Adopting a Data-Driven Quality Model: Essential for Quality and Compliance

On several occasions, Acting Commissioner of the U.S. Food and Drug Administration (FDA) Janet Woodcock has called attention to the importance of quality in product manufacturing. Given the high rate of drug shortages, she stresses that quality goes beyond simply meeting the FDA’s current good manufacturing practice (CGMP) requirements. Quality in manufacturing is the ability to reliably produce products in sufficient quantities to ensure that supply meets demand.

“Analysis of recent drug shortages indicates the need for rewarding more investment in quality. A team of FDA economists examined a sample of 163 drugs that first went into shortage between calendar years 2013 and 2017. They found that of the 163 drugs in shortage, 62% went into shortage after supply disruptions that were associated with manufacturing or product quality problems,” said Woodcock._¹

Data’s Pivotal Role in Quality

Life sciences companies have long generated vast amounts of data as a byproduct of their processes for developing and manufacturing products. Quality is one area of the business that is particularly dependent on data. Comparatively, quality gathers and uses more data than other business units in an organization.

As long as companies follow the regulatory guidelines for data management and have the required documentation in place, they can achieve regulatory compliance. However, quality processes that involve compiling and storing data on paper documents require more time and effort than is necessary. In fact, it fosters more of a reactive rather than proactive approach to quality. Quality managers often need to spend more time double checking documents and following up with stakeholders to ensure records are complete and accurate. The challenges associated with this approach include:

• Data is gathered in multiple formats and stored in various repositories, making it difficult to disseminate to those who need it.
• Stakeholders don’t have important data until it comes to them.
• Data sets are at risk of being incomplete or containing errors, which can adversely impact reports and submissions.
• Data is insufficient for effectively investigating and resolving procedure and product deviations.

Quality Data Is Essential for Producing Quality Products

Your quality efforts and objectives should be driven by the needs of your customers. Based on these needs, you will typically create a product specification that indicates a precise target for each of the product’s critical characteristics — with minimal variation around the target for quality. But how can you ensure you maintain that level of quality throughout the product’s life cycle?

Advanced technologies are making data more abundant and more focus-area specific. Incorporating the following technologies into your quality processes gives your company a tactical advantage:

• Internet of things (IoT) – An ecosystem of web-enabled smart devices that collect, send and act on data they obtain with a high level of accuracy.
• Big data – The ability to quickly gather and aggregate large amounts and various types of data in real time.
• Predictive analytics – The use of new and historical data to forecast industry activities, behaviors and trends. Using predictive analytics allows stakeholders to see around corners and make more confident decisions.

According to a Garter survey on internet of things (IoT) technology, life sciences companies are implementing digitized technologies specifically to gather more data and gain more value from it. For example, using predictive analytics, life sciences companies can quickly identify trends, spot unforeseen or overlooked risks and mitigate deviations before they cause delays or result in a product recall.²

Employing a data-driven quality model helps you apply quality processes more holistically throughout the organization, as quality needs to be more of a collective effort, not just the responsibility of one department. This approach to quality will become a key business strategy as you strive to boost production capabilities, reduce throughput times and pursue continuous improvement. Other benefits include:

• Quality and manufacturing can be better aligned and more transparent.
• Staff can focus on business-critical tasks, rather than reacting to events after they occur.
• Overall performance and right-first-time metrics can improve significantly.

A survey report from Accenture Life Sciences explains how the life sciences industry is at an inflection point. Companies throughout the industry now recognize that to remain competitive they need to make better use of all their data assets. It further cites that to be a leader in this new data-driven world, life sciences companies must fundamentally transform how they create, manage and effectively use all their data.³

Organizations that adopt a data-driven, platform-enabled quality model will augment their capacity to yield real-time quality intelligence and predictive insights. When equipped with the ability to collect and share data within a common platform, every function within an organization can have an appreciable impact on product quality.

References

1. “To Help Reduce Drug Shortages, We Need Manufacturers to Sell Quality — Not Just Medicine,” Janet Woodcock, FDA Voices, U.S. Food and Drug Administration (FDA), https://www.fda.gov/news-events/fda-voices/help-reduce-drug-shortages-we-need-manufacturers-sell-quality-not-just-medicine
2. “Predicts 2020: Life Science CIOs Must Digitalize for Business Growth,” Andrew Stevens , Jeff Smith , Michael Shanler , Animesh Gandhi, Gartner Research, Dec. 23, 2019, https://www.gartner.com/en/documents/3978690/predicts-2020-life-science-cios-must-digitalize-for-busi
3. “Digital Transformation in the Lab: Bridging Analog Islands in a Digital Ocean,” Survey Report, Accenture Life Sciences. https://www.accenture.com/_acnmedia/PDF-115/Accenture-Digital-Lab-Transformation.pdf#zoom=50