The biopharmaceutical landscape is on the cusp of a transformative shift as artificial intelligence (AI), particularly Large Language Models (LLMs) and machine learning, take center stage in redefining market access strategies. In an industry where understanding and integrating complex data sets becomes pivotal, AI emerges as a potent ally. But what distinguishes AI from static algorithms, and how do adaptive algorithms reshape the pharma market access paradigm?

In his prescient work, “Deep Medicine,” Eric Topol envisioned a future where AI’s role in healthcare extends beyond mere assistance – it becomes a cornerstone of medical innovation. As we narrow our scope to market access, we will set aside R&D and medical affairs, rather focusing on how AI will impact drug pricing, patient access, and the overall healthcare journey.

This post explores five significant ways AI is set to influence pharma market access: from altering patient-doctor interactions with more informed pre-consultation resources to aiding physicians in sifting through the ever-growing mountain of medical literature. We’ll unravel the implications of these advances and what they mean for biopharma manufacturers, who must now position themselves strategically in an AI-integrated healthcare ecosystem.

1. From “Dr. Google” to AI-Assisted Diagnostics in Pharma Market Access

Gone are the days when “Dr. Google” was the go-to joke for patients self-diagnosing online. Today, we’re witnessing the emergence of a more sophisticated advisor in artificial intelligence-powered platforms. AI and LLMs have begun to redefine the preliminary stages of patient healthcare journeys, often becoming the first point of consultation before a doctor’s visit.

The concept of ‘Dr. Google’ has evolved to become a harbinger of a new era where integrated AI systems combine the immediacy of search engines with the nuanced analysis of medical data. This disruption in patient education is in addition to the changes machine learning is bringing to fields like molecular pathology and dermatology, with their reliance on visual diagnostics, are particularly ripe for AI intervention.

Implications for Pharma:

• Bias in Training Data: As AI systems become a front-line filter for patient symptoms, biases inherent in their training data could lead to skewed diagnoses and treatment recommendations. (This bias presents a crucial health equity challenge, which is a post of its own.) For product selection, if an LLM relies on training data that does not include scientifically appropriate or balanced data on a specific medicine, the recommendations patients will see in the output of the AI will be suboptimal. For biopharma manufacturers, it is key to ensure that their products are represented fairly within these datasets, contributing to balanced AI guidance and maintaining trust in the healthcare system.
• Data Integration: The capability of AI to assess conditions through photos and molecular signatures will be leveraged for digital diagnostics. Particularly as these digital products become consumerized, biopharma companies must integrate their product data into these platforms, positioning their treatments at the forefront of AI-recommended solutions.

2. Empowering Physicians with LLMs: A Milestone for Market Access

The volume of medical literature is growing exponentially, making it nearly impossible for physicians to stay current without the aid of AI and LLMs, a key factor in pharma market access. These AI-driven systems can digest vast expanses of text, summarizing the most pertinent information for healthcare professionals (HCPs).

In the current climate of medical advancements, the volume of literature is not just growing: it’s exploding. A 2011 study estimates that medical knowledge now doubles every 73 days, and physicians are having trouble keeping pace. Particularly in fast-paced fields like oncology, the ability for LLMs to summarize research will be a welcome development to HCPs. This future will be realized when LLMs are able to more deeply incorporate real-time data as they are published, and when LLMs solve their “hallucination” problems.

a rate that is simply unsustainable for any human to keep up with, particularly in fast-paced fields like oncology. Herein lies the invaluable role of Large Language Models (LLMs) – AI-driven systems capable of digesting vast expanses of text to summarize and highlight the most pertinent information for healthcare professionals (HCPs).

Implications for Pharma:

• Empowering HCPs with AI Skills: Because this skill set will be new to providers, training in LLM prompt-engineering should become a part of pharma companies’ education programs, both disease-state education (DSE) and continuing medical education (CME). Fluency in prompt engineering on the part of HCPs can support a greater awareness of how to leverage these tools in support of patients.
• Influencing Payer Decision Making: Payers employ clinicians to help develop medical policy, review appeals, and determine formulary design; these clinicians face the same challenges as practicing providers in keeping up with the deluge of published clinical data. As payer medical directors increasingly rely on LLMs to summarize evidence, there is a pressing need for biopharma companies to ensure their health economic data is not only published in peer-reviewed journals but also accessible via SEO-optimized platforms. This dual publication strategy maximizes the likelihood that LLMs will retrieve and utilize their data, influencing formulary decisions and market access.

3. Machine Learning: Optimizing Prior Authorizations for Enhanced Pharma Market Access

Hub service providers are a critical link in the chain of patient access to medications, aiding provider offices through the intricate payer prior authorization steps, among other services. As biopharma products increasingly enter the market, the volume of claims and appeals these hubs handle offers a unique dataset ripe for analysis through machine learning. Specifically, because hubs have the potential to collect and analyze data on which language, processes, etc. lead to the most successful claims, these service providers have the opportunity to apply machine learning to these data sets to effect continuous process improvement.

Implications for Pharma:

• Leveraging Asymmetry in Data: A single hub service provider typically processes a higher volume of claims and appeals for a particular medication than any one payer or Pharmacy Benefit Manager (PBM). This data asymmetry means that hub providers are uniquely positioned to identify successful authorization strategies. By structuring their data effectively and overlaying machine learning, hubs can outpace payers in learning what works, leading to improved approval rates and faster access for patients to necessary medicines.
• Investment in AI for Business Efficiency: The investment in machine learning tools represents a significant business opportunity for hub providers. Those firms who capitalize on AI stand to gain substantial economic benefits by streamlining their approval processes. For biopharma manufacturers, understanding which hub services invest in these capabilities can be crucial when deciding on partnerships.
• Scrutiny Over “Technology-Driven” Services: As hub providers already tout “technology-driven” services and begin to pitch use of AI, market access professionals must approach these claims with a critical eye. While AI offers promising advancements, the real measure of success lies in demonstrable outcomes. Contracting with hub services should be informed by tangible metrics, for example approval rates and cycle times.

4. AI’s Role in Clinical Pathway and Treatment Guideline Design for Pharma Market Access

Clinical pathways and treatment guidelines are essential tools in modern healthcare, designed to streamline decision-making and standardize care. Both are critical in shaping treatment decisions, yet they also risk becoming quickly outdated in the face of rapid clinical innovation.

The integration of AI into the development and updating of these pathways and guidelines marks a significant shift. Initially, AI can support physicians and committees in interpreting the latest research and incorporating it into current practices (in the same way as #2 above). Eventually, AI could take a more central role, drafting these documents directly with sophisticated algorithms that keep pace with the latest medical advancements.

Implications for Pharma:

• Ensuring AI Accessibility to Evidence: For biopharma companies, the rise of AI in this domain underscores the necessity of making clinical and economic evidence AI-friendly. Ensuring that data on drug efficacy, safety, and cost-effectiveness are accessible to AI algorithms will be crucial. This accessibility may influence the speed and frequency with which their products are included in or excluded from clinical pathways and treatment guidelines.
• Proactive Engagement with AI Systems: Biopharma manufacturers must actively engage with the systems and platforms where AI draws its information. They must ensure their evidence is not only robust and peer-reviewed but also formatted in a way that AI can easily interpret and utilize.
• Monitoring AI-Driven Changes: As AI systems begin influencing or even creating clinical pathways and treatment guidelines, there is a potential for significant shifts in drug utilization patterns. Pharma companies will need to monitor these changes closely, as AI’s recommendations could rapidly and opaquely alter market dynamics and access for their products.

5. Leveraging Machine Learning in Revenue Cycle Management for Pharma Market Access

Healthcare revenue cycle management is a complex and nuanced process where billing codes and their sequencing can significantly impact revenue. Hospitals and healthcare systems already have substantial staff resources dedicated to these business needs and are starting to deploy machine learning to optimize these billing processes, ensuring the maximum revenue capture for each patient encounter.

Implications for Pharma:

• Navigating Site-of-Care Economics: The deployment of AI in revenue cycle management has direct implications for biopharma, especially concerning site-of-care decisions for their products. For example, machine learning can highlight economic variances between inpatient and outpatient administration that might otherwise be overlooked. For drug developers, even though these shifts may be outside of their control, understanding these variances is crucial, as they can influence everything from pricing strategies to patient access programs.
• Predictive Analytics for Billing Optimization: By using predictive analytics, healthcare provider systems can more accurately determine the most financially advantageous ways to code and bill for treatments, including pharmaceuticals and medical devices. This level of sophistication in billing practices is likely to lead to an escalation in the financial arms race between providers and payers, with payers in this case finding themselves at a disadvantage due to less data transparency.
• Strategic Insight for Access Professionals: For access professionals within biopharma, these AI tools offer a new frontier to model the economic impact of various coverage, coding, and payment scenarios. In pipeline decision making, mastery over these models will be essential for strategic planning, providing a clearer understanding of how AI-driven billing practices can affect market access for their products.

Conclusion: Embracing AI’s Role in Pharma Market Access

As AI continues to make indelible strides across the healthcare industry, biopharma will be impacted. From AI-assisted diagnostics to AI-informed clinical pathways, the role of AI in pharma market access is both transformative and imperative. Biopharma companies must ensure their data are accessible and actionable within AI frameworks and remain vigilant, ready to adapt their strategies in response to the AI evolution in pharma market access. The digital future of pharma market access is here, and the industry must embrace this shift without delay.