In this world of rapidly changing regulatory requirements and hyper-competitive global environment, life science companies need to pursue true digital transformation by leveraging comprehensive solutions to provide intelligence-driven insights at precisely the right moment within the organization’s workflows.
Historically, quality operations supporting product lifecycle activities had a significant percentage of time dedicated to inspection, verification and record-keeping activities. However, organizations are being driven to re-evaluate their approach to quality assurance in response to evolving and diverging regulations. The demands of today require management of a range of complex quality data to support optimized and timely analysis and decision making by industry professionals.
A comprehensive, AI-driven Quality Management System (QMS) provides a means to overcome today’s challenges of tightening standards for regulatory compliance and the competitive need to optimize productivity. Intelligent, data-driven insights provided by a digital, AI-powered QMS speed time-to-action and identify potential risks throughout the product lifecycle. With AI providing targeted insights, this ‘electronic eye’ can support the Safety, Regulatory and Quality employee to become a true, augmented professional.
Medtech & pharma goals converge while operations diverge
Pharmaceutical and Medtech organizations share the same key goals. The foremost of these are improving safety and performance, establishing and maintaining good practices (in product design, development, production and distribution), securing pre-market approval, and, finally, executing post-market activities – such as capture and timely reporting of Adverse Events (AEs).
Despite these shared principles in the pharmaceutical and medtech industries, there are significant differences between the two at the operational level. A differing breadth of product range and differing market size drives different economies of scale and the nature of the products themselves drive different approaches to product development and clinical activities. These fundamental differences present challenges when managing drug-device combination products that incorporate both pharmaceutical and medtech technologies. For example, some countries may consider a product like a professional prophy paste containing fluoride to be a medical device, while other countries may consider it to be a pharmaceutical product.
How the product is categorized by the local regulatory authority determines the data that is needed in a broad range of submission activities, and the production of this data is linked to a range of processes, technologies and systems that are used to track, assess and manage product design and development activities.
How can they avoid developing silos of data, which could be used to inform medtech/pharmaceutical quality operations in other regions?
What does this mean for companies that operate in more than one country and need to gather data for a range of development requirements? What about those companies who have products that require both a medtech and pharmaceutical perspective? How can they avoid developing silos of data, which could be used to inform medtech/pharmaceutical quality operations in other regions? How could they avoid missing key requirements that drive the need for country-specific data?
Connecting intelligence to drive practical insights
To support a range of pharmaceutical and medtech requirements, life science organizations need solutions that can manage and integrate operations and processes with a range of complexity. The capabilities of these solutions should include the handling of both data and documents, performing structured data builds, and integrating workflows, depending on the nature of the process and the target output for the QMS activity. In addition, the solutions will need to share files and information – in the form of data, documents, outputs, actions and activities – between the QMS, supply chain and other systems used by an organization.
Connected intelligence (CI) is the key to enabling and developing this new system for holistic quality management throughout the product lifecycle.”
Connected intelligence (CI) is the key to enabling and developing this new system for holistic quality management throughout the product lifecycle. In a CI system, codified regulatory intelligence captures the requirements, insights of how the requirements apply to a company’s product range, and the precedence (or past experience) of prior activities. This intelligence is then integrated within and across targeted QMS activities to optimize transactional workflows and to provide transformation insights, prompts and/or recommendations to be considered. This enables teams to capture in near-real-time the requirements that support decision making, for example in the case of product change control, to intelligently assess the impact of the planned change on the product registration/submission activities in different countries.
The CI-enabled QMS can then produce relevant insights into how to adjust operations to comply with local variances as well. These insights are generated by ensuring that regulatory intelligence covers a range of countries, product types and product risk classes. With CI, QMS data is available to examine an organization’s operational history or audit trail to better understand historic decisions and the outcomes produced by those decisions and provide prompts to the industry professional. This enables companies to take very complex regulations and immediately understand the practicality of how those new standards will affect operations, rather than hypothesizing about the regulation’s impact and theorizing about the next best action to take for compliance.
Leveraging AI to uplevel insights and operations
What enables all of this in-depth historical and regulatory intelligence analysis? The answer is artificial intelligence (AI). When embedded into CI-enabled QMS platforms, AI can analyze vast amounts of data contained in thousands of documents to generate insights that are simply impossible to discern through human analysis alone – especially when including analysis of structured, semi-structured and unstructured data. When applied to systems and functions across safety, regulatory and quality, AI enables organizations to approach holistic quality management the right way, i.e., with the right data, at the right time, deriving the right insights to drive the right actions.
For example, when considering quality operations, CI-enabled QMSs can identify in real-time the cost, timeline and “hot spots” of potential design changes.”
An AI-powered, CI-enabled QMS platform can supercharge the intelligence capabilities of pharmaceutical and medtech organizations. For example, when considering quality operations, CI-enabled QMSs can identify in real-time the cost, timeline and “hot spots” of potential design changes. This knowledge enables teams to identify, much sooner than was previously possible, the feasibility of proposed changes and the impact they would have on quality, and therefore on patient safety.
Vision and strategy for the future
In this world of rapidly changing regulatory requirements and hyper-competitive global environment, life science companies need to pursue true digital transformation by leveraging comprehensive solutions to provide intelligence-driven insights at precisely the right moment within the organization’s workflows. As more organizations adopt AI technology, possessing a well-thought-out vision and strategy regarding AI deployment will help organizations align with regulatory standards.
As more organizations adopt AI technology, possessing a well-thought-out vision and strategy regarding AI deployment will help organizations align with regulatory standards.”
Fundamental to that vision and strategy is understanding how to build the proper IT infrastructure, validation processes, talent and skillsets to create CI-enabled IT ecosystems that include the company’s QMS. Organizations that proactively approach CI- and AI-enabling technology deployment will be equipped with critical insights that their lagging competitors may not possess. Ultimately, this helps ensure the provision of safe and effective healthcare solutions to global populations.
Text by Michael King, Senior Director of Products & Strategy, Technology Solutions, IQVIA.
This Commentary was originally published in NLS magazine No 02 2023, out May 2023.