Data integrity principles for the CRC
4 lessons · 3 hours
Data integrity is the reason clinical trials exist. If the data are unreliable, the entire enterprise -- the science, the regulatory submission, the years of participant sacrifice -- produces nothing of value. The CRC is the first link in the data integrity chain: the person who creates source documents, transcribes data into case report forms, and responds to queries when discrepancies are found. This course teaches the principles, systems, and daily practices that produce data worthy of the trust we place in it.
This course is part of the Clinical Research Coordinator track. Enroll once to access all courses and exams.
Apply the ICH E6(R3) data integrity criteria -- attributable, legible, contemporaneous, original, accurate, complete, secure, and reliable -- to all source documentation and data collection activities, while recognizing the industry mnemonic ALCOA-CCEA (which adds Consistent, Enduring, and Available) as a complementary but distinct framework
Create source documents that withstand source data verification and regulatory inspection
Execute accurate data transcription from source documents to case report forms, whether paper or electronic
Navigate electronic data capture (EDC) systems with proficiency, including data entry, edit checks, and query resolution
Manage data queries efficiently and accurately, maintaining query resolution timelines
Implement data governance practices consistent with ICH E6(R3) Annex 1, Section 4 (Data Governance)
7 modules, 28 lessons, and 7 knowledge checks — all self-paced.
Select any lesson to preview — lessons marked Preview are free to read in full.
4 lessons · 3 hours
Enroll in the Clinical Research Coordinator track to access this course, all exams, and your certificate.
Data integrity is the reason clinical trials exist. If the data are unreliable, the entire enterprise -- the science, the regulatory submission, the years of participant sacrifice -- produces nothing of value. The CRC is the first link in the data integrity chain: the person who creates source documents, transcribes data into case report forms, and responds to queries when discrepancies are found. This course teaches the principles, systems, and daily practices that produce data worthy of the trust we place in it.
This course is part of the Clinical Research Coordinator track. Enroll once to access all courses and exams.
Apply the ICH E6(R3) data integrity criteria -- attributable, legible, contemporaneous, original, accurate, complete, secure, and reliable -- to all source documentation and data collection activities, while recognizing the industry mnemonic ALCOA-CCEA (which adds Consistent, Enduring, and Available) as a complementary but distinct framework
Create source documents that withstand source data verification and regulatory inspection
Execute accurate data transcription from source documents to case report forms, whether paper or electronic
Navigate electronic data capture (EDC) systems with proficiency, including data entry, edit checks, and query resolution
Manage data queries efficiently and accurately, maintaining query resolution timelines
Implement data governance practices consistent with ICH E6(R3) Annex 1, Section 4 (Data Governance)
7 modules, 28 lessons, and 7 knowledge checks — all self-paced.
Select any lesson to preview — lessons marked Preview are free to read in full.
4 lessons · 3 hours
Enroll in the Clinical Research Coordinator track to access this course, all exams, and your certificate.
Explain the data governance framework introduced in ICH E6(R3) Annex 1, Section 4, including data lifecycle elements, computerized systems requirements, and the CRC's role in implementing governance at the site level.
4 lessons · 3 hours
Parse the ICH E6(R3) definitions of source data and source documents, identify source for each data category, and understand certified copies and prospective source determination.
Apply worksheet design principles to create SDV-ready source documentation, incorporate quality prompts, evaluate worksheets against protocol requirements, and manage version control.
Determine when EMR data qualifies as source documentation, identify common pitfalls of clinical records in research, and apply strategies to make EMR data research-ready.
Apply the contemporaneous documentation requirement, understand memory decay science, identify how delayed documentation is detected, and implement real-time documentation strategies.
4 lessons · 3 hours
Identify CRF structural elements, apply completion guidelines for dates, units, and missing data, recognize common pitfalls, and understand the CRF as an investigator attestation.
Apply the read-transcribe-verify method, identify high-risk data elements, navigate transcription challenges, and execute CRF correction procedures for paper and electronic systems.
Execute a three-step self-QC routine, perform high-risk checks, build QC discipline into your workflow, and identify personal error patterns.
Compare paper and electronic CRF workflows, identify unique error risks for each, understand correction requirements by medium, and apply data integrity principles across both systems.
4 lessons · 3 hours
Navigate EDC systems, execute the data entry workflow, manage operational challenges, and leverage quality-supporting features.
Differentiate hard, soft, and cross-form edit checks, respond appropriately to each type, recognize edit check fatigue, and understand the clinical logic behind validation rules.
Understand the legal significance of electronic signatures, execute the sign-and-lock workflow, protect credentials, and navigate post-lock corrections.
Understand 21 CFR Part 11 requirements at the CRC operational level, apply system access practices, maintain training documentation, and recognize Section 4 alignment.
4 lessons · 3 hours
Differentiate three query types, identify triggers and response pathways, read query content effectively, and interpret query metrics as quality indicators.
Execute systematic query resolution, write effective responses, route queries to investigators when needed, and handle queries that reveal actual data errors.
Apply query resolution timelines, understand downstream dependencies, build tracking systems, and execute escalation procedures for delayed resolution.
Analyze query patterns, map prevention strategies to query types, distinguish systemic from individual issues, and track improvement with meaningful metrics.
4 lessons · 3 hours
Understand the SDV process, what monitors verify, how risk-based monitoring shapes SDV practices, and how centralized statistical monitoring detects data patterns.
Organize source documents for monitoring visits, achieve pre-monitoring data readiness, support SDV efficiently, and self-discover issues before the monitor arrives.
Process monitoring visit reports, execute data corrections, create source document late entries, and track recurring findings for quality improvement.
Understand remote monitoring approaches, fulfill CRC responsibilities for remote SDV, recognize centralized monitoring signals, and appreciate heightened self-QC importance.
4 lessons · 3 hours
Define ePRO and eCOA, describe CRC responsibilities for electronic outcome assessments, apply source documentation principles to ePRO data, and address common challenges.
Understand wearable devices in clinical trials, distinguish passive from active data collection, fulfill CRC responsibilities for device management, and apply source documentation principles to sensor data.
Understand EHR-to-EDC integration models, identify practical challenges, verify automatically transferred data, and determine source documentation in integrated environments.
Apply the Section 4 governance framework to emerging technologies, use proportionality with CTQ factors, and understand the CRC's evolving role as data quality steward.
Explain the data governance framework introduced in ICH E6(R3) Annex 1, Section 4, including data lifecycle elements, computerized systems requirements, and the CRC's role in implementing governance at the site level.
4 lessons · 3 hours
Parse the ICH E6(R3) definitions of source data and source documents, identify source for each data category, and understand certified copies and prospective source determination.
Apply worksheet design principles to create SDV-ready source documentation, incorporate quality prompts, evaluate worksheets against protocol requirements, and manage version control.
Determine when EMR data qualifies as source documentation, identify common pitfalls of clinical records in research, and apply strategies to make EMR data research-ready.
Apply the contemporaneous documentation requirement, understand memory decay science, identify how delayed documentation is detected, and implement real-time documentation strategies.
4 lessons · 3 hours
Identify CRF structural elements, apply completion guidelines for dates, units, and missing data, recognize common pitfalls, and understand the CRF as an investigator attestation.
Apply the read-transcribe-verify method, identify high-risk data elements, navigate transcription challenges, and execute CRF correction procedures for paper and electronic systems.
Execute a three-step self-QC routine, perform high-risk checks, build QC discipline into your workflow, and identify personal error patterns.
Compare paper and electronic CRF workflows, identify unique error risks for each, understand correction requirements by medium, and apply data integrity principles across both systems.
4 lessons · 3 hours
Navigate EDC systems, execute the data entry workflow, manage operational challenges, and leverage quality-supporting features.
Differentiate hard, soft, and cross-form edit checks, respond appropriately to each type, recognize edit check fatigue, and understand the clinical logic behind validation rules.
Understand the legal significance of electronic signatures, execute the sign-and-lock workflow, protect credentials, and navigate post-lock corrections.
Understand 21 CFR Part 11 requirements at the CRC operational level, apply system access practices, maintain training documentation, and recognize Section 4 alignment.
4 lessons · 3 hours
Differentiate three query types, identify triggers and response pathways, read query content effectively, and interpret query metrics as quality indicators.
Execute systematic query resolution, write effective responses, route queries to investigators when needed, and handle queries that reveal actual data errors.
Apply query resolution timelines, understand downstream dependencies, build tracking systems, and execute escalation procedures for delayed resolution.
Analyze query patterns, map prevention strategies to query types, distinguish systemic from individual issues, and track improvement with meaningful metrics.
4 lessons · 3 hours
Understand the SDV process, what monitors verify, how risk-based monitoring shapes SDV practices, and how centralized statistical monitoring detects data patterns.
Organize source documents for monitoring visits, achieve pre-monitoring data readiness, support SDV efficiently, and self-discover issues before the monitor arrives.
Process monitoring visit reports, execute data corrections, create source document late entries, and track recurring findings for quality improvement.
Understand remote monitoring approaches, fulfill CRC responsibilities for remote SDV, recognize centralized monitoring signals, and appreciate heightened self-QC importance.
4 lessons · 3 hours
Define ePRO and eCOA, describe CRC responsibilities for electronic outcome assessments, apply source documentation principles to ePRO data, and address common challenges.
Understand wearable devices in clinical trials, distinguish passive from active data collection, fulfill CRC responsibilities for device management, and apply source documentation principles to sensor data.
Understand EHR-to-EDC integration models, identify practical challenges, verify automatically transferred data, and determine source documentation in integrated environments.
Apply the Section 4 governance framework to emerging technologies, use proportionality with CTQ factors, and understand the CRC's evolving role as data quality steward.