For decades, outside forces have dictated how pharmaceutical and medtech companies approach quality assurance. The most influential force remains regulatory requirements. Both individual interpretations of regulations and feedback received during regulatory inspections have shaped quality assurance systems and processes. At the same time, mergers and acquisitions, along with the proliferation of different IT solutions and quality software, have resulted in a diverse and complicated quality management system (QMS) landscape. Historically, the cost of consolidating and upgrading legacy IT systems has been prohibitively expensive. Further challenged by a scarcity of IT support, many quality teams have learned to rely on the processes and workflows provided by off-the-shelf software without questioning whether they actually fit their company’s needs and evolving regulatory requirements.
In recent years, however, several developments have enabled a better way. New digital and analytics technologies make it easier for quality teams to access data from different sources and in various formats, without replacing existing systems. Companies can now build dynamic user experiences in web applications at a fraction of the cost of traditional, enterprise desktop software; this development raises the prospect of more customized, user-friendly solutions. Moreover, regulators, such as the FDA, are increasingly focused on quality systems and process maturity.1 The FDA also identified the enablement of innovative technologies as a strategic priority, thereby opening the door for constructive dialogue about potential changes.2
The time has arrived for pharmaceutical and medtech companies to act boldly and reimagine the quality function. Through our work on large-scale quality transformation projects and our conversations with executives, we have developed a new approach we call “smart quality” (see sidebar, “Smart quality at a glance”). With this approach, companies can redesign key quality processes and enable design-thinking methodology (to make processes more efficient and user-friendly), automation and digitization (to deliver speed and transparency), and advanced analytics (to provide deep insights into process capability and product performance).
The quality assurance function thereby becomes a driver of value in the organization and a source of competitive advantage—improving patient safety and health outcomes while operating efficiently, effectively, and fully aligned with regulatory expectations. In our experience, companies applying smart quality principles to quality assurance can quickly generate returns that outweigh investments in new systems, including line-of-sight impact on profit; a 30 percent improvement in time to market; and a significant increase in manufacturing and supply chain reliability. Equally significant are improvements in customer satisfaction and employee engagement, along with reductions in compliance risk.
Revolutionizing quality assurance processes
The following four use cases illustrate how pharmaceutical and medtech companies can apply smart quality to transform core quality assurance processes—including complaints management, quality management review, deviations investigations, and supplier risk management, among others.
1. Complaint management
Responding swiftly and effectively to complaints is not only a compliance requirement but also a business necessity. Assessing and reacting to feedback from the market can have an immediate impact on patient safety and product performance. Today, a pharmaceutical or medtech company may believe it is handling complaints well if it has a single software deployed around the globe for complaint management, with some elements of automation (for example, flagging reportable malfunctions in medical devices) and several processing steps happening offshore (such as intake, triage, and regulatory reporting).
Yet, for most quality teams, the average investigation and closure cycle time hovers around 60 days—a few adverse events are reported late every month, and negative trends are addressed two or more months after the signals come in. It can take quality assurance teams even longer to identify complaints that collectively point to negative trends for a particular product or device. At the same time, less than 5 percent of incoming complaints are truly new events that have never been seen before. The remainder of complaints can usually be categorized into well-known issues, within expected limits; or previously investigated issues, in which root causes have been identified and are already being addressed.
The smart quality approach improves customer engagement and speed
By applying smart quality principles and the latest technologies, companies can reduce turnaround times and improve the customer experience. They can create an automated complaint management process that reduces costs yet applies the highest standards:
- For every complaint, the information required for a precise assessment is captured at intake, and the event is automatically categorized.
- High-risk issues are immediately escalated by the system, with autogenerated reports ready for submission.
- New types of complaints and out-of-trend problems are escalated and investigated quickly.
- Low-risk, known issues are automatically trended and closed if they are within expected limits or already being addressed.
- Customer responses and updates are automatically available.
- Trending reports are available in real time for any insights or analyses.
To transform the complaint management process, companies should start by defining a new process and ensuring it meets regulatory requirements. The foundation for the new process can lie in a structured event assessment that allows automated issue categorization based on the risk level defined in the company’s risk management documentation. A critical technological component is the automation of customer complaint intake; a dynamic front-end application can guide a customer through a series of questions (Exhibit 1). The application captures only information relevant to a specific complaint evaluation, investigation, and—if necessary—regulatory report. Real-time trending can quickly identify signals that indicate issues exceeding expected limits. In addition, companies can use machine learning to scan text and identify potential high-risk complaints. Finally, risk-tailored investigation pathways, automated reporting, and customer response solutions complete the smart quality process. Successful companies maintain robust procedures and documentation that clearly explain how the new process reliably meets specific regulatory requirements. Usually, a minimal viable product (MVP) for the new process can be built within two to four months for the first high-volume product family.
In our experience, companies that redesign the complaint management process can respond more swiftly—often within a few hours—to reduce patient risk and minimize the scale and impact of potential issues in the field. For example, one medtech company that adopted the new complaint management approach can now automatically assess all complaints and close more than 55 percent of them in 24 hours without human intervention. And few, if any, reportable events missed deadlines for submission. Now, subject matter experts are free to focus on investigating new or high-risk issues, understanding root causes, and developing the most effective corrective and preventive actions. The company also reports that its customers prefer digital interfaces to paper forms and are pleased to be updated promptly on their status and resolution of their complaints.
2. Quality management review
Real-time performance monitoring is crucial to executive decision making at pharmaceutical and medtech companies. During a 2019 McKinsey roundtable discussion, 62 percent of quality assurance executives rated it as a high priority for the company, exceeding all other options.
For many companies today, the quality review process involves significant manual data collection and chart creation. Often, performance metrics focus on quality compliance outcomes and quality systems—such as deviation cycle times—at the expense of leading indicators and connection to culture and cost. Managers and executives frequently find themselves engaged in lengthy discussions, trying to interpret individual metrics and often missing the big picture.
Although many existing QMS solutions offer automated data-pull and visualization features, the interpretation of complex metric systems and trends remains largely a manual process. A team may quickly address one performance metric or trend, only to learn several months later that the change negatively affected another metric.
The smart quality approach speeds up decision making and action
By applying smart quality principles and the latest digital technologies, companies can get a comprehensive view of quality management in real time. This approach to performance monitoring allows companies to do the following:
- automatically collect, analyze, and visualize relevant leading indicators and outcomes on a simple and intuitive dashboard
- quickly identify areas of potential risk and emerging trends, as well as review their underlying metrics and connections to different areas
- rapidly make decisions to address existing or emerging issues and monitor the results
- adjust metrics and targets to further improve performance as goals are achieved
- view the entire value chain and create transparency for all functions, not just quality
To transform the process, companies should start by reimagining the design of the process and settling on a set of metrics that balances leading and lagging indicators. A key technical enabler of the system is establishing an interconnected metrics structure that automates data pull and visualization and digitizes analysis and interpretation (Exhibit 2). Key business processes, such as regular quality management reviews, may require changes to include a wider range of functional stakeholders and to streamline the review cascade.
Healthcare companies can use smart quality to redesign the quality management review process and see results quickly. At one pharmaceutical and medtech company, smart visualization of connected, cross-functional metrics significantly improved the effectiveness and efficiency of quality management review at all levels. Functions throughout the organization reported feeling better positioned to ascertain the quality situation quickly, support decision making, and take necessary actions. Because of connected metrics, management can not only see alarming trends but also link them to other metrics and quickly align on targeted improvement actions. For example, during a quarterly quality management review, the executive team linked late regulatory reporting challenges to an increase in delayed complaint submissions in some geographic regions. Following the review, commercial leaders raised attention to this issue in their respective regions, and in less than three months, late regulatory reporting was reduced to zero. Although the company is still in the process of fully automating data collection, it has already noticed a significant shift in its work. The quality team no longer spends the majority of its time on data processing but has pivoted to understanding, interpreting, and addressing complex and interrelated trends to reduce risks associated with quality and compliance.
Healthcare companies can use smart quality to redesign the quality management review process and see results quickly.
3. Deviation or nonconformance investigations
Deviation or nonconformance management is a critical topic for companies today because unaddressed issues can lead to product recalls and reputational damage. More often, deviations or nonconformances can affect a company’s product-release process, capacity, and lead times. As many quality teams can attest, the most challenging and time-consuming part of a deviation or nonconformance investigation is often the root cause analysis. In the best of circumstances, investigators use a tracking and trending system to identify similar occurrences. However, more often than not, these systems lack good classification of root causes and similarities. The systems search can become another hurdle for quality teams, resulting in longer lead times and ineffective root cause assessment. Not meeting the standards defined by regulators regarding deviation or nonconformance categorization and root cause analysis is one of the main causes of warning letters or consent decrees.
The smart quality approach improves effectiveness and reduces lead times
Our research shows companies that use smart quality principles to revamp the investigation process may reap these benefits:
- all pertinent information related to processes and equipment is easily accessible in a continuously updated data lake
- self-learning algorithms predict the most likely root cause of new deviations, thereby automating the review of process data and statements
In our experience, advanced analytics is the linchpin of transforming the investigation process. The most successful companies start by building a real-time data model from local and global systems that continuously refreshes and improves the model over time. Natural language processing can generate additional classifications of deviations or nonconformances to improve the quality and accuracy of insights. Digitization ensures investigators can easily access graphical interfaces that are linked to all data sources. With these tools in place, companies can readily identify the most probable root cause for deviation or nonconformance and provide a fact base for the decision. Automation also frees quality assurance professionals to focus on corrective and preventive action (Exhibit 3).
Pharmaceutical and medtech companies that apply these innovative technologies and smart quality principles can see significant results. Our work with several companies shows that identifying, explaining, and eliminating the root causes of recurring deviations and nonconformances can reduce the overall volume of issues by 65 percent. Companies that use the data and models to determine which unexpected factors in processes and products influence the end quality are able to control for them, thereby achieving product and process mastery. What’s more, by predicting the most likely root causes and their underlying drivers, these companies can reduce the investigation cycle time for deviations and nonconformances by 90 percent.
4. Supplier quality risk management
Drug and medical device supply chains have become increasingly global, complex, and opaque as more pharmaceutical and medtech companies outsource major parts of production to suppliers and contract manufacturing organizations (CMOs). More recently, the introduction of new, complex modalities, such as cell therapy and gene editing, has further increased pressure to ensure the quality of supplier products. Against this backdrop, it is critical to have a robust supplier quality program that can proactively identify and mitigate supplier risks or vulnerabilities before they become material issues.
Today, many companies conduct supplier risk management manually and at one specific point in time, such as at the beginning of a contract or annually. Typically, risk assessments are done in silos across the organization; every function completes individual reports and rarely looks at supplier risk as a whole. Because the results are often rolled up and individual risk signals can become diluted, companies focus more on increasing controls than addressing underlying challenges.
The smart quality approach reduces quality issues and optimizes resources
Companies that break down silos and apply a more holistic risk lens across the organization have a better chance of proactively identifying supplier quality risks. With smart quality assurance, companies can do the following:
- identify vulnerabilities by utilizing advanced analytics on a holistic set of internal and external supplier and product data
- ensure real-time updates and reviews to signal improvements in supplier quality and any changes that may pose an additional risk
- optimize resource allocation and urgency of action, based on the importance and risk level of the supplier or CMO
Current technologies make it simpler than ever to automatically collect meaningful data. They also make it possible to analyze the data, identify risk signals, and present information in an actionable format. Internal and supplier data can include financials, productivity, and compliance metrics. Such information can be further enhanced by publicly available external sources—such as regulatory reporting, financial statements, and press releases—that provide additional insights into supplier quality risks. For example, using natural language processing to search the web for negative press releases is a simple yet powerful method to identify risks.
Once a company has identified quality risks, it must establish a robust process for managing these risks. Mitigation actions can include additional monitoring with digital tools, supporting the supplier to address the sources of issues, or deciding to switch to a different supplier. In our experience, companies that have a deep understanding of the level of quality risk, as well as the financial exposure, have an easier time identifying the appropriate mitigation action. Companies that identify risks and proactively mitigate them are less likely to experience potentially large supply disruptions or compliance findings.
Many pharmaceutical and medtech companies have taken steps to improve visibility into supplier quality risks by using smart quality principles. For example, a large pharmaceutical company that implemented this data-driven approach eliminated in less than two years major CMO and supplier findings that were identified during audits. In addition, during the COVID-19 pandemic, a global medtech company was able to proactively prevent supply chain disruptions by drawing on insights derived from smart quality supplier risk management.
Pharmaceutical and medtech companies can approach quality assurance redesign in multiple ways. In our experience, starting with two or three processes, codifying the approach, and then rolling it out to more quality systems accelerates the overall transformation and time to value.
Smart quality assurance starts with clean-sheet design. By deploying modern design techniques, organizations can better understand user needs and overcome constraints. To define the solution space, we encourage companies to draw upon a range of potential process, IT, and analytics solutions from numerous industries. In cases where the new process is substantially different from the legacy process, we find it beneficial to engage regulators in an open dialogue and solicit their early feedback to support the future-state design.
Once we arrive at an MVP that includes digital and automation elements, companies can test and refine new solutions in targeted pilots. Throughout the process, we encourage companies to remain mindful of training and transition planning. Plans should include details on ensuring uninterrupted operations and maintaining compliance during the transition period.
The examples in this article are not exceptions. We believe that any quality assurance process can be significantly improved by applying a smart quality approach and the latest technologies. Pharmaceutical and medtech companies that are willing to make the organizational commitment to rethink quality assurance can significantly reduce quality risks, improve their speed and effectiveness in handling issues, and see long-term financial benefits.
Note: The insights and concepts presented here have not been validated or independently verified, and future results may differ materially from any statements of expectation, forecasts, or projections. Recipients are solely responsible for all of their decisions, use of these materials, and compliance with applicable laws, rules, and regulations. Consider seeking advice of legal and other relevant certified/licensed experts prior to taking any specific steps.