Black box testing for clinical data management


Dear Reader,

This article is about black box testing which I use a lot within clinical data management. It helps me to judge if system, people and procedures are able to assure quality clinical study data.
Therefore I do not exactly need to know how things are done. Instead I focus on what is done. Output versus input. Simply said, I check if what goes IN, is coming OUT as expected.
I use it within clinical studies to check accurate database preparation as well as for testing programmed edit checks. I use it for quality control activities to check accurate clinical study data handling. And for test scripts, carried out for computerized system validation.
Almost all clinical data management activities can be checked with the IN = OUT, unless… principle. 

Best regards,


 Black box testing for clinical data management  

4 core actions:
  1. Have your requirements thoroughly listed.
    What is your core business? Research & development, manufacturing, selling, contract research, patient care? If you need to carry out clinical trials or clinical studies, what are your requirements for these? Do you conduct clinical trials to obtain market approval for the product under subject? Is your product a drug, a medical device, a tissue-engineered product, or a combined product? Do you conduct clinical studies for post marketing surveillance? Sort out the applicable laws and guidances for your clinical trials and/or clinical studies (to be) conducted. Read and translate these regulations for your situation. Do you rely up on electronic records for data collection, data modification and/or data transfer? Then you’ll definitely need 21 CFR part 11 and its guidances. To have your requirements listed in thorough detail, external GCP courses and other regulatory courses are important to (have) join(ed). Discussing the practical implications of the law or guideline with the course facilitator, expert and other participants. In your own words, with the background of the organization you represent.
  2. Create a plan.
    Discuss how to investigate the fulfilment of each requirement listed. IN versus OUT testing works for most of these investigations. Big advantage is that it can be carried out and understood by your independent colleagues. Provide test data, the script to conduct and describe your expectations (expected results) per plan.
  3. Perform (IN versus OUT) testing.
    Just conduct the black box testing. And check if what went IN, is in the output as expected.
  4. Document, document, document along the way.
    Add action taken, date, name and signature on every plan, test script, test data set, test result and test report you’ve generate along the way. Take care that you document such that other people, e.g. colleagues, Auditors, can follow, understand and make their own judgement up on the results gained.


The challenge of clinical trial/study requirements is to clearly fulfil these.
In the center of each trial/study is the clinical data collected, stored, verified, updated and transferred. The more you rely up on your system, the more data handling activities can be automated and the more validation needs to be done.
The more you rely up on your procedures, the more specifications and documented evidence of correct performance you need to generate along the way. And the more you rely on your people, the more important quality control activities are to guarantee quality clinical data.


Good luck presenting your requirements and corresponding ‘black boxes’,

Kind regards,


© 2011, Maritza Witteveen, ProCDM

This is an article from ProCDM. Helping enthusiastic Clinical Research Professionals with a drive to use their expertise, so they get heard, are appreciated and can make a difference. Receive tips and the free e-book ‘Five strategies to get reliable, quality clinical data’ by subscribing via

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