Optimizing Generation of Clinical Evidence for Medical Devices

September 20, 2017
.  written by 
Jón I.
|Optimizing Generation of Clinical Evidence for Medical Devices|Cooperation between teams|

What is clinical evidence?

The latest MEDDEV 2.7/1 revision 4, defines clinical evidence as:

"The clinical data and the clinical evaluation report pertaining to a medical device."

Which is both a simple and valid definition - used to define the context in a regulated guidance document? Yet, in a broader perspective, one can also interpret clinical evidence as:

"The process to which you collect and document scientific arguments of clinical -safety, and -outcomes, when applying a method or a solution to a certain care pathway."

Generation of clinical evidence for medical devices requires both collection of data, and scientific documentation. The process requires cooperation between various specialized groups of people. Including, but not limited to Clinical-, engineering-, QA-, and regulatory know-how. All of which are often packed into clinical studies that involve sponsors, healthcare professionals, and patients.

The challenge with generating clinical evidence for medical devices

Throughout the years we have gained much experience working along medical device manufacturers and participated in the process both as actors and observes. The process of generating clinical evidence is often modeled as a “waterfall” process and the typical activities are as follows.

  1. Define requirements for study outcomes
  2. Design protocol and specify endpoints
  3. Setup study i.e. create documents, forms, questionnaires, SOP's, and etc.
  4. Choose system vendors (if relevant)
  5. Conduct data collection - Clinical Investigation
  6. Transcribe and "clean" data
  7. Analyze collected data
  8. Generate reports and outcome documentation.

We believe it’s safe to state that for most medical device manufacturers, this whole process is slow, troublesome, and inefficient. This is due to the fact that it is governed by analog tools and outdated methods. Paper is often the main method of data collection, and Excel the most dominant “database” (as previously discussed, Excel is not a database) Besides, there is a great lack of overview and often problems with missing data and loss of knowledge. This is due to a poor data structure and inconsistent storage of evidence. As a result, collaboration becomes inefficient and a lot of time (and money) is spent on status updates, and other time-consuming tasks – like transcription. Lastly, the new Medical Device Regulation (MDR) and its requirements for improved access to data, does not make it any easier process to complete.

Improve the process, not only the clinical investigation tool

In a recent case from a SMART-TRIAL medical device client, which was presented at CTMD2017, the client had primarily requested to improve their methods of data collection. The goal was to address the problem with SMART-TRIAL. The implementation process of SMART-TRIAL, however, ended up influencing change in the overall process, not just a part of it. This was mainly due to the fact that implementing modern software into an outdated and analogous process wasn't otherwise feasible. As a result, SMART-TRIAL inspired change which affected their employees and the clinical evidence process as a whole. There were three governing actions identified which influenced this.

#1 Digitize the clinical data collection

Why is it important to digitize the data collection in a clinical investigation? Because the age of paper has passed, we increase overview and improve security. Not only is paper bad for the environment, but it's costing us great amounts of time and money in transcription, missing data, and faulty collection. A computer screen can display numbers and reports on status faster than any analog reports. It can take hours when looking through paper-based reports or files. Data security is becoming increasingly important, especially in regards to the new GDPR. Access control to sensitive data must be done in coherence with standards of the 21st century and that doesn't limit access to it. Stowing paper-based data away in rooms/buildings with great access control doesn't help anyone if the location is too far away when data is shall be reviewed - it's not accessible.

#2 Start at the end

Focus first on the study end-results and how your graphs should look and be presented. Work backward from there, before you write your clinical study protocol. Don't jump from graphs to protocol writing, without testing your data collection methods and forms first. This results in less time spent on writing the protocol. Because by first creating the forms and designing the study flow, you end up spending less time on defining it when writing the protocol - because you've had a chance to visualize the whole setup. You make fewer amendments. Because you’ve had a chance to test your setup and design, which requires fewer amendments during the course of your study. Lastly, the study endpoints become more clear. Your data collection will represent endpoints that mirror your study-specific requirements. Not results from the adjusted protocol’s from a previous study, or from a protocol template. We've seen too many cases of a waterfall process, where older protocols end up being mirrored for an upcoming study. This results in unclear results and often useless data.

#3 Design better forms

We don’t gain anything by electrifying paper. Paper and electronic systems, or electronic case report forms (eCRF) and electronic patient reported outcomes (ePRO) look and work differently. By copying the paper or analogous forms, we neglect the advantages of the digital solution. We need to collect more quantifiable data. Quantifiable data is important when applying statistical methods to collect data. But people often end up collecting a lot of free text and other misleading data that doesn’t really help. An eCRF is not an EHR. By designing better forms that control the workflow, and guide people through the ‘fill out’ process, you’ll achieve improved compliance and less misleading data. Users will not have to wonder if certain fields are relevant or not, and won't be shown information that is irrelevant. This last action ended up becoming an inspiration for the latest SMART-TRIAL white paper on how to design an eCRF.

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Gains for Medical Device Manufacturers

From the case presented in Budapest, the client has now managed to optimize the overall process - far better than imagined. Firstly, they've cut down on time spent in planning for clinical investigation, from weeks (and sometimes months) to days. They have managed to save at least 2 hours of work for each subject, only in relation to paperwork, i.e. transcription, printing, and etc. Not to mention, improved data and status overview, cooperation, and data security. Storage of clinical evidence is also safe and fail-proof, and easily accessible, which allows them to gain access to any of the clinical investigation data if required.

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