Integrating Pharma and Medtech: Harnessing Connected Intelligence

Michael King, Director of Product and Strategy, IQVIA, discusses 10 areas where pharma and medtech differ and the importance of building end-to-end eQMS systems.

In the life sciences industry, the activities of product quality teams are receiving more and more attention. Once understood primarily as record-keepers, their role in the product lifecycle has evolved to include greater scrutiny related to efficiency and compliance. As a result, quality leaders are actively seeking avenues for optimization and enhancement, such as digital innovation, to revolutionize quality management practices. To deal with these growing pressures, organizations can greatly benefit from the implementation of a comprehensive artificial intelligence-driven (AI) quality management system (QMS). Such systems can provide intelligence-driven insights, accelerate tasks and results, automate processes to increase productivity, and predict and identify potential risks. Considering these factors, a strategic and operational convergence is necessary for the pharmaceutical (Pharma) and medical technology (MedTech) industries. This integration can be accelerated and optimized through the use of AI-enabled Connected Intelligence Systems across various QMS functions.

Alignment and Distinction: Patient Safety Principles in the Pharma and MedTech Industries

In developing new products, the pharma and medtech industries unite on the fundamental principle of patient safety. Although they share a similar commitment to ensuring patient well-being, subtle operational differences exist between these areas. Both Pharma and Medtech demonstrate an unwavering dedication to compliance with global and local regulations. Their main objective is to design, manufacture, ship and monitor products in a way that guarantees efficacy and safety.

When submitting new product applications for regulatory approval, pharma and medtech generally agree on four key principles:

1. Security and Performance, Demonstrate product safety and performance through comprehensive clinical and technical data throughout the product lifecycle.
2. good practices, Adopting industry best practices in product design, development, manufacturing, sales and distribution to ensure adherence to quality standards.
3. Pre-Market Approval: Seeking independent third-party review for pre-market approval to ensure compliance with regulatory requirements before entering the market.
4. Post-marketing activities: Engaging in post-marketing vigilance and surveillance activities to monitor trends and intervene in potential adverse events or misuse of the product.

By upholding these patient safety principles, the pharma and medtech industries strive to meet regulatory requirements and fulfill their commitment to safeguarding patient well-being throughout the product lifecycle. However, differences in the operational details between the two industries arise from differences in economies of scale and mechanisms of action.

Operational Differences in Pharma and Medtech: Challenges and Considerations

The pharma and medtech industries exhibit operational differences arising from product range, market size, economies of scale, and approaches to development and clinical activities. Management of drug-device combination products that combine pharma and medtech technologies presents challenges because of these differences. Local regulatory authorities classify products and set data requirements for submission, but international companies face diverse development requirements, resulting in data silos and the potential for oversight by country-specific regulations.

Key differences and considerations include:

1. market size: Pharma: $1,000 billion, Medtech: $455 billion.
2. Product Type: Pharma: Around 20,000 types, Medtech: More than 500,000 types.
3. Technology Range: Medtech covers a wider spectrum of technologies than Pharma.
4. risk: Unlike medtech, pharma products present biochemistry-related risks.
5. Effectiveness Factors: MedTech relies heavily on the skill of practitioners, affecting its effectiveness.
6. Development Lifecycle: Pharma products have a longer life cycle as compared to Medtech.
7. Clinical Study Length: The pharma goes through multi-stage, long randomized trials.
8. Clinical Study Factors: The course of MedTech studies depends on physician training, experience, and other factors.
9. Global rules: Medtech follows specific regulations for its products.
10. economies of scale: Medtech workflows are inherently linked to economies of scale.

Understanding and navigating these disparities allows companies to operate effectively across multiple countries and meet development needs for products that require a combined medtech and pharma perspective.

Integrating Pharma and Medtech Operations through Connected Intelligence Ecosystem

Life science organizations need solutions that can effectively manage and integrate operations and processes with varying levels of complexity to meet the needs of both the pharma and medtech sectors. These solutions should have the ability to handle data and documents, generate structured data, and integrate workflows based on the specific nature of the process and desired outcome for the QMS activities. In addition, these solutions should facilitate file and information sharing involving data, documents, outputs, tasks and activities between the QMS, supply chain and other systems used within the organization.

Connected Intelligence (CI) serves as the key to enable and develop a comprehensive QMS throughout the product lifecycle. In a CI system, regulatory intelligence is codified to capture requirements, information on how these requirements apply to a company’s product range, and knowledge gained from past activities. This intelligence is then integrated into targeted QMS activities to optimize transactional workflows and provide transformational insights, signals and recommendations for consideration, enabling teams to respond to real-time requirements that support decision-making processes. is allowed to catch. For example, when implementing product change controls, a CI-enabled QMS can intelligently assess the impact of planned changes on product registration/submission activities in different countries.

Furthermore, a CI-enabled QMS can provide relevant insights on adjusting operations to comply with local variations. These insights are generated by ensuring that regulatory intelligence covers a broad range of countries, product types and risk classes. With CI, organizations can analyze their operational history or audit trail using QMS data to gain a better understanding of past decisions and their consequences. This information can then power industry professionals, enabling them to navigate complex regulations and quickly assess how new standards will affect operations.

Using AI for Enhanced Insights in Connected Intelligence Systems

The integration of AI into CI-enabled QMS platforms holds significant promise for the future of quality management in pharma and medtech organizations. By leveraging AI’s ability to analyze comprehensive historical and regulatory intelligence data, organizations can overcome the limitations of human analysis and gain valuable insights. This holistic approach allows efficient access to relevant data, empowering informed decision making based on meaningful insights. AI-powered, CI-enabled QMS platforms provide substantial benefits in quality operations by enabling real-time identification of concerns related to design changes, facilitating early assessment of feasibility and impact on quality and patient safety. In a rapidly evolving regulatory landscape and competitive global environment, it is imperative for life science companies to achieve digital transformation through intelligence-driven solutions. Proactive adoption of CI and AI technologies with a well-defined vision, regulatory compliance and required IT infrastructure and talent enables organizations to gain critical insights and provide safe and effective healthcare solutions worldwide .