the study.

AI is not meant to replace the radiologists for their part in patient care. I went to the Radiological Society of North America (RSNA) annual meeting and convention in Chicago at the end of November 2022. There were hundreds of vendors at the meeting representing AI software and platforms for the radiology industry. Most of these software are FDA approved, but some are not. Due to the recent explosion in this area of technology, a focused and structured approach to adopting this technology into a hospital practice is needed, whether it is for one hospital or a larger system.

Visiting many of the AI booths at RSNA, it became apparent that vendors are taking several approaches to market and implement this technology. The first approach is the vendor develops their own AI software. The vendor would market their software in individual software offerings or in package offerings that include several pieces of AI software, directly to end users/facilities. This means they employ or contract with all their own software developers. Most of their offerings are FDA approved, but some may not be. 

A radiology AI implementation committee should be formed to make strategic decisions as to what software modules are most important and then determine how to implement that software.

The second approach vendors use is licensing third-party developers of AI software to market to their customers. Again, this can be marketed as individual software offerings or as package offerings that would assess images of several different areas of the body. Some of these vendors may have their own platforms that could be launched directly from the end user’s current PACS solution.

The third approach vendors use for marketing AI for radiology applications is a hybrid model of the first two. These vendors develop their own software and license 3^rd party software to the market to end users/facilities. This allows the vendors to market software to end users that they would not otherwise have in their offerings.

There are two aspects radiology departments should consider when assessing AI vendors; how the vendor platform interacts with the radiology physicians during image assessment, and how the AI platform real-time interfaces in order to communicate a positive finding to the care team. These two aspects are independent of each other and not all vendors will have either, or both, of these platforms. If the vendor has an interactive platform for the radiology physicians for when they are reading the exams, the way that platform is utilized by the physician is important, as it can greatly affect the physician workflow efficiency. One example of an interface used during the exam reading task is the vendor AI software would send an assessed image with an interactive icon to the facility’s native PACS system. That icon on the image may indicate that the AI software has identified a finding and the physician could click on that icon inside the PACS system which would take the physician to the AI software, opening directly to the image where the positive finding can be found. Once the AI software has identified a positive finding, if the software platform has a results interface, it would then send the findings to the care team that is signed into the platform at the time. For instance, a positive finding for Large Vessel Occlusion (LVO) could be sent to the Neuro Interventionalist, Stroke/Vascular Cath Lab team, radiologist, stroke coordinator, Hospitalist and/or ED physician, and any other clinical team member needing this information. Examples of software that a vendor may carry include intracranial, hemorrhage, LVL, brain aneurysm pneumothorax, aortic dissection, and C spine fracture, just to name a few.

Lastly, in order to determine a strategy on which of the vendor’s specific software modules to purchase and how to implement those modules, a formal process should be implemented. At my health system, we will create a Radiology Artificial Intelligence Summit Committee. This committee will consist of a radiologist from each of our radiology physician reading groups, members of radiology IT, the chair and vice chair of our system radiology directors’ council, and several SMEs from the areas of focus of the software we are assessing. As an example, for LVO, those SMEs could include stroke coordinators and/or neuro interventionalists. These SMEs would be adhoc and different for each software module that would be assessed by the committee.

With so many aspects to consider, adoption of AI in medical imaging can be an overwhelming and arduous project, especially in a large hospital system. Knowing what specific AI software modules are strategically important to your hospital system imaging departments is a key piece of information to determine what details to look for from AI software vendors. This would include pricing based on if the hospital would be purchasing one specific software module, a package of several software modules, and if the vendor has a comprehensive platform and interfaces for radiology exam reading and the ability to communicate acute positive findings to the clinical care team. A radiology AI implementation committee should be formed to make strategic decisions as to what software modules are most important and then determine how to implement that software.

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If you have worked in healthcare for even the smallest of time, you are aware of the nursing shortages, and the movement to travel nursing resulting in much higher wages. This has made the organization profit margins slim, or even fall into the red. What’s not captured in the headlines as much is the shortage of imaging technologists, primarily MRI, CT, X-ray, and mammography. While not as critical as nursing shortages, organizations are scrambling to offer similar sign-on bonuses, higher wages, or better hours in hopes to lure new technologists in, avoiding low staffing, or paying higher traveler cost. However, this comes at a heavy costs both financially and morale. 

Because of a fierce competitive job market, technologist who are unhappy for any reason, including pay, fair treatment, hours, or management treatment can easily find a new job without the need to relocate. Not only does this happen between organizations but often between departments within the same organization. The biggest question is, how do we retain staff at reasonable wages, and hours for both the staff member and the organization? 

Everyone has seen the jokes about management and pizza parties being “the best we can do”, however what employees don’t understand are the high cost and at times low payments for imaging studies. Sitting down with staff during meetings is a great way to improve transparency and bring to light the high cost and number of staff it takes to run a successful imaging department. Many employees have heard of the gross charge for an exam, and associate that with an actual payment which of course is vastly different. Hospitals struggle to collect 25% of what they charge while outpatient centers are better but still around 35-40%. To help simply that, providing an average payment for a particular study can be useful. For example, if a study pays $200, show the staff how far that money goes, not only for their pay, but the pay of the check-in person, the staff who performs prior authorizations, and equipment repair. CT tube, or mammo detector replacements can run over $100,000 easily, not including labor. Not only will staff be thankful for the education and knowledge, but they will also gain respect for the challenges we face as healthcare leaders. 

The biggest question today, how do we retain staff at reasonable wages, and hours for both the staff member and the organization?

Another challenge leader’s face are internal bidding wars, between different facilities inside one large organization. X-ray or other commonly used modalities can often bounce around from hospital to outpatient centers within the same organization and at times receive a pay increase. This is a two-fold issue with the first of losing the staff, and the second being a morale hit to the current department. As many of us know, employees do often communicate their pay to one- another, making it difficult for leaders to keep morale up. Keeping open lines of communication between leaders and having a simple agreement no pay increases for lateral moves can help both keep staffing levels in hospitals sufficient and morale at the desired level for your team. Directors, or VPs should be mindful of these issues and stress a consistent pay practice across the organization. 

While internal transfer do happen, the main concern is losing staff to outside organizations. How to slow down the bidding war to hire staff? Many organizations are already providing internal bonuses, and higher annual raises, but are still seeing employees exit for even more money. While staff mostly cite this as the reason for their departure, it is often deeper than financial reasons. Every employee, even outside healthcare has different needs from their leader. Some love one-on-one time for a meeting, while others may just enjoy a simple coffee every-so often. Leaders must almost keep a diary on their staff, recording what helps motivate them to go “above and beyond.” Knowing how employees like to be appreciated or engaged goes a long ways with staff, and can make them second-guess a move to another organization for a modest pay raise. Using e-cards, emails, or just praising a staff member individually vs. group goes a long ways to help staff feel wanted. Rounding daily but not only by the direct manager but by senior leadership help staff feel more engaged, and enjoy sharing their experiences or concerns with higher level leadership. 

Increasing new grads through technical schools is another way to hire for growth, or staff vacancies. With the work population at a higher age, improving access for students to train has never been more important. Healthcare organizations must work with their local education systems as early as high school to get students interested in imaging, and invest in clinical training for those at a technical school. Going to career fairs, is a great way to show all that imaging has to offer, with so much focus on nursing, imaging departments must show the benefits of becoming a technologist, such as hours, patient mix, and advanced technology. 

Since there is no one solution for every imaging site, leaders must use their best judgement with staff retention. However, continuing to increase pay without regard to fair practice pay, staff morale, or profit margins will place a strain on healthcare systems, limiting capital, or increasing staff volumes. Keeping an open dialogue with current staff members is critical, ensure that employees who do have an offer from another organization look at the “total picture,” including benefits, layoff history of completing organizations, and work/life balance. Sometimes putting things in perspective helps staff see the grass isn’t greener on the other side.

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The future of healthcare is here. Will physicians be a part of it?
Flying cars, interstellar space travel, and ubiquitous sentient intelligence running megacities. The idea of a technologically advanced future has mesmerized society for as long as one can remember. Yet, the past is riddled with false predictions by futurists. As one of the more technologically forward medical specialties, radiology is at the forefront of advances in healthcare artificial intelligence (AI). However, predictions of AI’s influence on radiology have not aged well. In 2016, Dr. Geoffrey Hinton, regarded as the “godfather of deep learning,” famously stated that society should stop training radiologists, that it was obvious radiologists would be replaced by software in the next five years. Having decided on radiology as a career early on in medical school in 2014, I would be lying if I said I wasn’t a tiny bit concerned about my career choice. Since then, seven years have passed and not a single radiologist has been replaced by autonomous software. Was Dr. Hinton completely wrong about AI? Or did he simply get the timing wrong?

Computers assisting radiologists are nothing new. Computer-aided diagnosis (CAD) was approved by the USFDA in 1998 for mammography, and CMS approved reimbursement for CAD in 2002. When a radiologist reads a mammogram, CAD software highlights sinister-looking calcifications and masses for a radiologist to review. While there was some initial concern that CAD would replace radiologists, that never materialized. On the contrary, CAD was another tool in a radiologist’s armamentarium in their quest to diagnose cancer. When today’s radiologists read about the current AI landscape, many dismiss the notion that their jobs are at risk. Many believe that, like CAD in the past, new AI technology will overpromise and underdeliver. Instead, I would argue that current AI software is completely different. We are in uncharted waters.

Today’s AI software is powered by deep learning algorithms, a subset of the machine learning that utilizes artificial neural networks such as convolutional neural networks (CNN) to recognize and make sense of imaging findings with significantly less need for manual filter labeling, a necessity of more archaic algorithms that was both tedious and resource-intensive. With high-quality annotated training data, researchers are able to use these sophisticated algorithms to make sense of the mountains of information in diagnostic imaging datasets. Most importantly, they are performing some tasks with an accuracy similar to or better than practicing radiologists.

What steps need to be taken to arrive in a world where machines have replaced radiologists?

In 2017, the world was introduced to CheXNet, a CNN-powered algorithm that exceeded the performance of practicing radiologists at the task of identifying pneumonia on chest radiographs. The algorithm was trained on ChestX-ray14, a large publicly WU 1 available dataset of frontal chest radiographs, and performance was compared to four practicing radiologists. It did not take long for the media to sensationalize the news, clamoring that the end was near for radiologists. Since then, AI software has made significant inroads in nearly all subspecialties of radiology. Whether it is software that automatically calculates and compares the volume of brain cancers, or a startup that sends stroke findings directly to a neurointerventionalist’s smartphone, we are seeing the discipline of radiology being shaped right before our eyes.

As of January 2023, the FDA has cleared almost 400 radiology AI algorithms, with more than half of the algorithms on the market receiving clearance from 2019 to 2022. What is driving this surge? For one, it is helpful that the optics around AI have shifted, thanks in
part to an optimistic outlook of a future where machines help us, not hurt us. Computing power has also advanced and is less cost prohibitive. But arguably, the biggest catalyst is reimbursement. Since CMS implemented new payment models for AI software, the
market has exploded with startups. Stroke software developed by companies have circumvented the diagnostic radiologist completely. These algorithms automatically review images to quickly identify patients who would benefit from immediate neurovascular intervention. If this software is used in a center where only a single radiologist is interpreting studies for the entire hospital, it’s not uncommon for the interventionalist to know which patients need treatment before the radiologist knows the exam has even been performed. Best of all? Hospitals using these algorithms can be reimbursed by CMS under a new pathway called New Technology Add-On Payment (NTAP), allowing for quick and widespread adoption.

Yet, we are nowhere near Dr. Hinton’s predicted reality. Where do we go from here? What steps need to be taken to arrive in a world where machines have replaced radiologists? For starters, the public needs to be comfortable with the idea of a machine telling them they have brain cancer. The financial incentives need to be there to allow competition to foster innovation. Above all, algorithms need to grow more sophisticated. It’s one thing to perform a singular task better than a radiologist. It’s something else entirely to replace an entire discipline of medicine.

Amara’s Law states that society tends to overestimate the impact of technology in the short term and underestimate in the long term. We are seeing this play out now. Despite all the growth we’ve seen in the short term, there are moments of obvious disconnect between reality and expectations. In a world marching towards automation, it is paramount that humanity remains at the heart of medicine. For all the things that computers excel at, being human is something it can never achieve. It is imperative that physicians serve as gatekeepers, to ensure that patients do not suffer at the hands of those who prioritize profit over care.

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