Clinical Data Standards for Medtech Will Fuel Innovation in the Era of Data Complexity

From simple lateral flow tests to complex next-generation sequencing, drug-coated balloons, and heart pumps, the diversity within the medtech industry means that every product charts a different path to market. But what unites every device journey is the need for regulatory speed. With growing clinical evidence requirements and increasing data complexity, clinical data standards are a key component to further data harmonization and accelerate regulatory reviews.

A baseline survey conducted by the Medical Device Innovation Consortium (MDIC) highlights the industry’s data fragmentation. Their assessment revealed that 100% of surveyed organizations use internal standards for reporting clinical trial data in support of regulatory submissions.¹ Often adapted from existing standards from the Clinical Data Interchange Standards Consortium (CDISC), these internal standards exemplify the fragmentation in medtech that’s a blocker to the benefits of broader standardization.

To combat the inconsistencies created by internally developed guidelines, MDIC’s research also shows broad support for industry-wide common standards. Clinical data standards can help medtech companies spend less time on data reconciliation and gain more time to make data-driven decisions, helping to develop innovative devices faster and speed up regulatory review.

The Case for Acceleration with Standards

While the biopharma industry has operated with mandatory data formats and many standardization initiatives like the Electronic Common Technical Document (eCTD), the EMA Data Standards Initiative, and the International Council for Harmonisation’s guidelines for Good Clinical Practice (ICH GCP), medtech has trailed in comparison, perhaps because of the challenges of mapping diverse device outputs and protocols. But the operational value of uniform data formats for medtech regulatory submissions has been proven in recent years.

During the COVID-19 pandemic, the U.S. FDA Emergency Use Authorization program demonstrated how structured review processes function under the pressure of time. To speed up access to critical diagnostics during a public health emergency, the National Institutes of Health (NIH) Rapid Acceleration of Diagnostics (RADx) Independent Testing Assessment Program (ITAP) expedited regulatory review by introducing a standardized data submission. This framework allowed the FDA to move with greater efficiency, supporting the production of more than 3 billion COVID-19 diagnostic tests since January 2022.

While the FDA requires CDISC standards for all drug applications to the Drug Evaluation and Research (CDER) and Center for Biologics Evaluation and Research (CBER), the Center for Devices and Radiological Health (CDRH) encourages (but doesn’t require) data or terminology standards for pre-market submissions and post-market reports for medical devices. Despite no current mandate for medtech, some companies are forging ahead with standards to streamline reviews process and enable the consistent use of data analysis tools by health authorities.

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In a 2021 paper, Edwards Lifesciences chronicled successful submissions in the U.S. and China using CDISC standards.² While certain amendments were made to fit aspects outside of the scope of existing CDISC formats, these trials indicate the eagerness to adopt standards to reduce the time from submission to approval. More must be done to establish clinical data standards with defined terms and formats that work for medtech but the industry is forging ahead.

Providing Structure for Real-World Evidence

The benefits of standardizing clinical data extend beyond pre-market submissions. Clinical evidence generation is moving toward a continuous model where data must flow seamlessly from early clinical investigations to post-market surveillance as companies now pull from a wider range of sources including traditional EDC systems, direct instrument data, imaging results, eCOA, and real-world evidence (RWE). Achieving this continuity requires data integrity across the total product lifecycle, a key factor for leveraging RWE.

As medtech organizations increasingly rely on RWE to complement randomized controlled trials, they’re seeking guidance on data quality standards and study design. At the same time, regulators are encountering great variability in submitted data formats and study methodology. This is where standardization provides a structured foundation for high-quality data and interoperability for both sponsors and regulators.

Standardization is a key part of the framework to turn unstructured real-world data into regulatory-grade evidence. With clear definitions and structured formats, standardization paves the way for data harmonization to support RWE. This creates consistency and reliability in regulatory decision-making to accelerate reviews. 

Effective AI Starts with Data

As medtech companies seek to integrate AI into clinical workflows, data cleanliness has become a strategic priority. The 2025 Veeva Clinical Benchmark shows that over half of medtech companies plan to prioritize data collecting and cleaning in the next year as part of their focus to optimize clinical processes.³ This is especially important as 72% of medtech organizations are planning to invest in the infrastructure to power AI, working to harmonize existing systems and standardize data to make it AI-ready.

Effective AI use requires rigorous governance to ensure high-quality data, and industry data standards can support this. Since AI tools cannot parse fragmented or unstructured data, simple inconsistencies in formats can derail models. Building studies with data standards in mind from the beginning can alleviate the data-cleaning bottleneck that can often stall AI projects and ensure high-integrity outputs to make data-driven decisions.

Ultimately, data standardization helps achieve two strategic objectives at once: streamlining day-to-day clinical operations as well as preparing the foundation to scale AI initiatives. Industry-wide standards can provide medtech companies the framework needed to tackle modern data complexity to set the stage for AI.

Building a New Gold Standard for Medtech

In the absence of mandated clinical data standards for medtech, leaders are exploring how standards can work in practice to move the industry forward. Abbott, Roche, and Siemens Healthineers are collaborating with CDISC, the FDA, and MDIC to form the Data Standardization Working Group (DWSG). The group focuses on the development of standards within in-vitro diagnostics (IVD), which inform 66% of clinical decisions in the U.S.

The working group’s recent review of the current Study Data Tabulation Model Implementation Guide (SDTMIG) and its equivalent medtech version, the SDTMIG-MD, revealed the majority of existing CDISC standards are also applicable for IVD. Their findings underscore that similar standards between biopharma, medical devices, and IVD will be important for interoperability, as using the CDISC framework ensures that data stays Findable, Accessible, Interoperable, and Reusable (FAIR) throughout its lifecycle.

Ultimately, the group aims to reduce the time and cost spent by both sponsors and regulators by developing fit-for-purpose standards for IVDs. These efforts at the DSWG lay the groundwork for automated data quality checks and more seamless data exchange across regulatory teams. The proof of concept they’re developing is meant to clearly exhibit the utility of standards, building off of previous work like that at Edwards.

Data standards have been proven to accelerate clinical trial execution and regulatory reviews in biopharma,⁴ and the medtech industry is ready to follow. By opting into clinical data standards and working with industry peers across functions, early adopters are shaping the standards that may become the industry standard for medtech.

Data Standards as a Key to Unlock Innovation

The future of medtech clinical research is more reliant on high-quality interoperable data, making standardization more important than ever. As clinical trials grow more complex and the demand for continuous evidence generation increases, RWE and AI will be key tools for medtech. They cannot run in isolation; both require consistent and machine-readable data and a unified technology foundation. 

Clinical data standards are a critical first step towards this data harmonization. Standards will help ensure clean, consistent data to improve transparency between sponsors and regulators as the industry moves from traditional data management to advanced data science. Early pilots in medtech and longer-term adoption in the biopharma world show the possibility of standards to reduce administrative friction and speed up the regulatory approval process.

Rather than viewing data standards as a compliance burden, medtech leaders are turning to standardization as a catalyst for innovation. By enabling AI and RWE and speeding up clinical execution and regulatory reviews, a collective effort to establish clinical data standards in medtech will ultimately help deliver devices to patients faster.

References

1. bit.ly/mposoftware07261
2. bit.ly/mposoftware07262
3. bit.ly/mposoftware07263
4. bit.ly/mposoftware07264

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With more than 20 years of experience in data management, clinical research, project management, and product management, John Acampado is focused on optimizing processes to enable compliant, end-to-end clinical trial execution and evidence generation. He holds an MBA and MPH and has served in leadership roles in medtech and biopharma companies prior to joining Veeva MedTech.