3. Rising demand for specialist imaging procedures
The utilization of CT and MR is forecast to grow from 21% share of overall U.S. diagnostic imaging procedures in 2021, to 23% in 2026. In absolute terms, this translates to volume growth of 6% (CAGR), significantly outpacing the growth of other modalities, especially X-ray (+2% CAGR). As demand for specialized modalities increases, so does the requirement for specialist radiologists capable of interpreting more complex examinations. Where access to subspecialty expertise can be limited in-house (e.g., in smaller hospitals), teleradiologist expertise provides an ideal solution.

4. Increased healthcare provider focus on operational efficiency, partly owing to reimbursement cuts

The Centers for Medicare and Medicaid Services’ (CMS) announcement (December 2020) to drastically cut diagnostic radiology reimbursement by 10% in 2021, and proposed 2022/2023 Medicare Physician Fee Schedule (MPFS) changes will impact the diagnostic imaging market. To some extent reduced reimbursement rates may act as a market barrier and reduce the attractiveness of setting up a teleradiology reading service. However, reimbursement cuts are expected to place further pressure on U.S. healthcare providers to reduce costs (e.g., downsize their internal workforce) and push to more outsourcing from hospitals, with an increased focus on operational efficiency; this is expected to drive additional demand for imaging IT/AI products and tools that enrich a provider or radiologist’s efficiency and productivity, whether that is increasing patient throughput, or faster reading and reporting via teleradiology services.

5. AI developments
Three of the most important factors that heavily influence the success of teleradiology reading service providers are the speed that teleradiologists perform and report on their reads; the accuracy of the reports produced by teleradiologists; and the workflow and decision support processes that service providers put in place to ensure urgent reads are prioritized and reported on quickly. Over time, AI can be used to support and improve all key ingredients for a successful teleradiology service.

Although AI usage in teleradiology image/clinical analysis remains relatively limited, most instances of AI adoption to date have been in relation to improving teleradiology workflow efficiency. In the short- to medium-term, AI workflow optimization is expected to move toward wider teleradiology market adoption and support improvements in turnaround times.

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