S P E C I A L I S T I N S I G H T
THE DESIRE TO BE MORE TRANSPARENT WAS MAGNIFIED BY THE DEVELOPMENT OF THE COVID-19 VACCINES .
such as the individual ’ s medical history , weight , preexisting medical conditions , reaction to drugs and other information . This is a significant data privacy risk for pharmaceutical companies , which could lead to steep regulatory fines as well as damage to the brand and reputation .
One solution to balancing transparency and data privacy is for pharmaceutical companies to use advanced quantitative analysis of trial results to evaluate how to anonymise patient-level information before sharing it with partners or the public . They can take the actual trial results and apply a statistical analysis specifically designed for clinical trial use cases using sophisticated software to help make smart decisions on which data can be retained and shared and which needs to be transformed or redacted in order to protect the privacy of an individual .
Instead of stating that Patient 41732-382 is 26 years old , a simple example is that it could be presented as patient 38291-381 is between 20 and 30 years old . The patient ’ s name and certain other personal information would automatically be omitted from the data revealed to the public or even partners while other data can be retained as-is . The quantitative methodology evaluates all of the patient information from the trial and systematically recommends transformations , redactions and retained data based on advanced statistical analysis . This approach to the anonymisation of the data protects the personally identifying characteristics within the data yet preserves data utility for secondary researchers looking to gain clinical insights from the information . The results will vary based on how the data will be shared . Public sharing requires additional safeguards versus internal sharing or with a partner when the intended use of the data is defined and the access to the information is controlled .
The objective is to retain the clinical meaning or value of the data and still be able to disclose it publicly while managing the risk of re-identification . Data privacy is more important than ever and people are more conscious about their personal information .
An alternative to quantitative analysis of clinical trial data is to redact any information pertaining to study results . This is what pharmaceutical companies have traditionally done to protect patient information . Anything personal or confidential was redacted so that it could not be read .
While redaction keeps personally identifiable information confidential , it renders otherwise useful attributes about that data unusable . It takes away all the useful insights and context for secondary use . On the other hand , a quantitative modelling approach uses advanced statistical methods to deidentify the information while maintaining the utility of the data .
This enables the information to be useful for readers of the study results , as well as meaningful for secondary research by academic institutions and other researchers . Given that one of the main reasons for sharing data and being transparent is to inform others , the quantitative modelling approach is more suitable for meeting this goal while keeping individuals ’ data safe . Most importantly , using the quantitative methodology to preserve data utility and clinical usefulness is what helps to accelerate secondary research and bring treatments to patients faster .
For clinical operations leaders and others responsible for ensuring transparency and privacy with clinical trial data , this newer approach provides the balance needed . It enables pharmaceutical companies to address the unique challenges they are facing all while protecting our most prized asset – individuals who volunteer to participate in clinical trials . �
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