How to Make 3rd Party Reconciliation in Clinical Data Management More Efficient

Third party reconciliation in clinical data management has long been a complex and painstaking process. However, by adopting certain approaches, organizations can ease this problematic process – ensuring cleaner data deliverables in shorter timescales.

Today, the need to modernize 3^rd party vendor data reconciliation workflows is no longer optional. It’s an essential requirement for stakeholders across the clinical data pipeline if they want to reduce pressures and comfortably meet submission deadlines.

This blog explores the ideal scenario when it comes to 3rd party data in clinical trials, as well as the key challenges and solutions to simplify the process and achieve higher-quality data, faster.

The success of clinical trials is heavily reliant on receiving clean, compliant data from vendors within specific timeframes. Not only is this crucial for ensuring the quality of research outcomes, it’s imperative for meeting regulatory submission deadlines. But given that external vendors now provide over 70% of the data collected in clinical trials, this task is nothing short of monumental.

The goal is clear: accurate datasets that meet vendor data transfer specifications and adhere to clinical data standards – supplied within agreed timescales. However, achieving this in a timely manner has become an immense challenge for the industry as a whole.

1. Volume and complexity

Handling large volumes of diverse data from multiple external vendors makes for a daunting scenario. Vendors supply a wide variety of data types and file formats when supplying clinical data. Anything from ECG reports, X-rays, and blood test results to radiology images and questionnaires . And these may come in a range of formats, such as spreadsheets, .csv files, image files (JPGs, PNGs), and even machine readable formats like xml and json.  That’s thousands of data points for hundreds of thousands of patients!

Put simply, the growing volume and complexity of today’s study data is putting a huge strain on data management, data standards and programming teams. And the absence of universal standards for vendor data delivery only compounds the issue.

2. Multiple systems in use for 3rd party reconciliation

Sponsors often rely on multiple systems when ingesting vendor data, performing validation, flagging issues, and communicating resolutions. In the majority of cases, discrepancies are managed manually. Without an integrated solution, this fragmented approach can lead to:

• Lack of visibility: Communication trails become difficult to trace across multiple email threads, chat channels, and spreadsheets.
• Disparate issue resolution: Ineffective collaboration hinders quick resolution of errors, data gaps and inconsistencies.
• Time-consuming processes: Managing and updating issue logs manually and keeping track of things like the status of fixes eats into resources.

3. Non-compliance with Data Transfer Specifications (DTS)

Consistently adhering to data transfer specifications is crucial for the provision of accurate, complete deliverables when it comes to third party reconciliation in clinical data management. However, vendors often deliver data that fails to align with specifications. This results in constant back-and-forth exchanges between sponsors and vendors as they struggle to resolve these deviations. This repetitive process of manually communicating about non-compliance issues and agreeing required fixes adds unnecessary delays and extra work for all parties.

4. Missed deadlines

Late or incomplete 3^rd party data submissions puts added pressure on sponsors to resolve issues in compressed timelines. These delays can directly impact regulatory submission deadlines – causing further delays in approvals, and ultimately jeopardizing lifesaving treatments reaching patients in need.

5. Non-compliance with regulatory standards

Compliance with clinical data standards, such as CDISC, is mandatory for clinical trial submissions. And non-compliance puts your submission at risk of delay – or even failure. But compliance is fraught with complexities, and it calls for a certain degree of knowledge and expertise.

One of the challenges encountered is that when external vendors supply data, there’s often a delay in data management teams retrieving and being able to validate that data.

This could be down to manual validation. In other words, someone having to manually check the delivered data for compliance issues, perhaps using Excel looks ups. As well as being inefficient and error prone, this can also pose compliance issues downstream if validation issues aren’t resolved at the point of data collection. For example, an invalid unit means a conversion can’t be run or a test code can’t be mapped – which puts a strain on timelines.

Delays could also be triggered when using ‘automated’ validation checks, such as SAS macros. In any case, these traditional methods often have limitations. For example, data may typically only be validated against proprietary standards – not against regulatory standards. And there’s commonly limited capabilities in terms of value level and terminology checks. Plus, there’s the issue of manually updating and maintaining these manual systems.

4 things you can do to streamline 3rd party reconciliation in clinical data management

With a fresh approach to handling non-EDC clinical trial data, it’s possible to ease the burden on teams, and overcome many of the challenges discussed. Below we discuss four key steps you can take to save time and improve the quality of vendor data.

1. Centralized collaboration

Centralizing data ingestion, validation, issue resolution, and communication in one location reduces siloed working practices and fragmented dialogue. When all stakeholders can access the same, up-to-date information, and communicate in one central place, in real-time, it removes the reliance on manual email chains and spreadsheets. As a result, it consolidates the end-to-end reconciliation process and unifies third party reconciliation in clinical data management.

2. Standardized 3rd party Data Transfer Specifications (DTS)

The best practice approach to DTS is to design and agree specs with your vendors upfront. In other words, you should align with 3^rd party vendors on clinical data requirements in advance. This ensures that expectations are clearly established from the outset, and  avoids discrepancies downstream.

Once data transfer specifications have been agreed upfront by both parties, they can be standardized. Creating  standards that can be reused across future studies not only saves time, it also increases data quality and consistency. Having a central library of reusable standards facilitates the provision of complete, accurate, consistent data from vendors.

So what impact does standardization have on your 3rd party data reconciliation process? At the very least it means:

• Efficiency: Significant time savings through upfront DTS agreements and reuse of standardized specs.
• Consistency: Ensures the provision of consistent data formats and structures.
• Quality: Fewer errors and increased data quality by using pre-approved, compliant specs.

3. Vendor accountability

With transparent expectations defined at the start, vendors can be held accountable for complete and compliant data delivery. Taking this a step further, it’s important to closely monitor vendor performance over time – to ensure timely data delivery and track progress across the data flow.

Keeping track of vendor metrics, such as data delivery timescales, progress scores and issue trends, puts sponsors firmly in control of enforcing both deadlines and quality where needed. By working closer with vendors, partnerships are strengthened, and the potential for increased data quality and reduced timelines can be realized.

4. Upfront validation and compliance checks

Digital platforms can automatically verify if the 3^rd party data being received matches DTS specifications. The ability to enforce end-to-end standards ensures downstream compliance with clinical data standards, including CDISC SDTM  controlled terminology, and formatting requirements. Any discrepancies are automatically flagged at the point of data delivery, and the necessary fixes are highlighted. This means that any errors or inconsistencies are resolved at the start, avoiding risky delays downstream.

Even implementing these four key measures will deliver tangible benefits across your 3rd party data reconciliation process. Through centralized collaboration, standardization, and in-stream validation, sponsors will be best placed to achieve:

• Reduction in time spent ingesting external data.
• Shorter data delivery timescales.
• Fewer resources for managing external vendor data workflows.
• Faster and smoother issue resolution.
• Greater visibility and control across the entire process.

These key benefits underscore the power of taking a modern approach to vendor data reconciliation.

To summarize, 3rd party reconciliation in clinical data management doesn’t have to be a bottleneck in your submission timeline. By collaborating centrally, implementing reusable standards and in-stream validation, sponsors can drive improved vendor performance -and transform a previously stressful process into a highly efficient one!