We work with you to create a decision tree that meets your needs, adapting to your data, product info, and logic. Get the Medcrypt advantage with a more scalable, evidence-based, and repeatable product cybersecurity assessment.
Contact us >With our bespoke decision trees, 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 decision tree methodology has proven to be easier to use than traditional error-prone scoring methods, while reducing reliance on human experts, and increasing efficiency. It also enabled capturing and integrating qualitative inputs better.
Making changes to your current product development to support new FDA regulations, such as adding a cloud-based patching strategy, can be costly and have a significant impact on your resource and budget planning.
Decision trees provide a clear and simple communication tool for internal and external stakeholders, increasing regulatory confidence in your risk management approach.
Using our decision tree methodology, medical device manufacturers have shown that they can save up to 8-12 months of R&D opportunity costs.
As you’re working your way through the new FDA cybersecurity requirements, our decision tree helps you feel confident that you’re making the right changes to your strategy to meet regulatory requirements.
Using our decision tree 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.
Scoring methods are based on individual human judgment, thus are not scientifically sound and repeatable. They are also not scalable, relying on constant use of human experts.
You run the risk of missing critical issues, as well as the FDA disagreeing with your risk scoring, all of which impacts your bottom line.
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.
Results:
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.
Decision tree:
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.