🔎 What is Hardcoding?
The public domain lacks a clear operating definition of hardcoding. Let’s review several definitions:
– Hardcoding: Inserting or correcting data directly into a program’s source code, bypassing external sources, when data is missing or needs correction [1].
– Hardcoding: Programmatic data changes in a database without an audit trail, resulting from incorrect data entry from a Case Report Form or other data collection medium [2].
– Hardcoding: Manually embedding specific information into a program, overriding data from the clinical data management system or other sources [3].
🧐Reasons why hardcoding is a bad practice [1, 2, 3]:
1. Data Integrity and Audit Trail Compromise: Hardcoding overrides clinical data management system controls, risking data discrepancies and impeding audit traceability, compromising data integrity.
2. Regulatory Compliance Risk: Federal regulations, like 21 CFR 11, stress accurate electronic recordkeeping. Hardcoding may breach these regulations, leading to potential regulatory actions and consequences.
3. Validity Concerns: Hardcoding may become invalid over time, hindering updates and causing database inaccuracies.
4. Accountability Impact: Hardcoding diminishes accountability, raising doubts about data entry responsibility and integrity.
5. Ineffective Data Monitoring: Hardcoding complicates data audit and interpretation, impacting data monitoring and clinical trial data reliability.
💼 Best Practices for Transparent CT
🔍 Ensure Proper Approval: Standardize approval for hardcoding instances. Document justifications and involve responsible individuals for accountability.
📝 Document the Agreement: Utilize a specific hardcoding agreement form to record all details of the approval process, which will serve as a crucial reference point for auditors and QA teams, ensuring transparency.
💬 Use Standard Comments and Logs: Enhance transparency in your code by incorporating standard comments and PUT statements in the log when applying hardcoding.
🔄 Explore Alternatives: Prioritize traceable approaches, avoiding permanent hardcoding when possible.
🤝 Collaborate for Quality: Foster open discussions within the team to address hardcoding challenges and ensure data integrity.
Maintain trustworthiness and compliance in clinical trial data through these practices.
1. Michael Nessly, PharmaSUG 2023 (https://lnkd.in/efCpiAYm)
2. Susan F., PharmaSUG 2000 (https://lnkd.in/ej8-UgRP)
3. Jack Shostak, SAS Programming in the Pharmaceutical Industry (https://lnkd.in/eMApjBcX)