ROI Potential with Ultrasound AI

Ultrasound AI Today

AI-enhanced ultrasound today allows for diagnostic accuracy and efficiency, enabling quicker and easier image capture and offering detailed image analysis that assists clinicians in detecting and diagnosing a variety of conditions more swiftly and with greater precision. This technology speeds up the diagnostic process, enables minimally trained users and minimizes human error, ensuring more consistent and reliable results.

Consequently, AI in ultrasound is seeing broader adoption in medical practices, particularly in specialties such as cardiology, obstetrics and radiology, where the nuances of image interpretation are critical. Precedence Research forecasts global ultrasound AI to grow at an 8.6% compound annual growth rate (CAGR) between 2023 and 2032, reaching over 2 billion dollars by 2032. At the same time, in Exo’s 2023 POCUS Survey Report, 25% of respondents who indicated an unwillingness to purchase ultrasound AI said they needed to see proof of return on investment (ROI).

A Holistic View of Ultrasound AI’s ROI

Quantifying ROI based purely on direct reimbursement from insurance companies and government payers shouldn't be the only determining factor for the investment. While there are some reimbursement codes for a few new AI technologies, many hospitals and clinics are looking at ROI through direct and indirect cost savings, labor efficiencies and increased revenue.

Here are a few ROI benefits to consider when investing in AI-enabled ultrasound:

Ultrasound AI systems can help streamline the scanning process. By alleviating repetitive elements like ultrasound image analysis, it can help reduce feelings of burnout among healthcare workers while enhancing facility-wide productivity.
Improved efficiency and faster diagnosis improve throughput and reduce labor costs, enhancing the financial returns on the investment. By reducing procedural complications and high-end radiology utilization such as CT, MRI or Interventional Radiology, AI-enhanced ultrasound systems contribute to cost savings and increased revenue.
The time saved using AI-driven ultrasound systems allows clinicians to increase the number of patients they can see daily. Medical professionals can also use saved time to focus more on direct patient care, improving patient satisfaction, ultimately leading to increased revenue. In an article covering Accenture research, Hospital and Health Management explains, “a hospital system earning $2B in revenue would have to cut 460 jobs (assuming a loaded salary of $100K) to achieve the same 2.3 percent margin benefit that improving the consumer experience might bring through revenue growth.”
Not every “imaging answer” must come from specialized department resources, teams, or medical professionals (radiology, cardiology, eco-lab, etc.); instead, these answers could be retrieved from the bedside, immediately from early POCUS users, nurses, or other qualified healthcare professionals with AI-enabled ultrasound. As a result, those specialized departments can focus on the patients that truly need their time, specialized care, and higher-cost care. Reducing the need to schedule specialized care in turn reduces patient wait times, unnecessary paperwork, and administrative tasks—all of which can reduce overall care costs.
Putting AI ultrasound into the hands of more minimally trained users rather than a health system’s top-trained professionals can save time since not every exam warrants transfer to an eco-lab. In turn, this reduces time spent on scanning and relieves the overburden on expensive specialist providers. Consequently, it improves patient experience with faster triage.

Enabling the Most Cost Efficient Resource for Answers

Since ultrasound is the only imaging modality that doesn’t have radiation as a byproduct, it is the least harmful to patients. With the growth of handheld ultrasound devices in hospital departments and outpatient clinics around the globe, health providers now have the ability to use AI applications in more places, making scanning more convenient and accessible than ever. AI ultrasound could likewise become available anywhere, at any time when AI applications are integrated into handheld POCUS devices. The more health systems embrace early POCUS users in adopting AI scanning for specific use cases with handheld ultrasound, the more it can help make POCUS ubiquitous in all areas of healthcare.

Calculating ROI On Savings

A high initial cost is a common objection to implementing new technology in hospitals and clinics. Looking at ROI over the long term instead of only considering the initial investment may paint a more accurate picture and make it easier to get buy-in from stakeholders. The following illustrative example shows that without adding any revenue, the savings from implementing ultrasound AI technology alone could provide an impressive return on investment.

Potential Costs of Ultrasound AI

Initial Costs:

Software/Subscription Fees: Cost of acquiring or subscribing to the AI software.
Hardware: Any additional equipment needed to run the AI software effectively.
Training: Costs associated with training staff to use the new system.
Integration: Costs related to integrating the AI with existing hospital or clinic systems.

Ongoing Costs:

Maintenance and Support: Regular maintenance fees and support services if applicable.
Updates: Costs for software updates and upgrades if applicable.
Operational Costs: Additional system running costs (may include increased bandwidth or data storage).

Potential Savings of Ultrasound AI

Increased Efficiency: Time saved in image capture, analysis and diagnosis, reducing labor hours.
Improved Diagnosis: Reducing errors and more accurate diagnoses, reducing the cost of unnecessary treatments and tests.
Resource Utilization and Allocation: AI can help with using the right resources at the right time. Not only could it help with quicker and better identification of critical cases, it can also optimize the use of medical resources.
Patient Outcomes: Reducing errors in scanning while also speeding up image acquisition helps reduce readmission rates and penalties, and it can also accelerate triage and more systematic bedside care.
Reputation: Enhanced hospital reputation can attract more patients and potential partnerships.
Speed: Faster image processing and reporting times equates to a faster health system overall. Good AI applications for ultrasound should perform in real-time.
Consistency: Standardized analysis and reporting reducing variability between different ultrasound exams.

If you are interested in learning more about the ROI on Exo’s AI tools, click here for more information.

Discover Your ROI Possibilities with Ultrasound AI from Exo

One incredibly important quality that is often missed when healthcare systems consider adopting ultrasound AI is whether or not that AI application is able to perform image analysis in real-time. Not very many do. If the need for quality insights is immediate, so too should be the ability of the AI application to deliver those insights at the time of need, not after.

Exo’s FDA-certified ultrasound AI uses are helping more healthcare facilities leverage POCUS AI to improve patients’ experience and save time and money. Learn more about Exo’s ultrasound AI commitment by reading Exo’s AI manifesto. We take a unique approach to designing and verifying our AI applications that extends beyond what Exo’s competitors do. To see Exo’s ultrasound AI in action with the Exo Iris™, view a virtual demo or schedule a live demo with our team.

Also, schedule a demo of Exo Works®, our POCUS workflow solution, and ask how Exo may provide a return on investment for your organization.