Before, clinical data was managed manually. It was in the 1990s and early 2000s, during the introduction of electronic data capture (EDC) systems, that the need for reconciliation was established. Ensuring consistency, accuracy, and compliance has become a priority since then.
To date, integrating 3rd party reconciliation remains a core process in discovering new medicines and treatments. Without it, clinical trials could end up with mistakes that delay new treatments or even harm patients.
So, what does the future hold for 3rd party reconciliation? Here’s how the process is changing with new technologies, shifting rules, and more.
Making Work Easier and Faster
First up is automation and artificial intelligence (AI).
Before, people often checked data by hand to spot errors. When a lab result didn’t match what’s in the trial’s main system, it was addressed manually. This took a lot of time and would sometimes lead to mistakes.
But today, AI does most of the heavy lifting. A model can quickly scan thousands of data points and spot when numbers don’t add up. It uses machine learning technology and is trained on what normal data looks like and then flags anything unusual.
For example, if a patient’s blood test from a lab doesn’t match their record in the trial, AI will catch it fast. It might even suggest how to fix it. This saves time and makes the data more trustworthy.
AI can also handle messy data that’s hard to compare. Natural language processing (NLP) can read texts and pull out key details to match with other records. So, as trials get bigger and more complicated, this level of automation is essential. This is why AI is considered revolutionary in clinical data management (CDM).
Fixing Problems Right Away
In the past, data checks happened in batches. Maybe once a week or a month. Teams would collect all the data from external sources, like labs or device makers, and then compare these pieces of information. If something was off, they’d find out too late, and fixing it could slow everything down.
But that’s changing. Thanks to cloud-based systems, data can flow in constantly. This allows for real-time reconciliation, enabling data to be checked and fixed without any delay.
You can see it on a fitness tracker sending a patient’s heart rate straight to the trial system. You can also see it on labs that upload test results to the cloud on the same day the test is conducted.
This means that reconciliation won’t have to wait. The system will check data as it arrives, resulting in fewer delays and cleaner data.
Why does this matter? Faster fixes mean trails can stay on the right track. If a mistake is caught early, it’s easier to solve before it snowballs into a bigger dilemma.
Letting the Pros Take the Wheel
As trials get trickier, another trend is popping up: outsourcing. CDM is a major undertaking, so some companies are turning to specialized vendors.
These external vendors have the right tech designed for specific kinds of trials, like cancer studies or heart research. They may offer imaging services or provide wearable devices. One of the major vendors of clinical trials, though, is contact research services. They’ll provide updated information on businesses or individuals that can be your trial’s participants.
However, if you’re outsourcing from many vendors, you may find it challenging to validate all of their deliverables. Thankfully, there are data exchange platforms for enhancing 3rd party reconciliation for compliance and data integrity.
Here, you can collaborate with vendors and reconcile all the data they’ll deliver. Instead of every trial team figuring out reconciliation on their own, they’ll hand it off to these platforms.
This doesn’t mean trial teams will lose control. They’ll still oversee everything. The only difference is that the study team will be more focused on designing the trial and its methodologies, while the outsourced platform will tackle all the nitty-gritty data work.
Speaking the Same Language
Organizing clinical database is another pain point that must be addressed. The reason for this is the tendency for data to be sent in different formats, and matching all that up would be a nightmare. This makes standardization important, which is what 3rd party reconciliation is there for.
There’s already the Clinical Data Interchange Standards Consortium (CDISC) that sets rules for how clinical data should look. Third party companies, like labs and device makers, are now following this playbook. When data comes in the same format, it’s way easier to compare and reconcile.
If a lab and a trial system both use the same way to label blood pressure results, a computer can quickly see if they match. No more guessing or reformatting. Plus, new tech like Application Programming Interface—tools that let systems talk to each other—will make this even smoother.
Keeping Data Safe and Transparent
Speaking of oversight, you must not forget the rules. Governments and health agencies, like the FDA in the U.S. or the EMA in Europe, care a lot about clinical trial data. They want to make sure it’s accurate and safe to use for approving new drugs. That’s why regulatory compliance shapes 3rd party reconciliation.
These agencies are now pushing for better audit trails—records that show every step of how data is checked and fixed. Automation, which was discussed earlier, will handle this, logging every change so nothing gets missed. This builds trust that the data is legit.
CDM can also be enhanced using blockchain. According to a review, given the impenetrable nature of this technology, it can act as a ledger for clinical database. Safe from adverse events brought about by hackers and other cybercriminals.
This is important because the rules get stricter, and these tools will help trial teams stay in the law’s good graces.
New Kinds of Trials
Zooming out a bit, clinical trials are changing. More of them are decentralized, meaning patients don’t always visit a hospital. Instead, they might use a wearable device at home or see a doctor online.
This creates a flood of new data from third parties like fitness trackers, telehealth apps, or home test kids. Reconciling all this with trial data is a big challenge. But it’s also an opportunity. Looking ahead, teams will use more smart tools to manage this variety.
For instance, if a patient’s smartwatch says their heart rate spiked, but their doctor’s report says otherwise, reconciliation tools will figure out what’s true. This flexibility will be key as trials become more patient-focused and spread out.
Building a Team Effort
Finally, there’s the promise of collaboration. Right now, trial sponsors, research organizations, and 3rd party vendors often work separately. Data gets passed around, and reconciliation happens after the fact. But what if they teamed up from the start?
Sure, there are advanced trials that already apply this principle, but others fail to integrate a strong collaboration. Now, withthe emergence of shared platforms where everyone plugs in their data at once, collaboration would be an ease.
You’ll see a massive online hub where a lab, a device maker, and the trial team can see the same info. If something doesn’t match or there’s a duplicate data entry, they can fix it together right then and there. This proactive approach cuts down on back-and-forth and keeps the data flowing smoothly.
A Bright Future for Reconciliation
So, where does this leave you? The future of 3rd party reconciliation in clinical data is truly exciting. Each emerging change builds on the last, creating a system that’s faster, smarter, and more trustworthy. The result is better data, safer treatments, and a smoother path to health. And it’s starting to take shape!
