How Do We Match the Right Drug to the Right Patient?
HealthSpark, Episode 20: Tanya M. Laidlaw, MD, Professor of Medicine at Harvard Medical School and allergist-immunologist at Brigham and Women’s Hospital, shows how treating severe asthma with advanced biologics can still feel like trial-and-error, and why linking patient experience, biomarkers, and research is critical to getting treatment right the first time.
What happens when precision medicine isn't precise enough?
Targeted therapies and biologic drugs promise to tailor treatment to individual patients, yet in practice, many decisions still rely on educated guesses. Clinicians and health systems may have multiple drugs that are theoretically appropriate, but few tools reliably predict which option will reduce hospital visits, prevent exacerbations, and restore quality of life. The gap between scientific potential and real-world decision-making highlights the need for stronger evidence, better decision-support tools, and more robust methods for measuring treatment effectiveness in routine care.
How can better biomarkers change the economics of care?
When it takes months to discover that costly therapy is not working, patients, payers, and providers all bear the consequences. Biomarkers and patient selection tools have the potential to shift this equation by more accurately identifying who is likely to benefit from which drug earlier in the care pathway. Doing this responsibly requires careful research, validation, and regulatory oversight, but the result is a system in which investment in precision pays off with better outcomes and more sustainable spending.
What would a 'learning loop' between the clinic and the laboratory look like?
For many conditions, the most important questions emerge directly from patient care. Why does one person respond to a drug while another does not? Which biological pathway matters most? How can treatment be tailored more effectively? Turning these questions into better therapies requires a continuous feedback loop between clinical practice, data science, and research labs. That loop must be built on trusted partnerships, high-quality data flows, and a commitment to testing new approaches that reflect real-world settings. Over time, this type of learning system can turn individual patient experiences into insights that can change the standard of care.
Key question to take forward:
As you watch the video and consider your own setting, you might reflect on:
How can insights from patient experiences, data, and research be better connected to guide treatment, product, and policy decisions?