As Pharmaceutical organizations continue to rapidly evolve their operations based on data-driven insights, there is a growing need to ensure security, privacy and appropriate access of their data across the enterprise. There is a need to adopt a privacy by design approach to data management.

In order to ensure data privacy and appropriate access, organizations are faced with the following challenges:

• Penalties for Non-compliance to data residency requirements

• Risk of Transferring sensitive data across borders, especially (PII), (PHI)

• Geo-infra-replication multiplies infrastructure expenses

CitiusTech's Cross Geography Data Privacy framework involves a detailed assessment of internal and external data flow design, current vulnerabilities, configuring a multi-layered data privacy policy grid and continuous monitoring of the compliance and effectiveness.

CitiusTech can help address Data Privacy initiatives from ideation to execution via consulting services, assessments & implementations.

By implementing CitiusTech's 'Healthcare Data Privacy Framework' Pharmaceutical organizations can leverage the following:

a. A Compliance-Driven Architecture that prioritizes compliance with data residency regulations. For eg. Data residing in the USA will have different fields anonymized as compared to data residing in China and being viewed in India.

b. In-depth and continuous data lifecycle management by defining policies & procedures wrt data collection, storage and archival.

c. Data anonymization and pseudonymization which protect patient & clinical trial participant privacy while still allowing the data to be used for analysis.

d. Policy Engine allows for policies to be configured via code which help automate enforcement and validation.

e. On-demand Anonymized datasets can be shared with specific external parties for additional analysis/specific use cases.

Key Business Use cases:

Global Digital trials: Teams across countries can view and analyze the digital data by using data protection techniques i.e. data anonymization, de-identification.

Patient identification for Clinical Trials: To identify the right patient for the right trial at right site by getting access to EMR data.