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The clinical data manager role is undergoing a significant shift across medtech. It is no longer just about cleaning data, it is about architecting the strategic flow of clinical evidence across the entire organization.

Denise Kendall, supervisor, clinical data management at Alcon, and Robin Solinsky, vice president of technology and innovation at Bright Research shared how the data manager role is changing to embrace cross-functional, agile methodologies that optimize study speed and maintain compliance.

A changing landscape

The regulatory landscape is pushing the medtech industry to rethink how it captures and views clinical data. With updates like the latest revision to ISO14155, the industry is moving away from treating clinical data as a series of flat, two-dimensional documents. Instead, medtechs must adapt to a three-dimensional world of records that fully encompass metadata, comprehensive audit trails, and system interactions.

“People do still have that mentality about records really just existing as a flat document when we’re all trying to adapt to this three-dimensional world of a record,” Robin Solinsky, VP, technology and innovation, Bright Research.

In this world of records, the immediate availability of digital data can tempt study teams to collect excess variables, but the value of simplicity cannot be overlooked. Rather than capturing data simply because a system allows it, modern data managers must act as digital architects, ensuring that they focus on variables necessary to support the clinical investigation plan for continued evidence generation.

Three approaches to agile database builds

Operating in a fast-paced medtech environment requires agility, but different products require distinct operational frameworks. Kendall and Solinsky highlighted three unique strategies to accelerate database builds using the ³Ô¹Ï±¬ÁÏ Clinical Platform:

Dual-role ownership model: At Alcon, the clinical data management team supports rapid, early-design trials, with timelines lasting just three to four weeks. To execute at this pace, Alcon has their data managers operate in a multifaceted role, building the study, executing user acceptance testing (UAT), and directly owning the study lifecycle from start to finish. They complement this with focused, case report form (CRF) review meetings with cross-functional stakeholders who arrive fully prepared to finalize designs. Utilizing pre-validated, standardized forms in the ³Ô¹Ï±¬ÁÏ Clinical Platform, the Alcon team can complete this entire process in less than ten days.

Hybrid mock CRF strategy: Other medtechs manage a vast array of complex modalities, ranging from imaging software to anesthesia machines and blood pressure cuffs. Because standardizing across such diverse products is challenging, the team utilizes a hybrid approach. They build mock CRFs for early visibility, allow stakeholders to enter test data in a development site to capture immediate feedback, and finalize the design before running strict UAT. This proactive process within ³Ô¹Ï±¬ÁÏ EDC prevents extensive post-go-live change requests.

Therapeutic master templates and cross-training: Bright Research, a medtech-focused contract research organization (CRO), drives efficiency by deploying master template databases tailored to specific therapeutic areas, such as cardiovascular or neurovascular workflows. These templates remain locked to ensure strict compliance with ISO 14155 and CFR Part 812.

Additionally, Bright Research cross-trains clinical research associates (CRAs) in data management. Because CRAs understand the clinical rationale and field realities, this cross-training streamlines the database design process.

Guardrails for quality and compliance

As leadership teams demand faster execution, data management teams must implement strong operational guardrails to ensure that speed does not compromise data quality.

1. Standardizing innovation: Implementing global libraries or master databases enables replication of standardized forms across multiple studies, ensuring consistency and saving weeks of clinical programming time and effort.
2. Predictable release cycles: Aligning database updates with a predictable, cyclical schedule helps manage backlogs systematically, giving sponsors, sites, and internal clinical teams clear visibility into timeline changes.
3. Early site collaboration: Bringing site coordinators or research managers into the CRF review process before going live ensures that sites can practically collect the requested data in the field, eliminating costly mid-study amendments.

What’s next

To remain competitive in today’s changing medtech ecosystem, data managers must expand their purview and lean into technological advancements. This allows them to maintain compliance and improve trial efficiency, all while preserving the human element in a rapidly evolving AI landscape.

For more details about how to get started, read about how other medtechs are adapting to the changing the medtech clinical trial landscape.