Full-Service CROs
Take a study end to end, from protocol support through database lock and submission.
INDUSTRY
CROs & Research Teams
Most clinical research is executed by organisations that don't own the product being studied. This page covers how CROs and research teams are structured, how a study actually runs, which standards govern the data, and what is changing in how studies are designed and monitored.
Discuss Your Use CaseTHE SECTOR TODAY
What Defines Contract Research
Most clinical trial work is outsourced. CROs provide the sites, monitoring, data-management and regulatory capacity sponsors need to run studies at scale.
The sector is not uniform. Four models dominate:
Functional Service Provision (FSP)
Places named CRO staff inside sponsor processes for a specific function — data management, biostatistics, monitoring — working to sponsor systems and sponsor SOPs. This model has grown, and it changes the technology question entirely, because the CRO is operating in someone else's stack.
Specialty And Niche CROs
Focus on a therapeutic area, a phase, a population or a geography, competing on depth rather than breadth.
Site Networks And Academic Research Organisations
Sit closer to the patient — SMOs coordinating investigator sites, and academic groups running investigator-initiated and cooperative-group research on different funding and governance models.
The commercial pressure is consistent across all four. Studies are won on cost and cycle time, executed on fixed budgets with change orders as the margin mechanism, and judged on data quality that only becomes visible late.
HOW A STUDY RUNS
Startup, Conduct, Close-Out
01
Study Startup
Longest phaseProtocol finalisation, regulatory and ethics submissions, country and site selection, feasibility assessment, contract and budget negotiation with sites, essential document collection, system builds and site training.
02
Site Activation
First-patient-inSites initiated, systems credentialed, supply shipped, first participants screened. First-patient-in is the milestone the whole startup phase points at.
03
Conduct
Longest durationRecruitment and retention, visit execution, data entry, query resolution, monitoring visits, safety reporting, protocol deviation handling and supply management.
04
Data Management
Toward lockDatabase design, edit check specification, data review, query management, reconciliation of external data from laboratory, imaging and device sources, medical coding, and preparation toward lock.
05
Monitoring
Runs throughoutSource data review, site oversight, risk signal review and corrective action. Modern practice is risk-based: attention is directed by data signals rather than distributed evenly across all sites and all fields.
06
Close-Out And Reporting
Reconstructable for yearsDatabase lock, statistical analysis, clinical study report, trial master file archival, and disclosure of results to public registries.
Every stage produces artefacts that must remain reconstructable years later. An inspector may ask, long after the study closed, who changed a value, when, and why.
THE REGULATORY FRAME
The Standards That Govern Study Data
ICH GCP (E6)
Global
Sponsor oversight, investigator responsibilities, data integrity, risk-proportionate quality management and quality-by-design in study conduct
ICH E8, E9
Global
General considerations for clinical studies, and statistical principles including estimands
EU Clinical Trials Regulation — (EU) 536/2014
EU
Single application and coordinated assessment through CTIS, with defined transparency requirements
21 CFR Parts 50, 54, 56, 312
US
Informed consent, financial disclosure, IRB review and investigational new drug requirements
21 CFR Part 11
US
Electronic records and signatures — audit trails, access control, record integrity, system validation
ISO 14155
Global
Good clinical practice for clinical investigations of medical devices
CDISC — CDASH, SDTM, ADaM, Define-XML
Global
Standardised collection, tabulation and analysis structures required for regulatory submission
MedDRA / WHO Drug
Global
Controlled terminologies for coding adverse events and medications
GDPR / HIPAA
EU / US
Lawful basis, consent scope, minimisation, pseudonymisation and subject rights over participant data
The practical consequence for study systems: field-level audit history, controlled and role-based access, versioned study and form definitions, documented validation, and lineage that survives from source through to submission dataset.
WHERE THE DATA LIVES
What A Single Study Runs On
EDC
Electronic data capture. Where case report form data is entered, validated and queried. The system of record for study data.
CTMS
Clinical trial management system. Study, site, milestone, visit and payment tracking. The operational layer.
eTMF
Electronic trial master file. The regulated document repository, structured to a reference model and subject to inspection.
IRT / RTSM
Interactive response technology. Randomisation, kit assignment and drug supply management.
ePRO / eCOA
Electronic capture of participant- and clinician-reported outcomes, on provisioned devices or participants' own.
eConsent
Electronic informed consent, including version control and re-consent management.
Safety Database
Adverse event capture, case processing and expedited reporting.
Statistical Computing Environment
Analysis datasets, programming and output generation under strict version control.
External Data Feeds
Central laboratory, imaging and device data arriving on their own schedules and formats, requiring reconciliation against the EDC.
A single mid-sized study can touch eight or more of these, procured separately, integrated partially, and reconciled manually at the seams. In FSP arrangements a CRO may be working across several different combinations of them simultaneously, one per sponsor.
WHAT'S CHANGING
Five Shifts Reshaping Clinical Research
01
Decentralised And Hybrid Designs Became Standard Options
Remote consent, telehealth visits, home nursing, direct-to-participant supply and wearable data capture moved from exception to routine design choice.
02
Quality By Design Replaced Quality By Inspection
Current GCP thinking emphasises identifying the factors critical to reliability and participant safety at design time and building proportionate controls around those.
03
eSource Is Closing The Double-Entry Gap
Pulling data directly from the electronic health record into study systems, using FHIR-based approaches, reduces transcription and the queries that follow it.
04
Sponsors Are Pushing On Cycle Time And Cost
Startup timelines and per-patient costs are under sustained pressure, pushing CROs toward reusable study configurations and automation of administrative work.
05
AI Is Being Applied To Review, Not Decisions
The credible applications are narrow: surfacing anomalies and missing data earlier, drafting queries for human review, and reducing manual reconciliation of external data.
COMMON FRICTION POINTS
Where The Work Usually Breaks Down
Study Startup Rebuilt From Scratch Each Time.
Forms, edit checks, roles and reports reconfigured per protocol, with the same decisions re-argued each round.
Sites Entering The Same Data Twice.
Source in one system, EDC in another, a paper worksheet in between. Every duplication becomes a query.
Queries Arriving After The Visit.
Validation running downstream, so a coordinator learns about a problem after the participant has left.
Monitoring Working From Stale Exports.
Risk signals living in a spreadsheet pulled last week, so trends are visible only in hindsight.
Participants Lost To Long Follow-Up.
Manual reminders and an afterthought participant experience, with completion rates carrying the cost.
Reporting Reassembled By Hand.
Datasets, decisions and provenance in different places, so producing a defensible submission dataset becomes a project of its own.
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