‘Quick and dirty’ engineering – proving your technical feasibility

Building confidence in early stage ideas and concepts

As discussed in our article on front end innovation, the early discovery phase of your development will involve a lot of ideation and narrowing down of ideas. During this stage, it is important to align your team on what you are hoping to achieve with your product. A useful way to do this is through a Target Product Profile (TPP), which can be used to capture the specific goals, desired characteristics and attributes of your product. This not only helps to streamline the development process and provide a framework to guide future decisions, it can also be used to feed into the formal User Requirements Specification (URS) and Product Requirement Specification documentation later on, as required by the regulations.

Once you have determined your TPP and some concepts to move forwards with, the next step is to determine their feasibility. At Team, we begin to prove out most of our ideas with rapid and informal feasibility testing so that we can find out what works and what doesn’t. This can be done in a number of ways.

Often, the most cost effective and fast approach is to apply engineering tools such as math modelling to quickly determine if a concept would be feasible. Alternatively, it might involve building a ‘quick and dirty’ prototype that focuses on the core enabling idea or technology, ideally with readily available off the shelf components, easy modifications and a few bespoke parts where necessary.

While it may not be as accurate and reliable as more rigorous testing methods, quick and dirty feasibility testing allows you to rapidly obtain performance feedback and insights to determine the viability of an idea in a short amount of time. This approach is usually low-cost, agile and allows you to identify potential challenges, limitations and risks early on that could influence the direction of the project.

Creating a demonstrator - the proof-of-concept phase

Having determined a potential idea, the next step is to prove the viability of the concept to determine whether the idea is worth pursuing and what might be required to bring this to fruition. This typically involves building a small-scale or simplified version of the product or technology to test its key features and functionalities.

The goal of the proof-of-concept phase is to move from an abstract concept to a tangible demonstrator, aimed at validating its feasibility and viability, before committing to full-scale development.

It is an iterative process with a framework to refine and improve the concept based on feedback and learnings from each iteration. The process aims to either enable the development of the original idea, or identify reasons to consider alternative concepts, potentially re-directing the development pathway before committing to the wrong solution.

As the concept evolves and confidence grows, it is also important to progress the fidelity of the demonstrator to accurately represent the intended concept and demonstrate its fundamental functionality through testing. Whilst an early prototype may lack refinement in terms of aesthetics or advanced features, it convincingly shows that the key concept works.

Enhanced demonstrators may also replicate the intended user experience to gather feedback from potential users, helping to validate the concept’s appeal and potential demand. Striking the balance between user centred design and robust engineering at this stage can also help to avoid expensive or timely modifications later in development.

Demonstrating a functional proof of concept provides tangible evidence of the concept’s viability and can be crucial for gaining support and buy-in from stakeholders, including investors, management and potential customers. A well-designed proof-of-concept demonstrator can effectively communicate the essence of a complex idea or technology to non-technical investors, bridging the gap between technical details and broader understanding.

The engineering toolkit

As mentioned, there are many engineering tools that can be used to rapidly create a functioning demonstrator at a low cost. Critical thinking, risk management, market insights, engineering analysis and math modelling are some of the key engineering approaches we use at Team.

Critical thinking

One of most important tools in engineering is the ability to demystify a complex problem through critical thinking. This might involve breaking a problem down into individual workstreams, focusing on a specific or set of interacting functions to make the problem-solving process more manageable and efficient. Developing different functions separately often enables a more agile approach to finding the right solution for each requirement, before bringing these sub-systems together during an integration phase.

During the early phases of development, critical thinking also involves keeping an eye on performance and robustness – identifying issues or limitations of the system that are susceptible to sensitivities. The aim is to understand a system’s design limits and the impact of variation, as well as to identify and de-risk technical challenges that could be potential showstoppers, allowing you to make informed decisions to fine-tune performance and manage trade-offs.

Risk management

Risk management plays a critical role in developing safe MedTech devices. As patient safety is paramount, it’s crucial to identify, assess and mitigate potential risks throughout the device development lifecycle.

In early phase development, risk assessment should be approached pragmatically. The aim is not to eliminate all risks, but to manage them in a way that aligns with the project’s goals and constraints. It’s about making informed decisions to increase the likelihood of successful outcomes: addressing potential issues early, reducing patient harm, improving device reliability, enhancing user satisfaction and maintaining compliance with regulatory standards.

Risk assessment is an iterative process. New risks may emerge and existing risks might change in significance as the project evolves. It is important to understand the scope of potential risks, prioritise them and focus resources on addressing the most critical ones. Mitigation strategies should be developed to reduce the probability of the risk occurring, or to minimise its impact if it does happen.

Addressing potential risks early on allows you to incorporate necessary safety features or design changes to meet regulatory requirements, reducing the likelihood of costly rework or non-compliance issues later.

Market insights

While many MedTech innovations are novel by nature, there will likely be similar devices or technologies available on the market which insights can be drawn from. Applying key learnings from alternative industries can significantly speed up the innovation process and lead to more efficient and effective engineering outcomes. Some of the key tools for gaining market insights are as follows:

Product teardowns

Teardowns can provide valuable insights into competitors’ products, identifying areas for improvement to gain a competitive edge. They also allow engineers and designers to deconstruct and optimise existing products, as well as to benchmark their own designs against industry leaders.

Medical device characterisation testing

When developing a PoC demonstrator, it is often important to test certain characteristics of the device throughout. Medical device characterisation testing refers to the process of evaluating and assessing device performance. It involves a series of tests and analyses conducted to understand and quantify various characteristics and parameters.

For example, performance testing is used to evaluate how well the device performs against its intended function. In a diagnostics device, this might involve testing its accuracy, sensitivity, specificity and precision.

Market research

Conducting market research and analysing similar existing products or solutions will provide insights into the technical challenges faced by others in the industry and help you anticipate potential roadblocks. By comparing your own designs with those of successful products, you can also identify gaps, assess market trends and better understand customer expectations.

Engineering analysis

Engineering analysis plays a crucial role in ensuring the safety, reliability and optimal performance of medical device development. It is an essential step in the engineering design and problem-solving process, helping to streamline the development process by enabling virtual testing, optimising designs, predicting failures, improving performance, reducing costs and mitigating risks.

Sometimes referred to as a ‘digital twin’, engineering analysis models are a digital representation that simulate the characteristics, behaviour and performance of their real-world counterpart in real-time. These digital representations of your product provide valuable insights and help to identify potential issues early in the design process.

By leveraging analytical tools and methodologies, engineers can make informed decisions, iterate rapidly, and reduce the reliance on physical prototyping, ultimately accelerating the development timeline. One of the key tools within engineering analysis is mathematical modelling.

Math modelling – early-stage analysis

A mathematical model is a simplified representation of a system using mathematical equations and techniques. It is useful to apply throughout your development, however, can be particularly useful in the early stages for determining technical feasibility.

One of the key benefits of a mathematical model is the ability to understand complex systems without having to physically develop and test them. By formulating mathematical equations and relationships, you can gain insights into the behaviour, interactions and dynamics of complex phenomena. This can be used to predict how certain components will interact with each other for example, or to estimate the probability of failure or certain events occurring.

Mathematical models also often reveal patterns, relationships and dependencies that might not be immediately apparent. Through visual representations, such as graphs, charts and simulations, models can also help to visualise different aspects of the product and its technical feasibility.

When developing a math model, it is important to consider the level of complexity required. If the model is too simple, then the absence of unconsidered variables can lead to inaccurate results. If it’s too complex, the model can become hard to use. A good model should only be as complex as is necessary to achieve reliable results.

Once you have created a math model to represent the system and refined the model to target specific outputs, it’s important to move quickly into the ‘prototype and test’ iterative cycle to validate the theoretical understanding of the system.

Realising the demonstrator

Having understood the system and with an informed design schematic, industry standard Computer Aided Design (CAD) software can be used to realise the design in three dimensions. Once the final design is complete, the 3D CAD model also provides precise measurements and specifications that act as a blueprint for the prototyping process. This data can be directly used to create physical prototypes through 3D printing or alternative manufacturing processes.

Rapid prototyping such as this allows for quick and cost-effective production of physical prototypes that closely resemble the digital design. What’s more, feedback from functional testing and user evaluation can quickly be incorporated into the 3D CAD model to refine the design as well.

Overall, the iterative process of updating the CAD model and producing new prototypes helps optimise the product’s performance, appearance and usability.