1.

Enabling smarter therapy selection

Traditional treatment models rely heavily on population‑based evidence. Clinicians select therapies that work for most patients and adjust them based on observed responses. While effective at scale, this approach does not account for the biological and genetic variability that causes patients to respond differently to the same therapy.

Diagnostics are changing that dynamic by enabling more informed decisions before treatment begins.

Pharmacogenomic (PGx) testing is a clear example. Notably, 99% of individuals carry clinically actionable variants that affect drug metabolism. By analyzing these variants, PGx testing helps clinicians identify from the start which therapies are most likely to be safe and effective for a specific patient. This is a foundational shift from reactive care to predictive, personalized decision‑making that reduces the risk of adverse events and prevents delays in effective treatment.

The impact is already evident in clinical practices, including in oncology, where genetic profiling of both patients and tumors has refined treatment to align therapies with a patient’s unique cancer biology. One study from 2024 showed that 28% of patients with cancer who underwent PGx testing during chemotherapy experienced adverse side effects compared to 73% among patients who did not get the testing.

2.

Supporting real‑time personalization

Personalized care does not end once a treatment plan is selected. Patients’ responses can change over time, influenced by metabolism, adherence, disease progression, and immune response. Therapeutic drug monitoring (TDM) helps clinicians adapt care amid this variability.

TDM measures drug concentrations and, in some cases, antidrug antibodies, to offer insight into adherence and biological response to ensure medications remain within the therapeutic range. This is especially important for drugs with narrow therapeutic windows, where even small dosing changes can significantly affect safety and efficacy. With this objective data available, clinicians can more easily assess whether a therapy is working as intended, minimize toxicity, and make faster adjustments when needed.

Transplant medicine is an example of a specialty using this type of diagnostic to improve outcomes. TDM helps clinicians manage immunosuppressive therapies following solid organ transplantation by providing the data needed to balance the risk of rejection against the risk of toxicity. Studies show meaningful improvements in overall mortality when dosing is guided by diagnostic data rather than fixed protocols.

3.

Driving innovation

The rise of diagnostics as a cornerstone of treatment has made interdisciplinary collaboration between laboratories and colleagues in other areas essential to innovation. When clinicians or investigators pursue novel ways to personalize treatments, they increasingly turn to laboratory experts for help designing diagnostic tools to deliver the data they need to guide their therapies. Conversely, laboratorians often detect emerging trends in the data — such as unexpected responses, patterns in therapeutic drug monitoring, or genetic markers — that spark new lines of investigation for other specialists.

This growing integration ultimately translates into accelerated discovery of better and more personalized therapies for patients.

At Mayo Clinic, the collaboration between laboratorians and clinicians has resulted in more than 200 assays covering toxicology, PGx, TDM, and more, with ongoing research driving even more solutions in the future.

4.

Fueling analytic and AI tools

Data has always been central to patient care and medical research, but today, it’s driving healthcare transformation at an unprecedented pace. Much of the data fueling these tools originates from clinical diagnostic tests, making laboratories essential to the ongoing development and implementation of data-driven, personalized care solutions.

Advanced analytic tools now allow us to process massive datasets at remarkable speed and depth, revealing patterns that were previously undetectable. These insights are leading to new breakthroughs in disease diagnosis, treatment, and prevention. Clinicians are also increasingly using AI tools to anticipate health issues before they arise and adapt interventions to deliver care that is more precise and proactive.

As this transformation takes place, laboratorians are central to ensuring these data-driven tools effectively support more personalized healthcare. Their expertise is essential to accurately interpret laboratory data and ensure that the AI tools leveraging the data are clinically valid, ethically sound, and actionable.