Data Collection

This initial step involves the gathering of clinical data from various sources, including electronic health records (EHRs), case report forms (CRFs), laboratory results, patient interviews, and medical devices. Data may be collected through on-site visits, remote monitoring, or electronic data capture (EDC) systems.

Data Entry, Validation, & Transformation

In the data management workflow, we start with Data Entry and Validation, where information is securely input into a database and undergoes validation checks to spot and fix errors, inconsistencies, or missing values. Next is Data Cleaning, which systematically corrects discrepancies, outliers, and anomalies, enhancing data quality by addressing issues like duplicate records and transcription errors. Finally, Data Transformation may be required to standardize data for consistency and comparability, including tasks like unit conversion, data normalization, and coded terminology mapping (e.g., SNOMED CT, LOINC).

Encryption, Security, and Auditing

To safeguard clinical data’s confidentiality and integrity, comprehensive security measures are in place, including encryption and stringent access controls, ensuring compliance with regulations like HIPAA and GDPR. Additionally, an audit trail is maintained, tracking all data changes to establish a transparent history of alterations, including who made them, when, and for what purpose. This practice is vital for both data integrity and regulatory adherence.

Data Reconciliation & Quality Control

In clinical trials, data reconciliation is crucial for maintaining consistency and accuracy by comparing information from various sources like CRFs and laboratory reports. Quality Control (QC) further reinforces data integrity through comprehensive processes that ensure adherence to predefined standards and protocols. QC activities encompass data review, cross-checking, and data validation.