Building Better Medical Devices Faster: The Strategic Role of CM&S
TL;DR
- CM&S delivers the most value when targeted at high-severity, hard-to-test failure modes early in development, not applied broadly or brought in late.
- Model trust must be earned through structured verification, validation, and uncertainty quantification — the rigor required scales with the criticality of the decision.
- Simulation is not a replacement for physical testing; the strongest workflows alternate between modelling and experimentation, with each sharpening the other.
- Inputs — material properties, boundary conditions, and assumptions — govern model accuracy more than solver sophistication; sensitivity analysis should guide where to invest in better data.
- CM&S becomes a genuine organizational capability when it is integrated into design workflows, risk analyses, and documentation rather than treated as a standalone tool.
Computational Modeling & Simulation (CM&S) has quietly shifted from a niche engineering tool to a central pillar of modern medical device development. Yet many teams still underuse it, often bringing it late in device validation, when key decisions have already been made. That approach leaves much of the value of CM&S untapped.
Iterative guesswork is replaced with predictive insight, allowing teams to identify risks earlier, reduce uncertainty, and design with a clearer understanding of how devices will behave in real-world conditions.
Realizing that value depends not just on applying simulation but on how it is executed, particularly in terms of model credibility, documentation, and the structured assessment of confidence throughout development.
Target the Problems Where CM&S Pays Off
Not every problem needs simulation. The highest value comes from targeting areas where physical testing is limited, can’t be scaled, or is expensive. These tend to cluster around several themes:
- High-severity failure modes
- Difficult to physically monitor or detect
- Strong dependence on design parameters
- Meaningful clinical variability
Consider some examples: thermal damage thresholds in energy-based devices, fatigue life under physiological load, or aerosol transport in drug delivery systems. Each of these would be difficult to replicate experimentally or would require impractical test matrices, but simulating allows conditions to be probed across parameters (such as device geometries, patient population anthropometrics, drug formulations, etc.).
A useful diagnostic question when scoping work: could we have predicted this failure? If so, it belongs in your CM&S scope.
Model Credibility Has to Be Earned
A model provides value with its predictions only if they are trusted, but this trust must be earned. Regulatory expectations are increasingly explicit on this point, particularly with frameworks like ASME V&V 40. That means being clear early on about what the model is for and how much confidence it needs to carry. A model guiding early design exploration doesn’t require the same rigor as one supporting a regulatory submission on safety or efficacy – treating them the same is inefficient.
Effective teams tier their models. Early-stage work prioritizes speed and directional insight. When decisions become more critical, teams add carefully selected, documented confidence boosters. These include model verification, validation, and uncertainty quantification. This leads to higher trust and dovetails into supporting traceable regulatory claims with their assumptions, inputs, and limitations.
Explore Design Space Before Hardware Exists
As CM&S is key for early decision making, one of its most practical advantages is the ability to explore design space before hardware exists. Rather than lengthy iterations through prototypes, teams can agree on varied parameters, run DOEs, and map out how to optimize performance.
The result is clarity on two fronts: which variables drive performance, and where the design is most likely to fail.
The payoff shows up quickly: instead of converging on a viable design from small bouts of physical tests, teams can rule out entire regions of poor performance early, putting energy into areas with higher confidence of success.
Misconceptions about CM&S as a full replacement for physical testing persist. In truth, it augments physical testing and makes it more targeted and informative. A strong workflow alternates between modelling and experimentation. Initial models guide what to test, with experimental results exposing discrepancies. Those discrepancies drive model refinement and confidence, in turn expanding the value of the simulation’s predictive capabilities.
The complementary aspect is crucial. The most valuable tests are ones that challenge assumptions and reveal where the model breaks down. That’s where understanding deepens.
Design for the Patient Population, Not the Average
Medical devices operate in a wide range of conditions, often both uncontrolled and critical for patient safety. Anatomy varies. Physiology varies. Environmental conditions and user preferences too. Designing around a single average case often carries hidden risks.
CM&S addresses this directly. Population-based modelling, stochastic simulations, and worst-case analyses build robustness into a design. It also informs safety margins, reducing over-conservatism by grounding them in realistic simulated conditions.
Input Quality Governs Model Accuracy
Model accuracy is a natural focus for the analyst. Details like solver sophistication and mesh density enable refinement necessary for detecting failure points and optimizing performance. What’s often missed though is the importance of inputs in governing accuracy. Material properties, boundary conditions, parametric ranges, and simplifying assumptions can introduce more uncertainty than the model’s numerical foundations. Sensitivity analysis then becomes essential and DOEs help narrow down which inputs have the greatest influence on outputs. Teams then can prioritize where to invest in better data – whether through targeted experiments or literature reviews. Keeping this front of mind in the planning stages ensures refinement effort is put in the right place.
Simulation Belongs in the Design Process, Not Beside It
When done well, simulation doesn’t sit off to the side. It’s a core part of how systems are designed, informing requirements, verification, and providing evidence that risk controls are effective. This integration improves traceability. Simulation finds its way into design plans and risk analyses, tying assumptions, decisions, and outcomes to the design history narrative.
Where CM&S Programs Commonly Break Down
Complexity
Even experienced teams fall into familiar traps: building overly complex models before the system is understood, relying on qualitative agreement for validation, or letting software capabilities dictate model strategy.
Documentation
A recurring issue is poor documentation. The model itself cannot hold full context for inputs, numerical models, assumptions, or validation steps. While documentation depth should match the credibility required, there should always be a basic record before and during model development. These find themselves in a simulation plan and report, which provide scope-dependent inputs and context, along with results and their meaning. If the model then progresses to regulatory submission, these documents become a platform for appropriate confidence assessments.
Documentation also helps other engineers come on board quickly, whether due to scope scaling or timelines tightening. Finally, it helps reproduce results; if another engineer can’t pick up the model and run with it, then there isn’t enough context provided.
CM&S Becomes a Capability When It’s Built Into How Teams Think
Leading organizations realize that CM&S is more than a collection of tools – it’s a strategy to integrate into their teams’ thinking. They invest in standardized workflows, maintain curated data for material properties, and ensure simulations are version-controlled and reproducible. Dovetailed with this is team integration across functions. When engineering, clinical, and regulatory perspectives are incorporated early, models are built better suited to address the questions that matter.
CM&S doesn’t eliminate uncertainty but reshapes it. Teams anticipate problems, quantify them, and form optimized designs rather than reacting to problems as they come up. The resulting development process is more efficient and fundamentally more informed, producing better products and patient outcomes.
Nathan Muller is a former StarFish Medical Mechanical Engineer – Analysis and Design. His focus is in simulation engineering using computational modelling. As part of a design and development team, he frequently leads the development of mechanical design and device integration across disciplines, including targeted optimization and derisking activities through computational modelling and simulation (CM&S).
Images: Adobe Stock
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