Does Precision Medicine Have a Communications Problem?
And no, a new tagline won’t fix it
Precision medicine – “the right treatment for the right patient at the right time” – is the rallying cry of modern healthcare. It’s also one of the most under-explained concepts in medicine today. For researchers, it’s a scientific inevitability. For investors, it’s a bet on smarter, more predictable returns. But for clinicians, payers, and patients, it can still sound like a distant, expensive experiment.
This isn’t a question of better branding or sharper taglines. It’s about finding ways to make the science meaningful to the people who need to use it and trust it. If we can’t explain precision medicine in ways that feel relevant and actionable, we risk slowing its adoption and missing opportunities to improve care when it’s most needed.
The AI double standard
AI and precision medicine share an awkward similarity: both promise to fundamentally reshape how we solve problems. But only one of them is allowed to be vague.
AI companies can get away with sweeping claims – “transformational,” “revolutionary”, “solving everything” – without having to explain the plumbing. Precision medicine can’t.
Partly, that’s because medicine is held to a higher evidentiary standard – and rightly so. But it’s also because AI’s champions have mastered narrative framing. They speak in terms of inevitability (“It’s coming, get ready”), scale (“This will touch every industry”), and familiarity (“Your phone already uses AI every day”). They create metaphors and mental shortcuts. They sell the story before they sell the specs.
Precision medicine has mostly done the opposite – leading with technology, acronyms, and statistical models. We explain how it works, but not why it matters in human terms. James Tabery, author of Tyranny of the Gene, notes that even Gleevec – hailed as the “poster child” for personalized medicine – came with caveats. It worked brilliantly in one context, but failed to generalise. Add in aggressive pricing and you have a public narrative that quickly soured. The lesson isn’t “don’t promise too much,” it’s “explain what’s realistic, and for whom, before others define it for you.”
Until precision medicine finds a common, everyday language – the kind AI has – it will remain easier to dismiss than to adopt.
Beyond Oncology – precision’s unexplored terrain
Precision medicine has been tethered to oncology for over two decades.
That’s not a bad origin story – cancer was the logical proving ground for targeted therapies and companion diagnostics. But it’s become a communications straitjacket. Ask the average clinician or policymaker about precision medicine, and they’ll answer with a cancer example.
The reality is that the very same approaches can now be applied to cardiovascular disease, autoimmune disorders, respiratory disease, metabolic syndromes, and neurodegenerative conditions. Adam Platt, VP of Translational Science and Experimental Medicine for Respiratory and Immunology at AstraZeneca, recently observed that precision approaches in chronic disease have “lagged behind” oncology, but that new tools and datasets are closing the gap.
Take type 2 diabetes. Today’s treatments are largely trial-and-error, with patients cycling through drug classes until something works. Genomic and proteomic insights could allow us to pre-select the therapy most likely to succeed based on a patient’s metabolic subtype.
In heart failure, biomarkers could stratify patients into those who will respond to beta blockers versus those who need alternative mechanisms.
In rheumatoid arthritis, a cytokine profile could determine the most effective biologic from the outset, instead of cycling through three or four until symptoms improve.
The science is moving – but public perception is frozen in the oncology era. Until we widen the lens, we’ll keep underestimating both the scope and urgency of what precision medicine can do.
The Business Fear – demystified, demolished
Within pharma boardrooms, precision medicine can trigger a reflexive flinch:
“But won’t that shrink our addressable market?”
The concern isn’t irrational – segmentation can look like commercial self-sabotage when you’re used to dreaming about blockbusters.
The flaw in that thinking is it ignores the cost of failure. A late-stage drug that fails in Phase III wipes out not only years of sunk R&D but also billions in direct expenditure and untold opportunity costs. Compare that with a drug that works spectacularly for 30% of patients:
Faster regulatory approval because the trial data shows a clear, high-response signal.
Higher reimbursement odds because payers can see the cost-effectiveness in advance.
Rapid adoption because clinicians can have companion diagnostic tools, driven by patient stratification biomarkers, to identify eligible patients.
A 30% success rate in a large patient population can be worth billions annually. And those wins have a compounding effect – success breeds further investment, strengthens pipeline confidence, and builds the brand equity needed to expand indications.
The risk of doing nothing – of clinging to an “all patients or bust” mindset – is far greater.
Alzheimer’s – breaking a monolith is progress, not defeat
Few diseases illustrate the perils of the one-size-fits-all approach better than Alzheimer’s. It’s been the graveyard of countless drug candidates, with failure rates north of 99%. Billions have been poured into therapies aimed at treating all patients, despite decades of evidence that the disease’s biology varies significantly between individuals.
Alzheimer’s is not one disease – like most complex, chronic diseases, it’s multiple biological endotypes hiding under the same clinical label: amyloid-driven, tau-driven, vascular, inflammatory, and mixed forms. A universal cure is almost certainly impossible. But targeted therapies for each subtype? That’s feasible – and transformative.
Imagine an amyloid-clearing drug for the ~30% of patients whose disease is driven primarily by amyloid plaques. A vascular-protective therapy for those whose dementia is driven by blood vessel pathology. An anti-inflammatory approach for another large subset. None of these would be a “cure” for all, but together they could help hundreds of millions – and generate tens of billions in annual revenue for their developers.
Breaking Alzheimer’s into smaller, treatable pieces isn’t giving up. It’s playing the odds intelligently.
From COVID swabs to mechanistic matchmaking
One of the overlooked legacies of the COVID-19 pandemic is the infrastructure it created for rapid, scalable, low-cost sample collection. At-home nasal and saliva swabs became routine for hundreds of millions of people. The systems to distribute, process, and return results at scale are now proven.
That same infrastructure can be repurposed to collect biological samples for precision medicine.
Instead of detecting viral RNA, these swabs could identify genetic variants, protein signatures, or metabolomic markers associated with disease mechanisms. Coupled with advanced analytics like combinatorial analysis, this allows for:
Rapid identification of suitable patients for clinical trials.
Earlier and more accurate diagnosis.
Matching approved therapies to the patients most likely to benefit.
The beauty is the familiarity – patients already know how to use the tools. The challenge is explaining that what was once a COVID test could now be the gateway to a treatment plan designed uniquely for them.
Voices from the field – real stories, real gaps
The most compelling arguments for precision medicine aren’t in white papers – they’re in the lived experiences of patients and clinicians.
Take Bryce Olson, diagnosed with advanced prostate cancer. Standard treatments failed him. By sequencing his tumour, he found a trial for a PI3K inhibitor that matched his cancer’s specific mutation. It bought him more time – time that blunt-force standard of care could not. His story isn’t just about science; it’s about agency.
On the other side of the coin is Dr. Lillian Siu’s account, shared with the Financial Times. She’s faced the wrenching moment of telling a patient, “I know this drug could help you… but it isn’t covered.” It’s a reminder that the communication gap isn’t just scientific – it’s financial and systemic. Without alignment between innovation, reimbursement, and delivery, the science stops short of the patient.
Equity, trust, and the ‘Missing Ones’
Precision medicine risks becoming a two-tier system if access is uneven. Dr. Kashyap Patel has warned that precision approaches, without equitable implementation, “may live shorter lives compared to those represented in drug development.” The danger is that minority and rural populations – already underrepresented in trials – are also the last to benefit from precision diagnostics and therapies.
Building trust requires more than translating leaflets into multiple languages. It means engaging communities directly, respecting cultural perspectives, and being transparent about how data will be used. The Alaska Native Tribal Health Consortium, for example, used locally produced videos, workshops, and community champions to explain genomics in relatable terms. Participation and acceptance increased – because the message wasn’t parachuted in, it was co-created.
Leading the reframe – a communication playbook that works
Clinicians need biomarker information embedded into decision tools, not buried in journals. They need language that turns molecular probability into patient relevance.
Payers need a return-on-investment story. The Netherlands’ DRUP model – in which pharma covers the first cycle of an expensive targeted therapy, and insurers pay only if the patient responds – shows that creative payment structures can unlock access and make precision affordable.
Patients need to see themselves in the story. They don’t want risk allele percentages; they want to know, “Does this mean I’ll have more good years?”
Policymakers need the macro view – that precision medicine isn’t a boutique luxury, it’s one of the few viable strategies for controlling chronic disease costs in an aging population.
Iceland’s deCODE – trust as a force multiplier
The deCODE genetics project in Iceland remains one of the best examples of scaling precision medicine through trust. By returning actionable results – such as BRCA2 mutation status – to participants, it turned a research program into a national early-warning system. Hundreds of people took preventive action that likely saved lives.
The key was transparency: people understood what was being collected, why, and what they’d get back.
It’s proof that when people trust the system, they don’t just tolerate precision medicine – they can embrace it.
Privacy, bias, and the burden of trust
Precision medicine is data-intensive by definition. That makes it uniquely vulnerable to breaches of privacy, algorithmic bias, and unintended social consequences. These risks aren’t hypothetical – they’re already visible in examples where genetic data was used in ways participants didn’t anticipate.
Public trust will be won or lost on how openly we acknowledge and address these risks. Ignore them, and precision medicine will be seen as something done to people rather than for them.
Final thought
Precision medicine doesn’t need a new slogan – it needs a clearer conversation.
The science is already here in many areas, but too often it’s discussed as if it’s still on the horizon. We need to bring it into the present, using real examples, relatable language, and a focus on what it changes for patients, clinicians, and health systems.
That means making the benefits visible, the pathways understandable, and the trade-offs honest. The more we can do that, the faster precision medicine will move from promise to practice.
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