Unlock the Evidence Data Hidden in R&D of Medical Devices

October 30, 2017
.  written by 
Jón I.
|Evidence is gold for Medical Devices|Evidence quote|Medical Device Evidence is gold|Unlock the evidence hidden in RD|Hidden Evidence in R&D of Medical Devices||Medical Device Product Life Cycle Data||

Verification and validation tests are a crucial part of the R&D process for medical devices. They are time-consuming and costly but generate a lot of data. Imagine if there's a way to speed up the process, and not only save time but encapsulate the valuable evidence and data hiding there within. We've actually touched upon this subject before in the 3 things ignored in MedTech R&D

Manufacturers are Looking the Wrong Way

All medical devices will undergo testing at some point before reaching the market. Anything from early design validation, to mandatory engineering tests for regulatory compliance. Most manufacturers perform multiple R&D tests before anything else, while mandatory verification and verification is only conducted after ensuring that everything works as expected in-house. The problem is that manufacturers aren't paying enough attention to the hidden possibilities of their tests. People tend to only focus on what's required at any given time. They lack overview. Besides, data collection is often only done to fulfil regulatory requirements. And if somebody mentions data collection, the first thing that pops up into everybody's head is a 'Clinical Trial'. Why does evidence only have to be 'clinical'? Why don't manufacturers collect any serious data outside of clinical trials as well?

Because of the following 3 problems

1. There are no standards for how to collect or store valuable evidence from R&D. Which makes it difficult to conduct continuous testing with good results.2. There is no easy way to review evidence data or access results in a collaborative manner. Which makes it troublesome for future improvement or due diligence.3. People rely too much on analogue tools, such as paper, Word, and Excel, to perform tests and collect data. Such tools are time-consuming, they lack overview, and don't support further improvement.

Are we breaking the law by using Excel in medical R&D?

All of which results in less amount of valuable evidence.

Medical Device Evidence is gold

“Evidence, whether it’s clinically related or not, is the gold of a Medical Device and should be treated as such.”[/caption]Evidence is not only valuable for a proof of concept or marketing. It's the essence of the device itself and the organization as a whole. The potentials behind the collection of more evidence should not be understated.

"But collecting more data takes time and can slow down the whole process."

Well, if you're still using pen and paper it definitively will. But, there's a different way.

Should Medical Devices take Reference from Pharma?

In short yes, but not completely. The pharmaceutical industry has been using state-of-the-art data solutions for decades. The MedTech industry has however been lacking both options and funds to keep up with it. If you look at the life cycle for a medicinal product, you'll quickly grasp how much data manufacturers actually withhold. From idea to market, manufacturers will have to collect large amounts of evidence, both clinical and non-clinical, to reach consumers. Compared to MedTech manufacturers, the amount of collected data is staggering. Why is this? Firstly, Pharma has to collect a lot more data on their products before going to market. Secondly, the funds available in the Pharma industry offer the possibility of collecting more data with more sophisticated tools. The upcoming regulations for the EU MedTech industry will change this. Especially with the new Medical Device Regulation (MDR).The MDR will indirectly improve the overall evidence collection by medical device manufacturers. But, without the right tools or procedures in place, this might become troublesome.

It's Not Just About Clinical Trials

The amount of software as a service (SaaS) solutions have been growing exponentially over the past decade. Today you're able to find software solutions that fit almost any given problem in the modern workplace. But, most of the data solutions available for MedTech are marketed for quality management, clinical trials, or post-market surveillance.

Medical Device Product Life Cycle Data

Medical Devices can produce large amounts of data in the product life cycle. Data that is often left unused. Electronic Data Capture (EDC), electronic Case Report Forms (eCRF), electronic Patient Reported Outcomes (ePRO), electronic Clinical Outcome Assessment (eCOA), Clinical Trial Management System, Clinical Data Management Systems, and etc. These are all valid solutions for their given purpose. But what about the rest of the product lifecycle? What about the non-clinical trial related evidence data? We believe, that if the industry is to go forward from here and collect more evidence, we need to look at it differently. Manufacturers need data solutions that can integrate into the complete product lifecycle - not just a part of it. Solutions that allow you to dynamically collect evidence to any given scenario.

More Evidence in Less Time

From a case study conducted with one of SMART-TRIAL's leading medical device manufacturers, we found out that you can save months of work, by just adjusting some of the basic processes behind the evidence generation process. As a result, you gain more overview, better collaboration among colleagues, and more quality evidence data. By keeping all evidence in one place, you have the possibility of observing device data with user feedback, clinical outcomes, and other engineering data. Not to mention, the ease of access to valuable information for due-diligence or regulatory purposes, and improved data security and compliance. Such combination of data will become increasingly valuable to manufacturers as we step into the age of AI and machine learning. From our experience, most manufacturers have the correct mindset in place but are lacking the right tools and guidance. We wrote a blog and a white paper on this subject, to better depict the factors at play. You should take a look

Read the blog