Dr. Ron Boucher serves because the Chief Medical Officer of Teleradiology at Experity, a software and services company focused on the U.S. urgent care market.
Experity offers an integrated operating system that features electronic medical records, practice management, patient engagement, billing, teleradiology, business intelligence, and consulting solutions. Nearly half of the urgent care clinics within the U.S. use Experity’s platform. Experity’s teleradiology overread services address the shortage of radiologists by providing clinics with prolonged support. These services are recognized for his or her industry-leading turnaround times, 99.97% accuracy, and real-time access to radiologists. The mixing of AI into scan reads goals to further enhance each efficiency and accuracy in care delivery.
For readers who’re unfamiliar with this term, what’s Teleradiology?
Teleradiology is a medical service that permits radiologists to supply clinical interpretation services on X-rays, Ultrasounds, and other diagnostic imaging without having to be physically present with the patient. Within the case of urgent care, the teleradiologist functions as an extension of a clinic, offering faster turnaround times, real-time consultation, and even sharpened accuracy.
With teleradiology, patients receive faster and more precise care, clinic staff save time by receiving timely responses, and clinic providers can confidently depend on diagnoses reviewed by board-certified radiologists. Moreover, clinics that produce a small volume of radiology exams can save a major amount of cash by not having a dedicated radiologist onsite and only pay for the exams performed. This is especially necessary at any time when a subspecialist radiologist is required, typically only available at larger institutions and academic centers.
Could you elaborate on the important challenges you have encountered with AI integration in teleradiology, and the way have you ever addressed these challenges?
The challenges we’ve faced to date have been primarily clinical, with the biggest being a small group of radiologists that aren’t ready to include AI of their workflows. This is generally as a consequence of clinicians wanting to know the technology and maintain their independence as providers and utilizing traditional practices. Because the technology experts behind the AI integration, we understand that AI is supposed to facilitate and improve the usual workflow, not replace the role of radiologists. With the continued advancements being made to AI and other technologies that enable providers to enhance their practices, we urge providers to take care of an open mindset toward the tools that may also help make their jobs easier and, in tandem, deliver more efficient and higher care.
One other challenge is trying to know the strengths and weaknesses of the fracture detection software with which we’ve got integrated. Once those are identified, the radiologist, as they gain more confidence within the software, can adjust the workflow to enhance the general accuracy and care delivery process. It’s our job at Experity to indicate and advocate for the true value that AI brings to radiologists’ workflows once those initial adoption challenges are overcome.
Why do you suspect that adopting AI in healthcare settings, particularly in radiology, is more useful than avoiding it?
Most hesitancy surrounding AI stems from concerns of replacing humans, but within the case of teleradiology, radiologists are still required to interpret results. AI augments the radiologist’s tasks, but board-certified clinicians are still required to oversee the method. Each speed and quality of care are drastically increased with AI’s integration into radiology overread services.
One key advantage of AI on this capability is the numerous improvement within the efficiency and accuracy of imaging interpretation. For example, our AI software assists radiologists by identifying fractures in adults and pinpointing potential injury locations – each of that are particularly useful in teleradiology where patient histories could also be incomplete or when the study is sub-optimally performed or positioned
AI helps reduce the time radiologists spend trying to find abnormalities, which results in a 15-20% increase in speed. This efficiency allows for faster patient care without compromising quality. In truth, the standard of reads with this integration has improved by about 40%, as AI helps prevent missed diagnoses, ensuring more accurate and reliable results. Patient expectations for quality and efficiency will only increase in the longer term, especially for urgent care, so selecting to embrace AI and maximize the support it offers will help to best meet those needs.
How has AI integration in teleradiology specifically contributed to higher patient outcomes?
AI not only increases speed on workflow, but in addition improves patient care by enhancing the detection and diagnosis of fractures. These fractures might otherwise be missed, so AI is significantly increasing the opportunity of higher outcomes for patients. Systems that utilize AI can discover additional fractures that radiologists might overlook as a consequence of their subtlety or because they occur alongside more obvious injuries. This capability is crucial for comprehensive patient care and seeing the complete picture, no matter medical records being available.
AI in teleradiology has also contributed to faster turnaround times. This speed is especially useful in urgent care settings where quick diagnosis and treatment are essential. Physicians profit from the rapid availability of accurate diagnostic information, enabling them to treat patients more efficiently and discharge them quicker, thus improving overall patient satisfaction and clinic success.
In what ways has AI technology improved operational efficiencies and accuracy in radiology readings?
Prior to AI, clinics and practices would work to treat and release patients as efficiently as possible, but the standard of care was jeopardized with this rushed approach. Now with a national shortage of radiologists, finding ways to streamline operations while maintaining quality of care is crucial to the success of a practice. By improving turnaround times and maintaining high-quality standards, AI helps the teleradiology industry thrive by meeting its high demand for quick and precise diagnoses.
Patients will ultimately seek care from those that can deliver a satisfactory balance of quality and efficiency – each innate qualities of urgent care which can be only amplified with using AI. At Experity, our teleradiology overread services have an industry-leading turnaround time with 99.94% accuracy rates. Our AI technology helps radiologists discover equivocal and obscure abnormalities that otherwise might not be indicated by the patient’s history, exam, or records, expanding the accuracy of reads with a further component of timeliness.
What do you see as the longer term role of AI in healthcare and the way can healthcare providers prepare for these changes?
Once I attended the Radiology Society of North America’s conference this 12 months, AI took up about 30% of the ground space. AI is the direction we’re headed in, and it may well impact every aspect of our workflows as radiologists. For individuals who decide to carry on and ignore AI, many practices will eventually turn into obsolete. The physicians and practices who decide to embrace technology can be the survivors of the transition. For example, when teleradiology services became mainstream, this process can be heavily reliant on leveraging advanced technology. Radiologists might want to adapt to the changing landscape of AI integration. AI won’t replace radiologists, but as a substitute will enhance their roles as a clinical provider by improving patient care and quality while reading more efficiently and accurately. Radiologists who don’t adopt AI of their workflows in some manner can be obsolete.
How do you balance the advantages of AI automation with the necessity for human oversight in radiological assessments?
Our goal with integrating AI into our teleradiology services is for it to be supplemental and help our urgent care partners deliver one of the best care possible. AI doesn’t involve emotions or understanding a patient’s history, so those components must be manually integrated with the history and knowledge provided by a clinician. One Danger of AI is a clinician or patient taking the AI result at face value without the skilled insight of a radiologist or clinical expert to make sure the output is accurate and verified.
Mistakes can occur, which is why maintaining human oversight is crucial for the answer’s integration. The algorithm can mark false negatives or positives, but its ability to indicate areas of interest within the Radiology exam reduces the human error rates more effectively and outweighs reading exams without AI involved.
Are you able to discuss any regulatory hurdles related to using AI in healthcare and the way Experity is navigating these?
I’m very optimistic about AI and the role it is going to play in Radiology. Nonetheless, it is going to take time to know the legal implications. Regulations surrounding AI are going to drastically change over the following few years, and this drives meaningful resistance amongst radiologists. If an AI product identifies an abnormality and the physician disagrees with it, how does it impact a lawsuit if something were to go flawed within the care delivery process?
Without regulations, the default results in tort law, which just isn’t optimal to make sure patient safety. Physicians are ultimately chargeable for the diagnosis and image reporting. There aren’t any set legal ramifications currently, which might result in uncertainty from each patients and providers as cases occur. Radiologists are the licensed physicians delivering care to patients, so there are gray areas that must be explored and addressed as AI becomes more distinguished across the industry.
Are you able to discuss how AI in teleradiology has impacted access to healthcare services, particularly in underserved or rural areas?
As I previously mentioned, the specialty of Radiology is an area of healthcare that’s feeling more severe effects of the national physician labor shortages. Teleradiology alone provides recent opportunities for patients to receive care in rural areas with an absence of medical resources and care available. Partnering with a 3rd party to supply the skilled imaging interpretation process vastly expands a clinic’s capabilities and increases the sort and quality of care they deliver. It brings subspecialty care to their patients.
With AI being integrated into these more rural practices, the standard and efficiency of care will be prioritized more and even standardized to the care a patient would receive in a more urban setting. Not only is the care available more extensive, however the accuracy and efficiency can be improved.
