Once a clinical study starts, every form, entry, query, correction, reconciliation, and review step affects whether the final dataset can actually support analysis, reporting, and regulatory use. Problems in data management often stay hidden early, then become expensive when a team is approaching an interim milestone, database lock, final report, or regulatory submission.
Qmed provides data management support for medical device and IVD studies across Europe. We help manufacturers build and run controlled data-management workflows that support the study as it happens and prepare the dataset for reporting, evidence generation, and downstream regulatory use.
What this service covers
Data Management at Qmed can include database planning and setup support, CRF or eCRF review, edit-check review, data-management planning, user-role structure, query handling, data review, discrepancy management, reconciliation activities, cleaning support, lock preparation, and transfer-ready outputs for analysis and reporting.
Where relevant, we also support the interfaces around the data itself: coordination with study management, monitoring, safety workflows, statistics, and technology vendors so issues are identified early and do not sit unresolved between functions.
Some clients need full operational support for the data-management workstream. Others need targeted help around build review, query control, clean-up, lock readiness, or recovering control over a study that is already live.
How support works across the study lifecycle
Set-up
We help establish the structure behind the data before the study becomes active. That can include database planning, eCRF input, edit-check review, role and access structure, data-review planning, vendor coordination, and the groundwork needed to make the study data flow manageable from day one.
Live phase
During study conduct, we support the control activities that keep data moving in a usable way. That can include query handling, discrepancy review, cleaning activities, data-status oversight, communication across stakeholders, and practical follow-up so the dataset does not drift while the study continues.
Close-out
At the end of the study, we support the work that turns an active dataset into a reliable final output. That can include final cleaning, reconciliation support, lock preparation, transfer-ready outputs, and coordination with the wider close-out and reporting process.
Why this matters
Data management is not just an administrative function sitting behind the study. It is one of the control layers that determines whether the study can produce usable evidence on time.
A well-run data-management workstream helps reduce avoidable rework, surfaces issues earlier, supports cleaner reporting, and makes it easier to show how the final dataset was built, reviewed, and controlled. That matters not only for study outputs, but also for the regulatory documents and evidence decisions that depend on them later.
Typical situations
Usually, clients are experiencing one or more of the following:
- the study is underway, but no one owns the data-management workstream in a structured way
- the database is in place, but query handling, review, and cleaning are not being controlled tightly enough
- the team needs clearer visibility into data status before an interim milestone, final report, or lock
- internal clinical or regulatory teams need more confidence in the quality and traceability of the dataset
- a post-market study or PMCF project needs hands-on data-management support without building the function internally