Introduction

SPEAKERS:
Gurjeev Singh, Head of Product, Ostro

KEY TAKEAWAYS:

• 87% of consumers cannot comprehend standard pharma content
• Non-generative architecture achieves 100% PRC/MLR approval across dozens of implementations
• Oncology brand achieved 10X increase in HCP asset downloads over five months
• Independent study validates 200% script lift increases and 125% brand retention improvements
• Multi-channel orchestration enables asynchronous HCP engagement between exam rooms and patient touchpoints

The pharma industry faces a crisis hiding in plain sight. While brands invest millions in patient education materials and HCP resources, most of that content remains functionally invisible to the people who need it most. The accessibility problem runs deeper than distribution—it's about fundamental health literacy gaps and user experience expectations shaped by consumer technology.

Gurjeev Singh explained at Pharma Customer Engagement USA that "before I joined this industry, I didn't even know what a copay card was," illustrating how specialized pharma terminology creates barriers even for educated professionals. As AI transforms how patients and healthcare providers interact with information across every other industry, life sciences stands at an inflection point: adapt to consumerized engagement standards or watch competitors capture market share through superior digital experiences.

The Health Literacy Crisis Driving Underperformance

Singh opened his presentation with a statistic that reframes the patient engagement challenge entirely.

"87% of consumers lack the reading comprehension skills required to understand the average pharma content published—87%. That's a huge problem,"

he stated. This isn't merely a patient education issue; it represents a fundamental commercial barrier preventing brands from converting content investments into engagement outcomes that drive prescribing behavior.

Traditional one-size-fits-all content strategies fail because they assume uniform comprehension levels across diverse patient populations. Brands create comprehensive asset libraries—patient management guides, dosing resources, financial assistance tools—only to see minimal utilization rates. The problem isn't content quality or therapeutic messaging; it's discoverability and personalization at the moment of need.

Ostro addresses this through AI-powered semantic matching that understands user intent regardless of how queries are expressed. The system interprets misspellings, cross-language requests, and colloquial phrasing to surface relevant approved content. Singh emphasized the platform's regulatory foundation and implementation velocity: 

"We have 100% success rate through PARC/MLR. We have interoperability with existing systems, we implement in less than eight weeks, and we're built to scale."

The commercial impact appears most clearly in asset utilization data from production implementations. An oncology brand case study revealed dramatic improvements over a five-month comparison period before and after deployment. Singh reported the results:

"46 HCP downloads of the Patient Management Guide before Ostro was implemented. After these tactics were implemented? 570 HCP downloads. That's a 10X increase in HCP engagement."

Long-term data downloads similarly increased from 54 to 1,559—nearly 28X growth. These weren't new assets or additional marketing spend; they were existing approved materials that became accessible through better discovery mechanisms that matched how users actually search for information.

Purpose-Built Architecture for Regulatory Velocity

The presentation distinguished Ostro's approach from consumer-grade generative AI tools that create regulatory uncertainty in life sciences environments. Singh explained the fundamental architectural decision that enables both innovation and compliance:

"We don't do anything generative in nature and that's intentional. That's by design... What we're doing is we're taking inbound requests from the user, understanding semantic intent, and then we're matching pre-approved PARC/MLR content."

This design choice eliminates hallucination risks while maintaining the personalization benefits users expect from AI systems shaped by ChatGPT and similar consumer tools. The platform doesn't create new responses or synthesize information across multiple sources; it surfaces existing approved content based on semantic understanding of user queries.

When relevant content doesn't exist in the approved asset library, the system routes users to appropriate resources like rep scheduling or medical information contacts rather than attempting to answer beyond its knowledge base.

Pharmacovigilance requirements receive similar purpose-built treatment. Singh detailed the safety monitoring approach: 

 "Our system has a two-tier adverse event reporting mechanism. So we have the ability to identify adverse events that are coming in both from an AI model standpoint and detect that. And then the second piece is we have human-level review."

The hybrid AI-human monitoring addresses regulatory obligations that typically slow digital innovation in pharma, creating confidence for legal and regulatory reviewers evaluating deployment proposals.

The 100% PARC/MLR approval rate across dozens of implementations with top 20 pharma companies stems from implementation methodology, not just technology features. Singh revealed the success factor: 

"Oftentimes it starts with the concept review, and our team works hand-in-hand with the brand team to make sure the concept is reviewed properly. If you have a concept review that goes successfully, it's almost guaranteed that you have a successful MLR result."

This upfront collaboration approach contrasts with post-development remediation models that create approval delays and rework cycles. The sub-8-week implementation timeline—from contract signature to production deployment—demonstrates how regulatory confidence accelerates time-to-market for commercial outcomes.

Multi-Channel Orchestration Matching HCP Workflow Realities

Singh demonstrated three complete patient and HCP journeys showing seamless transitions between web chat, email, SMS, and rep scheduling. The strategic insight centers on matching actual workflow constraints rather than forcing users into brand-preferred channels or desktop-dependent interactions.

The HCP time constraint example proved particularly compelling. Singh drew from personal observation:

"My wife is a physician. I see it all the time. She comes home from work, she's charting at night, she has zero time to do notes in between patients."

This reality makes synchronous, desktop-dependent interactions functionally invisible during the micro-moments when prescribing decisions happen—between exam rooms, during brief documentation windows, or when patients ask unexpected questions about new treatment options.

The platform's SMS capability enables HCPs to text between exam rooms requesting mechanism of action information or dosing details, receiving instant PARC/MLR-approved responses without waiting for scheduled MSL appointments. Email engagement provides follow-up after web interactions, allowing continued asynchronous dialogue when providers have time to review more detailed clinical information.

The ecosystem approach—Navigate for consumers, Tailor for HCPs, Airmark for email, Prompt for SMS—creates continuous engagement regardless of where users enter the journey or which channel fits their current context.

Patients face similar comprehension challenges around financial assistance, coverage verification, and pharmacy networks. Multi-channel orchestration enables contextual education at the moment of need rather than requiring users to navigate complex information architectures or remember details from earlier touchpoints.

From Engagement Metrics to Commercial Outcomes

The presentation culminated with commercial validation that distinguishes script-level impact from vanity metrics like page views or session duration. Singh cited independent research results: 

"We've done independent third-party studies, activated triplet studies. We've seen things like 200% increase in script lifts, we've seen 125% increase in retaining brand RX."

Additional outcomes included new-to-brand patient acquisition and competitive therapy switches measured through prescription data analysis.

These results matter because they connect digital engagement improvements to prescription behavior change—the ultimate measure of commercial success in pharma marketing. Most pharma brands optimize for intermediate metrics without validating whether asset downloads or form fills actually drive prescriptions. The third-party study methodology provides measurement frameworks that link touchpoints to script data, enabling ROI calculations for digital investments.

The broader context positions pharma's digital transformation as business imperative rather than experimental initiative. Singh noted enterprise AI adoption trends: 

 "More than 80% of organizations are now regularly using generative AI. We're seeing up to $2 trillion of spend projected for AI in 2026."

These investment levels reflect AI's impact on user expectations across all technology interactions, not just pharma-specific applications.

The consumerization comparison to transportation, entertainment, and food delivery proved strategic rather than aspirational. Patients and HCPs increasingly judge pharma digital experiences against best-in-class consumer standards, not pharma peer benchmarks.

To get you highlights of Pharma Customer Engagement USA faster, we are using generative AI technology to summarise the transcripts of the sessions. If you have any feedback about the summary, please contact lucy.fisher@thomsonreuters.com.

Discover more on this topic at Pharma USA 2026 (March 17-18, Philadelphia) - North America's largest cross-functional pharma gathering. Visit the website here.