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Query Management in Clinical Trials
Definition of query management
Query management describes the process of identifying, prioritizing, analyzing, and addressing (resolving) questions/issues that arise during clinical trials - most commonly discrepancies in clinical data. Developing and implementing a systematic approach for recording and resolving queries allows for transparency and accuracy throughout the entire trial process.
Importance of query management in clinical data management
Effective query management is critical in order to ensure that data gathered during clinical trials is accurate and reliable. High-quality query management procedures help safeguard against data errors or discrepancies that may occur during the collection, transcription, or processing of study data. Good query management practices also help maintain ethical standards by ensuring that all queries are addressed appropriately and documented correctly, which also has implications for participant safety and well-being. Relatedly, proper documentation of queries assists with analysis of the trial's progress and compliance by review boards and regulatory bodies.
Types of queries in clinical trials
Clinical trials involve collecting, analyzing, and interpreting a considerable amount of health data. In doing so, a variety of queries may arise which need to be addressed by the clinical trial team - either at sites, by a central monitor, or even by external stakeholders/auditors. Most commonly, queries are related to data discrepancies, protocol deviations, and participant safety.
Data discrepancy queries
Data entry queries are typically requests for clarification on data entered into a subject's clinical record, or case report form (CRF). They are generated to replace or adjust data that was entered incorrectly into the CRF, CDMS, or EDC or other system, or which is missing altogether. Data discrepancy queries involve identifying potential errors or discrepancies between what was entered into the system versus what was observed by researchers or investigators on site at the time of the visit (so-called “source data”), such as measurements of participants’ vital signs. This type of query requires review/correction by members of the clinical trial staff - generally by the person responsible for the original data entry.
Queries may be generated as part of routine data review, such as in source data verification (SDV), or when an investigator or the sponsor notices a suspicious data point, such as values that are incompatible with life (i.e., pulse rate 700 bpm).
Protocol queries
Protocol queries may arise when there is deviation from any step(s) outlined in the protocol (e.g., a change in visit sequence). Queries related to protocol deviations may require detailed investigation and responses from trial staff as well as regulatory bodies in order to ensure that trial operations remain compliant with ethical and legal regulations.
Safety queries
Queries related to participant safety could involve discrepancies between adverse events identified via monitoring activities versus those reported through standard practices like SAE reporting. Queries could also be generated directly during monitoring activities if data is identified that could indicate a potential adverse event or serious adverse event. This process could also be automated, such as in the case of risk-based monitoring, which may be set up to flag certain outlier data points that could indicate participant safety issues. Such queries will likely require a comprehensive review process involving the sponsor, monitor, and perhaps a study clinician.
Query management in clinical trials
Clinical trials are complex endeavors that require a structured approach and collaboration between numerous teams to ensure accuracy and compliance with protocol. Query management represents the formal system used for communicating and resolving discrepancies between the sponsor, investigator, sites, and research personnel. The clinical research coordinator plays an especially important role in query management by developing an appropriate query management plan and protocols.
The role of the clinical research coordinator in query management
A clinical research coordinator will typically be responsible for developing a query management plan that meets all regulatory requirements in order to minimize the risk of errors and compliance violations during a trial. This requires an understanding of applicable regulations and ethical standards, so that query management protocols are designed in such a way that queries are managed in line with these standards, regardless of who resolves the query.
Developing a query management plan
When creating a query management plan, it is important to consider how queries will be identified, documented, escalated (when needed), analyzed for accuracy and completeness, tracked until resolution is achieved, closed, and - perhaps most importantly - communicated between team members.
In order to developing the query management plan, the following protocol aspects need to be clarified:
- Determine whether manual processes or electronic (and possibly automated) systems (or even both) will be used for collecting data from different sources
- Decide whether queries will be managed manually or electronically (or in an automated fashion)
- Develop criteria for initiating audit trails when discrepancies are found
- Establish methods for communicating and tracking queries
- Decide how queries and query resolution will be documented throughout the trial, so they can easily be accessed later, if necessary, by external auditors or regulators
Methods of Managing Queries in Clinical Trials
There are different options available for managing queries; the appropriate query management process will depend heavily on the specific trial protocol, any technological systems used in operations, and the preferences of the sponsor and investigators.
A. Manual query management
Manual query management describes the traditional means for handling queries submitted during a clinical trial. It involves manually entering information related to each query into a system such as Microsoft Excel, or even on paper records, and tracking the status of each query throughout the review process. Although manual query management may be effective when performed carefully, it is time-consuming and prone to errors, which can lead to delays in resolving queries or even instances of non-compliance if not managed properly.
B. Automated/electronic: Query management system
Automated query management is an increasingly popular alternative to manual query management. Software solutions, such as an electronic quality management system (eQMS), enable sponsors and investigators to electronically manage queries without relying on outdated paper forms or spreadsheets. These systems enable faster recording and tracking of all data related to each query, and streamlined management of simultaneous queries. Electronic query management systems will require initial setup costs and ongoing maintenance in order to remain operational over the course of the trial, but the initial investment tends to pay off due to increased operational efficiency and reduced error rate.
Query resolution and documentation
Swift and appropriate resolution of queries is essential for the success of a clinical trial, as unresolved issues can lead to legal concerns or safety issues. Additionally, it is important that records be kept regarding when and how each query was resolved in order to facilitate audits and remain compliant.
Best practices for query resolution and documentation
As a clinical trial sponsor, there are a few best practices you might wish to follow in order to maximize the efficiency of query management and resolution. The following considerations can help you ensure smooth and swift query management.
Successful query management strategies
1. Leverage automation and electronic query management tools
Automated query generation tools and electronic query management systems can help streamline query generation, tracking, and resolution by reducing the risk of manual data entry mistakes, automating parts of the workflow (for example, automatic notifications), and increasing the speed of response times. These tools can be used in conjunction with other “eClinical” tools such as clinical trial management systems (CTMS), EDC, and risk-based monitoring tools.
2. Use clear language in queries and provide explicit instructions for users
Providing users with simple but clear instructions on how to properly respond to any given query can help ensure that they provide accurate answers in a timely manner and avoid unnecessary delays or complications due to misunderstandings. While a query may not suggest a specific solution, it should indicate exactly which data field/concept is problematic, and where/how it might be verified.
→ Poor example:
Subject 140 date of birth is unclear, should be 12/12/1990; please verify at source.
→ Better example:
Subject 140’s date of birth is unreadable. Please verify from the case report form and re-enter in the format DD/MM/YYYY.
3. Respond to user comments and feedback
Feedback or comments from the person assigned to resolving the query can be invaluable for identifying ways to improve the clarity of queries generated. For example, if an extensive back-and-forth discussion is required to resolve a simple query for correcting a single data field, this could be an indication that the instructions in the original query were not sufficiently clear.
4. Monitor query metrics to set targets for future improvements
Monitoring queries can provide insights into the frequency of discrepant data, how quickly queries are resolved, and the overall management of queries. This information can be used to identify where improvements can be made and make changes to enhance the management of queries in future trials.
Conclusion
Efficient query management procedures can accelerate trial timelines and enhance patient safety, while also ensuring the quality and integrity of trial data and results. Having consistent processes in place - and in particular, using electronic systems - reduces risk associated with errors due to human oversight or miscommunications.
As technology continues to advance, new opportunities will emerge for improving how queries are managed within clinical research. Advanced data analytics may provide deeper insights into trends across all aspects of a study, bringing further transparency into how queries are handled throughout different stages of development. Artificial intelligence (AI), which is already being increasingly applied in various aspects of clinical research, could reduce manual work associated with handling queries while cutting down response times even further. These technologies present opportunities for improving query management and overall trial efficiency, but they also present challenges that will require careful consideration when implementing them into existing workflows, particularly with regard to compliance and patient data privacy.