Pharma generates more insight than ever. Translating that insight into timely action is where the system falters, and the gap runs deeper than most commercial leaders are willing to admit.
Over the past decade, omnichannel systems have matured considerably. CRM platforms improved and digital orchestration sharpened. Targeting models grew more sophisticated as data science moved from descriptive to predictive. On paper, the machinery delivered what it
promised: reach the right healthcare professionals with the right message through the right mix of channels.
And yet most commercial leaders will admit the same frustration. Investment continues to rise, but the impact does not reliably scale with it.
That tension is usually framed as a failure of omnichannel. In reality, the system has been optimizing the wrong objective and measuring the wrong things in order to validate it.
The metrics problem
Most omnichannel programs are still governed by legacy KPIs: reach and frequency, email opens, click-through rates, touch counts. The industry still runs on heuristics like “it takes 16 to 20 touches before you see prescribing uplift.” Sixteen to twenty what, exactly? Emails? Rep
visits? Banner ads? Scientific events? The number persists without any real understanding of which sequence of actions moves a physician from awareness to consistent prescribing for appropriate patients.
Activity is easy to measure. Proving real impact is far more difficult. When the only story a campaign can tell is how many physicians opened an email or clicked through to content, the system has optimized for visibility rather than behavior change. Attribution to meaningful outcomes becomes secondary, and engagement proxies begin to resemble objectives.
This is where the plateau becomes visible. Omnichannel has improved coordination and reach, yet it has not reliably accelerated how quickly the right patients receive the right therapy. In the blockbuster era, that gap mattered less. When patient populations run into the tens of millions, the funnel can absorb inefficiency. Diagnostic delays, referral friction, and missed connections do not necessarily undermine the economics.
Precision medicine changes the math entirely. Therapies built for populations of 40,000 or 80,000 patients cannot tolerate the same lag.
The patient the system can’t see
Every delayed diagnosis represents a meaningful share of the people the therapy was built for. The delays are not abstract. A patient senses something is off and visits a primary care physician who does not recognize the pattern. Referrals follow, sometimes to the wrong place, often without the full clinical story traveling with them. What should be a direct path turns into fragmented consultations and inconclusive testing. Months pass in that loop. Occasionally, years.
For rare diseases, diagnostic timelines stretching five to ten years are well documented. The pattern extends well beyond rare conditions. Certain autoimmune disorders, metabolic diseases, and cancers carry delays that would surprise anyone who has not experienced them
directly. The therapies exist. The science is established. Yet patients often fail to reach the physician positioned to recognize the pattern in time.
This part of the journey rarely appears in brand dashboards or launch readiness decks. It also reveals a second blind spot, one that compounds the limitations already embedded in the commercial model.
Commercial systems have become genuinely skilled at modeling physician behavior, from prescribing tendencies to responsiveness to content and likelihood of trial adoption. Far less attention has gone to understanding where patients are before they reach a specialist’s office. The system is built to reach the physician. It was never designed to see the patient who has not yet arrived.
That gap sits between development and commercialization. Development is built on the expectation that patients will be identified and treated. Commercial operations, by contrast, focus on reaching and influencing the physicians who will prescribe. When identification slows, both sides are operating on assumptions that no longer hold.
Two blind spots, in a maturing system
The failures are connected. A commercial model that cannot see patient journeys before diagnosis and cannot measure whether engagement changed behavior is operating with two blind spots at once. Blockbuster portfolios could tolerate that kind of imbalance because volume compensated for inefficiency. Precision therapies leave far less room for that compensation.
The data needed to start closing the patient-side gap is not theoretical. Claims records, lab results, referral patterns, and procedure codes trace fragments of the path patients travel before a diagnosis is named. No single element explains the trajectory. Viewed over time and at scale, however, structure appears. Some patients cycle through the system in ways that signal an unrecognized condition, while other journeys stall in predictable places.
This argument does not depend on predicting individual diagnoses or replacing clinical judgment. The boundary between clinical expertise and commercial activity exists for good reason and should remain intact. Separation, however, should not translate into blindness. When commercial teams understand which physicians are likely to encounter patients moving through certain referral and testing paths, outreach can align with real decision points. The objective is improved timing.
Where execution breaks
Many organizations already generate predictive insight. They score physicians, identify opportunity segments, and surface signals that point to potential demand. The friction often occurs at activation. Static personas, manual campaign sequencing, siloed systems, and med-legal timelines slow the transition from insight to action, creating a lag between what the data indicates and what the market ultimately experiences.
AI becomes relevant here because it can integrate fragmented commercial and de-identified patient-level signals into usable guidance without displacing clinical judgment. Once unified, those signals begin to reveal why certain engagement sequences gain traction and others stall.
Teams can test and refine approaches in shorter cycles, often measured in weeks instead of quarters, and field recommendations reflect observed behavior rather than fixed segmentation models. Decisions remain human. The system adjusts as more outcomes are observed. That opens the door to a more adaptive commercial model rather than a more critical one.
The objective of omnichannel is shifting. Earlier models concentrated on orchestrating channels around physician profiles, an effort that strengthened coordination across teams. The next step requires attention to the interval between emerging patient need and appropriate treatment, along with evidence that engagement influenced behavior in ways that mattered for patients.
What success looks like now
The focus shifts away from touch counts toward whether a sequence of engagement made it easier for a physician to treat the right patient at the right moment. That may show up as movement from ignoring content to engaging with it, from skepticism to selective use, or from
trial to consistent prescribing for appropriate patients. Those transitions become clearer when commercial and behavioral data are viewed together, even if they rarely appear in traditional engagement metrics.
As pricing pressure intensifies and expectations around demonstrated value tighten, the need for reorientation becomes harder to dismiss. Therapies introduced earlier in the disease course generate different real-world evidence than those introduced later. Over time, those differences influence health economic narratives and long-term commercial credibility.
Omnichannel largely delivered against the objectives it was given. Whether those objectives still reflect the landscape precision medicine has created is a separate question.
When success is defined primarily by activity and reach, performance approaches a natural ceiling under current definitions of success. Defining success in terms of compressing the time between first signal and appropriate treatment requires a wider lens. That lens includes patients who have not yet arrived in a specialist’s office, as well as evidence that engagement, once they do, influenced behavior in ways that mattered.
For precision medicine to deliver on its promise, execution has to catch up with insight, and that insight must extend to the patient’s journey.
Author Bio
Pete Harbin, ODAIA Chief Strategy and Customer Officer
With over 25 years in Life Science, Pete Harbin is known for his market influence and expertise in Business Intelligence, Information Management, and Customer Science. Formerly a Partner/Managing Director at Deloitte Consulting, Vice President at Veeva Systems, and General Manager at IMS Health (IQVIA), Pete has impacted 100+ Life Science companies.
