Revolutionizing the Complaint Management Process with Artificial Intelligence

Digital technology and artificial intelligence (AI) are changing the way organizations manage quality processes. As industries shift to Industry 4.0 at an unprecedented speed, companies must manage customer satisfaction. Companies can move to a proactive value-based customer approach using digital technology and AI—”smart quality.”

McKinsey & Company coined the term “smart quality.” According to McKinsey, “Smart quality builds on the existing objectives of unconditionally delivering on patient safety and regulatory requirements at the lowest-possible budget. However, it achieves these objectives in new ways and expands their scope by reframing the role of quality in the modern enterprise. Quality is a value-added partner and coach that helps integrate compliance into regular operations while enabling speed and effectiveness.”

Quality management systems (QMS) with advanced analytics and AI are components of McKinsey’s smart quality approach.

The result is intelligent decision-making based on factual data, feedback, and trending. Let’s consider how the AI-embedded QMS will improve a complaint management system, enabling greater visibility into adverse events and helping to close complaints faster.

Challenges with a Manual Complaint System
Complaints indicate issues, defects, or dissatisfaction with a product or device. This is the first step toward correcting these issues, or in more severe cases, recall or product removal. Multiple complaints about the same category and a sole product indicate a trend, whereas a single complaint can be fixed with a corrective action. 

Managing adverse events and providing effective complaint handling is a regulatory expectation. Classifying, evaluating, and identifying the root cause of adverse events, then determining the next course of action to prevent recurrence relies on interpreting a magnitude of data. Quality teams must also have the visibility needed to identify and prioritize high-risk quality issues.

Poor complaint handling is the next-highest observation the FDA provides medical device companies. Insufficient complaint management can come in many forms, one of which is taking a manual approach to handling complaints.

Often, manual or legacy systems challenges include siloed processes, poor communication, lack of visibility, inadequate procedures, and not meeting regulation requirements. Some organizations still use manual processes and disparate systems to manage complaints, which can result in challenges like:

Slower Response Time: Organizations using manual processes for complaint management must reconcile complaints from various systems and sources, including email and phone calls. Complaints may be overlooked without a central location for managing the complaint process, resulting in delayed response time.

Poor Communication: When using multiple systems, repetitive entries will increase the risk for human error and mismanaged data. At the beginning of the process, the groups collecting all complaints may not capture all relevant data needed for the investigation. When collecting data from different groups, communication breakdowns can occur, further prolonging the complaint resolution process.

Lack of Visibility: Effective complaint handling requires collaboration across various groups. Lack of visibility into the complaint handling process of various groups makes it challenging for quality personnel to identify trends and prevent additional issues from occurring.

No Standardized Process: Without a single complaint management system, various groups across the enterprise may use their own workflows and terminology. The same departments in multiple countries may also operate differently. Lack of a standardized process becomes a challenge when identifying, resolving, and reporting common product issues.

A manual process is time-consuming with little to no structure, which creates inconsistency in managing complaints.  Ultimately, companies are left with late resolution of complaints and an overcomplicated complaint trending process with incomplete data.

A digital QMS provides the capabilities needed for a more effective complaint management process. 

How AI-Embedded Quality Management System Transforms Complaint Handling
AI-embedded QMSs change the way companies manage complaints with capabilities like complaint risk categorization, which provides suggestions on type, reportability, and severity.

Due to increasing volume and complexity, quickly classifying and triaging quality events is critical to prioritize the focus of quality teams. Users can classify and triage quality events, reduce errors, and compliance risk—along with the associated regulatory actions.

This minimizes recurring product issues and reduces the risk of negative impact on customers. Real-time analytics provide a method for monitoring processes and allows organizations to prevent failures. Automated workflows and email notifications with automatic reminders provide a seamless way to keep track of tasks. Automatic root cause investigations pinpoint the complaint’s root cause so organizations can prevent these issues from recurring.

An AI-embedded QMS uses algorithms to sort through all data in single records, individual complaints, or quality events. It then suggests the most likely critical data categories within each record, along with a statistical probability.

The result is complaint management that augments human decision-making and provides a timely response to all complaints.

Benefits of AI-Enabled Complaint Management 
Applying smart quality methods to complaint management reduces the total cost of quality assurance. According to a McKinsey use case, a medtech company reduced the cost of complaint management by more than 25 percent.

This approach enables management of larger volumes of complaints and redirects valuable quality resources to high-risk areas.

Users can leverage historical quality data and identify previously invisible correlations and patterns. They can then review this data at a glance for more context, learning from existing data and identifying possible trends. In addition, systematic identification of related records removes redundancy and adds higher consistency and traceability to investigations, providing insight into rich quality data history to use AI data for faster, better decisions as well as other benefits:

Greater Visibility into the Status of all Complaints: Visibility of complaints from submission to resolution provides accountability. In a traditional siloed system, management has no global view of complaints. Using a digital complaint management system provides visibility into all complaints and actions across the organization.

Ease of Complaint Submission: A digital QMS provides ease of complaint submission, automatic acknowledgments, and, as a result, increased customer satisfaction.

Insight into High-Risk Events: Organizations can quickly escalate high-risk complaints (with auto-generated reports), reducing patient risk. An AI-embedded complaint management system detects signals of potential high-risk events to prioritize and respond accordingly.

Performance Tracking: Users will be able to track performance of complaint handlers for KPIs. An automated global complaints process should be part of a company’s business strategy to effectively and efficiently track and monitor complaint handling performance.

Advanced Reporting and Analytics: Advanced analytics enable organizations to track, categorize, and identify trends. Storing data in a central location provides visibility and reporting capability across the value delivery chain.

Integration with Key Systems: An enterprise QMS seamlessly integrates complaint management with other critical processes like document management, providing a closed loop. This integration enables trending across all sites.

Better Efficiencies: Users can reduce errors and accelerate productivity by enabling quality teams with smarter systems, best practice processes, automation, and cross-functional collaboration.
Meet Regulatory Compliance: The visibility achieved from a digital QMS enables organizations to better meet regulatory compliance. An automated system minimizes human bias and errors and reduces compliance risk and associated regulatory actions.

A Smarter Approach to Complaint Handling
Poor complaint handling is the second-highest observation provided to medical device companies from the FDA. AI-embedded quality systems provide a better way for streamlining complaint management, providing organizations with the tools to increase customer satisfaction.

When complaints are managed using a centralized system, firms can quickly identify and address critical issues. Doing so will increase customer satisfaction, prevent patient harm, and prevent damage to brand reputation.

According to McKinsey, “Pharma and medtech companies that adopt smart quality and incorporate technology into each step of the process can free up business resources to focus on higher-value tasks.”

Taking a “smart quality” approach allows complaint submissions and statuses to be routed and tracked throughout the organization, ensuring complaints are resolved. It enables continuous improvement and helps achieve proactive quality by using information from data analytics.

An AI-embedded QMS is key toward achieving this and accelerating the path to proactive quality. 

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Zillery A. Fortner is the product marketing manager of Life Sciences at Sparta Systems, a Honeywell company. She earned a bachelor’s degree in health science from South University.   Fortner has 20 years of experience in the medical device arena related to quality assurance, regulatory affairs, and JACHO. She served 10 years in the military as a certified surgical technician. Fortner is an active member of ASQ and AAMI.