Enabling Traceability & Oversight from Data Collection to Final Analysis

In Clinical Development, Quality Data Trumps All

From data managers to clinical operations personnel, clinical trial teams rely on accurate, quality data to support their efforts. That data has to go through many iterations to ensure it’s being used properly and efficiently.

However, the need to alter and clean data throughout the varying steps of a clinical trial can result in gaps between clinical data management processes.

You could have the highest quality clinical data being analyzed by highly capable professionals, but if you don’t have the ability to see the chain of custody of that data from end to end and communicate how datasets were created and secured, your final submissions will ultimately fall flat.

Not only does oversight and traceability ensure everyone is working from a singular version of the truth (and thus ensures more accurate insights in the end), it also demonstrates to auditors that you’re compliant with regulatory guidelines.

Enabling traceability and oversight within your clinical trials is a must to preserve the integrity of your collected data and instill full confidence that your final submissions are justifiable and accurate.

Let’s take a closer look at the importance of traceability and oversight in clinical trials, and explore how your teams can work to ensure data is being collected, analyzed, and reported on in a controlled, cohesive manner.

Why Are Traceability and Oversight Important?

In a recent report from PharmaIntelligence, 30% of survey respondents stated that the complexities of handling and analyzing data were interfering with effectively using trial data to assist with strategic decision-making.

Another 25% of respondents felt their time was spent on tasks and issues with minimal impact on outcomes. When oversight and traceability are effectively baked into every step of a clinical trial, teams can more easily access clinical data, make better sense of it, and ultimately spend their time on more high-value tasks.

Before we get into the specifics of enabling traceability and oversight within your clinical trial, it’s important to understand why these two concepts matter in the first place. For starters, traceability ensures that you have a controlled version of your data from start to finish.

From a regulatory perspective, clinical trials can get into hot water when there are steps in the data management process that are not controlled. This most often occurs when data is transitioned from one system to another system, or when data transformations are not adequately validated.

When your data is traceable, it means that auditors (and your clinical trial teams for that matter) can determine where any singular data point originated from, right down to the exact file and initial calculation. Knowing how your data evolved from collection to final analysis makes it easier to justify and support your findings while also minimizing your chances of running into compliance issues.

Traceability is just one component of oversight. With the myriad of responsibilities and tasks put on clinical trial teams, oversight can often be overlooked. But high-quality oversight needs to be at the core of every clinical trial. Otherwise, everyone’s hard work may end up being in vain.

How to Enable Traceability & Oversight Within Your Clinical Trial

How your clinical trial teams create, store, and access patient data will dictate the success and validity of your final outcomes. For that reason, enabling traceability and oversight of your data at every stage of the clinical trial needs to be a main focus from the get-go.

There are a few ways you can ensure your clinical trial data is always accurate, traceable, searchable, and complete.

Take a Hard Look at Your Data Strategy

Before you get into the weeds of enabling traceability and oversight, you need to start by considering what you need to collect, how to collect it, and then how to use the data as it comes in. How will data be transferred throughout different steps of the clinical trial? Who will need access to datasets at which stages of the clinical trial? The answers to these questions should help you take a critical look at your data management strategies and identify where there are potential gaps in control and oversight.

Looking at every step where data transition or manipulation occurs will help you map out a better data strategy that minimizes the likelihood of something going wrong along the way.

Pay Attention to Data Access Control

When someone leaves or changes roles within the company, access rights to clinical trial data need to be updated accordingly. This can be done easily when you’re working from a centralized platform where most of the processing steps occur, but it can get complicated when multiple data management systems are in play.

When you’re reviewing access control for all current and former employees across a wide range of systems, it can be very easy to miss one or two cases of inappropriate access. For this reason, you should consider implementing a centralized clinical data management system that makes it easier to confirm that the right people have access to the right data.

Utilize Automated Data Classification

The average Phase III clinical trial today has data coming in from ten different sources.  Each source system requires separate configuration by different teams and can lead to discrepancies that need to be reconciled. Now add in different project teams working on different studies and you have a recipe for inconsistencies to creep in.  Compound this with the drive to improve efficiencies through feeds from health record systems, decentralized trials solutions, and unstructured data resulting in a barrage of variability. With automated data classification, you can expedite the identification of data to subsequently drive automation of downstream processes.

By automating configuration based on natural data keys, your teams can seamlessly merge two different tables without significant programming work. This delivers a better user experience for your clinical trial teams and requires less handoff and support while data is being analyzed.

To make automated data classification a reality, you’ll need to have the proper tools and technologies at your disposal.

Embrace Technology

Human error is often the culprit of incorrect datasets or unusable final submissions. If one person inadvertently assigns the wrong variables to the wrong elements in an array, it may not seem like a huge problem initially. However, small errors like this can lead to an entire submission getting recalled. If you’re working in a black box where you don’t have insight into what’s going on with your data behind the scenes, small issues like the one previously mentioned would never be caught before it’s too late.

With a centralized clinical data management system, you can have peace of mind knowing your data is consolidated, your work is traceable, and your submissions are completely accurate. Having a single source of truth for data analysis and reporting not only results in higher quality submissions, but also ensures you’re adhering to regulatory compliance standards.

To streamline and simplify your data management processes, embrace a future-forward clinical data management system that does the heavy lifting for you.

eClinical’s elluminate^® clinical data cloud has been helping life sciences companies seamlessly integrate and unify all their data sources – including EDC, eCOA, and labs – for streamlined data review, exploration, and new data insights. To see how our revolutionary software can empower your teams, request a demo.

Robert Musterer

Vice President, Product Management, eClinical Solutions

A life sciences industry veteran and clinical systems expert, Rob Musterer has over 25 years of experience in clinical programming, biostatistics and data management in large pharmaceutical companies including Bayer Healthcare and Schering-Plough Pharmaceuticals. Rob most recently served as President of ER Squared, Inc., a life sciences consulting firm with an e-clinical focus. Rob began in pharma as a data manager. Seeing possibilities to improve the model, he engaged in early deployments of electronic data capture (EDC) systems and championed the change management required to adopt EDC. Rob has dedicated his career to helping the life sciences industry obtain greater time-to-value from their solutions and break from historical clinical research processes through innovation. His expertise extends far beyond EDC and includes data management services, clinical trial programming, data repositories, data visualization, clinical trial management systems (CTMS), standardization, validation and more.