Discover how Aidoc’s AI platform offers seamless end-to-end integration into a facility’s existing IT infrastructure enabling implementation of AI at scale.
Discover how Aidoc’s AI platform offers seamless end-to-end integration into a facility’s existing IT infrastructure enabling implementation of AI at scale.
Artificial intelligence (AI) has made significant strides in its capabilities and applications over the past few years, particularly in the healthcare sector, and has long since shed the perception that it’s little more than hype and science fiction. AI, deep learning, neural networks, and algorithms now form the foundations of reliable solutions that healthcare organizations and practitioners can rely on to reduce workloads, streamline processes and improve patient care. AI is now in use across multiple areas of the healthcare industry, but it’s potential is being fully realized within diagnostic imaging services.
The state of diagnostic imaging services today
In 2018, the American College of Radiology (ACR) released the ACT Commission of Human Resources Workforce Survey to find out who comprised the radiology workforce so that private practices and teleradiology practices could gain a deeper understanding of the profession, and to help improve decision making around new hires and skills development. In the United States, the radiology market can be broken down into two primary groups – private practices and hospitals – and the ACR survey uncovered some interesting results that highlighted how the workforce is distributed across these two groups.
The survey, that included input from 30% of all practicing radiologists in the US, found that around 55% of the workforce is employed in private practice, which includes commercial companies. Academic hospitals have 19% of the workforce, hospitals have 22%, and 4% are in clinics. In private practice, two distinct subgroups are found – commercial companies that hire radiologists with salaries, and partnerships that are made up of radiologists that are equity partners. In the commercial company sector, there can be anything from 100 to 500 radiologists working for a company while private practices tend to have around 30-100 radiologists on the team. Over the past few years, there has been a shift in the market with many of the larger practices buying out the small to medium sized practices. This consolidation of practice and radiology capability within larger companies is creating powerful entities that are competing for market share and patient care. The other trend is an increase in the hiring of skilled radiologists in breast imaging, neuroradiology and interventional radiology.
These results are interesting as they stand, but they are even more interesting when held up alongside the growing move towards AI within imaging services. In diagnostic imaging services, AI is increasingly recognized by private practices as a tool that allows for improved patient care, faster diagnosis, earlier detection, and streamlined workflows – all boxes that the sector wants to tick to retain an advantage in both competition and reputation.
Improving the efficiency and quality of diagnostic imaging services with AI
Diagnostic imaging AI is fast becoming a measurable advantage for the radiology practice that wants to shift gears and gain market traction. A great example of how it can fully support the radiology practice is in value-based reimbursement. One of the major innovations implemented by the Affordable Care Act, these value-based reimbursements have redefined how payments are structured from government health plans. The value-based model gives groups a bonus reimbursement if they can quantifiably prove greater quality and efficiencies within their practice. These are measured against a specific list of metrics such as turnaround times and detection rates and, if a practice can show that they have delivered on these requirements, then it has an equally measurable impact on the bottom line. The new payment models account for up to 20% of radiology reimbursements which means that achieving the goals set out by the metrics achieves improvements in both patient care and financials.
However, this best-case scenario isn’t easy to achieve. Tracking the metrics, reporting on their success and ensuring that they show improvements is complex and difficult. Practices need solutions that allow them to both measure and report on these metrics – solutions that are precise, accurate and reliable.
This is where diagnostic imaging AI comes in. An analysis released by the Journal of the American College of Radiology found that AI can cut down the amount of time that healthcare professionals spend on reporting, admin, paperwork and documentation while equally improving the quality of these tasks. The report found that physicians spent up to 78% of their working day creating notes and reviewing medical records which costs both practice and patient. Diagnostic imaging AI that’s been embedded into workflow and system has the ability to not only support radiologists in diagnosis and managing imaging volumes, but in managing reporting, reminders, and clinical processes. In environments where systems are often fragmented and impacted by legacy technology and process, diagnostic imaging AI has the potential to refine processes and improve how practices measure metrics and performance. The analysis cited above concluded that AI can ‘improve quality and increase value in the era of value based payment system(s)’ and this is just one of the advantages offered by AI.
Diagnostic imaging AI in private practice is an investment into a solution that supports the radiologist while improving workflow, analysis and control. It can be used to reduce the impact of tasks that are labor intensive and time consuming, thereby increasing the amount of time that can be spent with the patient providing care rather than on paperwork and admin. It is also immensely valuable in changing how radiologists manage workflows and priorities – diagnostic imaging AI is capable of flagging acute abnormalities which allows for radiologists to manage which patients receive care first. While AI will most probably never replace the radiologist, it does offer an extra pair of eyes that allow for streamlined care and improved management of time and resources.
The value of AI in imaging services is far richer than just service, access and support, but right now, in an environment driven by metrics and measurements, it’s an invaluable investment. One that can shift the boundaries of practice capability while providing essential support to radiologists feeling the pressure of time, workload and admin enabling improvement of quality of service and reputation in a competitive market.
