Beyond the ICD Code: Why Patient Finding Must Lead to Commercial Action

Author

This is the first post in a series exploring how life sciences organizations can turn patient-finding insights into commercial action. This blog gives TGaS Advisors’ perspective.

For years, the life sciences industry has invested heavily in finding patients earlier, more accurately, and at greater scale. As data sources expand and machine learning models improve, patient-finding programs have become more sophisticated. A new generation of AI capabilities now promises to unlock signals from clinical notes, referral pathways, genomic data, and other sources that traditional approaches have historically struggled to incorporate.

Yet an important question often goes unasked: what happens after a patient is found? Most conversations about patient finding focus on identification: how many additional patients can be uncovered, which models perform best, and what new data sources should be incorporated. Those are important questions, but they tell only part of the story.

Finding more patients creates commercial value only when organizations translate those insights into action. Signals must reach the right teams, inform targeting and field planning, and feed back into the organization’s understanding of what works. Without those capabilities, even a sophisticated patient-finding model can become another dashboard disconnected from commercial action.

Increasingly, patient finding is evolving from an analytics exercise into an organizational capability.

Which Patients Traditional Data May Miss

Historically, commercial analytics has been built around patients who are already visible in the data. Claims data, diagnosis codes, prescribing histories, structured electronic health records, and patient journey analyses remain foundational to how organizations estimate patient populations, identify target HCPs, and prioritize field activities, offering valuable insight into what has already happened and who is currently treating patients.

The limitation of these methods is that they all share a common starting point: the patient has already become visible somewhere in the data. Traditional patient-finding workflows may not capture patients who are:

• Undiagnosed
• Misdiagnosed
• Coded under broad disease categories
• Described only in unstructured clinical documentation

In many therapeutic areas, those patients represent a meaningful share of the population organizations are trying to reach.

This challenge is especially pronounced in disease states with diagnostic complexity. Rare diseases, autoimmune conditions, neurological disorders, and biomarker-defined oncology populations often involve lengthy diagnostic journeys, overlapping symptoms, and referral barriers. By the time patients become visible in traditional datasets, opportunities to support diagnosis or treatment may have passed.

Patient finding, in other words, is no longer simply about understanding patients who have already been diagnosed. It is increasingly about understanding the population that remains outside traditional visibility altogether.

How AI Expands Patient Visibility

Machine learning has helped organizations identify previously unseen patient signals for some time. What is changing is the breadth of information teams can analyze and their ability to extract meaningful signals from data sources that have historically been difficult to use. This includes clinical notes, referral patterns, genomic information, and symptom progression data that often sit outside traditional commercial workflows.

Claims data, structured records, and historical patient journeys remain essential to commercial operations. Newer AI capabilities are best understood as an extension of that foundation rather than a replacement for it. They give organizations a more complete view of the patients they are trying to reach.

This shift has implications for commercial strategy. In many disease areas, organizations can identify additional patients through approaches that go beyond standard claims-based queries. The more pressing question is how those insights fit into the broader commercial model once patients are identified.

What Happens After a Patient Signal Is Identified

Many organizations invest considerable resources in patient-finding models, then encounter obstacles as those insights move beyond the data science environment. Common obstacles include:

• Signals do not reach the field quickly enough to inform action
• Priorities are not embedded in the workflows reps and account teams already use
• Teams do not understand or trust the recommendation

When a newly identified patient is eventually diagnosed and starts therapy, that outcome can go uncaptured. The model then continues to operate on the same assumptions rather than improving with each cycle.

In situations like these, the technology may be functioning exactly as intended while the broader initiative still falls short of its potential because the barrier is operational, not technical.

This distinction matters because commercial value is not created when a patient is identified. It is created when that insight changes a decision, shapes an engagement, or improves an outcome. Capturing that value requires more than a well-performing model. It requires workflows that connect analytics teams, field organizations, commercial operations, and medical affairs around a shared signal. It also requires clear ownership and a mechanism for learning from results over time.

Organizations that treat patient finding as a standalone analytics initiative may find that value harder to sustain than organizations that treat it as an enterprise capability built to last beyond any single model or brand team.

The Next Phase of Patient Finding

For years, discussions about patient finding centered on data access and model sophistication. Those topics remain important. The industry is now entering a phase in which the more consequential question is whether an organization is prepared to act on what its models reveal.

As patient finding advances, commercial leaders should ask:

• Can new patient signals translate into field action that reps and MSLs trust and use?
• Can they influence account planning and targeting strategy rather than add another list to review?
• Can organizations build feedback mechanisms that improve performance over time and make patient finding a repeatable commercial capability rather than a series of disconnected analytics projects?

The organizations that gain the greatest advantage may not be those with the most advanced algorithms. They may be those that connect analytics, field execution, and organizational learning into a single coordinated capability.

At TGaS, conversations with commercial insights and advanced analytics leaders increasingly reflect this shift. These discussions are moving beyond what patient-finding technologies can do and toward how organizations can operationalize those capabilities. Finding a patient is only the beginning. What happens next is where value is created.

Is your organization prepared to act on what its patient-finding models reveal?

Connect with us to discuss how to integrate those insights into commercial planning and field execution.