Technological Evolution in Clinical Data Management

Armand Matejunas is a distinguished R&D leader with expertise in Clinical Data Management, Clinical Operations, Clinical Technologies, Vendor Alliance Relationship Management, and IT. He focuses on quality, delivery, and cost efficiency. Throughout his management career, he has collaborated internally and externally to ensure the timely and budgetoriented delivery of clinical trial data. He has worked with service providers in a partnership model to drive cost savings via standardization and automation. As a strategic leader, Armand Matejunas has established organizational and service provider models that best suit the company’s needs, including prioritization and execution.

Please provide insight into your roles, responsibilities, and day-today tasks in your organization.

I’ve been in the industry for four decades, starting as a Fortran programmer at Hoffmann-La Roche and progressing through data management, operations, and leadership roles. I lead clinical data operations at Pacira Biosciences, a relatively small company with about 750 employees globally. Our product range is limited, and we typically conduct three to five studies annually. My main focus is ensuring we have the proper data capture and cleaning infrastructure, which involves close collaboration with our CRO to gather data from various sources like wearables, images, labs, and ECGs. We also prioritize industry standards to enhance the quality and efficiency of our operations.

How has the industry evolved over the past few years, and what challenges are leaders facing today regarding their roles and responsibilities?

I see various challenges ahead for the industry. Firstly, there’s uncertainty surrounding the practical applications of AI and how it can be effectively integrated. Many struggle to grasp its value in data capture and document structuring. Additionally, there’s growing skepticism toward the CRO industry as more large companies opt to handle operations internally. Despite these challenges, there remains a gap between desired outcomes in data and technology and the willingness to invest in them. Companies often express the need for specific capabilities but tend to deprioritize such projects during budget allocation. The successful companies poised to make an impact in the next decade or two prioritize investment in technology and data-driven approaches while maintaining investment in other areas. CEOs and heads of R&D face tough decisions about resource allocation, especially considering the significant cost and commitment required for these technologies to succeed. Achieving desired outcomes remains challenging without sufficient financial backing and personnel allocation.

How have you adjusted your leadership approach to address these challenges? What strategies have you employed?

Justifying investments in technologies for crossportfolio and big data analysis can be challenging for small companies. Convincing the head of IT, head of R&D, and CEO of the necessity of such investments is often tricky. Prioritizing and solving past issues, like locating protocols from years ago, precedes futureoriented challenges. Document management solutions might be lacking in small companies, emphasizing the need to address the current problems rather than focusing on future expansion. Consequently, promoting new technologies in small companies becomes a formidable task.

Please share any current project initiatives that you are working on.

My company is discussing a project aimed at organizing all our data and document assets by categorizing and cataloging them to make them easy to find. This is to avoid missing out on opportunities related to Intellectual Property (IP) because of disorganization.

What advice would you give your peers to navigate this changing space, especially regarding AI?

Before investing in new technologies like AI, doing your homework is crucial. Take the time to thoroughly understand the landscape, be well-versed in the discussions, and grasp all the intricacies. Avoid getting swayed by flashy sales pitches and glossy magazine images. There’s often a tendency to rush in life, but that’s not always the wisest strategy. Many times, slow and steady progress wins the race. Aim to demonstrate early successes when strategizing, but don’t set unrealistic expectations for monumental outcomes in five years. Instead, gradually break your plans into manageable chunks and introduce new capabilities, similar to app development. This approach is more pragmatic and feasible, especially when dealing with disruptive technologies like AI.