Why Fast-Tracking Clinical Use can Slow your Medical Device ProgressMar 30, 2022
Clinical use is a significant milestone for most medical device companies. Investors often see this milestone as a significant de-risking activity – one that can come with valuation bumps and subsequent rounds of funding.
But is this early milestone helping or hurting your long-term business success?
Here’s what typically happens with the hyper-focused fast-track clinical use approach:
1. It takes a lot longer to get there than you think
It typically requires (deep inhale..) design control, document control, GMP manufacturing, bench testing, process validations, clinical protocol development, institutional review board (IRB) reviews, investigational device exemptions (IDEs), patient recruitment, enrollment, site engagement, along with the documentation, routing, and waiting involved for each of these steps.
Despite optimistic thinking, this process is typically measured in years, not months.
2. Your MVP design compromises the clinical data you seek
You are looking for an MVP – a device that can help you get into clinicals. It doesn’t have to have all the bells and whistles. It doesn’t have to be an elegant design. What matters is getting the clinical data fast -- Right?
Not quite -- more on MVP in Medtech here: When MVP Goes Wrong.
What typically happens is that companies rush to FIH with a hacky, immature design. And the very thing that you aim to collect – compelling clinical data – is often compromised due to the design shortcut you took. The device will probably not be reliable. It certainly will not be focused on usability and clinical workflow.
There’s nothing quite like getting into clinicals only to fail due to design shortcomings that could have been addressed through pre-clinical methods.
3. You get stuck in a long redo loop
Now that you’re in clinicals, and you haven’t gotten the compelling data you sought, you will want to implement design changes and redo clinical steps. You’ll need to reestablish the validity of your device, in a clinical setting. The challenge here is that design changes of any significance will require you to redo all the steps listed in Item 1.
The clinical redo loop is painfully long and slow. In many cases, the time and cost it takes to redo the clinical loop requires more funding that companies have and investors are willing to shell out.
4. You have done little to prove safety or efficacy
You have raced to early clinical use, and even if the procedure went well, what have you really proved?
Typically, there’s not nearly enough data to suggest safety or efficacy.
You have simply shown that your device can be used clinically without catastrophic results (hopefully). Is this really any different than doing the same work pre-clinically through simulators, cadavers, or animal studies?
Has this really de-risked your medical device?
A Better Way..
Almost always, the perceived short cut turns out to be the long haul.
You may think that your early FIH data is going to put you on the fast track to success, but if your design is not ready for this milestone, it will set you back big time.
So, what to do instead?
>> Isolate the risks
Clinical risk is there – no doubt. And you’ll need to address this, at the right time. Wrapped inside the clinical risk are several other linked risks. These relate to reliability, usability, procedural workflow, biological response, and more.
Are there ways that you can isolate these risks and conduct simple, fast tests to address each one separately? For instance, can you create quick usability mockups that are solely focused on workflow, user precision, and efficiency? Can you create rapid breadboards or test rigs that allow you to test reliability of a critical feature or function?
By isolating the risks and completing rapid iterations – you will get the answers you seek far faster than attempting to iterate a fully integrated device design.
>> Prototype to learn – NOT to prove
When you are developing your medical device and entering the prototyping stage, it is a mistake to aim to “prove the concept.” Most often, this is massive waste of time and resources. In these endeavors, companies spend tremendous time and money developing show-and-tell models, typically building things that are conceptually obvious. More on this topic here: Proof-of-Concept Pitfalls in Medtech.
Instead of aiming to prove your concept, it is more important to understand – what is the right concept? Companies need to flip the prototyping strategy on its head – what are the critical assumptions that we are making about viability? What are the critical risk factors that need to be evaluated? What are the reasons that the medical device may fail?
Focusing prototyping and testing on LEARNING rather than proving will accelerate your progress.
>> Find the medium that allows rapid iteration
When you aim to learn about the risk factors previously addressed, it is important to determine the medium that will enable you to rapidly develop and iterate.
Sometimes this medium is a 3D printed model. Other times, it is an electrical breadboard. When risks center around usability and/or customer perception, sketch level illustrations may due the trick. Visual mock-ups (without any internal functionality) can also be suitable to share concepts and obtain user feedback.
If you are investing in injection molding or stamped sheet metal at the early stages, you have probably chosen the wrong medium. It will take you too long to iterate, and you will burn up a lot of cash in the process. If you have small components, consider scaling them up to 3X or 5X models. Doing so may open rapid, less expensive methods, such as 3D printing. Bank on many iterations required – and figure out the medium that will enable you to pursue this process as efficiently as possible.
>> Integrate AFTER isolated subsystems have been optimized
If you have followed this advice so far, you will have created mechanism prototypes, usability prototypes, electrical prototypes, user interface prototypes – all as isolated subsystems. You will have focused on the testing processes that helped you rapidly learn, refine, and optimize these subsystems.
Resist the temptation to integrate these subsystems too early!
Once you integrate these subsystems, your iteration cycle time will increase significantly. Subsystem iterations are often measured in days, whereas integrated system iterations are often measured in quarters. If you believe that integration will get you to the finish line sooner – you’ll find that it’s quite the opposite.
>> Plan for clinicals AFTER you have maximized learning through pre-clinical methods
You don’t need to run a clinical study to understand procedure workflow with a new medical device. This can often be completed through a simulation study. You don’t need to run a clinical study to evaluate wearability of a new medical diagnostic. This can often be completed through a usability or formative human factors study. You don’t need to run a clinical study to understand how a device interacts with tissue. This can be evaluated through an ex-vivo tissue model.
In some cases, such as in-vitro diagnostics, you do need early human data to understand viability. Human data may be the only means of evaluating sensor reliability and confirming accuracy of algorithms. In such cases, you may qualify for IDE Exempt status -- a topic that is covered by the FDA guidance, "In-Vitro Diagnostic (IVD) Device Studies - Frequently Asked Questions."
Otherwise, clinical studies should be completed when there is a specific human-related risk that can not be captured through other pre-clinical means. Save your clinical studies (and the related time and cost) to focus on these risks as the right time.
You need to capture clinical data – but racing to get there too early can set your progress back in a significant way. This is a long and expensive road. When you get to the clinical stage, you don’t want your data to be compromised because your design has not been well vetted.
A better approach is to isolate, iterate, and de-risk through benchtop, simulator, cadaveric, and animal testing. If you conduct these rapid preclinical iterations, you will be maximizing your chances of success during your clinical stages. Furthermore, this preclinical iteration and optimization will ensure that your device is much closer to a successful market launch after the clinical validation has been completed.
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