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Pricing models will have an impact on adoption of AI in medical imaging

Provider uptake could be affected based on the contracts that they are offered by those aiming to add advanced capabilities.

A technician looks at scanned imagery in the control room of the diagnostic imaging area at the Hong Kong Integrated Oncology Centre in Hong Kong, China, on Tuesday, Nov. 3, 2015. Equipped with biopsy facilities, body scanners, and quiet ‘VIP’ chemotherapy rooms, the Hong Kong Integrated Oncology Centre is the first of a string of […]

With the introduction of cloud-based AI solutions, subscription and fee-per-study models are becoming more prevalent and are forecast to account for an increasing share of the market in the coming years.

Until fairly recently, most medical image analysis software was workstation-based and sold with a perpetual (one-time) software license. However, with the introduction of cloud-based AI solutions, subscription and fee-per-study models are becoming more prevalent and are forecast to account for an increasing share of the market in the coming years. Subscription models are typically preferred by vendors as they create sustainable and recurring revenue streams.

While the general trend is towards software-as-a-service (SaaS) licensing models, this does not necessarily mean the end of the perpetual license, and for some customers this is still the best option. SaaS models replace the upfront capital outlay of buying software licenses with ongoing subscription or ad hoc fee-per-study payments. This can be appealing to smaller customers with lower scan volumes. However, subscriptions typically cost more over a several-year period, and for that reason, many medium-size and large providers still prefer to purchase software outright.

To maximize their market reach, vendors should consider offering both perpetual license and SaaS options, ideally with a strategy to convert perpetual license customers to a SaaS model over time, to take advantage of the more sticky and predictable business.

Vendors who adopt a subscription model then need to set a pricing strategy that is attractive to provider organizations.

Providers’ costs for Imaging IT systems, such as PACS, is often based on the number of users allowed to access the system at any one time, known as concurrent users. Because many providers are already familiar with this pricing strategy, it lends itself well to AI-based image analysis software, particularly for established vendors that are looking to sell AI tools to provider organizations that are their existing customers.

Pricing can also be based on scan volumes, a strategy that works well for smaller providers and for those that want to start small and scale up if the software proves to be of value. Many healthcare organizations prefer a volume-based pricing model with fixed prices for pre-agreed scan quotas—many healthcare IT execs believe that this this is easier to budget for.

Volume-based pricing can work well for nascent markets, such as AI tools for medical image analysis, as it’s more affordable and lowers the risk of investing in new technology. However, with the general trend in medical imaging towards longer contracts, it can be challenging for providers to predict longer term scan volumes at the start of a multi-year contract.

An alternative is an unrestricted usage subscription, will all potential users of the software receiving unlimited access. This can be a useful strategy for start-ups and new market players, as it maximizes the vendor’s exposure and engagement with customers, and hence the perceived value of the software. Assuming the software is widely and routinely used, this strategy helps to support customer retention and renewals.

Subscription-based licensing also lends itself to the “freemium” licensing model, where customers can access a free version of an application for a limited period, or a version with restricted functionality, for evaluation purposes. While a useful strategy, particularly for new market players, it comes with its challenges and needs careful management.

The fee-per-study (FPS) model, where customers are charged based on their usage of the software, is well established in medical imaging, particularly for neurology image analysis where several vendors offer this, with pricing typically in the region of $200 per scan. The FPS model gives provider organizations maximum flexibility, the lowest entry costs and limits their risk for investing in new technologies.

For vendors, the benefits for the FPS model are much the same as with volume-based subscriptions, in that the lower cost (compared with perpetual licenses) can help to build brand awareness and seed the market. Often vendors will offer both FPS and subscription options, with customers starting out on the FPS model and switching to a subscription as usage increases.

As the market for AI-based image analysis software ramps up and more solutions come to market, we expect fee-per-study pricing will fall. Zebra Medical Vision has taken an early lead in this respect with its AI1 (“All in One”) business model, whereby it charges a flat fee of $1 per scan for access to its algorithms. While this strategy is somewhat unproved in medical imaging, the low price point is expected to be attractive to customers, particularly those with lower scan volumes, and it is likely that other vendors will adopt similar pricing strategies. Vendors considering this strategy need to be mindful that it will likely require significant capital investment in the early years, as a high number of low volume customers can be expensive to support, not to mention the increased administrative and billing overhead.

In the broader healthcare IT market, there is growing interest in risk-based contracting, under which software vendors receive a share of the cost savings from using their software, or by meeting pre-agreed performance metrics. If the vendor’s technology or software solution fails to meet the agreed targets, financial penalties are incurred.

For vendors, the attraction of risk-based contracting is the potential for more sizeable and lucrative contracts when compared with the business models described above. They can also foster closer customer relationships and lock-in customer long term.

On paper, this model is well suited to AI tools for medical image analysis, as the main value proposition of these products is typically improved radiologist productivity. Additionally, key performance indices (KPIs) can be developed around a variety of operational and clinical metrics, such as increased modality utilization rates, reduced diagnostic error rates and improved clinical outcomes. However, most health providers do not have robust systems in place for measuring KPIs, let alone return on investment (ROI). Without accurate and robust data, it may prove challenging for providers to hold their vendors to account when KPIs are missed, or for vendors to prove the real value of their solutions.

At this early stage of the market development for AI-based medical image analysis tools, most customers will be reluctant to commit to complex risk-based contracting and the more traditional business models outlined in the previous sections are better suited. However, as confidence in the technology grows, and more clinical validation on the benefits of these solutions becomes available, the case for risk-based contracting will become more compelling. In the short-term, vendors are encouraged to offer a variety of licensing options, including perpetual licensing, fee-per-study and annual subscriptions, in order to appeal to the wide array of customer needs.

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