Building the Data Foundation for Successful Clinical Studies
Clinical study data quality starts long before the first participant is enrolled. It begins with how the protocol is translated into CRFs, how the study database is configured, and how data quality is managed throughout the study lifecycle.
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Touchcore provides end-to-end study build and clinical data management services that transform study requirements into structured, operationally ready data collection environments.
We support clinical research teams from protocol review and CRF design through EDC study build, validation, data cleaning, query management, and database readiness. Our approach brings together clinical data management and technology expertise to help teams collect accurate, consistent, and analysis-ready data.
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This is clinical data infrastructure designed around the study, not the other way around.
From Protocol to Analysis-Ready Data
Built for Data Quality
throughout the Study Lifecycle.
We design study environments around how data is collected, reviewed, validated, and ultimately used.
From the first CRF specification to final data review, our focus remains on reducing ambiguity, preventing avoidable errors, and maintaining data integrity.
What We Provide
Integrated study build and clinical data management services that help sponsors and research teams establish reliable data collection systems and maintain high-quality study data throughout the lifecycle.
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Our teams combine clinical research understanding, data management practices, and technology expertise to support studies from initial build through database readiness.
CRF and eCRF Design
We translate study protocols, objectives, endpoints, and data requirements into clear and structured CRFs and eCRFs. Forms are designed to capture the data required by the study while minimizing ambiguity, unnecessary fields, and data-entry burden.
EDC Study Build and Configuration
We configure study databases based on protocol requirements, including forms, fields, visit schedules, dependencies, branching logic, calculations, validation rules, and study-specific workflows. The build is structured to support both operational usability and downstream data requirements.
Edit Checks, Data Validation and UAT
We design and test edit checks, validation rules, calculations, workflows, and study logic to identify data issues early and verify that the study operates as intended. We also support UAT, defect resolution, and study readiness before go-live.
Clinical Data Cleaning and Query Management
We provide ongoing data review to identify missing, inconsistent, and potentially incorrect data throughout study conduct. Queries and discrepancies are systematically managed to support timely resolution and maintain clean, reliable clinical datasets.
Study Amendments and Database Management
We support the study database throughout its lifecycle, including protocol amendments, CRF updates, configuration changes, validation updates, testing, and controlled releases. Changes are managed with a focus on traceability, study continuity, and data integrity.
Data Quality by Design
Clean clinical data is not created at database lock. It is engineered throughout the study.
We build quality controls into study design, configuration, and ongoing data management from the beginning.
Protocol-Driven
Study Architecture
Forms, fields, visits, validation rules, and workflows are mapped to study requirements so that the database reflects how the protocol is intended to operate.
Quality Controls at the
Point of Capture
Field constraints, edit checks, dependencies, controlled values, and validation logic help identify potential data issues as early as possible, reducing downstream cleaning and rework.
Designed for
Study Operations
Study databases are built around practical workflows for sites, investigators, monitors, and data teams. We focus on reducing unnecessary complexity while making critical data easier to capture, review, and manage.
Controlled Data Management Lifecycle
Study configuration, data review, queries, amendments, testing, and database changes are managed through structured processes that support consistency, traceability, and reliable study execution.
Featured Projects
Discover the many ways in which our clients have embraced the benefits of the Touchcore way of engineering.
Artificial Intelligence and Augmented Reality powered telemedicine platform for a US based firm.
A machine learning powered mobile app that enables tele-rehab, sports
medicine, fitness exercises, and mobility assessments for individuals and
patients.
50K+
App Downloads
± 3°
Accuracy (Validated through Gait Lab)
28
​No. of Key Points Tracked
Frequently Asked Questions
It means deploying AI systems that operate reliably inside real workflows with monitoring, governance, and accountability in place.
Through monitoring, drift detection, retraining strategies, and clear rollback mechanisms.
When outputs cannot be controlled, validated, or audited within a workflow.
By designing decision structures, confidence thresholds, and traceability from the start.
Yes, when they are engineered with governance, oversight, and compliance requirements built in.