Unifying pharma and medtech: Harnessing connected intelligence
Michael King, director of product and strategy, IQVIA, discusses 10 areas where pharma and medtech differ and discusses the importance of creating an end-to-end eQMS system.
In the life sciences industry, the activities of product quality teams are garnering increasing attention. Once perceived mainly as record-keepers, their role in the product lifecycle has evolved to include greater scrutiny related to efficiency and compliance. Consequently, heads of quality are actively seeking avenues for optimisation and enhancement, such as digital innovations, to revolutionise quality management practices. To navigate these mounting pressures, organisations can greatly benefit from the implementation of a comprehensive artificial intelligence-powered (AI) Quality Management System (QMS). Such a system can provide intelligence-driven insights, expedite actions and outcomes, automate processes to boost 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 unification can be accelerated and optimised through the utilisation of an AI-enabled connected intelligence system across various QMS functions.
Alignment and distinctions: Patient safety principles in pharma and medtech industries
In developing new products, the pharma and medtech industries converge on the fundamental principle of patient safety. While they share a common commitment to ensuring patient well-being, nuanced operational differences exist between these sectors. Both pharma and medtech demonstrate an unwavering dedication to compliance with global and local regulations. Their core objective is to design, manufacture, ship and monitor products in a manner that guarantees efficacy and safety.
When submitting new product applications for regulatory approval, pharma and medtech generally align on four key principles:
- Safety and performance: Demonstrating product safety and performance through comprehensive clinical and technical data throughout the product lifecycle.
- Good practices: Embracing industry best practices in product design, development, manufacture, sale and distribution to ensure adherence to quality standards.
- Pre-market approval: Seeking independent third-party review for pre-market approval to ensure compliance with regulatory requirements before entering the market.
- Post-market activities: Engaging in post-market vigilance and surveillance activities to monitor trends and intervene in potential adverse events or product misuse.
By upholding these patient safety principles, the pharma and medtech industries strive to meet regulatory expectations and fulfil their commitment to safeguarding patient well-being throughout the product lifecycle. However, differences in economies of scale and mechanisms of action lead to divergences in operational details between the two industries.
Operational differences in pharma and medtech: Challenges and considerations
The pharma and medtech industries exhibit operational differences stemming from product range, market size, economies of scale, and approaches to development and clinical activities. Managing drug-device combination products that merge pharma and medtech technologies presents challenges due to these distinctions. Local regulatory authorities categorise products and set data requirements for submissions, but international companies face diverse development requirements, resulting in data silos and potential oversight of country-specific regulations.
Key differences and considerations include:
- Market size: Pharma: $1,000 billion, medtech: $455 billion.
- Product types: Pharma: around 20,000 types, medtech: over 500,000 types.
- Technology range: Medtech encompasses a wider spectrum of technologies compared to pharma.
- Risk factors: Pharma products introduce biochemistry-related risks, unlike medtech.
- Effectiveness factors: Medtech heavily relies on practitioners' skills, influencing its effectiveness.
- Development lifecycle: Pharma products have longer lifecycles compared to medtech.
- Clinical study length: Pharma undergoes multi-phase, lengthy randomised trials.
- Clinical study factors: Medtech relies on clinician training, experience, and other factors during studies.
- Global regulations: Medtech adheres to specific regulations tailored to its products.
- Economies of scale: Medtech workflow is inherently linked to economies of scale.
Understanding and navigating these disparities allows companies to effectively operate in multiple countries and fulfil development requirements for products requiring a combined medtech and pharma perspective.
Unifying pharma and medtech operations through a connected intelligence ecosystem
Life science organisations require solutions that can effectively manage and integrate operations and processes with varying levels of complexity to meet the requirements of both pharma and medtech sectors. These solutions should possess capabilities to handle data and documents, perform structured data builds, and integrate workflows based on the specific nature of the process and the desired outcome for QMS activities. Furthermore, these solutions should facilitate file and information sharing, encompassing data, documents, outputs, actions, and activities between the QMS, supply chain, and other systems used within the organisation.
Connected Intelligence (CI) serves as the key to enable and develop a comprehensive QMS throughout the entire product lifecycle. In a CI system, regulatory intelligence is codified to capture requirements, insights into how these requirements apply to the company's product range, and knowledge gained from previous activities. This intelligence is then integrated into targeted QMS activities to optimise transactional workflows and provide transformational insights, prompts, and recommendations for consideration, allowing teams to capture real-time requirements that support decision-making processes. For instance, when implementing product change control, the CI-enabled QMS can intelligently assess the impact of the planned change on product registration/submission activities across different countries.
Moreover, the 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 wide range of countries, product types, and risk classes. With CI, organisations can analyse their operational history or audit trail using QMS data to gain a better understanding of past decisions and their outcomes. This information can then prompt industry professionals, enabling them to navigate complex regulations and quickly assess how new standards will affect operations.
Harnessing 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 organisations. Leveraging AI's capability to analyse extensive historical and regulatory intelligence data, organisations can surpass the limitations of human analysis and derive 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 offer substantial advantages 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, achieving digital transformation through intelligence-driven solutions becomes imperative for life science companies. Proactive adoption of CI and AI technologies, accompanied by a well-defined vision, regulatory compliance, and the necessary IT infrastructure and talent, enables organisations to gain critical insights and deliver safe and effective healthcare solutions worldwide.