data security – Buonaiuto@Work

The critical role of HL7 and FHIR in Pharmaceutical Innovation: A Case Study Approach

Massimo2023-04-060286 views

Highlighting the transformative role of Health Level Seven International (HL7) and Fast Healthcare Interoperability Resources (FHIR) in the pharmaceutical industry, emphasizing their importance in enhancing drug development, patient outcomes, and regulatory compliance. HL7 and FHIR facilitate seamless data exchange across healthcare systems, enabling pharmaceutical companies to leverage comprehensive patient data for accelerated drug development and improved therapeutic strategies. The integration of FHIR in a global R&D company is showcased as a case study, illustrating significant improvements in clinical trial efficiency, data security, and regulatory compliance. The implementation process involved strategic planning, infrastructure upgrades, software development, and rigorous data validation to ensure compliance with FHIR standards. This case study underscores the critical role of data standardization and advanced analytics in pharmaceutical innovation, highlighting the benefits of real-time data access, efficient patient recruitment, remote monitoring, and streamlined regulatory processes.

Data management in practice

Massimo2023-03-140429 views

Addressing challenges in data management involves designing robust strategies, implementing quality frameworks, and ensuring compliance with regulations. From data migration to security protocols, each step plays a crucial role in maintaining accurate, accessible, and secure data for organizational success.

Unlocking the Power of Data Standardization: A Guide to CDISC and FAIRplus Cookbook for Streamlined Research. Transforming data management to foster adoption of FAIR principles

Massimo2022-01-010425 views

In the realm of life sciences research, the FAIRplus Cookbook emerges as a pivotal guide for integrating the FAIR principles—Findability, Accessibility, Interoperability, and Reusability—into data management practices. This invaluable resource offers a series of practical recipes for the FAIRification of data, catering to a diverse audience, including researchers and data stewards. It underscores the significance of data standardization, as demonstrated through the application of CDISC-SDTM models for clinical trial data, to enhance the quality and utility of scientific data. By leveraging the FAIRplus Cookbook, the scientific community can navigate the complexities of data management, ensuring that research data are not only standardized but also aligned with ethical considerations and regulatory requirements. This guide thus stands as a testament to the collaborative effort to foster a culture of open, standardized, and ethically responsible data sharing in life sciences.

Total Data Quality Management: A Comprehensive Approach to Data Quality

Massimo2021-08-150408 views

Total Data Quality Management (TDQM) embodies a holistic approach to enhancing data integrity across all facets of an organization’s data lifecycle. This methodology prioritizes the accuracy, completeness, consistency, and relevance of data, ensuring its strategic alignment with business objectives. TDQM integrates practices such as data profiling, cleansing, governance, and quality monitoring to mitigate risks and elevate decision-making capabilities. Central to TDQM are the principles of data governance and management, which establish the framework for data quality standards, stakeholder roles, and the implementation of data strategies. Additionally, TDQM stresses the importance of data security and privacy, safeguarding the organization’s and stakeholders’ trust. Through comprehensive components including data analysis, integration, and continuous quality monitoring, TDQM ensures data serves as a robust, strategic asset, facilitating competitive advantage in a data-centric business landscape.

How many fields in data science?

Massimo2021-06-270400 views

Data Science is a research activity… mostly Data-driven scientific discovery is regarded as the fourth science paradigm The twenty-first century has ushered in a new age that is coined as data science and big data analytics. Data-driven scientific discovery is regarded as the fourth science paradigm. Data science has been a core driver of the new-generation science, technologies and economy, and is driving new researches, innovation, profession, applications and education across both disciplines and business domains. There are many scientific and technical challenges associated with big data, ranging from data capture, creation, storage, search, sharing, modeling, representation, analysis, learning, visualization, explanation, and decision making. Among the many data characteristics and complexities to be addressed, I mention the hybridization of heterogeneous, multisource, hierarchical, interactive, dynamic,…