Small and Medium-sized Enterprises (SMEs) need to navigate many constraints in order to succeed. Whilst driving commercial growth and bringing innovative and potentially industry changing therapeutic solutions to market, they need to operate in a complex and evolving regulatory environment with significantly less resources, less global footprint, and less lobbying power than their MedTech and In-Vitro Diagnostic Top 50 counterparts. It is for this reason that having an industry voice (such as the Association of British Healthcare Industries (ABHI) and the British In Vitro Diagnostic Association (BIVDA) is key as collectively their influence is greater when they use the sum of the SME parts.

In this three-part blog series roundtable discussion, Stuart Angell, Chair of Regulatory Affairs Working Party, BIVDA and Managing Director, IVDeology Ltd. and Phil Brown, Director, Regulatory & Compliance, at the ABHI join Mike King, Senior Director, Product and Strategy, IQVIA share their unique perspectives on the challenges of SMEs in the MedTech industry regarding Quality Management System (QMS) digitization and AI adoption, considering their resource constraints and specific operational context. They look at the variables at play for SMEs in their QMS digitization journey, when SMEs would likely uptake AI, and how industry could best help the Quality Assurance and Regulatory Affairs (QA/RA) professional who often wears many hats at an SME.

Part 1 discusses:

• The main challenges facing SMEs as they seek to digitize their QMS.
• The impact of resource constraints.
• What a typical QMS setup entails, the steps SMEs should take towards digitization.

Q1: What are the main challenges that SMEs face in digitizing their QMS?

Stuart Angell, Chair of Regulatory Affairs Working Party, BIVDA and Managing Director, IVDeology Ltd. (SA): Many SMEs are currently focused on basic document digitization - converting their paper documents into PDFs and storing them in platforms like SharePoint or Google Drive. This approach is popular because it's well understood and easy to adopt, requires minimal resources and funding, quickly demonstrates compliance with basic QMS requirements, and leverages tools they often already have access to as part of existing software packages.

However, there's an important distinction to make between simply digitizing documents and truly utilizing data. While storing PDFs in SharePoint is a valid starting point, it has limitations. The challenge is that many SMEs stop at this basic level because they lack the specialized skill sets needed to develop more robust systems, they don't have additional funding for more sophisticated solutions, and they need to implement solutions quickly with minimal resources.

So, while this basic digitization meets immediate needs, it doesn't provide the scalability or advanced capabilities that a more comprehensive digital transformation could offer. The trade-off is between quick, easy implementation now versus developing more sophisticated systems that could provide greater long-term benefits.

Phil Brown, ABHI - Association of British Healthcare Industries (PB): SMEs face a multi-layered set of challenges when approaching quality management system digitization. At the most fundamental level, organizations must first develop a thorough understanding of their existing quality management system before they can effectively digitize it. While digitization can enhance every aspect of a QMS, the key is understanding what your quality system currently does for your organization and then identifying how digital processes can meaningfully improve those functions.

The decision-making process is complicated by both internal and external constraints. From a practical standpoint, SMEs often struggle with budget limitations, as implementing comprehensive digital solutions requires substantial financial investment. There's also the critical consideration of regulatory compliance – any digital transformation must ensure that conformity assessment bodies and notified bodies can effectively review and understand the digitized system during audits.

This creates a complex environment where SMEs must balance their aspirations for digital enhancement against practical limitations. Success requires careful consideration of both internal capabilities and external requirements, making the journey to digitization more nuanced than simply implementing new technology. The path forward demands strategic thinking about which aspects of the QMS would benefit most from digitization while remaining within organizational constraints and maintaining regulatory compliance.

SA: I can add one more thing as a factor. Are they incentivized by the funders? The question of external incentives plays a crucial role in how SMEs approach QMS digitization. Without pressure or encouragement from funders or industry stakeholders to develop comprehensive digital systems early on, many organizations opt for minimal solutions like basic document control. This decision makes practical sense at the outset, especially when considering immediate needs and resources.

However, this creates a strategic tension. QMS naturally evolve and require additional functionality as organizations grow and mature. Starting with a basic system, while cost-effective in the short term, may necessitate more complex and expensive upgrades later. The challenge lies in finding the right balance – SMEs must weigh the immediate practicality of minimal solutions against the potential future costs of having to significantly upgrade their systems.

The absence of incentives to front-load QMS development as part of early SME growth can lead to a pattern of reactive rather than proactive system development. This raises important questions about value assessment: organizations must carefully evaluate what level of QMS sophistication truly adds value at each stage of their development, while remaining mindful of how their needs will evolve over time.

Mike King, IQVIA (MK): Yes, this is the discussion. From a strategic perspective, there are three critical factors that influence how SMEs approach quality management:

1. Commercial Viability: SMEs, like most businesses, need to prioritize profitability. Their financial goals typically align with one of two paths: either positioning themselves as attractive acquisition targets for larger companies or seeking new investment opportunities. Strong financial performance is crucial for either outcome.
2. QA/RA Expertise Limitations: In SMEs, quality and regulatory affairs professionals often wear multiple hats. This creates a challenge as these professionals must divide their attention across various responsibilities, potentially limiting their ability to develop deep expertise in any single area.
3. Innovation Origins: Many SME products can originate from university research, where the primary focus is on technology and therapeutic applications rather than quality management or regulatory documentation. This academic origin can create gaps in the documentation needed for market entry.

These factors typically result in SMEs adopting broad-based QMS that are often paper-driven and variable in nature. The specific implementation depends heavily on factors such as the organization's route to market, their therapeutic area focus, and the innovation pipeline structure.

When approaching digitization for these companies, understanding both their current position and future goals is essential. Different business models require different priorities. R&D-focused companies may benefit most from digitizing document management systems and technical documentation, while direct-to-consumer products with high volume sales might prioritize digital complaint management systems.

A common challenge in digitization efforts is data readiness. Before implementing digital QMS, companies often need to undertake significant work gathering and structuring their existing information. This presents a stark contrast to large corporations, which typically benefit from dedicated departments, specialized teams, and the economies of scale that make comprehensive electronic systems more immediately viable.

SA: Is it also true to say that an SME's approach to QMS digitization heavily depends on their strategic goals? To answer my own question, for companies aiming primarily for acquisition, investing heavily in a sophisticated digital QMS may not be cost-effective. A basic paper-based system that demonstrates fundamental quality and regulatory compliance could suffice, since the acquiring company will integrate the SME into their existing enterprise QMS. The decision to digitize thus hinges on the organization's long-term objectives rather than just technical capabilities or best practices.

Q2: How do resource constraints impact an SME’s ability to implement AI-driven automations in their QMS processes?

MK: The implementation of digital QMS in SMEs typically follows a two-phase approach. First, organizations address scattered paper-based quality management processes by consolidating them through basic digital tools like SharePoint and ShareFiles. This consolidation then enables progression toward more sophisticated electronic enterprise QMS solutions with specific process controls.

For AI implementation in SMEs, it's crucial to understand that technology itself is not the deliverable. This principle is particularly relevant for SMEs pursuing either acquisition or additional investment rounds, as AI solutions must demonstrate tangible commercial value. Solutions that enable companies to accomplish more with existing resources can be attractive investments. However, AI systems requiring substantial IT infrastructure management become less appealing, particularly for companies anticipating acquisition within a few years.

The key consideration lies in aligning AI capabilities with the SME's strategic journey. The value proposition of AI must directly support the company's specific growth trajectory and objectives, which can vary significantly between different SMEs. This strategic alignment, rather than technology adoption for its own sake, should drive decisions about implementing AI-driven QMS automation.

Resource constraints significantly shape how SMEs approach AI automation in their QMS. Success depends not on the sophistication of the technology, but on how well it serves the company's immediate needs and long-term strategic goals while remaining manageable and compliant within existing resource limitations.

PB: I attended a recent discussion with the Shelford group, a ’collective’ for teaching hospitals. The discussion highlighted an emerging shift in how organizations view AI implementation. Rather than seeing AI as a technological constraint or infrastructure burden, organizations are beginning to conceptualize it as additional headcount – effectively treating it as a new team member.

This perspective shift is already taking practical form, with organizations deploying AI in roles traditionally held by junior researchers. The approach fundamentally changes how we evaluate resource constraints' impact on AI implementation, particularly for SMEs. Instead of focusing solely on technological and financial barriers, organizations are assessing AI through the lens of workforce augmentation and capacity enhancement.

This evolving understanding suggests the need to reconsider traditional assessments of resource constraints on SMEs' AI capabilities. The key question shifts from whether an SME can afford to implement AI solutions to how they can strategically integrate AI as a productive team member. This perspective is particularly relevant for QMS processes, where AI could function as a dedicated quality assurance resource rather than just another system to maintain.

SA: The value proposition of AI in QMS needs careful consideration, particularly for very small organizations. For a three-person operation, the manual QMS burden might be minimal, raising questions about the return on investment for AI implementation.

This leads to a more fundamental question about SMEs' expectations of AI in quality management: Are organizations looking to AI not just for automation, but as a source of expertise? The concept of AI serving as a regulatory expert—potentially guiding companies through complex processes like CE marking for diagnostic products—represents a significantly different value proposition from basic process automation.

The feasibility of developing AI systems sophisticated enough to guide companies through regulatory compliance remains uncertain. While current AI capabilities might not yet be able to fully construct and implement a QMS for Conformité Européene - European Conformity - (CE) marking, this potential future application could transform how SMEs approach quality management. The key question becomes whether AI can eventually replicate the nuanced understanding required for regulatory compliance in specialized fields like medical diagnostics.

MK: I always use a broad base as an example, because if you look across the medical device arena and I include In-Vitro Diagnostics (IVDs), the scale of medical device regulation (MDR) and IVD regulation (IVDR) presents significant challenges for comprehensive AI automation. The industry encompasses over 500,000 product types and if we use a number of 100 countries, each with their own regulations and standards which we could number at 100, with each of this holding 100 points of codification. If we multiply this together, building and maintaining a system to handle this complexity would require managing approximately 500 billion data points, not including verification and validation requirements. This level of comprehensive automation remains aspirational rather than achievable in the current landscape. That said, practical AI applications can still provide immediate value for SMEs in specific areas:

1. Regulatory Guidance: While fully automating and submitting global registration documentation isn't feasible today, AI systems can help identify relevant regulatory standards for specific countries and highlight key differences between European, American and other global standards. This assists SMEs in prioritizing their market entry efforts more effectively.
2. Complaint Monitoring and Processing: Traditional manual processes for monitoring customer feedback across social media and various communication channels are time-consuming and prone to human oversight fatigue. AI-powered Natural Language Processing (NLP) can automate this surveillance, enabling real-time monitoring and data capture, thereby allowing staff to focus on reviewing and addressing issues rather than manually searching for potential cases and entering data.

These practical applications offer tangible benefits for SMEs by enabling QA/RA professionals to improve operational costs, enhance regulatory compliance, and drive business profitability. While more ambitious AI applications may be years away, these focused solutions provide immediate value through resource optimization, time savings, and enhanced compliance monitoring. For SMEs, these benefits directly support their business objectives while remaining achievable within current technological limitations.

PB: One thing that you were talking about there, Mike, and it's one thing which became apparent to me some time back was that when you look at QMS, quality management extends beyond regulatory compliance to become a fundamental business control mechanism. When properly implemented, it drives business improvement and operational excellence.

The value of AI in this context is the ability to create connections both vertically within the quality management system and horizontally across different business functions. These connections can link quality processes to various operational aspects such as raw material supply, sustainability goals, and procurement processes.

This comprehensive integration transforms basic process control through procedures into a more strategic approach. By connecting various business aspects through quality management, organizations gain better visibility into their product and company trajectory. Quality management then becomes one component of a larger, interconnected system that provides insights and control across the entire operation. This integration enables better decision-making and strategic planning, as quality data and metrics become indicators of broader business performance and direction.

The regulatory environment has transformed dramatically over the past decade. While previously it might have been possible for an individual regulatory person to maintain comprehensive knowledge of the field, today's landscape has become too complex for any single expert to master completely. This complexity has been further amplified by the UK's regulatory divergence, the global variations in AI regulation, and numerous other emerging factors.

The proliferation of standards has created an increasingly challenging environment for regulatory professionals. The key opportunity lies in using AI to help condense and organize this vast amount of information, identifying crucial focus points and revealing synergies between different standards. This capability would be particularly valuable as these standards are foundational to QMS. The ability to understand and navigate these interconnections within regulatory requirements could significantly enhance how organizations implement and maintain their QMS, making complex regulatory landscapes more manageable and actionable.

Q3: Can you describe a typical QMS setup, and the steps SMEs should take towards digitization?

SA: Let's start with the basics - what's a QMS for a startup? Often, they don't even know what one is. They just build it organically based on what they think their company needs, growing it alongside their business planning. But here's the thing - a QMS is sensible process management. International standards are built on best practices, focusing on what a business needs to do things right the first time and keep doing them right consistently. It's a wide-ranging subject, because you could extrapolate it down, so are you storing all your raw materials at the right temperatures. Inherently, SMEs are doing some form of quality management as an inherent, embedded part of the business operations.

Even without formal quality knowledge, startups are already doing some of this stuff. They're storing data somewhere; they're doing risk assessments (even if it's just in their heads and not written down). They're running parts of a quality system through their discussions and thought processes.

The real key is documenting these processes. At its simplest, you're taking the experts' mental processes and ideas about their diagnostic or device product and writing them down in a way other people can understand. This documentation might be for investors, team members, or anyone else they need to communicate with. That's really what a QMS starts as for an SME - it's that simple. Everything else can be built from there.

PB: QMS can be simpler than many people think. Experience shows that effective quality systems can be built on a set of basic, fundamental principles. For standards like ISO 13485 and ISO 9001, there's a finite, minimum number of required procedures - and it's surprisingly modest. A well-functioning quality system can be maintained with around a dozen core procedures.

When consulting with organizations about their QMS, you often encounter situations where companies have created an excessive number of procedures. Many of these procedures serve no real purpose – they're neither read nor followed, existing merely for the sake of documentation.

A quality management system should be tailored to fit the organization's actual needs and business operations whilst meeting the necessary global standards. It should encompass essential elements including company structure and setup, management hierarchy, employee training processes, material procurement for product manufacturing, and post-market surveillance (though this may not apply to all SMEs depending upon their business structure, product and deliverables).

While there are 12 or so core principles that all organizations should follow and that regulatory bodies recognize, no two QMS should be identical. Each system needs to control various aspects depending on the specific business context. As the saying goes, it's "horses for courses" – the system must be appropriate for its intended use.

The key is to create a system that meets the mandatory regulations whilst effectively supporting business operations without unnecessary complexity or redundant procedures.

SA: So, Phil, I'm sure you've seen this example too, where you have a QA/RA professional from a large company implement their previous company's system – often consisting of 150 procedures, each 50 pages long – into a startup. While this might be a comprehensive Quality Management System on paper, it's usually not appropriate or usable for a smaller company. This approach typically leads to organizational paralysis and a loss of confidence in QA/RA as a profession.

When working with SMEs, the focus should first be on building a quality culture rather than implementing complex procedures. By establishing the right cultural foundation, the QMS will develop organically in a way that's appropriate for the organization's size and needs. This natural evolution ensures the system remains practical and effective rather than becoming an impediment to operations.

PB: Absolutely. And of course, the Chief Executive Officer of the company is the most important person to drive that and the worst situation you can have is where you turn around as a Quality Manager and you say to somebody in the organization, “right, we're going to need a procedure now that's going to look after this.” And that person bemoans another procedure that they feel is just going to stop them doing their job. That's not what a procedure is there to do. It doesn't stop you from doing your job. It just makes sure that you do that job the same as you do on Wednesday when you do it on Friday.

The implementation element when you move into AI and are working to digitize everything is to do so manageably. You don't do everything at once. Rather, you pick off the obvious low-hanging fruit and do it, and that proof of concept permeates through the organization.

MK: Let me build on the discussion about digitization. Consider a small company with over 200 processes currently managed by people. I can say with confidence that many employees probably don't have current training records for all these processes. If you were to simply transfer these 200 processes into an electronic system, you'd likely overwhelm your organization with training notifications, creating unnecessary noise and disruption. That's why before implementing a digital quality management solution for document control, you must step back and evaluate what's absolutely necessary.

I must smile when I think about this because I've seen it happen: People join a new company, bringing their previous company's QMS approach with them. They're often quite risk-averse in their approach, but they haven't taken the time to understand their unique environment – the clinical context of the products, the therapeutic areas, target markets, risk appetite, organizational culture. Not taking the appropriate amount of time to understand this context is where things start going sideways.

When considering digitization, you really need to understand three key things: First, what's the SME's culture and what journey are we discussing? Second, which processes and activities are priorities for digitization? And third, before starting the project, you need to assess your current state. Many SMEs end up with too many processes because they've received non-conformances and corrective and preventive actions (CAPAs) from audits, and their default response is to write new processes as objective evidence to address gaps.

Take design control, for instance. If your company likes to make quick changes and tends to be reactive about changing product configurations, specifications, or suppliers, you can run into trouble without proper design control and change management. Companies often stumble here. This connects directly to verification and validation activities – you need proper records showing your design is under control and meeting requirements before going to market.

So, what I'm suggesting is two-fold: review what you currently have and determine what is necessary and what can be consolidated or removed and additionally identify gaps from quality standards and business operations that need addressing. This is where having good consultation at the beginning makes a real difference, before you start your digital transformation journey. A consultancy led approach that clearly defines scope, sequencing of deliverables and that communicates value as part of the journey drives a targeted approach to digitization and AI implementation.

PB: Digitization enables you to implement a more sophisticated quality system that you can actively query and monitor, helping you stay current with requirements. Meanwhile, AI can serve in a complementary role by analyzing your system to identify gaps and recommend specific actions needed to address them. Together, these technologies create a more proactive and intelligent approach to quality management.

MK: You know, this really makes me smile because I keep seeing these fantastic companies developing AI-driven medical advice and IVD solutions, and when you dig deeper into their underlying data, there's often a gap – they're still collecting it through old-school analog methods.

I was chatting with a friend recently who works at a company developing medical software. They were excited, telling me "Great news." Our software is more than 90% accurate compared to human clinical review." So, I asked them what happens with the 10% when it gets things wrong. The look I got was priceless – like I was just being a typical risk averse QA/RA professional.

But here's the thing: it absolutely matters. If those percentage points where the system misses are clinically insignificant, then yes, you've got an excellent product. But if those misses could have serious clinical implications – things that human intervention could have caught – you're going to have a tough time getting that product to market and need to go back to the risk control and design of the product.

I mention this because it's particularly relevant for SMEs trying to bring innovative products to market. This isn't just me being curious – it's a crucial consideration for CE marking and global product registration. Your technical documentation needs to include thorough risk assessments and clinical benefit analysis backed by solid science.

This circles back to some of our earlier discussion points. You might have an amazing product that you're passionate about, but if you haven't properly addressed the regulatory and quality framework requirements needed for market access, you could end up with a brilliant product that you simply can't commercialize due to missing documentation.

Conversely, great products designed and manufactured by great companies, with underlying QMS that support a dual focus on patient safety and commercial performance is where the global healthcare industry can make a difference in the provision of patient solutions. And how a SME digitizes their QMS is a critical part of this journey.

Go to Part 2