We work with you to create a customized regulatory risk model that meets the needs of your current and future devices throughout their lifecycle. Get the Medcrypt advantage with a scalable, repeatable model to improve your risk mitigation strategies to meet FDA requirements.Contact us >
With our custom Lifecycle, Environment, Network, and System (LENS) model, we enable you to proactively identify risks and opportunities. We work with you to model the decision tree based on your data, logic, and products, producing repeatable and reliable results.See case study >
Our LENS model enables you to handle legacy device risk, as well as predict risk for next-generation devices, with a statistical breakdown of risk. Identify and fill critical gaps in your cybersecurity architecture.
Enhance your confidence in your cybersecurity R&D roadmap. Make strategic changes to your current risk mitigation strategies to maximize return on investment and reduce unexpected delays.
Ensure you understand the impact of recent regulatory changes included in the “PATCH Act”, as well as the likelihood that the FDA will flag your submissions for connected devices due to cybersecurity deficits.
We found that certain device characteristics increase the presence of regulatory risks, including deficiencies and the possibility of getting a Not Substantially Equivalent (NSE) determination for your next-generation devices.
Get the benefit of a scalable, repeatable risk modeling method of analyzing and mitigating risk to ensure patient safety and gain FDA approval.
Ensure you understand which device characteristics will increase your regulatory risks for both your legacy and next-generation devices. Our model is based on known regulatory considerations (FDA deficiencies).
We created the LENS model in partnership with Hubbard Decision Research (HDR). HDR was founded by Douglas W. Hubbard, the creator of "Applied Information Economics" (AIE). Hubbard developed AIE as a practical application of scientific and mathematical methods to the most complex and risky decisions - even when they seem driven by seemingly "immeasurable" factors.
Don’t just take our word for it. Our MDM client saved up to 12 months in R&D opportunity cost using one of our custom decision trees. They were able to make strategic changes that they felt confident would not impact timelines significantly and would help them meet FDA cybersecurity regulations.
Problem: Devices did not support over-the-air updates for patching
This MDM’s devices did not support secure over-the-air (OTA), or cloud-based, updates. Adding OTA capabilities across all device lines would significantly impact their development timelines, which would in turn delay their time-to-market.
However, with the new FDA cybersecurity regulations, particularly around patching strategies, they were worried that with their current non-wireless system of updating, they would not get FDA approval for their devices. Could they make smaller, less system-wide changes that would meet cybersecurity requirements while not putting them getting their devices to market in a timely manner in jeopardy?
Client’s original approach: Prone-to-error scoring
This MDM was using an error-prone scoring method and threshold to determine the products that were at highest regulatory risk, possibly requiring strategic shifts which would significantly impact R&D costs and time-to-market.
Because their method was based on individual human judgment, it was not scientifically sound and repeatable, thus they also ran the risk of the FDA disagreeing with their risk scores, which would further impact their bottom line.
Our client was able to maintain their product development timelines. Where they did need to delay timelines, they were able to demonstrate ROI with reduced regulatory risk.
Using decision trees preserved or reduced patient risk, minimized additional costs to patients, and maintained or improved business outcomes, including cost of goods sold (COGS), timelines, and project scope.
Where the client did determine they needed to implement OTA capabilities, they felt confident in the value of this investment, as well as decreased uncertainty of regulatory rejection. This enabled them to realize a savings of 6 to 12 months of R&D opportunity cost.
We developed a bespoke decision tree that adapted the MDM’s existing data, logic, and product information to model their decision ecosystem, thereby enabling them to accurately see the risks that would result from each decision, eliminating uncertainty and speeding time-to-market.