Kari Miller, Director, QMS Regulatory and Product Management, IQVIA10.26.21
Quality management is one of the largest obstacles to new product launches for most life sciences and medical technology organizations, yet it’s historically talked about only in the past tense: what went wrong and how can we fix it next time?
What’s more, the business case for ensuring that quality goes beyond error correction and prevention to regulatory and industry compliance is increasingly essential. Harmonizing processes between Quality and Regulatory divisions can enable organizations to ensure quality at scale while also mitigating potential regulatory compliance issues and improving costs, efficiency, and customer satisfaction in the process. However, these pillars have remained mostly siloed within life sciences companies to date. Then, the pandemic started.
Remote work orders during the pandemic meant that organizations could not physically audit their suppliers and regulator inspections were now inspecting data rather than physical clinical sites and manufacturing floors. Being able to work in parallel was paramount. Technology and collaborative process adoption have become essential to ensuring that the inability to be in-person cannot impede the quality and compliance of medical products.
As organizations explore long term implementation of these strategies, it opens up the opportunity to explore Industry 4.0 technologies influencing automation and data exchange in manufacturing such as artificial intelligence (AI) and machine learning (ML) to drive better business outcomes overall.
When people work in siloes, most activities are happening twice, and the work being done in each of these siloes is highly prone to error when it is transposed. Furthermore, functions move much more slowly in siloes, due to their sequential nature. As pressures mount to deliver products more quickly, especially during the pandemic, this reality does not lend itself to success. Contrarily, breaking down these siloes can contribute to bringing down the total cost of delivery, time to market, and overall customer satisfaction – all of which companies are measured on to calculate their return on investment. By eliminating silos, companies can function as a well-oiled machine, optimizing information sharing and mitigating repetitive work in the process.
Breaking down these barriers will indeed entail a challenging cultural shift that organizational leaders must navigate in close partnership with their staff and stakeholders. However, the capabilities of technology have reached a critical inflection point in their advancement, which will significantly aid in this shift.
Industry 4.0 technologies like AI/ML will enable automatic data sharing between Quality and Regulatory, allowing organizations to remove redundant work from their quality management processes. In turn, this can reduce stress on individual teams to manually transpose information and allow them to re-focus more on the tasks that drives the business forward and improve the products they deliver. In terms of quality assurance, this will make way for a new standard of automated audit trails, removing the process of retroactively compiling quality documents in the event of an audit while also helping to assure its completeness and accuracy. And reducing these administrative burdens is just the beginning.
While quality assurance has historically been conducted largely in retrospect – looking at problems that have occurred in the real world and how we can correct and prevent them in the future – we have the opportunity to begin looking forward rather than back with AI/ML. Predictive analytics working within an organization’s ecosystem allow teams to predict results, concerns and problems, as well as correlate those outcomes to various drug demographics.
As a result, organizations can proactively project to the regulators what’s going to happen based on historical data. Furthermore, they can predict when a quality protocol will fail and use analytics to decide the next best steps to prevent it in the real world. In short, organizations can learn through AI to prevent mistakes before they are made.
As a crucial added benefit, the institutional insight that AI/ML provides will aid organizations in satisfying their requirements for continuous improvement under ISO 9001. There will always be issues to address in manufacturing and opportunities to improve the process. The implementation of AI/ML will give leaders a much deeper understanding of where the areas for improvement are and what changes can be made to achieve those goals. These insights will feed into regulatory information management to help ensure that products remain compliant, on the market, and safe for the patients who use them.
The move to automation necessitates departmental, operational, and functional cooperation and collaboration, which often requires conversations about change management and employee training. Regardless of how flexible administrators attempt to be, or how well the benefits and procedures related to change are communicated, there will be challenges in orchestrating user adoption.
To bridge the gaps in change management, leaders can best prepare by evaluating their needs and expectations before envisioning a solution. To this end, it’s critical to involve employees in the change process, asking for their insight, such as obstacles they might have faced prior versus those they anticipate during the transformation, and incorporating these concerns into necessary training and communication. Technology is the single answer to organizational challenges, but with strategic implementation in key areas of need, organizations can holistically improve their quality management practices. This will be the underpinning for better quality and regulatory compliance, as well as faster delivery of better products that ultimately drive better outcomes for patients.
As the QMS Regulatory and Product Management Leader for IQVIA, Kari Miller is responsible for driving the strategic product roadmap, and delivery of industry best practices and regulatory compliance solutions for quality management. Kari has more than 25 years of experience delivering software solutions for life sciences. She brings that knowledge to her current team as they focus specifically on translating market and industry requirements into industry-leading enterprise quality management solutions that meet the needs of the heavily regulated life sciences QMS market. Kari earned a Bachelor of Science in Business Administration and a Bachelor of Science in Psychology from Marian College of Fond-du-lac, Wisconsin.
What’s more, the business case for ensuring that quality goes beyond error correction and prevention to regulatory and industry compliance is increasingly essential. Harmonizing processes between Quality and Regulatory divisions can enable organizations to ensure quality at scale while also mitigating potential regulatory compliance issues and improving costs, efficiency, and customer satisfaction in the process. However, these pillars have remained mostly siloed within life sciences companies to date. Then, the pandemic started.
Remote work orders during the pandemic meant that organizations could not physically audit their suppliers and regulator inspections were now inspecting data rather than physical clinical sites and manufacturing floors. Being able to work in parallel was paramount. Technology and collaborative process adoption have become essential to ensuring that the inability to be in-person cannot impede the quality and compliance of medical products.
As organizations explore long term implementation of these strategies, it opens up the opportunity to explore Industry 4.0 technologies influencing automation and data exchange in manufacturing such as artificial intelligence (AI) and machine learning (ML) to drive better business outcomes overall.
The Case for Harmonizing Quality and Regulatory
The potential benefit of harmonizing Quality and Regulatory is two-fold. First and most importantly, patients will benefit from safer products that are delivered more quickly. Furthermore, integration of processes will support compliance maturity of the organization.When people work in siloes, most activities are happening twice, and the work being done in each of these siloes is highly prone to error when it is transposed. Furthermore, functions move much more slowly in siloes, due to their sequential nature. As pressures mount to deliver products more quickly, especially during the pandemic, this reality does not lend itself to success. Contrarily, breaking down these siloes can contribute to bringing down the total cost of delivery, time to market, and overall customer satisfaction – all of which companies are measured on to calculate their return on investment. By eliminating silos, companies can function as a well-oiled machine, optimizing information sharing and mitigating repetitive work in the process.
Breaking down these barriers will indeed entail a challenging cultural shift that organizational leaders must navigate in close partnership with their staff and stakeholders. However, the capabilities of technology have reached a critical inflection point in their advancement, which will significantly aid in this shift.
The Role of Industry 4.0
The digital era is now upon us and has been in process for decades. In fact, we are approaching the post-digital age, where advanced technology is delivering new capabilities to organizations that are reshaping their businesses.Industry 4.0 technologies like AI/ML will enable automatic data sharing between Quality and Regulatory, allowing organizations to remove redundant work from their quality management processes. In turn, this can reduce stress on individual teams to manually transpose information and allow them to re-focus more on the tasks that drives the business forward and improve the products they deliver. In terms of quality assurance, this will make way for a new standard of automated audit trails, removing the process of retroactively compiling quality documents in the event of an audit while also helping to assure its completeness and accuracy. And reducing these administrative burdens is just the beginning.
While quality assurance has historically been conducted largely in retrospect – looking at problems that have occurred in the real world and how we can correct and prevent them in the future – we have the opportunity to begin looking forward rather than back with AI/ML. Predictive analytics working within an organization’s ecosystem allow teams to predict results, concerns and problems, as well as correlate those outcomes to various drug demographics.
As a result, organizations can proactively project to the regulators what’s going to happen based on historical data. Furthermore, they can predict when a quality protocol will fail and use analytics to decide the next best steps to prevent it in the real world. In short, organizations can learn through AI to prevent mistakes before they are made.
As a crucial added benefit, the institutional insight that AI/ML provides will aid organizations in satisfying their requirements for continuous improvement under ISO 9001. There will always be issues to address in manufacturing and opportunities to improve the process. The implementation of AI/ML will give leaders a much deeper understanding of where the areas for improvement are and what changes can be made to achieve those goals. These insights will feed into regulatory information management to help ensure that products remain compliant, on the market, and safe for the patients who use them.
Next Steps for Leaders
Life sciences organizations are continuing to fully embrace digital quality systems, not only for the purpose of regulatory compliance, but to provide better products to patients. The industry is increasingly recognizing the superior efficiency and consistency that the breaking down siloes can enable and how AI/ML automation can support that shift. Still, investments and related costs can’t be relegated to technology and equipment alone, and these changes will require a break with traditional approaches and a fundamental shift in internal culture.The move to automation necessitates departmental, operational, and functional cooperation and collaboration, which often requires conversations about change management and employee training. Regardless of how flexible administrators attempt to be, or how well the benefits and procedures related to change are communicated, there will be challenges in orchestrating user adoption.
To bridge the gaps in change management, leaders can best prepare by evaluating their needs and expectations before envisioning a solution. To this end, it’s critical to involve employees in the change process, asking for their insight, such as obstacles they might have faced prior versus those they anticipate during the transformation, and incorporating these concerns into necessary training and communication. Technology is the single answer to organizational challenges, but with strategic implementation in key areas of need, organizations can holistically improve their quality management practices. This will be the underpinning for better quality and regulatory compliance, as well as faster delivery of better products that ultimately drive better outcomes for patients.
As the QMS Regulatory and Product Management Leader for IQVIA, Kari Miller is responsible for driving the strategic product roadmap, and delivery of industry best practices and regulatory compliance solutions for quality management. Kari has more than 25 years of experience delivering software solutions for life sciences. She brings that knowledge to her current team as they focus specifically on translating market and industry requirements into industry-leading enterprise quality management solutions that meet the needs of the heavily regulated life sciences QMS market. Kari earned a Bachelor of Science in Business Administration and a Bachelor of Science in Psychology from Marian College of Fond-du-lac, Wisconsin.