Data has always played a central role in patient care and medical research. But today, data is driving healthcare transformation at an unprecedented pace. From the rise of EHRs to the emergence of AI, the way we collect, interpret and use data is changing.
This makes healthcare data more valuable than ever before — and it should come as no surprise that diagnostic information coming from the clinical laboratory is a big part of this value creation. It comprises a significant amount of quantitative data in the medical record.
From EHRs to AI: the evolution of healthcare data
From patient-reported symptoms and physical measurements — such as blood pressure and heart rate — to biospecimen testing and diagnostic imaging, data has informed how we classify, diagnose, treat and conduct research for centuries. The evolution and widespread adoption of EHRs in the 21st century made it easier than ever to collect, store and query all of this data. The sources of health data also continue to expand, with wearable technology advancing rapidly and passive measures, such as thermal monitoring, being evaluated. While the amount of data continues to grow astronomically, it is often underutilized because its volume and complexity make it overwhelming for individuals to analyze and comprehend.
In response to this “data explosion” across multiple sectors of society, there has been growth in talented bioinformaticians and computer scientists, computational power and advanced analytics tools. We now have the expertise and technology to fully harness our data to improve patient outcomes. With the rapid evolution of AI — particularly large language models — we can now analyze data and draw insights in ways that were unimaginable just a few years, if not months, ago.
AI and data converge to accelerate discovery
One major outcome of the convergence of data and advanced capabilities is the acceleration of innovation and medical research. We can now analyze massive datasets at unprecedented speed and depth, uncovering patterns that were previously undetectable. This leads to novel insights and unveils better solutions in disease diagnosis, treatment and prevention.
As AI systems continuously learn from expanding datasets, they not only improve diagnostic precision but also spark new clinical questions. This feedback loop — where data drives discovery, and discovery generates more data — is bolstering research and development across the healthcare ecosystem, including diagnostics.
Clinical labs emerge as architects of personalized care
The changing data landscape also highlights the role of laboratories in developing and using technologies to support more personalized care. Clinicians are increasingly using AI tools to anticipate health issues before they arise and adapt interventions to deliver care that is both more precise and proactive. This shift toward personalized medicine promises to improve outcomes, reduce unnecessary treatments and enhance the overall patient experience.
Much of the data in healthcare that will feed these tools will continue to originate from clinical diagnostic tests, making laboratory professionals essential partners in developing and applying solutions that harness this data to enable more personalized care.
Laboratorians must evolve into data stewards and innovators
In this environment, laboratorians have both the opportunity and responsibility to shape emerging innovations. As a leader, I’ve been encouraging our staff to go beyond generating results and embrace their roles as stewards of data integrity, guides in interpretation and developers of new tools. They must also help colleagues understand the results and data that are driving their clinical decisions.
With my staff, I’ve highlighted their deep understanding of the nuances behind test results — insights often rooted in the methodologies used — and a unique grasp of how this data connects to patient presentation and clinical context. We know what a test result means and just as importantly, what it does not. This insight is essential not only for improving patient outcomes but also for appropriately designing and implementing data models, identifying potential risks and shortcomings of AI, and ensuring that healthcare experts apply innovative technologies ethically and effectively.
This shift calls for a rethinking of medical education and professional development. We must train students, residents and practicing clinicians to use AI as a tool. They do not need to become coders or data scientists, but they do need to understand the principles by which these tools work and how they can support deductive reasoning applied to laboratory-derived data in clinical practice, as well as support the use of lab data to create new data-driven solutions.
A couple of ways we’ve achieved this at Mayo Clinic Laboratories is by featuring AI use cases in staff meetings and offering a broad range of educational options. Staff have access to everything from on-demand courses and journal clubs to formal degree and certificate programs, allowing them to tailor their education to their needs and position.
Balancing opportunity with data governance and stewardship
The growing value of diagnostic data is attracting attention, with insurers, pharmaceutical companies and tech firms increasingly interested in incorporating this data into their data pipelines and AI models. For the clinical laboratory, this interest opens the door to new collaborations and investment while also underscoring the need for strong data governance and a clear understanding of how data is used, shared and protected.
Unlocking the full promise of healthcare’s data revolution
As healthcare continues its digital transformation, data is emerging not just as a byproduct of technology advancement, but as a strategic asset. Realizing the full potential this growing data resource creates requires equipping laboratory professionals and clinicians with the skills to interpret, use and govern data responsibly. By embracing evolving roles, the clinical diagnostics industry can ensure that data serves its highest purpose: to improve care, empower clinicians and transform lives.
To learn more about trends in clinical diagnostics, follow me on LinkedIn or tune in to the “Answers From the Lab” podcast, where we explore the latest in laboratory science, healthcare advancements and leadership.
Dr. Morice serves as president and CEO of Rochester, Minn.-based Mayo Clinic Laboratories, a global leader in advanced diagnostics. He is a professor of laboratory medicine and pathology, and a Mayo Clinic consultant in hematopathology.
