Separation of Patient Profile Data

Structuring your data

• Patient profiles typically include comprehensive information about individual participants, such as demographics, maybe some kind of summarised medical history, and even some sort of treatment exposure.
• There is no sound reason why the basic data of the patient would not be kept entirely independently of the medical history and exposures to really sustain anonymity.
• By maintaining a clear separation between basic patient identifiers (e.g., name, date of birth, contact information) – data often found in CMDM, and keeping this separate from sensitive medical details, the risk of unauthorized access or accidental disclosure of PHI is reduced. This compartmentalization aligns with the HIPAA principle of “minimum necessary” access to PHI.
• Placing these distinctive elements in different data stores like the Pretectum CMDM, enhances efficacy, data privacy protection and data safety outcomes.
• When patient or participant basic data is stored independently from medical records and trial information, it becomes more challenging to re-identify individuals, even if a data breach occurs. This approach supports the goal of anonymization and helps maintain patient confidentiality.
  

Privacy and Compliance:

• HIPAA supports separating protected health information (PHI) from non-PHI data, though HIPAA does not explicitly mandate that protected health information (PHI) must be stored as encrypted data; instead, it categorizes encryption as an “addressable” implementation specification within the Security Rule.
• While encryption is highly recommended to protect electronic PHI (ePHI), it is therefore not an explicit strict requirement, Pretectum CMDM stores patient and participant data profiles in an encrypted and access controlled state, by default.
• All that said, keeping patient profiles separate from trial data helps with better compliance. Such a separation protects sensitive personal information while allowing medical practitioners and researchers to access necessary data for diagnostics, analysis and reporting.
• Researchers and authorized personnel may only require access to specific aspects of patient data based on their role in accordance with the Pretectum CMDM RBAC model.
• By keeping basic identifiers separate, access controls can be more granular, allowing personnel to view medical histories without necessarily having access to direct patient identifiers.
• Patients and participants may also feel more comfortable participating in clinical trials if they perceive that their basic personal information is being handled with an extra level of care and separation from sensitive medical data. This can enhance patient trust and willingness to share information.
• Failure to implement encryption or equivalent protective measures could lead to severe consequences, including financial penalties and reputational damage, especially if a data breach occurs that could have been mitigated through proper segregation of data encryption.

Data Management Practice

• The Clinical Data Interchange Standards Consortium (CDISC) is a global, non-profit organization that develops and supports data standards to enhance the quality and interoperability of data in medical research and healthcare.
• CDISC aims to create standardized formats for clinical research data, in the acquisition, exchange, submission, and archiving of medical and biopharmaceutical data in product development. This standardization helps streamline the clinical trial process and improves data quality and efficiency.
• Since December 2016, CDISC standards have been mandatory for submissions to the U.S. Food and Drug Administration (FDA). This demonstrates the importance of CDISC in ensuring that clinical trial data is presented in a consistent and interpretable manner for regulatory review.
• SDTM is a foundational standard that organizes clinical trial data into a structured format for regulatory submission. It defines how to represent data collected during clinical trials.
• The Study Data Tabulation Model (SDTM) defines several domains that are crucial for organizing and standardizing clinical trial data.

Two specific SDTM domains of interest are Demographics (DM) and Subject Characteristics (SC). They fall into the Special Purpose domain category in that that they are designed for specific types of data that do not fit neatly into the general observation classes. These domains address unique aspects of clinical trials and provide additional context or structure to the data.

Special purpose domains have a specific structure defined by CDISC and cannot be extended with additional qualifier or timing variables beyond what is specified.

Demographics (DM) Domain

The DM domain captures essential demographic information about each subject participating in clinical trials. This data is critical for understanding the population involved in the study and for analyzing the impact of demographic factors on trial outcomes.

Key Variables:

• USUBJID: Unique Subject Identifier, ensuring each subject is distinctly identified.
• AGE: Age of the subject at the time of consent or enrollment.
• SEX: Gender of the subject (e.g., Male, Female).
• RACE: Race of the subject, which can be important for demographic analysis.
• ETHNICITY: Ethnic background of the subject.
• COUNTRY: Country of residence or participation.

The information in this domain is typically collected once at the beginning of the trial and does not change throughout the study. This consistency is vital for maintaining the integrity of demographic data. The DM domain follows standardized definitions and formats, allowing for easy aggregation and comparison across different studies and trials.

Subject Characteristics (SC) Domain

The SC domain provides additional characteristics of subjects that may not be captured in the DM domain. This domain is helpful for analyses related to quality of life, risk-benefit assessments, and other demographic-related inquiries.

Key Variables:

Similar to the DM domain, SC data is collected only once per subject at the beginning of the trial. This data is not expected to change during the study, ensuring stability in the characteristics being analyzed. The SC domain utilizes a normalized data structure, meaning that it captures data in a way that maintains clarity and consistency. Each characteristic is represented by separate variables, which helps in organizing and analyzing the data effectively.

By capturing demographic and subject characteristic data in standardized formats, researchers can conduct more robust analyses regarding how these factors influence trial outcomes. The use of SDTM domains, including DM and SC, is essential for compliance with regulatory requirements, particularly for submissions to agencies like the FDA in support of improved transparency and efficiency in clinical research.

Clinical data management systems often utilize standardized formats (like CDISC SDTM) for patient profiles, which can be independently reviewed and analyzed without necessarily directly linking to trial results. Such a practice supports data integrity and facilitates easier updates and maintenance of patient information, especially for longitudinal studies where the same participants may be present for numerous trials over a protracted period.

The Pretectum CMDM naturally supports the implementation of customer profile data completely independently of PHI and stores this data in a secure and access controlled manner.