The Medical Device industry has come a long way. With more innovative devices on the market, clinical trials are getting more complex. And increasing amounts of data are being collected from so many different sources. Clinical data is gaining a lot of traction within the Medical Device industry right now. Partly because of the Medical Device Regulation (MDR) but also because of its potential to contribute to other areas of medical device clinical activities.
In this blog, we go through seven of the most common mistakes made by Medical Device companies when collecting clinical data and what you can do to mitigate them.
The seven pitfalls medical device manufacturers encounter when collecting clinical data
As an Electronic Data Capture (EDC) Vendor, there are certain pitfalls, that we see time and again when medical device companies intend to collect clinical data.
1️. Collect too much data
Companies get so caught up in the “need” for clinical data, that they end up spending much time, effort, and money on data that’s not even useful. We’ve heard of many cases where studies or projects have been running for years. Then ending up with useless data, only because the goal wasn’t clear enough in the beginning. The solution to your lack of clinical evidence is not large amounts of clinical data – but the right data.
2.Forgetting the individual
Focusing solely on collecting information from clinical staff and medical records are one-sided. It puts a limit on the data that’s collected. Adding real-world evidence collected directly from patients will allow you to get access to otherwise unknown data which can enhance your existing claims. The combination of the data sources can even potentially support new claims.
3. Starting off on paper
It’s common for Medical Device companies to start with paper CRFs – later realizing that EDC would have been a more efficient option.
Lack of Overview
Paper CRF’s make it difficult to get an overview of data and study progression. An overview is crucial to Medical Device companies when it comes to clinical investigations or PMCF activities, especially when you need to oversee activities across different sites or product segments.
Tedious to monitor and transcribe
GCP compliance can be tedious to maintain with paper CRF’s because there is no system to assist. Monitoring needs a thorough review of all data. Furthermore, on final analysis data transcription can be time-consuming and potentially induce new errors.
Lack of data security and access control
Paper CRF’s make it difficult to manage data security and access control. This requires physical barriers/controls and manual processes to comply with regulations. Furthermore, with paper CRF’s it is difficult to back-up, thus in order to minimize data loss, procedures for data back-up must be implemented and followed as soon as data is acquired by clinical operations.
Lack of validation leads to a higher risk of erroneous data
Because paper has no way of validating data input on the go there is a higher risk of errors and missing data. If not handled correctly this can lead to misleading results and time-consuming data management and cleaning, resulting in higher cost of data collection.
4. Mixing methods of data collection
With more clinical activities comes more complexity. By mixing methods of data collection across the medical device lifecycle, you risk chaos and increased operational cost.
More Clinical Activities
Recently there has been an increase in the variety and number of clinical activities across all phases of the Medical Device lifecycle. Both changes in regulation and pressure from the media and the public are forcing MedTech companies to gather evidence on the performance and safety of their products.
With more variety comes more complexity especially when it comes to the choice of data collection methods. When data collection is not standardized across teams and divisions, it can lead to:
- Valuable data not being put to correct use (or no use at all)
- Time and money wasted on repetitive work
- Lack of regulatory compliance
5. Not taking the clinical workflow into account
A good study protocol does not equal quality data.
Clinical studies (especially for Medical Devices) often rely on a series of (additional) tasks, which can complicate the standard workflow and collection of data.
Variation between sites and countries
Even though a study protocol does not place additional requirements on clinical staff, the smallest variation in the ways of work between sites/staff can have a major impact on the data quality if not mitigated.
6. Forgetting GCP & Validation
A medical device manufacturer that doesn‘t take GCP and validation into account during clinical data collection, is not complying with the MDR.
Risk of non-compliance
The MDR is very clear on what is considered as quality clinical data. A medical device manufacturer that does not take GCP and validation into account on all levels of clinical data collection is not complying with the regulation.
Higher risk of errors
Microsoft Excel or other data/survey software that is not validated according to relevant industry standards should not be used to collect clinical data. Non-validated software increases the risk of errors and data manipulation. This can render data useless for regulatory affairs. If your data is to be eligible to comply with the MDR, you will have to use a system that is validated for clinical data collection. Make sure to select a vendor that has the necessary quality procedures in place.
7.Relying too much on Key Opinion Leaders
A one-sided clinical strategy can be a risky plan for your clinical evaluation. Many companies rely on their relationship with a handful of key opinion leaders for the signoff on their clinical plans, clinical evaluation, and they approach the same KOLs for data collection. At some point, there may be a perceived conflict of interest or bias. This is something you can avoid by for example involving other physicians in your post-market activities.
Mitigate the pitfalls – Create a road map for better collection of clinical data
The below-mentioned safeguards can prove useful when collecting clinical data. It is important to think of these safeguards as contributing to a better roadmap for your clinical data.
So we recommend Start at the end. Don’t rush into data collection. Always follow these four steps: (1) Define a hypothesis, (2) Determine a statistical analysis plan, (3) Define a data collection plan, (4) Initiate data collection.
Next, include ePRO/eCOA into your clinical data set and get access to otherwise unknown data to enhance your existing clinical claims or support new ones.
Remember to Go digital from day one. This will not only help you keep the cost down but increase data quality and overview.
Additionally, it is imperative to implement a common standard. By defining a standard for data collection for all activities you can regain overview and control of your data.
Test and seek feedback, spending time on testing your study setup thoroughly is cheaper (and better) than ending up with an unusable dataset. Take control of your data quality. Don’t rely solely on clinical staff to do so.
Remember, Compliance first, make sure to comply with ISO14155 also in registries, surveys, and cohorts.
Lastly, go beyond clinical evidence, identify new attributes and collaborators that can further support your clinical claims.
Find out how SMART-TRIAL can assist you with your clinical data collection strategy at any stage of the medical device lifecycle.
For more info visit: www.smart-trial.co