What is it? Why is it important?
Automated Data Processing is the use of technology with the aim to convert data into a more useful format or needed information (e.g. age upon study start, BMI based on height and weight). Automated processing has an important advantage, as it excludes human calculation errors.
Data processing can be done either before or after being entered in the study database.
Automated processing requires:
- An electronic database (e.g. CDMS)
- Ability to program processing procedures (e.g. calculations, classification)
- Ability to perform applicable validations procedures to ensure automated processing was correctly implemented
Calculation of creatinine clearance requires the input of raw data into an applicable formula (age, weight, serum creatinine, and gender). Upon automated data processing the creatinine clearance number is no longer raw data but automated processed data.
What do I need to do?
As a SP-INV and based on data needed to answer your study question:
- Make a list of study data (variables) that can only be obtained through automated processing
- Define which variables are needed for the automated processing
- Ask your data manager to program any required automated processing procedure(s) in the study database.
- In the event processing procedures are performed prior to being entered in the study database, provide applicable staff guidelines and ensure the technical equipment is validated for its intended use (e.g. pocket calculator, stop-watch). An added control measure supporting the detection input-errors are upper and lower input limits to input variables in the database.
- Include a test phase to ensure automated data processing is correctly implemented
- Document any performed validation procedures
- Describe any automated processing in the DMP
Where can I get help?
Your local CTU↧ can support you with experienced staff regarding this topic
Basel, Departement Klinische Forschung, CTU, dkf.unibas.ch
Lugano, Clinical Trials Unit, CTU-EOC, www.ctueoc.ch
Bern, Clinical Trials Unit, CTU, www.ctu.unibe.ch
Geneva, Clinical Research Center, CRC, crc.hug.ch
Lausanne, Clinical Research Center, CRC, www.chuv.ch
St. Gallen, Clinical Trials Unit, CTU, www.kssg.ch
Zürich, Clinical Trials Center, CTC, www.usz.ch
ICH GCP E6(R2) – see in particular guideline
- 5.5 Trial management, data handling, and record-keeping