“If it wasn’t documented, it didn’t happen.”
It’s a timeworn phrase that’s been heard often — and probably repeated — by anyone who has ever worked in a regulated industry.
It’s also the main reason why documents have historically been the core artifacts quality management revolves around. The rationale that has fueled the predominance of the document-centered approach to quality is straightforward: quality activities are most commonly rooted in historical information that is maintained in reviewable documents, so it makes sense to focus on those documents. And so, for as long as there have been regulations, documents have been the quality function’s primary concern.
But times change. Industries that are subject to regulatory oversight are discovering — some of them the hard way — that the document-centered approach to quality management is insufficient in the digital era. The life sciences industry in particular is witnessing a massive shift away from quality management methodologies that focus on “data objects” (read: documents) and toward a model that facilitates granular access to the data contained within those documents. This industrywide transformation is enabling organizations to improve their ability to connect and analyze data across product life cycles so that quality issues can be predicted and preventive actions can be prescribed with greater efficiency.
Have no fear, though, quality managers and document control professionals. Your precious documents aren’t going anywhere. The quality function is simply improving its ability to zero in on the vital data contained within the documents traditionally esteemed to be the lifeblood of quality. Thanks to the efficiencies provided by increasingly innovative technologies, connected data is now the engine that powers the quality management machine.
The urgency to adopt a more data-centric approach to quality and compliance is best understood through the lens of unstructured data, which accounts for more than 80% of data in the life sciences development, production and commercialization life cycle.(1) Think about all the locked PDFs, scanned files, uploaded images and other documents used every day during the course of conducting routine quality and compliance activities. They are all elements that can be “managed” within a quality management system (QMS). But they are also artifacts that contain nearly incalculable amounts of granular data and insights that are difficult to extract and impractical for most organizations to correlate and analyze in real time.
There are many motives for wanting increased access to the insights hidden in data objects, but there are three overriding reasons why it’s critical to overcome the hazards this unstructured data poses in today’s evolving regulatory landscape:
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There is no question that quality data has value. Yet there is a major gap between the perceived effectiveness of data analytics and the actual relationship of data attainability and usefulness, according to the latest Gartner Quality Analytics Research Model report.(2) In fact, the research indicates that most companies with stalled quality data optimization projects are struggling to advance their efforts because they are unwilling to make the investments necessary to unlock the potential of their unstructured data.
Regulators worldwide continue to strive toward more closely aligned quality standards and coordinated efforts. In doing so, they are adopting new data-driven approaches and increasing their capacity to share and harmonize data. As regulatory standards are updated and as new regulations go into effect, they are placing increased emphasis on risk. Regulators are shifting from a one-size-fits-all regulatory philosophy toward a data-driven, segmented approach, and the latest research from Deloitte indicates that this trend is expected to continue. The principal goal of regulators’ renewed focus on data is to enable faster regulatory approvals and subsequently improve their ability to share information about the risk and value of products to the public, according to Deloitte research.(3) This regulatory posture places added importance on — and increases the value of — quality data.
As emphasized in the opening quote, in the eyes of regulatory authorities, quality evidence that cannot be substantiated upon request may as well not exist. However, a compliance model that mainly focuses on the object containing the compliance information regulators are seeking (i.e., a document) is insufficient in the current regulatory environment.
Alternatively, a “continuous readiness model” that is supported by sufficiently effective digital technology is inherently more efficient and is capable of drastically lowering overall compliance costs, according to recent reports from the Deloitte Centre for Health Solutions.(4) The Deloitte reports also suggest that combining the continuous readiness model with expedient compliance risk assessments would further improve the effectiveness of compliance functions. But adopting this type of approach to quality and compliance requires that a company first overcome the obstacle of unstructured data.
Everyone wants access to more and better data and the subsequent insights it can yield. But, like their peers in R&D and marketing before them, quality professionals can quickly find themselves inundated with too much data. Besides being flooded with machine performance, product performance, process performance and observational data, the quality function also has access to “internet of things” (IoT) data, social media data, structured customer feedback and human sentiment data. With all this data available, the expectation is that quality departments should be able to do more, but research shows that only 12% of quality organization staff have analytics comfort.(5) Many companies are left wondering how they can feasibly bring order to the deluge of available quality data.
Even regulators are experiencing the same kinds of difficulties as they try to keep pace with the impact of evolving technology on the life sciences, according to Bakul Patel, division of digital health director at the U.S. Food and Drug Administration (FDA).
“If the volume and pace of digital transformation continues to remain the way it is, the existing regulatory approach won’t work,” Patel said. “There is a disconnect between the speed, iterative development and ubiquitous connected nature of digital health technologies and the existing regulatory structures and processes. The current regulatory approach is not well-suited to support that fast pace of development.”(6)
Just as regulatory agencies are recognizing the impact of the accelerated evolution of quality, companies doing business in regulatory environments are at a turning point in their approach to managing quality data. Those that continue to pursue a document-oriented quality management model and fail to invest in quality management platforms that streamline data collection, management and analysis while simultaneously improving the analytics comfort level of their quality professionals will at best only suffer from inefficiencies, production delays or product defects. At worst, they’ll experience the harsh consequences of noncompliance or product recalls.
Most modern QMS solutions are a giant leap forward from the antiquated paper-based quality management processes of yesterday, but they have the capability to be much more effective and efficient.
To achieve a state of truly connected quality, a company must move beyond the idea that it is sufficient to merely digitize document-centered processes. Forward-thinking companies must adopt a holistic approach that allows quality and compliance professionals to access, analyze and apply insights — knowledge gleaned from both structured and unstructured data alike — from within the same system across the product life cycle. Attaining and maintaining such a state of connectivity requires the implementation of innovative digital tools and methodologies that unite the entire quality life cycle, extend the quality ecosystem, and unlock the hidden intelligence and predictive capabilities that are waiting to emerge from unstructured quality data.