4 Reasons to Stop Mixing Your Clinical Data Collection Methods

April 17, 2023

4 Reasons to Stop Mixing Your Clinical Data Collection Methods (new)

They say that variety is the spice of life—but when it comes to blending clinical data collection tools, it’s more like dumping the whole spice rack in the pot.

One of the most common clinical data collection mistakes I see comes from mixing clinical data collection tools. It often happens inside a company unintentionally, with one researcher using Excel, another using an existing eCRF platform, and yet another running everything on paper. It may seem innocuous, but this failure to establish a standardized software tool for clinical data collection can have seriously negative results.

Let’s take a look at how mixing clinical data collection tools impacts your teams, your processes, your medical device business, and—of course—your data itself.

Cross Functional teams need standardized clinical data collection

Collaboration is an essential element of any successful clinical trial. A collaborative culture promotes open communication, reinforces shared goals, and increases visibility for all stakeholders, including CROs, sponsors, project teams, and vendors.

However, collaboration isn’t something that happens by accident—it requires intentionality. And when clinical data is being collected and stored using a myriad of disconnected tools, it makes cross-functional teams feel anything but functional.

This typically occurs when there’s no standardization or documented process for clinical data collection—and the impact can be felt across the board. For one, mixed data collection tools are a breeding ground for data silos. Not only do these impact the completeness and accuracy in a clinical study, the lack of visibility and access to real-time data makes it difficult to spot trends, make on-the-fly adjustments, or glean valuable insights. 

It also can present major frustrations for growing medical device companies. As companies scale, MedTech executives depend on clinical data to support their pitches and generate funding from new investors and revenue from customers. But, when they’re forced to sift through different drives, emails, and spreadsheets to find the most current figures, the result may very well be a significant detriment to the company’s future.

On top of that, the lack of a standardized clinical data collection process means a company can’t ensure compliance across departments. Non-validated methods such as emails and Excel spreadsheets do not provide a foolproof centralized database for maintaining cross-functional communications and collaborations, and do not comply with the requirements of ISO 14155:2020.

The solution is to define a standard operating procedure (SOP) for data gathering (which can be part of your QMS) where you specify that a specific software tool should be used. By taking a standardized approach and unifying your efforts in a single platform, you implement a single source of truth for clinical data. 

BONUS RESOURCES: Click here to access the free, on-demand webinar recording and PDF slides of Key Pitfalls to Avoid in MedTech Clinical Data Collection.

Mixing data collection tools is tedious

In my experience, if people don’t know which ‘’correct’’ tool to use to solve their problem, they try to find some sort of solution themselves. And while I admire the self-starter attitude, this is precisely how you end up with one colleague using Excel for case reports, and another using a survey tool for clinician feedback. Then, before you know it, you’re knee deep in tedious and mind-numbing data integration work.

Manually entering clinical data is an imperfect process—one study found that when manually entering data into complex spreadsheets, the probability of human error was 100%. Considering the complexity and importance of clinical data, I can say confidently that mixing data collection tools means there will be a need for data cleaning and preprocessing. 

Data from different sources may contain errors, outliers, or missing values that need to be identified and addressed before analysis can proceed. This can be a time-consuming process, as it may require manual inspection of large datasets or the development of custom scripts or software tools to automate the process. 

Combining data collection tools often leads to compatibility issues. Clinical data collection tools may have different file formats, data structures, or APIs that make them incompatible with each other. For example, one tool may collect data in a specific format that cannot be easily integrated with another tool. 

There’s also the issue of employee training to consider. Each tool may have its own unique features, interfaces, and workflows that require specific training for effective use. This can add additional time and complexity to the integration process, as users may need to learn how to use multiple tools simultaneously. 

BONUS RESOURCES: Click here to access the free, on-demand webinar recording and PDF slides of Key Pitfalls to Avoid in MeTtech Clinical Data Collection.

Clinical data integrity is everything

It’s difficult to overstate the importance that clinical data plays in the medical device industry. For one, it serves as the basis for regulatory submissions and premarket approval. But clinical data collection also provides a crucial layer of protection against harm for patients, in the form of post-market surveillance. Suffice to say, the integrity of your data is essential to producing safe and effective medical devices.

Unfortunately, mixing clinical data collection tools significantly impacts the validity and reliability of your data. We already have touched on how manually inputting data can lead to human error, but there’s also the issue of missing or incomplete information. 

This is especially true for electronic patient recorded outcome forms (ePRO), which participants fill out and are usually sent via email or text. Patients may forget to complete the ePRO form, or they may be unwilling or unable to answer some questions. For example, if a patient is experiencing a high level of pain or discomfort, they may not be able to provide accurate or detailed information about their symptoms. 

Another issue is that mixing data collection tools can limit the scope of your data collection efforts, which can hurt the quality of your findings. If you can't reach all the relevant data sources, your analysis might miss important insights that could have been useful.

On top of that, mixing clinical data collection tools can create a lack of traceability and centralized access, which can make it hard to audit the data effectively. When you're dealing with sensitive patient data, it's important to have a clear record of who accessed what and when. Without this, you can't be sure that your data is trustworthy.

BONUS RESOURCES: Click here to access the free, on-demand webinar recording and PDF slides of Key Pitfalls to Avoid in MedTech Clinical Data Collection.

Your clinical data should give you a competitive edge

In MedTech, you’ve got to learn how to engage and sell to a customer base that is, by definition, one of the most risk-averse in the world—healthcare buyers. Whether you’re selling to hospitals, private practices, or distributors, you’ll encounter what’s called Value Based Purchasing (VBP).

VBP is a payment model that rewards healthcare providers based on the value of care they provide to patients, rather than the volume of care. To succeed under VBP, medical device companies need to be able to demonstrate the effectiveness and cost-effectiveness of their products. 

Clinical data can also provide a significant competitive advantage in this regard, specifically if it can provide meaningful insights. However, in order to provide you with a competitive edge, your clinical data needs to be collected in a manner that makes it easy for your team to quickly analyze and translate into business-facing insights.

The only way to generate data that takes advantage of the competitive edge it provides? You guessed it—standardizing your clinical data collection methods. Standardizing how data is collected throughout the organization ensures that quality of clinical data is coherent across various departments, which opens up the possibility to use the clinical data for multiple purposes, such as regulatory and marketing. 

BONUS RESOURCES: Click here to access the free, on-demand webinar recording and PDF slides of Key Pitfalls to Avoid in MedTech Clinical Data Collection.

Greenlight Guru Clinical gives you all your clinical data in one place

Mixing your clinical data collection tools can leave a bad taste in your mouth. Thankfully, there’s an easy solution for these concerns—centralizing data collection with the industry-leading electronic data collection (EDC) platform, Greenlight Guru Clinical.

Greenlight Guru Clinical is the leading clinical data collection toolbox, purposefully built for MedTech. Collect and manage clinical data in pre and post-market clinical studies, including registries, cohorts, surveys, human factor testing, design validation, and more. Greenlight Guru Clinical meets the regulatory requirements of the FDA, EU, and most other countries, and ensures compliance out-of-the-box with GCP and ISO 14155:2020. 

If you’re ready to learn more, contact us today for your free personalized demo of Greenlight Guru Clinical..

Páll Jóhannesson, M.Sc. in Medical Market Access, is the founder and Managing Director of Greenlight Guru Clinical (formerly SMART-TRIAL). Páll was previously the CEO of Greenlight Guru Clinical where he led the team to create the only EDC specifically made for medical devices.

Key Pitfalls to Avoid in MedTech Clinical Data Collection
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