Data protection

Data set reference and name

Data are the “raw materials” with which an evaluation is built. Evaluation data are systematically collected information relevant to the project that will be used in assessing objectives of the program.

Evaluation data can come from the content of surveys, questionnaires and interviews, tallies from logs, information from scales and self-assessments. Data collection itself involves administering instruments as well as gathering and organizing responses and measures for analysis.

Data set description

To achieve the objectives of the program there is a need of two types of data.

TYPE1: Data without references to human subject

An Authentication and Authorization infrastructure is in place to control and protect the project data repository.

TYPE 2: Personal data and privacy issues

Data privacy refers to the standards surrounding the protection of personal data. Personal data can be defined as any information relating to a research subject, which can lead to the identification, either directly or indirectly, of that subject. Evaluation data with reference to personal information which make an unambiguous assignment of a person possible such as: First and last name, full date of birth (Month, Day, Year) , E-Mail Address, Telephone number or other identifier Ex: Insurance number, National number, etc. , are to be protected and require the subject’s consent prior to collection.

Standards and metadata

The ICH Guideline for Good Clinical Practice (GCP) states: “The confidentiality of records that could identify personal data should be protected, respecting the privacy and confidentiality rules in accordance with applicable regulatory requirement(s).”

Privacy protection afforded to research subjects includes:

• Right to informed consent

• Right of the individual to withdraw consent

• Right to notice of disclosure

• Confidential collection and submission of data

Minimum Standards

• Education of all personnel who directly or indirectly handle personally identifiable data

• Designing of data collection instruments with the minimum subject identifiers needed including the design of survey, databases, data transfer specifications and any other area of data collection that may contain personal information.

• Anonymize or redact any documentation submitted to data management that contains subject identifiers beyond those needed to link it to a database record.

• Ensure ongoing review and updating of data management processes to ensure consistency.

Methods for protection and confidentiality of identifier:

1. Anonymization 

Irrevocable anonymization of personal data puts it outside data protection requirements as the data can no longer be linked to an individual and therefore cannot be considered to be personal data. Ideally such anonymization of data for research purposes should be an automatic process performed as patient data is processed through IT or manual systems, whichever is the case. Where personal data is anonymized, there is no need from a data protection perspective to seek the consent of human subject for the use of the data for research purposes.

2. Pseudonymization

Equally, it is recognized that linking episodes of care and preventing duplicate records in research may require that information can be matched or linked. This can be achieved through appropriate pseudonymization methods (e.g., use of initials or coding) without retaining all identifying characteristics with the data.

Where sufficient measures are put in place to ensure that personal data is neither accessible to nor likely to be identifiable by parties external to the data controller, pseudonymized data may be used for research purposes. The key linking codes to individual patients is stored separately and is accessible only to authorized staff at the treating center.