Commercializing new treatments requires a coordinated effort from diverse teams within life science organizations—brand, field, finance, contract, pricing, and market access, to name a few. Navigating the complexities of today’s healthcare and payer systems, reimbursement policies, and regulations requires collaboration and data-driven analysis.
Pricing and market access teams in particular face an added layer of challenges—an emerging perfect storm ⛈️—of imminent patent expirations, limited drug pipelines, high R&D expenditures, escalating rebates, and more. These challenges, akin to the threatening waves in Hokusai’s woodblock print, pose a serious risk of capsizing the business. 🌊
In this post, we’ll outline how AI-powered approaches to pharmaceutical and biotech pricing, contracting, rebates, and revenue management can help commercial and market access teams navigate this storm. In the end, they’ll emerge more unified, agile, and data-driven, ultimately driving cost-effective access and better patient outcomes. Let’s dive in!
In This Post
The impending storm
Pricing and rebate dynamics are complex.
Manufacturers, wholesalers, PBMs, payers, pharmacies, regulators, and market access teams must align incentives to get the right drug into the right hands at the right price.
(Image: The Center for Improving Value in Health Care)
On top of typical complexities, several headwinds are converging to create the perfect storm for market access and pricing teams:
- Impending patent cliffs. According to research from ZS Associates, patent expiration is expected to cause the top 10 pharmaceutical companies to lose around $200 billion in revenue over the next few years. To put that into perspective, that’s 45-50% of current revenue.
- Soft drug pipelines. Typically, companies would fill this gap with promising drug candidates. However, today’s pipeline replenishment rates are not sufficient to fill the revenue gap, likely only able to mitigate current revenue risk by a few percentage points.
- Increased R&D spend. R&D spending patterns have shifted as companies are investing more in R&D and innovation as a percentage of revenue to try to identify the next blockbuster drug to shore up impending revenue gaps. According to ZS, R&D spend is expected to accelerate from 14.4% of sales to 17% over the next 10 years.
- Surging rebates. Rebate spending touched $200 billion in 2021 and is growing at a compound annual growth rate (CAGR) of 6%, driven by a combination of market dynamics, negotiation intricacies, and competitive pressures.
- Executive pressure to optimize access cost. The top 25 pharma companies will be expected to cut over $32 billion in operating spend over the next decade, or roughly $3 billion annually. Leadership is focused on increasing operating efficiency and finding ways to fund growth within current operations, especially given market access investments have a material profit and loss (P&L) impact.
These headwinds are interacting and amplifying the effects of each other. For example:
- Patent cliffs drive R&D spend—which increases cost control pressure.
- Competitive markets drive more rebate concessions—which drives up cost.
Pharmaceutical leaders at the center of this storm must find both incremental and innovative approaches to optimizing profitability.
Navigating uncharted waters
Data and analytics is useful for optimizing profitability across the entire revenue management lifecycle of commercial life sciences. This includes the upfront strategy development and price setting, contract execution and management, and assuring and monitoring for compliance with rebate and chargeback plans to improve gross-to-net (GTN). (GTN is the difference between gross sales of product and net sales after accounting for various deductions, discounts, rebates and chargebacks, etc.)
Below in more detail are a few ways data and analytics, coupled with AI-powered automation, can help drive efficiencies:
Revolutionize pricing strategies. AI-powered analytics analyzes vast datasets to identify market trends, competitive landscapes, and HCP prescription behaviors. With insights and anomalies detected, pricing teams can adjust or propose changes to pricing more quickly to ensure optimal pricing points. This enables them to maximize profitability and maintain competitiveness, as well as assess the effectiveness of pricing and discount strategies across different market segments and regions.
👉 This automated approach surpasses manual methods by rapidly processing complex data, providing accurate insights, and adapting swiftly to market fluctuations.
Streamline contracting processes. Data and analytics is a critical component of the contracting processes by analyzing historic, related data to help determine contract terms, performance metrics, and incentives. It can accelerate contract negotiations through intelligent algorithms that identify mutually beneficial terms, reducing manual workloads and expediting the entire contracting lifecycle.
👉 The result is a more efficient and responsive approach, minimizing errors and enhancing collaboration between market access teams and stakeholders.
Optimize rebate programs. AI analyzes extensive datasets to identify patterns, assess market dynamics, and predict the impact of various rebate structures. It tailors rebate programs to align with strategic goals, ensuring a balance between competitiveness and profitability.
👉 Compared to manual methods, AI-driven optimization is more precise, adaptable, and capable of anticipating market shifts, thereby minimizing revenue leakage and maximizing the effectiveness of rebate initiatives.
Enhance overall revenue management. AI integrates data from multiple sources to provide a holistic view of market access performance. It employs predictive analytics to forecast revenue outcomes, allowing teams to proactively adjust strategies in response to evolving market conditions.
👉 This comprehensive and proactive approach surpasses manual revenue management, ensuring market access teams can navigate the complexities of healthcare systems and regulatory environments with agility and foresight.
Reconcile chargeback and rebate claims more easily and quickly. Pharmaceutical companies can use AI-powered anomaly detection to reconcile chargeback claims from wholesalers/distributors for pricing discrepancies, glean insights on patterns or anomalies, and spot disputable rebate dollars.
👉 AI-powered analysis, by orchestrating and automating data processing, enrichment, and monitoring—and then applying automated insights to uncover root cause analysis across all sources to spot disputable rebate dollars—is far more efficient than manual claims processing.
Dynamic scenario planning and cashflow prediction. AI facilitates dynamic scenario planning by simulating various market scenarios based on changing variables such as regulatory policies, competitor actions, and economic factors. It allows market access teams to strategize and optimize their approaches for different scenarios, enhancing resilience and preparedness.
👉 This dynamic planning capability surpasses manual methods, providing teams with a strategic advantage in navigating uncertainties and making informed decisions in a rapidly changing market.
Mitigate compliance risks. AI automates the monitoring and analysis of regulatory changes and compliance requirements. It ensures that market access strategies align with evolving regulations, reducing the likelihood of costly compliance errors.
👉 Compared to manual tracking, AI-driven compliance management is more thorough, efficient, and responsive, safeguarding market access initiatives from regulatory pitfalls.
Optimized government pricing compliance. AI automates the analysis of complex regulations and reimbursement policies. It ensures accurate calculations for government pricing programs, reducing the risk of compliance errors and financial penalties.
👉 This AI-driven optimization surpasses manual approaches, providing market access teams with a reliable and efficient means to navigate intricate government pricing requirements.
Real-time patient access monitoring. AI analyzes healthcare data, treatment adherence, and access barriers. It provides insights into patient journeys, helping market access teams identify and address access challenges promptly.
👉 This real-time monitoring capability surpasses manual tracking, allowing teams to proactively enhance patient access and adapt strategies based on evolving healthcare dynamics.
Data-driven, unified, and agile
Revenue generation, operations, and risk mitigation is a team sport.
But all too often, process and analytics silos hinder the teams from effectively collaborating to identify optimal pricing points, negotiate contracts effectively, and implement rebate programs that strike the delicate balance between competitiveness and profitability.
Failures in these areas, in turn, mean life science organizations are unable to proactively address leakage and wastage, maximize pull-through, or ensure a resilient market position.
How does AI-powered analytics help?
Data-driven. Modern AI-powered analytics platforms use intelligent analytics automation and intuitive UI to streamline the process of going from data to decisions through natural language exploration (i.e., a Google-like Q&A interface), automated insights generation, and accessible advanced analytics.
By providing an approachable no-/low-code analytics platform that encourages self-service analytics and upskilling through contextualized results and suggested next-best analytics actions, modern AI-powered BI platforms help teams avoid analytic bottlenecks that typically drive heuristics- or gut-driven decision-making, rather than data-driven decision-making.
Unified. Market access, pricing, contracting, and finance teams have different data and analytics needs based on their function and technical acumen. For example, market access teams care about making sure drugs are available and affordable, so they look to perform competitive analysis and historic formulary coverage analysis for similar drugs, whereas finance may need to do more advanced forecasting and reporting work.
A modern AI-powered analytics platform has the breadth of capabilities to work for each function, such as lightweight dashboarding, on-the-fly automated visualization, intelligent reporting, and AutoML, to quickly perform predictive analytics without needing to tap the data science team.
This is built all in a single, approachable tool, with built-in, industry-specific functionality, such as out-of-the-box market share analysis with a Google-like search, enabling market access teams to quickly identify changes in their drug’s performance. By unifying the analytics experience and offering a collaborative environment for multiple stakeholders to work together, market access and pricing teams can more quickly make data-driven decisions and take actions that move the GTN needle.
Agile. Unlike traditional BI or analytics tools, modern AI-powered analytics tools are geared toward rapid iteration and answering typically time-consuming ad hoc questions quickly, rather than focusing on formatting pixel-perfect dashboards.
This focus on speed and agility, by design, equips commercial pharma teams with the ability to capture market opportunities faster. It short-circuits typical analysis loops by answering questions with auto-visualized answers, performing multidimensional analysis of key drivers and root-causing using automated insights that parse millions of variables automatically (and quickly), and generally helping answer the harder questions that typically gum up days or weeks of analysis time so that everyone can move forward.
Setting sail into the future
As market access teams set sail into uncharted territory, it’s important to explore AI’s potential in pricing, contracting, rebates, and revenue management. Doing so can help them emerge from this storm stronger, more adaptive, and poised for continued success. AI is transforming the commercial pharma landscape—so if you haven’t already, it’s time to set your sights on this technology. ⚓
Learn more about how Tellius’ next-gen AI-powered analytics platform helps market access and pricing teams at leading pharmaceutical companies through this 3-minute video. 👇