The Need for QMS Transformation & Structured QMS Data

The Need for QMS Transformation & Structured QMS Data

Kari Miller, Regulatory and Product Management Leader, Pilgrim Quality Solutions, an IQVIA company

As product recalls, product bans, drug shortages, plant shutdowns, and enforcement actions continue to rise, industry and regulators alike are looking for answers on how to change the perspective of the Life Sciences industry from one of Compliance to one of Quality.

Is the solution to increase the amount of data we capture and report within our documents? After all, we so carefully record a great deal of data in a Quality Management System (QMS). No, data in and of itself, is not the answer. While data/metrics go a long way toward accomplishing the shift from compliance to quality, that data needs to be transformed into intelligence that is informed, actionable, proactive, and predictive.

The notion of Quality Metrics is not about getting better at gathering metrics; it’s about making business better through quality in all things. The Center for Devices and Radiological Health (CDRH) and the Center for Drug Evaluation and Research (CDER) recognized this need, and both have launched Quality Metrics initiatives in the last few years to extend their industries’ and the Food & Drug Administration’s (FDA) focus beyond compliance and toward a higher assurance of quality.

Both Centers have similar goals but analyze different metrics to demonstrate quality. Some of these metrics may already exist at some level in your organization.

CDRH – Right the First Time Mentality

In 2011, the CDRH launched “The Case for Quality” to begin the shift in focus from one of compliance and enforcement action to one of device quality. The goal of this initiative is to identify ways for the medical device industry to proactively and predictively measure the risk to its own product quality, thereby enabling the industry to focus on improving product quality proportionate with the need.

The program directive is to focus on the three major lifecycle stages for a product and analyze the key metric(s) in each phase. These three phases and the quantifiable product quality metric(s) within each are below:

  • Pre-production: the metric will measure total number of changes (product and process) for each project to move towards 0 post-design transfer changes.
  • Production: the metric will be the Right-First-Time production result that many organizations already measure. This metric not only assesses production efficiency, it allows for the identification of opportunities for improvement.
  • Post-production: a vast array of metrics are being assessed in the area of Complaints, Service Records, MDRs, and Recalls.

CDER – Product Quality

FDA announced its Quality Metrics Initiative for the Pharmaceutical industry in 2013 to determine data the industry could submit to FDA that would accurately reflect potential risk to product quality. The metrics framework was built upon driving a mindset of continual improvement that includes feedback loops across the entire enterprise to design quality into the product proactively at the source, instead of reactively catching inadequate quality after manufacture.

To achieve this, the following QMS metrics are assessed:

  • Lot Acceptance Rate
  • Product Quality Complaint Rate
  • Invalidated Out-of-Specification (OOS) Rate

Making the Transformation at Home

So, how do our organizations also shift our focus from Compliance to Quality? We can look to these FDA initiatives to build our own enterprise-wide QMS transformation roadmaps, including how we structure our data within the quality management system and then report against it.

In this age of big data, structuring your global eQMS information is imperative. Properly structured data sets are a must. The goal of properly structured data sets? Turning data into actionable information through its transformation. When properly structured, quality data can be transformed into KPIs, metrics, and measures that can be used consistently at a business unit, division, or site level. For each process, for products and product families, for customers and suppliers around the globe, this data is essential within a global enterprise Quality Management System.

Sorting Out All that Data

Quality records often require data from other systems such as the Failure Mode and Effects Analysis (FMEA) table or the Product Master Data from PLM; the production, inventory or finance data from ERP; or shipments and vendor information from Supply Chain Management solutions. Quality metrics are no different. To ensure that there is only one master data source, an eQMS needs enterprise-level integration to allow for the use of key data from other systems.

To extend the concept of a single master data source, there must be a Single Source of Truth for quality data as well. Providing the organization with that single source of quality truth is the catalyst to transforming the business. It allows us, as quality professionals, to make a case for quality transformation. The FDA’s Quality Metrics initiatives discussed above can give us some insight into how we do this, by shining a light on quality and its transformative impact on the business.

The Case for Transformation

When making the case for QMS transformation, don’t stop at the FDA metrics, however. Speak the language of the business, not just the language of quality. Focus in on the customer and operational excellence. It will be important to speak to metrics that the organization already understands. As Quality professionals, one of our jobs is to help the organization understand how the business is impacted by a quality journey.

And ultimately, ensuring the health of the business will ensure the goal of any Life Sciences organization – to provide quality products intended to improve the lives of the patients using those products.

The Case for Quality System Transformation

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Kari Miller

Regulatory & Product Management Leader, IQVIA Quality Compliance