A single mistake in a pharmaceutical company can cost the lives of hundreds of people and the company’s reputation. In December 2022, Uzbekistan’s Ministry of Health reported deaths [1] of 18 out of 21 children with acute respiratory diseases. The deaths were attributed to gross contamination of the cough syrups made by the manufacturer.
The root cause stemmed from the inherent deficiencies in quality assurance and control systems. Given the pharmaceutical industry’s substantial reliance on data quality, ensuring high-quality data is essential. It plays a pivotal role in various aspects, including the interpretation of clinical findings and the evaluation of each drug’s safety, effectiveness, and purity in relation to patient health outcomes.
This article discusses insights of data quality assurance, significance and benefits.
Data Quality Assurance (DQA)
DQA assurance refers to a collective term for processes utilized to ensure that the integrity of the data housed within different databases remains intact. This process is essential for businesses to achieve high data quality and sustain the same.
The DQA process aims at ensuring that the pharmaceutical products are safely manufactured using high quality materials and controls, and the quality, safety and efficacy of the finished product remains stable and consistent. In simple words, this continuous process ensures the safety and health of the public.
Data quality also means the trustworthiness of data, its integrity, validity, security and timeliness. Since the process deals with the health and safety of the public, data quality plays a critical role in delivering quality medicines.
1. Reputation and Trust
A pharmaceutical company’s reputation is based not only on the quality of products but also the quality of data behind. None wants to buy products of a company with poor quality and especially when suspected to be unsafe or sub-standard. Hence, product and data quality governance are essential to save not only the reputation of the companies but also earn them the trust of patients.
2. Drug Safety
Defective products not complying with established product quality standards are not only potentially dangerous to patients but also detrimental to the company’s reputation and reliability. It becomes more evident when the authorities discover bad data and product quality combined with bad documentation practices.
3. Regulatory Compliance
Pharmaceutical companies are no strangers to GMP (Good Manufacturing Practices) and undeniably comply with stringent regulations enforced by the regulators. Manufacturers in developing markets struggle with the imbalances of increasing cost, competition, and an evolving regulatory landscape.
Significance of Data Quality Assurance
Data quality assurance is essential in the pharmaceutical industry for several reasons. It is critical to safeguard patient health, maintain regulatory compliance, support research and development efforts, and ensure the overall integrity and success of pharmaceutical products and processes. Key reasons are:
- Patient Safety
- Regulatory Compliance
- Clinical Trials
- Product Quality Control
- Research and Development.
- Supply Chain Management
- Risk Management
- Data-Driven Decision-Making
- Data Integrity
- Efficiency and Cost Savings
Benefits of Data Quality Assurance
Good quality practices not only help serve satisfied consumers but also save companies from legal or pharma-industry challenges . Since the consumers (doctors and patients) trust the brands due to good quality it leads to the growth of ethical businesses.
However, any oversight of data quality assurance leads to drugs having little to no effect on the patient. Moreover, the medicines can also become toxic and cause adverse reactions leading to tremendous waste of resources and fragile reputation. Major benefits are:
- Informed Decision-Making
- Enhanced Operational Efficiency.
- Improved Customer Satisfaction
- Regulatory Compliance
- Better Data Integration
- Reduced Errors and Risk
- Effective Marketing and Sales
- Trust and Credibility
- Competitive Advantage
- Long-Term Sustainability
Future of Data Quality Assurance
According to McKinsey,[3] quality assurance is likely to be dominated by artificial intelligence (AI) automation, and digitalisation in the near future. Pharmaceutical companies inject unforeseen amounts of investment and efforts to improve product and data quality through the adoption of digital technologies and tools.
Consequently, quality assurance teams will need to adapt to new digital capabilities and data analytics tools. In fact, the teams must be tech-savvy and advance their knowledge & expertise.
Read on: AI in Healthcare: AI Transforming the Healthcare Industry
Conclusion
Since the pharmaceutical industry deals with the public health and safety of billions of people, it is absolutely critical to ensure every detail of the process from making through distribution of drugs is supported with reliable and quality data. Hence, a product and its data quality assurance are inseparable and critically important and timely to prevent tragedies of the healthcare industry.
Image credit: Freepik.com
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