One of the more telling healthcare AI developments of 2026 was not about replacing diagnostics with an algorithm. It was Roche’s agreement to acquire PathAI, bringing AI-powered pathology more deeply into a diagnostics business built around precision medicine, companion diagnostics, and biomarker discovery.
That direction matters for pharma and biotech. The future is not AI instead of clinical data. It is AI becoming more valuable because it can interpret increasingly precise clinical signals.
The same principle applies to commercial analytics.
Life sciences organizations now have access to an extraordinary and growing volume of patient information: claims, EHR records, prescription activity, engagement data, digital health signals, consumer information, and more. AI can synthesize those sources at a scale that was not practical only a few years ago, creating new opportunities to understand patient journeys, identify relevant populations, and engage the healthcare professionals most likely to influence treatment decisions.
But there is an important distinction between more intelligence about data and more certainty about a patient’s clinical state.
As AI becomes more capable, precise and harmonized clinical and genomic lab data will not become less relevant to HCP and patient targeting. In many use cases, it will become more valuable.
Healthcare is becoming more distributed. A patient’s story may now span health systems, independent specialists, virtual-care providers, retail clinics, pharmacies, wearable devices, direct-pay services, and concierge or membership-based care.
For patients, that can mean greater access, convenience, and choice. For organizations trying to understand the longitudinal patient journey, it can create a harder data problem.
An encounter with one physician may not appear cleanly in another provider’s system. A diagnosis code may represent suspicion rather than confirmation. A prescription may be written but never filled. A claim can indicate that a service occurred without fully explaining the clinical state that prompted it.
The paradox is straightforward: the industry can have more data about a patient while still struggling to understand what is clinically happening with that patient.
AI can help organize and interpret those signals. It can recognize patterns across enormous datasets and connect observations that would be difficult to reconcile manually. But AI still depends on the specificity, quality, and timeliness of the information underneath it.
It can make evidence more useful. It cannot create a clinical signal that was never captured.
This is where laboratory data plays a distinct role.
Lab results can provide direct evidence of biomarkers, disease characteristics, severity, progression, monitoring behavior, and treatment response. That gives analytics teams a different type of signal than utilization or transactional data alone.
Claims may suggest that an HCP is evaluating a patient for a condition. Laboratory results can add evidence about whether the relevant clinical characteristics are present.
An EHR diagnosis may indicate that a disease appears in the record. Lab values may help clarify where a patient sits within a clinically meaningful population or disease trajectory.
A prescription claim may indicate treatment initiation. Subsequent testing may provide additional insight into monitoring patterns or changes in the patient’s clinical picture.
For patient identification and HCP audience development, that distinction can sharpen the difference between possible relevance and clinically supported relevance.
And in an increasingly fragmented data environment, that clinical specificity matters.
It is tempting to think that increasingly sophisticated models will eventually compensate for gaps in individual datasets. Give an algorithm enough information, the reasoning goes, and it can infer what is missing.
That is not where the greatest value of AI is likely to come from.
The strongest models make high-quality inputs more useful. A model designed to identify patients who may be progressing, for example, can incorporate dozens or hundreds of variables. But when clinically meaningful laboratory signals are available, those signals can help distinguish meaningful patterns from weaker proxies and indirect indicators.
The same is true for HCP targeting.
Patient volume alone does not necessarily identify the HCPs treating the populations most relevant to a therapy. Knowing which HCPs are connected to patients with particular biomarkers, test results, diagnostic patterns, disease stages, or monitoring events can create a more actionable view of where education and engagement may matter.
This is especially relevant as precision medicine becomes more precise. Increasingly, therapies are developed for populations defined not simply by a diagnosis, but by a mutation, biomarker, laboratory threshold, or other measurable clinical characteristic. AI can help find patterns and prioritize opportunities, but the clinical signal often determines why a patient is relevant in the first place.
AI is the intelligence layer. Lab data provides the clinical signal that helps ground that intelligence in what is actually happening with the patient.
Of course, having access to laboratory records is not the same as having analysis-ready lab data.
Laboratory information can itself be fragmented across sources, schemas, terminology, units, test names, and reporting conventions. Raw volume without normalization can simply introduce another layer of complexity.
That makes harmonization and enrichment critical. Standardizing clinical and genomic lab data, interpreting results consistently, connecting those signals to the broader patient journey, and linking them appropriately to treating HCPs can turn disparate records into information that commercial analytics teams can use.
This is where Prognos Health fits naturally into the conversation. Its focus on harmonized, enriched, analytics-ready clinical and genomic lab data is designed to help life sciences teams turn fragmented diagnostic records into signals they can use across patient journey analysis, HCP targeting, and commercial decision-making.
This is an important part of the AI conversation that is sometimes missed. The competitive advantage does not come simply from feeding more records into a model. It comes from giving that model precise, timely, clinically meaningful information in a form that can be analyzed and acted upon.
In other words, the quality of the intelligence layer is constrained by the quality of the data foundation beneath it.
The continued growth of virtual care, concierge medicine, direct-pay models, retail healthcare, and other alternative care channels makes this even more important.
The assumption that a patient journey can be reconstructed primarily through one health system—or through conventional claims pathways alone - is becoming harder to maintain.
A patient may receive primary care through a membership-based practice, see a specialist within a large health system, use a virtual provider for another condition, fill prescriptions through several channels, and undergo diagnostic testing somewhere else entirely.
No single source necessarily captures the complete journey.
Laboratory data does not solve that fragmentation on its own, nor should it be treated as a complete representation of the patient. But clinically grounded signals can serve as important anchors within a distributed journey, helping analytics and AI systems distinguish meaningful clinical events from an expanding volume of contextual data.
For pharma and biotech commercial teams, that can support earlier patient identification, more precise HCP segmentation, a clearer understanding of testing and treatment patterns, and more timely engagement around moments that matter in the patient journey.
The next generation of healthcare targeting will not be built around choosing claims or EHR data or laboratory information or AI.
It will come from combining complementary sources intelligently.
Claims can illuminate healthcare utilization and treatment patterns. Clinical records can provide additional context. Consumer and behavioral information can help explain access and engagement. AI can connect these signals, surface relationships, and accelerate the path from data to decision.
Harmonized clinical and genomic lab data contributes something different: a precise and often timely view into the clinical characteristics that make a patient relevant to a particular disease, treatment, or intervention.
As therapies become more targeted and patient journeys become more distributed, that specificity becomes increasingly valuable.
The AI era will not reduce the need for clinical signal. It will create a premium on it.
For Prognos, that creates a clear opportunity: help life sciences organizations make those clinical signals easier to access, harmonize, connect, and act on within the broader data and AI ecosystem.
The organizations that get the most from AI will not simply be the ones with the most patient data. They will be the ones that can give AI the right clinical signals and translate those signals into action at the right moment.