Oct 20 2026
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Treatment Pathway Mapping: Uncovering Hidden Unmet Need in Real-World Care
Background:
A mid-size pharmaceutical company was preparing to advance a late-stage asset in a chronic, specialist-led disease area. Epidemiological modeling pointed to a sizable addressable population, but early commercial assessments flagged a persistent gap between theoretical prevalence and the smaller number of patients actively receiving treatment. Available treatment options were not the limiting factor; the disconnect pointed instead to how care was actually being delivered versus how it was designed on paper. This ambiguity made it difficult for the company to size its market with confidence or build a commercialization narrative grounded in reality rather than assumption.
Challenges:
Three interlinked problems anchored the engagement:
- Unexplained attrition between diagnosis and treatment: A meaningful share of the epidemiologically eligible population never appeared in treated-patient counts, and standard market-sizing tools offered no explanation for where or why they were lost.
- Opacity in real-world treatment behavior: Prescribing data showed patients cycling through multiple lines of therapy without durable control, but claims and registry data alone couldn’t distinguish why efficacy failure, tolerability, or access barriers each produce a similar signature in the data.
- Invisible risk-averse prescribing patterns: Anecdotal input suggested clinicians were extending earlier-line or off-label regimens well past intended duration, but this behavior was not captured in any structured dataset the company already had access to.
Together, these gaps meant the company’s forecast rested on an addressable population that looked credible on paper but was not defensible against real-world evidence.
Approach and Methodology:
The team designed a structured treatment pathway mapping exercise across three priority markets, built around four sequential workstreams:
- Pathway reconstruction: Standards of care were mapped end-to-end; primary care entry, diagnostic workup, specialist referral, treatment initiation, and ongoing management, using guideline documents, real-world prescribing data, and payer coverage rules as triangulating inputs rather than any single source of truth.
- Physician and care-team validation: Structured interviews with specialists, prescribing clinicians, nurses, and care coordinators tested where daily practice diverged from guideline intent, converting anecdote into a comparable, coded dataset.
- Discontinuation and switching analysis: Every transition point in the pathway was scored against three failure modes; loss of response, tolerability, and access-driven drop-off, to separate clinical from structural causes of attrition.
- Off-label and suboptimal use assessment: Conservative dosing, prolonged first-line use, and off-label substitution patterns were catalogued specifically among patients unable to tolerate or access guideline-recommended options.
The methodological discipline here was treating “why patients leave the pathway” as a classification problem, not a residual, each drop-off point had to be attributed to a specific, evidenced cause rather than lumped into generic non-adherence.
Solution Implemented:
The output was a stage-by-stage pathway model, cross-validated against prescribing data and physician input, that reclassified the “missing” patient population into three named, quantifiable sub-segments:
- Patients discontinuing due to durability or tolerability failure
- Patients held on suboptimal or earlier-line therapy by risk-averse prescribing
- Patients lost to access-driven attrition (step therapy, prior authorization, delayed initiation)
This reclassification let the company move from a single undifferentiated “unmet need” figure to a segmented model, each segment carrying distinct clinical and commercial implications.
Results and Impact:
The analysis showed that unmet need was not a function of limited therapeutic choice; it was concentrated in durability, tolerability, and access failures largely invisible to topline epidemiology or prescription-count data alone:
- Poor durability/tolerability emerged as the largest single driver of pathway attrition, with a substantial share of switching patients cycling through two or more prior therapies without sustained control.
- Prolonged suboptimal treatment, driven by clinician risk aversion and monitoring complexity, kept a material subset of patients on earlier-line or off-label regimens well beyond clinically intended duration.
- Access-related attrition like step edits, prior authorization, and delayed initiation accounted for a further identifiable slice of otherwise-eligible patients who never reached appropriate therapy.
Grounding the model in these real-world pathways allowed the company to recalibrate its addressable population upward, formally reintroducing a high-value segment that had been excluded from prior forecasts. The findings directly shaped three downstream deliverables: a medical narrative centered on durability and tolerability gaps, an evidence generation plan targeted at documented discontinuation and switching drivers, and a field strategy built around referral-pathway and access-barrier conversations.
Reflective Analysis: Why It Worked, and What’s Next
The exercise succeeded because it treated “missing” patients as a measurement problem rather than a forecasting assumption; each attrition point was investigated and attributed, not smoothed over with a generic conversion rate. This is the core transferable lesson: standard epidemiology and prescription-volume data are structurally blind to why patients don’t reach or stay on treatment, and closing that blind spot requires primary validation (physician and care-team interviews) layered onto secondary data, not secondary data alone.
The natural extension of this work is longitudinal tracking, revisiting the same pathway markers at 12- and 24-month intervals post-launch to test whether the durability, tolerability, and access hypotheses hold as real-world usage data accumulates, and to refine the segmentation model as the asset’s actual performance becomes observable.