EDC & eCRF Design Bootcamp

Electronic Data Capture has fundamentally transformed clinical data management — replacing paper CRFs and manual data entry with real-time electronic data collection, automated edit checks, and immediate discrepancy detection across global multisite trials. But the quality of an EDC system is entirely determined by the quality of the eCRF that was built into it — and a poorly designed eCRF generates data quality problems that no amount of downstream cleaning can fully correct. Fields that are ambiguous generate inconsistent data entries across sites. Edit checks that are incorrectly configured generate spurious queries that burden site staff and delay data cleaning. Visit structures that do not align with the protocol's schedule of assessments generate missing data patterns that compromise statistical analysis. And an EDC system that lacks appropriate access controls, audit trail integrity, and data security configuration fails 21 CFR Part 11 compliance — making the data it generates potentially unacceptable to regulatory authorities.

This program builds the complete EDC and eCRF design competency stack from the ground up across three tightly integrated operational layers. The first is the clinical trial documentation and AI foundation — understanding GCP documentation standards and their implications for electronic data capture systems, and the emerging role of AI in clinical research data management including AI-assisted CRF design, automated field suggestion, and intelligent edit check generation. These foundations establish the regulatory and technological context within which every EDC and eCRF decision is made. The second layer is the core eCRF design and EDC operations curriculum — CRF design principles and regulatory requirements, data collection methodology and field specification standards, AI-assisted CRF automation tools and output validation, CRF amendment and version control management, and EDC system introduction covering system architecture, user management, and operational configuration. The third layer is the clinical data quality and management curriculum — schedule of assessments alignment and visit-level form consistency verification, source data verification workflows within EDC environments, query management from generation through resolution and closure, missing data handling strategy and EDC-level prevention mechanisms, and data security and confidentiality requirements for electronic clinical trial data systems. These three layers are trained as an integrated clinical data management system — because an eCRF designer who cannot manage the query workflow their form generates, or who does not understand the SDV process that will verify their data fields, is building for a data quality standard they cannot sustain.

By the end you carry a complete live EDC and eCRF build portfolio — annotated eCRF with visit architecture, field specifications, edit check logic, AI automation validation records, amendment documentation, query management records, and data security configuration documentation — advisor-reviewed and published to your professional portfolio. In clinical data management hiring, the ability to produce a live eCRF build rather than describe what one should contain is the specific capability distinction that separates shortlisted candidates from all others.

You are assigned to the clinical data management function of a CRO building the EDC system for a Phase II oncology trial. The protocol has been finalised. The schedule of assessments has been approved. Your job is to translate that protocol into a live, validated eCRF that clinical sites can use to collect data that will support a regulatory submission:

Begin with protocol review and eCRF architecture planning. Open the protocol and schedule of assessments. Map every assessment at every visit to a required CRF page — screening visit, baseline, treatment visits at defined intervals, end of treatment, and follow-up. Identify every data field required to capture each assessment — what type of field is appropriate for each data point? Is this a numeric field with defined range limits, a date field, a categorical dropdown, a text field for narrative adverse event descriptions, or a checkbox for concomitant medication flags? Build the complete eCRF architecture map before opening the EDC system — a CRF designed inside the system before the architecture is planned generates structural errors that are expensive to correct after data collection has begun.

Open the EDC system. Configure the trial-level settings — study name, protocol version, randomisation configuration, site list, and user access roles. Assign access permissions — which user roles can enter data, which can query, which can approve, which can view only? Apply 21 CFR Part 11 access control requirements — each user account must be individually assigned, passwords must meet complexity requirements, and the audit trail must record every data entry, modification, and access event with a timestamp and user attribution.

Build the eCRF visit by visit. Start with the screening visit. Open the demographics page — build the date of birth field with appropriate date format validation, the sex field as a categorical dropdown, the height and weight fields as numeric fields with defined acceptable range edit checks. Build the eligibility criteria page — each inclusion and exclusion criterion as a binary yes/no field with a system rule that prevents study entry if any exclusion criterion is answered yes. Build the medical history page — a repeating form that allows multiple concurrent medical conditions to be entered with onset date, status, and severity fields.

Move to the baseline visit. Build the laboratory results page — each laboratory parameter as a numeric field with reference range edit checks that flag values outside the normal range for query review. Build the tumour assessment page — target lesion measurements as numeric fields, non-target lesion status as categorical fields, overall response as a system-calculated field driven by the measurement inputs. Verify that the calculation logic matches the protocol's response assessment criteria exactly.

Configure edit checks across the eCRF. An edit check fires when a data entry triggers a logical inconsistency that requires query or correction. Build the date sequence checks — the informed consent date must be earlier than the screening visit date, which must be earlier than the treatment start date. Build the range checks — laboratory values outside clinically plausible ranges trigger a query for confirmation or correction. Build the completion checks — mandatory fields that cannot be left blank without a system-enforced reason code. Build the cross-form consistency checks — if an adverse event is recorded on the AE page, a corresponding concomitant medication entry should exist on the medication page if treatment was administered.

Implement AI-assisted CRF automation. The AI tool has suggested field labels, data types, and edit check parameters based on the protocol therapeutic area and standard clinical data models. Review every AI suggestion systematically — does the suggested field label match the protocol's exact terminology? Does the suggested data type capture the required information format? Does the suggested edit check parameter align with the protocol's defined normal ranges and assessment criteria? Accept suggestions that are correct, modify those that are close but imprecise, and override those that are incorrect with documented rationale referencing the specific protocol section that specifies the correct parameter.

Validate eCRF against schedule of assessments. Open the protocol schedule of assessments table. Verify that every assessment listed at every visit has a corresponding eCRF page and field in the built form. Identify every gap — an assessment with no data capture field, or a data capture field with no corresponding assessment in the protocol schedule. Resolve every gap before the eCRF is released for testing.

Manage an SDV workflow in the EDC environment. A monitoring visit at Site A has been completed. The monitor has conducted source data verification for five subjects. Three SDV findings have been entered as queries in the EDC system. Review each query — is the query text specific enough for the site coordinator to identify the discrepancy and take the appropriate action? Are the queries assigned to the correct site personnel? Manage the query resolution cycle — two queries have been resolved with corrections, one has been answered but the answer does not resolve the discrepancy. Generate a follow-up query with additional clarifying instruction.

Handle missing data. Three subjects have missing values at a defined assessment timepoint — two due to a missed visit, one due to an assessment that was not performed per site error. Apply the missing data classification framework — is this missing due to subject withdrawal, administrative error, or protocol deviation? Apply the appropriate reason code in the EDC system. Verify that the statistical analysis plan's missing data handling strategy can accommodate the pattern of missingness in this dataset.

Execute a CRF amendment. A protocol amendment has been approved — a new safety biomarker assessment has been added to the treatment visits. Design the new CRF page for the biomarker data. Assess the impact of the new field on the existing dataset — subjects who have already completed treatment visits will have missing values for this new field. Document the amendment, update the eCRF version number, issue the amendment to sites with implementation instructions, and update the SDV guidelines to include the new field.

Review data security configuration. Verify that the EDC system's data transmission is encrypted, that subject identifiers are anonymised in all exported datasets, that audit trail records are tamper-evident and complete, and that the system access log shows no unauthorised access events since trial initiation.