The integration of artificial intelligence (AI) in medical imaging is not just buzz; it's a revolution transforming the landscape of healthcare. While much of the conversation rightly highlights AI's potential to enhance diagnostic accuracy and clinical decision-making, the potential financial implications are also compelling. Imagine a world where efficiency is maximized, utilization is optimized, and documentation is flawless, all thanks to AI enabled diagnostics and workflows. This article explores three applications of AI in ultrasound workflow, and the hypothetical financial gains and cost savings they may deliver.
Increasing Quality and Efficiency via AI-Guided Ultrasound
Economic data points to consider:
Procedure time
Number of scans per day
Repeat rate
Staffing levels
AI-enabled ultrasound solutions are delivering efficiency and quality gains that could eventually be translated into financial value. One example is scan guidance.
Ultrasound devices equipped with AI provide operators with real-time feedback on the completeness and quality of a scan. Mid-procedure prompts advise on proper probe positioning, identify anatomy, and pre-populate accurate measurements. This guidance can facilitate faster, more efficient capture of thorough and high-quality images on the first attempt, regardless of an operator’s experience level, and enable standardized image quality across different operators and care sites. This is a timely advancement amid the current the current shortage of sonographers, which has strained imaging departments and led to less experienced operators performing exams. It is also a valuable resource given the rise of handheld ultrasound utilization among clinicians with little or no formal scan training.
The financial impact of scan guidance has yet to be documented but there are several avenues to explore. Boosting the quality of initial images, even amid an influx of new ultrasound users, could reduce both procedure times and the rate of repeat scans, which could in turn enable an ultrasound department to manage more exams without increasing headcount. At the point of care, AI-enabled scan guidance can help operators conduct assessments that enable more targeted patient management and referral decisions, and support the appropriate use of clinical staff and equipment. There is also potential to enhance patient satisfaction by reducing wait times and improving the overall care experience.
2.) Optimizing Radiologists Workflow via AI Assistance
Economic data points to consider:
Cases per day
Read time
Reporting time
Backlog rate
Overtime costs
Burnout in radiology is increasing globally, with prevalence estimates reaching 88% and 62% for overall and high burnout, respectively1. AI's ability to automatically analyze and prioritize ultrasound images can be a valuable assistive function to radiologists. Some AI algorithms can pre-process images, identify areas of concern, and auto-populate measurements, reducing the workload and cognitive strain on radiologists.
Mathias Goyen, MD, a radiologist, and Chief Medical Officer EMEA, GE HealthCare noted, “AI-enabled ultrasound devices can help you increase productivity, provide a better patient experience, and ease the wear and tear on your body by reducing clicks and automating certain repetitive tasks2.
The financial value of AI-assisted radiology workflow could be measured in terms of throughput impact—a radiologist’s ability to handle more cases, within the same timeframe. It could also be explored in terms of job satisfaction and retention. Automating routine and repetitive tasks may enhance a radiologist’s professional experience by creating more time to focus on complex cases and multi-disciplinary collaboration.
3.) Reducing Payer Friction via AI-Enabled Ultrasound Documentation
Economic data points to consider:
Rate of reimbursement
Medical coding / billing costs
Rate of reject insurance claims
Rate of phantom scans
Leveraging the power of AI in ultrasound documentation may become a means to reduce payer friction and enhance financial outcomes for healthcare providers. One of the critical factors influencing ultrasound reimbursement is the quality of reports. Studies indicate that up to 20% of abdominal ultrasound reports have incomplete physician documentation, which can result in a 5.5% income loss3. AI-powered systems offer a solution by automating the documentation process, generating comprehensive reports that include precise measurements, detailed observations, and even suggested diagnoses.
The challenges associated with point-of-care ultrasound (POCUS) reimbursement are somewhat different. Here, the primary issue isn't the quality of the documentation but the lack of documentation and archiving of bedside scans. This oversight can have substantial financial repercussions. For instance, a study revealed that nearly 77% of emergency department ultrasounds might go unbilled4, leading to an estimated annual revenue loss of up to $3.28 million5.
The financial gains from automated reporting could be substantive. AI can ensure consistency and accuracy in documentation, which could enhance billing practices and reduce the likelihood of rejected insurance claims due to incomplete or incorrect information. It could support improved reimbursement rates and a more efficient revenue cycle.
Working Towards ROI with AI in Ultrasound
Quantifying the value of AI-enabled technologies in healthcare is new terrain—there is no definitive return on investment (ROI) calculation. The ultrasound-specific scenarios in this article aim to serve as a starting point for exploring financial impact that may accompany transformative patient-centered benefits.
Learn more about AI-enabled ultrasound solutions that enhance image acquisition, simplify exams, and provide clinical support, empowering providers to overcome clinical challenges and elevate patient care.
REFERENCES
1. Nader Fawzy et al., “Incidence and factors associated with burnout in radiologists: A systematic review,” European Journal of Radiology Open 11, no. 100530 (2023).
2. “The Power of Ultrasound + Artificial Intelligence”, GE HealthCare last modified 2023 https://landing1.gehealthcare.com/nurture-simplifying-workflows-en-lp1.html
3. Richard Duszak Jr et al., “Physician documentation deficiencies in abdominal ultrasound reports: frequency, characteristics, and financial impact.” Journal of the American College of Radiology 9, no. 6 (2012): 403-8.
4. Stephen Alerhand et al., “Attrition in emergency department point-of-care ultrasound workflow adherence for the evaluation of cutaneous abscesses,” Journal of Hospital Management and Health Policy 4, no. 36 (2020).
5. Figure calculated based on average critical care/emergency department procedure reimbursement and the assumption of 30 POCUS systems not connected to the EMR in a facility with each performing 5 exams per day. Actual results may vary.
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