Pharmaceutical
Speed time to market, ensure regulatorily
compliance and improve decision making.
Data Governance is necessary for compliance with current regulatory expectations for data integrity in pharmaceutical R&D and manufacturing enterprises.
A company should consider whether it has a data governance policy and, if so, whether it is comprehensive and effective. Data governance policies have become a regulatory expectation as one of the core quality system policies. It has been stated by the Medicines and Healthcare products Regulatory Agency (MHRA), European Medicines Agency (EMA), World Health Organization (WHO), Pharmaceutical Inspection Cooperation Scheme (PIC/S), and the Australian government’s Therapeutic Goods Administration (TGA) that a data governance system should be an integral part of the pharmaceutical quality system.
Data Integrity
Issuance of a quality system policy, ensuring the integrity of data unless the policy addresses all relevant aspects of the company’s operations, including personnel behaviors and actions. Data integrity breaches can result from poor practices, or inadequate systems/procedures.
Effective Data Quality
Stakeholders have the ultimate responsibility to ensure an effective pharmaceutical quality system is in place to achieve the quality objectives, and that roles, responsibilities, and authorities are defined, communicated, and implemented throughout the company.
Data Risk Management
Decision-makers are responsible for the implementation of systems and procedures to minimize the potential risk to data integrity, and for identifying the residual risk, using risk management techniques. In addition to the legal and ethical responsibilities for ensuring patient safety, the financial risks of poor data integrity justify significant engagement by senior management.
How does the Data Insights Platform
support pharmaceutical manufacturing?
Data Insights Platform delivers a range of Data Governance capabilities to improve your pharmaceutical manufacturing performance so you can deliver safe, effective drugs and other therapies to patients with greater data efficiency and confidence. Robust data governance and quality systems in pharma enterprises not just pave seamless clinical care, but also makes it more reliable for the patients. These are the key drivers of explicable patient outcomes and experiences as well as the productivity of the enterprise.
Clear Definition of Data
Robust Data Quality
- Pharma data must be organized as a valued and strategic enterprise data asset by defining and maintaining the Data quality consistently across the data life cycle through Amurta’s Data Quality Management
- Data quality affects operational profits and clinical care and patient deliverables such as Accuracy of patient’s medical data entered/retrieved
- The redundancy of patient records or possibly duplicated or merged patient records
Regulatory Policy Management
- Enlisting a set of guiding principles for quality data governance that is compliant with the service provider’s mission, vision, and fundamental health care values.
- The governance process must be managed to follow internal and external rules and regulations by defining the regulatory principles in the form of policies and controls to apply to the source data eliminate occurrences of harmful outcomes in health care operations
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