The foundational assumptions of biopharmaceutical sales forecasting, once a bedrock of strategic planning, are crumbling. Legacy models, meticulously built on the relative predictability of volume-based reimbursement, are proving alarmingly ill-equipped for a future where revenue is no longer a simple function of prescriptions written but is instead contingent on the nebulous, shifting concept of “value.” The consequences of this obsolescence are not trivial. A comprehensive analysis of over 1,700 forecasts for 260 drugs revealed a staggering reality: actual peak sales diverged by an average of 71% from predictions made just one year before launch, with many forecasts overstating projections by more than 160%. This is not a rounding error; it is a strategic crisis, a multi-billion-dollar blind spot that can derail development pipelines, misallocate precious resources, and lead to profound disappointment in the market.

This failure of traditional forecasting is not merely a technical problem to be solved with a better algorithm. It is a symptom of a deeper, more systemic misalignment. Many biopharma organizations remain structured, operated, and incentivized around the principles of a bygone era—an era of fee-for-service dominance where volume was king. Yet, the market has begun a seismic and irreversible shift toward a new economy, one that pays for demonstrable outcomes and proven value. This chasm between internal operating logic and external market reality creates a “strategic debt” that manifests as wildly inaccurate forecasts. Simply layering a new “value” variable onto an old, volume-based model is akin to putting a GPS in a horse-drawn carriage; it acknowledges the new destination without fundamentally changing the capacity to reach it.

This report serves as a comprehensive roadmap for biopharmaceutical leaders to navigate this complex and uncertain terrain. It is designed to guide the transformation of the forecasting function from a retrospective, often siloed reporting exercise into a proactive, integrated, and strategic compass. We will dissect the evolution of reimbursement models, from the fee-for-service guard to the vanguard of value-based contracting. We will deconstruct the new variables and uncertainties these models inject into the forecasting equation and detail the advanced analytical tools, data sources, and organizational structures required to manage them. Ultimately, this guide aims to provide an actionable framework for not only adapting to this new reality but turning its inherent complexities into a durable competitive advantage, enabling more precise strategic planning, more effective risk mitigation, and a more confident journey into the future of healthcare.

Part I – The Shifting Sands of Reimbursement: From Volume to Value

To understand why biopharma forecasting must change, one must first appreciate the tectonic shift occurring in the landscape upon which it is built. The very ground rules of how healthcare is paid for are being rewritten, moving from a simple, transaction-based system to a complex, performance-based ecosystem. This transformation renders old maps obsolete and demands a new understanding of the territory.

The Old Guard: A Look Back at Fee-for-Service Dominance and Its Influence on Forecasting

For the better part of a century, the U.S. healthcare reimbursement system was defined by a single, dominant model: Fee-for-Service (FFS). With historical roots in the mid-19th century, FFS became widespread after World War II, creating a direct and powerful link between the delivery of a medical service and the payment for it. Under this paradigm, clinicians and healthcare providers received payment for each individual service, test, or procedure performed. The economic incentive was clear and linear: more services rendered meant more revenue generated. This model prioritized the quantity of care over its quality or outcome.

This FFS-dominated environment was the fertile ground in which traditional pharmaceutical forecasting methods took root and flourished. Because revenue was a direct function of volume, forecasting became a relatively straightforward exercise in predicting the drivers of that volume. The key variables were tangible and measurable: the total number of potential patients (epidemiology), the rate of diagnosis, the number of prescribers in a specialty, the anticipated market share a new drug could capture from competitors, and the promotional effort required to influence prescriber behavior.

Consequently, forecasting methodologies were designed to model these volume-based inputs. Common approaches included ⁴:

“Top-Down” Analog Analysis: Forecasting based on the historical performance of similar products in the same or adjacent therapeutic areas. This method relied on readily available public data and was particularly useful in early-stage product development.
“Bottom-Up” Epidemiology Models: Building a forecast by starting with the total population and progressively applying filters such as disease prevalence, diagnosis rates, treatment rates, and market share assumptions. This method provided a granular, patient-centric view of potential demand.
Trend-Based and Statistical Methods: Using techniques like moving averages, exponential smoothing, and Holt-Winters models to project historical sales data into the future, assuming that past trends would continue.
Prescriber Behavior Models: Estimating the number of potential prescribers, the rate at which they would trial a new product, and their adoption curve for regular use.

These methods, while distinct, shared a common foundation: they operated under the assumption of a stable, predictable relationship between prescribing volume and revenue. The FFS system provided this stability, making the forecaster’s primary challenge one of accurately estimating uptake, not questioning the fundamental value of each unit sold.

The Value-Based Revolution: Charting the Rise of a New Healthcare Economy

The stability of the FFS world has given way to a period of profound disruption, driven by the rise of a new paradigm: Value-Based Care (VBC). VBC fundamentally inverts the FFS incentive structure. Instead of paying for the quantity of services, VBC models compensate healthcare providers based on the quality of care and, most importantly, on patient health outcomes.³ The goal is to deliver better outcomes at the same or lower cost, shifting the focus from volume to value.⁷

This revolution is not a single event but an ecosystem of new payment and delivery models designed to foster accountability, care coordination, and efficiency. The U.S. Department of Health and Human Services (HHS) has categorized this evolution, providing a framework that charts the journey from pure FFS to full value-based reimbursement.

Category 1: Fee-for-Service with No Link to Quality. The traditional model with no performance incentives.
Category 2: Fee-for-Service with a Link to Quality. FFS architecture with added bonuses for quality performance or penalties for poor performance (e.g., high readmission rates).
Category 3: Alternative Payment Models (APMs) Built on FFS Architecture. These models move beyond simple bonuses to create mechanisms for shared savings and risk.
Category 4: Population-Based Payment. The most advanced models, where providers are paid a set amount to manage the total health of a defined population, taking on full financial risk.

The most prominent APMs driving this transition include ²:

Accountable Care Organizations (ACOs): These are groups of doctors, hospitals, and other healthcare providers who come together voluntarily to give coordinated, high-quality care to their Medicare patients. The goal of coordinated care is to ensure that patients get the right care at the right time, while avoiding unnecessary duplication of services and preventing medical errors. When an ACO succeeds in both delivering high-quality care and spending healthcare dollars more wisely, it shares in the savings it achieves for the Medicare program. Some ACOs even operate under a “two-sided risk” model, where they are also responsible for a portion of any losses if spending exceeds the budget.
Bundled Payments: In this model, a single, comprehensive payment is made for all services associated with an episode of care, such as a knee replacement or cardiac surgery. This single payment covers services from the initial procedure through post-acute care and rehabilitation over a defined period.² This incentivizes providers to coordinate efficiently and eliminate wasteful or redundant services across the entire care continuum.
Patient-Centered Medical Homes (PCMHs): This model focuses on transforming primary care by emphasizing care coordination, communication, and a patient-centric approach. While not a payment model in itself, it is a foundational delivery system reform that enables success in other value-based arrangements.

The shift is not theoretical. In 2015, HHS set an aggressive goal of tying 50% of traditional Medicare payments to these alternative models by the end of 2018. This deliberate, policy-driven push signals a fundamental and permanent restructuring of the healthcare economy. For biopharma, this means the end customer—the healthcare system—is no longer being paid based on how many pills it prescribes but on the ultimate health outcomes of its patients.

The Tectonic Forces of Change: Why the Shift to Value is Inevitable

The transition from a volume-based to a value-based healthcare system is not a fleeting trend but an inevitable evolution propelled by a confluence of powerful, self-reinforcing forces. Understanding these drivers is critical for biopharma leaders, as they reveal the deep-seated and permanent nature of this new reality. Any strategy based on a potential return to the old FFS world is destined to fail.

1. Unsustainable Cost Inflation:

The primary catalyst for change is the crushing weight of healthcare costs. For decades, medical care prices have consistently outpaced general inflation, creating an unsustainable financial burden for governments, employers, and patients. Since 2000, medical care prices in the U.S. have surged by over 121%, compared to an 86% increase for all other consumer goods and services. More recently, medical cost trend—the measure of growth in per-capita medical spending—is hovering at rates reminiscent of 15 years ago, with a projected trend of 8.5% for the group market in 2026. Pharmacy spending is a major contributor, with spending expected to increase by $50 billion in 2024 alone. This relentless inflation puts immense pressure on payers to find new ways to control spending, making value-based models that reward efficiency and outcomes an economic necessity.10

2. Government and Policy Intervention:

Faced with these spiraling costs, governments have become powerful agents of change. Landmark legislation has systematically dismantled the old system and built the scaffolding for the new one. The Affordable Care Act (ACA) was a pivotal moment, establishing the Center for Medicare and Medicaid Innovation (CMMI) with a mandate to test, evaluate, and scale new payment and delivery models like ACOs and bundled payments.

More recently, the Inflation Reduction Act (IRA) of 2022 represents the most significant healthcare reform since the ACA, directly targeting pharmaceutical pricing. Its key provisions—granting Medicare the authority to directly negotiate drug prices, imposing inflation rebates on manufacturers whose price increases outpace inflation, and redesigning the Part D benefit—are projected to reduce manufacturer revenues by hundreds of billions of dollars over the next decade. This act fundamentally alters the power dynamic, giving the government direct leverage to force prices down and compelling manufacturers to justify their prices with robust value propositions. Simultaneously, a wave of state-level reforms is targeting Pharmacy Benefit Managers (PBMs), prohibiting practices like spread pricing and patient steering, and in some cases, breaking up the vertical integration between PBMs and pharmacies. These policy interventions are not isolated; they are a concerted effort to rein in costs and shift the entire ecosystem toward value.

3. The Empowered and Engaged Consumer:

The patient is no longer a passive recipient of care but an active, empowered consumer. This shift is driven by two key factors. First, patients have greater financial skin in the game due to the prevalence of high-deductible health plans, making them more sensitive to costs. Second, they are armed with unprecedented access to information. Data from wearable devices, genetic testing, and AI-powered tools like ChatGPT empower consumers to research their conditions, question treatment options, and demand more from the healthcare system. Their expectations have been shaped by other industries; they now demand convenience, cost-effectiveness, and control over their health journey. Their patience for a system fraught with friction—from high out-of-pocket costs and insurance delays to a lack of care coordination—is wearing thin. This consumer-driven pressure forces providers and payers to deliver a more seamless, efficient, and valuable experience.

4. The Data and Technology Tsunami:

The value-based revolution would not be possible without the technological means to support it. The explosion of data and advanced analytics provides the very foundation upon which VBC is built. For the first time, it is possible to measure and link treatments to real-world outcomes on a massive scale. Key technological enablers include:

Real-World Data (RWD): The routine collection of data from electronic health records (EHRs), medical claims, and patient registries provides a rich source of information on how treatments perform outside the controlled environment of a clinical trial.²
Data Analytics and Predictive Modeling: The ability to apply sophisticated analytics, including artificial intelligence (AI) and machine learning (ML), to these vast datasets allows for the identification of high-risk patients, the prediction of outcomes, and the efficient allocation of resources.
Interoperability: While still a challenge, progress in health information technology is enabling the data sharing and care coordination necessary for models like ACOs to function effectively.

These four forces do not operate in isolation. They form a powerful, self-reinforcing feedback loop that continuously accelerates the shift to value. Unsustainable costs trigger government intervention like the IRA. The IRA empowers payers to demand lower prices, which in turn forces manufacturers to prove their product’s value. To prove value, companies must invest heavily in generating Health Economics and Outcomes Research (HEOR) and Real-World Evidence (RWE). The availability of robust RWE then provides the necessary data infrastructure to design and implement even more sophisticated and demanding value-based contracts. This cycle ensures that the momentum toward value-based care will only increase, making any “wait-and-see” approach by biopharma companies a strategically perilous choice. The pressure is systemic, sustained, and coming from all directions simultaneously.

Part II – Deconstructing the New Models: A Forecaster’s Guide to Value-Based Contracts

As the healthcare economy pivots from volume to value, the contracts that govern pharmaceutical reimbursement are undergoing a radical transformation. For forecasters, understanding the mechanics of these new agreements is no longer optional; it is the core of the job. These contracts introduce a host of new variables, risks, and uncertainties that must be meticulously modeled to produce a credible revenue projection. This section provides a detailed guide to the most prevalent and emerging forms of value-based contracts.

The Litmus Test of Value: A Deep Dive into Outcomes-Based Contracts (OBCs)

At the forefront of the value-based movement are Outcomes-Based Contracts (OBCs), also known as performance-based or risk-sharing agreements. These are confidential agreements between a biopharmaceutical manufacturer and a payer that explicitly tie a drug’s price, rebate level, or reimbursement to the achievement of specified patient outcomes in a real-world setting.¹⁷ OBCs are the ultimate litmus test of a product’s value proposition—if the drug delivers on its clinical promise, it commands its negotiated price; if it falls short, the manufacturer shares in the financial risk.

The core mechanic of an OBC is relatively straightforward in concept, though complex in execution. A manufacturer and a payer agree on a set of clear, measurable clinical or economic endpoints. The drug is placed on the payer’s formulary at an initial price. The payer then tracks the performance of the drug against these predefined metrics in its patient population. If the agreed-upon outcomes are not met, the manufacturer is obligated to provide additional, performance-linked rebates or refunds to the payer.¹⁸

To move from theory to practice, it is essential to examine how these contracts work in the real world. Several high-profile examples illustrate the various forms OBCs can take:

Novartis’s Entresto (sacubitril/valsartan): In pioneering deals with payers like Aetna and Cigna, Novartis linked rebates for its congestive heart failure drug, Entresto, to its ability to reduce the rate of heart failure-related hospital admissions. If the drug did not achieve the admission reductions seen in its pivotal clinical trials, Novartis would provide an additional rebate. In exchange, Entresto received preferential formulary status, ensuring patient access.¹⁸
Amgen’s Repatha (evolocumab): Amgen entered into innovative contracts with Harvard Pilgrim and other payers for its cholesterol-lowering PCSK9 inhibitor, Repatha. These agreements tied rebates to the drug’s success in lowering LDL cholesterol to specific targets. One particularly bold contract with Harvard Pilgrim offered a full refund for any patient who, despite being on Repatha, still suffered a heart attack or stroke, directly linking payment to the ultimate desired clinical outcome.
Boehringer Ingelheim’s Jardiance (empagliflozin): A groundbreaking contract with UPMC Health Plan took the concept a step further. Instead of linking reimbursement for the diabetes medicine Jardiance to a single clinical endpoint, the contract tied it to the total cost of care for the entire population of members with diabetes. This moved beyond product-specific outcomes to hold the manufacturer partially accountable for the drug’s overall economic impact on the health system.
Alkermes’s Vivitrol (naltrexone): In the behavioral health space, UPMC also entered into a value-based contract for Vivitrol, a treatment for opioid dependence. The goal of this contract was to align incentives around improving patient adherence to the once-monthly injection, a critical factor for successful treatment outcomes.

These examples highlight the dual-sided appeal of OBCs. For payers, they offer a powerful tool to mitigate financial risk and reduce uncertainty when covering new, high-cost therapies. They can prevent wasteful spending on drugs that may not be as effective in the messy, heterogeneous real world as they were in the pristine environment of a clinical trial. For manufacturers, OBCs can be a strategic lever to gain and secure market access, especially in crowded therapeutic classes where they can use an outcomes guarantee to differentiate their product and avoid being excluded from formularies.¹⁸

However, the path to implementing OBCs is fraught with challenges. The complexity of designing and executing these contracts is a major hurdle. Key challenges include:

Defining and Measuring Outcomes: Agreeing on meaningful, objective, and easily measurable endpoints is difficult. Many contracts rely on surrogate markers (like lab values) available in claims data, which may not perfectly correlate with long-term health outcomes.¹⁸
Data and Administrative Burden: Collecting, cleaning, and analyzing the necessary data to track outcomes is a significant operational and financial burden for payers.¹⁸
Legal and Regulatory Hurdles: Navigating complex regulations like the federal Anti-Kickback Statute and Medicaid Best Price rules creates uncertainty and can deter both parties from entering into these arrangements.²⁰
Building Trust: The success of an OBC ultimately depends on a strong, trusting partnership between the manufacturer and the payer, which can be difficult to achieve in a historically adversarial relationship.

Sharing the Stakes: Understanding Risk-Sharing and Managed Entry Agreements

While Outcomes-Based Contracts represent a direct link between payment and performance, they are part of a broader category of agreements designed to manage uncertainty and share risk between manufacturers and payers. These are often collectively referred to as Risk-Sharing Agreements (RSAs) or Managed Entry Agreements (MEAs).¹⁹ An RSA is any contract in which a payer agrees to provide access to a drug in exchange for the manufacturer accepting some of the risk associated with its use, whether that risk is clinical, financial, or both.

MEAs can be broadly divided into two main categories, creating a spectrum of risk-sharing arrangements :

1. Financial-Based Agreements:

These agreements are primarily designed to manage the budgetary impact and financial uncertainty of a new drug, without necessarily linking payment to clinical outcomes. They help payers predict and control their expenditures. Common types include:

Simple Discounts/Rebates: The most basic form, where a manufacturer provides a confidential discount off the list price.
Price-Volume Agreements (PVAs): In a PVA, the manufacturer agrees to provide a higher rebate on all sales that exceed a pre-negotiated volume or expenditure threshold. This protects the payer from unexpectedly high costs due to greater-than-forecasted utilization.¹⁹
Capitation Agreements: The manufacturer agrees to a fixed price per patient or per member per month to cover all costs for their drug within a specific patient population. This provides the payer with complete budget predictability.
Free Initiation / Compassionate Use: The manufacturer agrees to cover the cost of the initial treatment period for patients, reducing the upfront cost for the payer.

2. Performance-Linked / Outcome-Based Agreements:

This category encompasses the OBCs discussed in the previous section. These agreements are designed to manage clinical uncertainty regarding a drug’s effectiveness or safety in the real world. They link payment directly to the value the product delivers, whether measured by clinical endpoints, biomarker improvements, or other performance metrics. Another variant in this category is the Conditional Coverage or Coverage with Evidence Development (CED) agreement. Under a CED, a payer grants temporary or limited reimbursement for a new drug on the condition that the manufacturer collects additional real-world data to resolve uncertainties about its long-term effectiveness or cost-effectiveness. The final coverage decision is then based on this new evidence.

The strategic rationale for engaging in these various forms of RSAs is clear. For a manufacturer launching a drug with promising but immature data (e.g., based on a single-arm trial in a rare disease), an RSA can be the key to unlocking market access. By agreeing to share the financial or clinical risk, the manufacturer can overcome payer reluctance and secure a place on the formulary, allowing the product to start generating revenue while more definitive evidence is collected.²⁶ For the forecaster, this means that the revenue stream is not a simple “yes/no” decision but is instead governed by the complex terms of the specific RSA in place.

The Next Frontier: Subscription Models and the Future of Reimbursement

Beyond outcomes-based and risk-sharing contracts lies a more radical vision for pharmaceutical reimbursement: models that completely “delink” a manufacturer’s revenue from the volume of its product sold. This represents a philosophical shift from “paying for a product that works” to “paying for a public health solution.” The most prominent of these emerging models is the subscription-based payment, often referred to as the “Netflix model”.

Under a subscription model, a payer—often a government entity or large health system—makes a large, lump-sum payment to a manufacturer. In exchange, the manufacturer provides an unlimited supply of a specific drug to treat a defined patient population over a fixed period. The payment is for access to the cure or treatment, not for each individual pill or vial.

This model has gained traction primarily for high-impact therapies that address significant public health challenges, where broad, uninhibited access is the primary goal. Real-world examples demonstrate its application:

Hepatitis C (HCV) Cures: To combat the HCV epidemic, the state of Louisiana and the Australian government implemented subscription models. They paid a fixed amount to a manufacturer (Asegua Therapeutics in Louisiana’s case) for an unlimited supply of curative antiviral medication, allowing them to treat a large number of patients in their Medicaid and public health systems without facing per-patient budget constraints.³⁰
Cystic Fibrosis (CF) Portfolio: The National Health Service (NHS) in England negotiated a landmark agreement with Vertex Pharmaceuticals. The NHS agreed to pay a guaranteed sum over a period of years in exchange for access to Vertex’s entire portfolio of existing and future CF therapies, ensuring all eligible patients could receive these transformative medicines.
Antimicrobial Resistance (AMR): The subscription model is also being actively explored as a solution to the market failure in antibiotic development. Because new antibiotics must be used sparingly to preserve their effectiveness, a volume-based sales model provides no incentive for R&D. A subscription model would “delink” revenue from sales, paying manufacturers for the availability of a novel antibiotic, thus creating a viable market and encouraging innovation.

The subscription model offers a compelling win-win proposition. For payers, it provides budget predictability and allows them to tackle major health issues at a population level without worrying about runaway costs. For manufacturers, it guarantees a significant and predictable revenue stream, de-risking their investment. However, implementation is a major challenge. These models require a complete departure from traditional pricing, reimbursement, and Health Technology Assessment (HTA) processes, demanding new legal frameworks and sophisticated negotiation capabilities.³⁰

Beyond subscriptions, other innovative models are emerging to handle the unique challenges of modern therapies:

Indication-Based Pricing: This model involves setting different prices for the same drug when it is used for different indications or diseases. The price is based on the specific value the drug provides in each distinct patient population.
Staggered or Annuity-Based Payments: For extremely high-cost, one-time curative treatments like cell and gene therapies, payers face immense “sticker shock.” Staggered payment models allow payers to spread the cost over several years, making these transformative therapies more affordable. These payments can also be linked to the continued achievement of outcomes, blending the annuity concept with a performance guarantee.²⁷

For forecasters, these frontier models represent a paradigm shift. The forecasting exercise is no longer about modeling patient flows, prescriber adoption, and market share. Instead, it becomes a high-stakes assessment of securing a single, large-scale government or payer contract. The key drivers of the “sale” are no longer physician behavior but rather the political will to address a public health crisis, the payer’s budget cycles, and the manufacturer’s ability to negotiate a complex, multi-year, lump-sum deal. This requires an entirely new set of forecasting skills, rooted in health policy analysis, government relations, and macroeconomic modeling.

Part III – The New Variables of Value: Remodeling the Forecasting Equation

The transition to a value-based reimbursement landscape does more than just change the philosophy of payment; it fundamentally alters the mathematical DNA of the sales forecast. The familiar inputs that have long served as the building blocks of revenue projections are being replaced by a new set of complex, interdependent, and often uncertain variables. To remain relevant, forecasters must deconstruct their old models and rebuild them with these new components.

The Anatomy of a Value-Based Forecast: A New Set of Inputs

The core task of the forecaster is to translate market dynamics into a revenue number. In the FFS world, this was a relatively linear translation. In the value-based world, it is a multi-variable equation where the final revenue is contingent on performance. The table below provides a direct comparison of the key variables that must be considered in a traditional versus a value-based forecasting model, highlighting the dramatic increase in complexity.

Table 1: A Comparative Analysis of Forecasting Variables: Traditional vs. Value-Based Models

Forecasting Dimension	Traditional Model Variable	Value-Based Model Variable	Key Data Sources & Dependencies
Target Population	Total Addressable Patient Population	Contract-Eligible Patient Cohort	Payer-specific formulary criteria, prior authorization rules, clinical trial inclusion/exclusion criteria mirrored in contracts.
Uptake & Share	Peak Market Share (%)	Performance-Adjusted Uptake Rate	Real-World Evidence (RWE) on adherence, discontinuation rates, prescriber behavior under risk, competitor performance.
Pricing & Revenue	Gross/List Price or WAC	Net-Net Price after Performance Rebates	Terms of the specific Outcomes-Based Contract (OBC), including outcome metrics, measurement periods, and rebate percentages.
Performance Risk	Not Applicable (N/A)	Outcome Achievement Rate (%)	RWE, claims data, Electronic Health Records (EHRs), Patient-Reported Outcomes (PROs) to model the probability of meeting contractual endpoints.
Time to Peak Sales	Standard Adoption Curve (e.g., S-curve)	Milestone-Gated Revenue Recognition	Contract terms defining evidence development milestones (in CED agreements) or payment triggers (in annuity models).
Competitive Impact	Competitor Market Share	Competitor Real-World Performance	Head-to-head RWE studies, competitor OBC terms (if known), impact on shared savings pools in ACOs.

Elaborating on these shifts reveals the depth of the new forecasting challenge:

From Addressable Population to Eligible Cohort: A traditional forecast might start with the total number of patients with a disease. A value-based forecast must begin with a much smaller, more specific number: the subset of patients who meet the strict clinical and administrative criteria defined within a payer’s contract. This requires a granular understanding of formulary design and prior authorization policies.
From Market Share to Performance-Adjusted Uptake: Forecasters used to model a simple peak market share. Now, they must model a “performance-adjusted” uptake. The effective market share is no longer just a function of prescribing, but is discounted by factors like patient non-adherence or early discontinuation, as these directly impact the ability to achieve outcomes and can trigger rebates.
From Gross Price to Net-Net Price: The concept of a single price is obsolete. The forecast must model a dynamic price that changes based on performance. The final “net-net” price is the wholesale acquisition cost (WAC) minus standard rebates, and then further reduced by any performance-based rebates triggered by a failure to meet outcomes.¹⁸ This final rebate is an unknown variable at the start of the forecast period.
Introducing the Outcome Achievement Rate: This is the most critical new variable. The forecaster must now predict the future: what percentage of the patient population will achieve the clinical or economic outcomes specified in the contract? This prediction is the linchpin of the entire forecast, as it determines the magnitude of the performance rebates. An analysis of sales forecasts submitted for reimbursement in Austria found that, on average, forecasts overestimated actual sales by 33%, with over half being wildly inaccurate, suggesting this is an incredibly difficult variable to predict.

Quantifying the Unquantifiable: The Central Role of HEOR and Real-World Evidence (RWE)

If the new forecasting equation is filled with these complex and uncertain variables, how can a forecaster possibly solve for them? The answer lies in two interconnected disciplines that have moved from the periphery to the absolute center of biopharma strategy: Health Economics and Outcomes Research (HEOR) and Real-World Evidence (RWE).

HEOR is the function within a biopharma company responsible for generating and communicating evidence of a product’s value.⁶ It uses economic and clinical research methods to demonstrate how a drug impacts not just clinical endpoints, but also patient quality of life, healthcare resource utilization, and overall costs.

The primary fuel for HEOR is Real-World Data (RWD), which the FDA defines as data relating to patient health status and/or the delivery of healthcare routinely collected from a variety of sources. These sources include:

Electronic Health Records (EHRs)
Medical and pharmacy claims data
Data from product and disease registries
Data from digital health technologies and wearables

When RWD is analyzed, it generates Real-World Evidence (RWE)—the clinical evidence about the usage and potential benefits or risks of a medical product.

Historically, RWE was used primarily for post-marketing safety monitoring. Today, its role has expanded dramatically. RWE is now the critical input for value-based forecasting because it provides the data necessary to model and predict real-world performance.³⁶ While randomized controlled trials (RCTs) demonstrate efficacy in a controlled, idealized setting, RWE shows how a drug actually performs in the messy, heterogeneous patient populations that payers cover.

Forecasters must now work hand-in-hand with HEOR teams to leverage RWE to:

Establish Baselines: Use RWE to understand the natural history of a disease and the outcomes associated with the current standard of care. This provides the baseline against which a new drug’s performance in an OBC will be measured.
Model Treatment Effects in the Real World: Analyze RWE to predict how a drug’s efficacy, as seen in an RCT, might translate to a broader, more complex patient population. This helps in setting a realistic “Outcome Achievement Rate” for the forecast model.
Identify Patient Subgroups: Use advanced RWE analytics to identify patient subpopulations who are most likely to benefit from a therapy, allowing for more targeted contracting and forecasting.
Incorporate Patient-Reported Outcomes (PROs): A crucial component of RWE is data that comes directly from the patient. PROs are measures of a patient’s health status—such as pain levels, functional ability, or quality of life—reported directly by them without interpretation by a clinician.³⁹ As value-based care increasingly prioritizes the patient perspective, PROs are becoming key metrics in OBCs. The PROMIS initiative, a USD 100 million NIH project, has developed standardized tools to capture this data, and its use is growing rapidly. One study found that 82% of healthcare executives believe VBC improves patient outcomes, underscoring the focus on metrics that matter to patients.

The High-Stakes Game: Forecasting for Specialty, Orphan, and Curative Therapies

The challenges of value-based forecasting are not distributed evenly across the pharmaceutical landscape. They are most acute and the stakes are highest in the very areas where innovation is most profound: specialty medicines, orphan drugs, and curative therapies.

Specialty Drugs: This category, which includes treatments for complex conditions like cancer, rheumatoid arthritis, and multiple sclerosis, is the fastest-growing segment of pharmaceutical spending. Specialty medicines are projected to account for 43% of global spending by 2028 and over 55% in developed markets like the U.S.. However, they present a perfect storm of forecasting challenges:

High Costs: Their high prices make payers intensely focused on value and prime candidates for OBCs.
Complex Administration: Many specialty drugs are infused or injectable, introducing complex supply chain and site-of-care variables that are difficult to model.
Data Gaps: A lack of integrated pharmacy and medical data often hides the true total cost of care, making it difficult to demonstrate economic value.
Pipeline Uncertainty: The sheer volume of new entrants, especially in oncology where 100 new treatments are expected over five years, makes the competitive landscape incredibly dynamic and hard to predict.

Orphan Drugs and Rare Diseases: Forecasting for rare diseases turns traditional methods on their head. The fundamental assumptions of analog analysis and large-scale epidemiological models break down due to:

Small Patient Populations: The limited number of patients makes it difficult to conduct large clinical trials, leading to greater uncertainty in the evidence at launch.
Poorly Understood Disease Progression: For many rare diseases, the natural history is not well documented, making it hard to establish a baseline for measuring treatment benefit.
Lack of Comparators: Often, a new orphan drug is the first and only treatment for a condition, meaning there are no existing competitors to benchmark against.

In this environment of high evidence uncertainty, payers and HTA bodies are moving beyond standard cost-effectiveness analysis. They are adopting frameworks like Multi-Criteria Decision Analysis (MCDA), which evaluates a drug based on a broader set of criteria, including “unmet medical need,” “disease severity,” and “societal value,” in addition to cost and efficacy.⁴⁵ This has a profound implication for forecasting. The forecaster must now attempt to model the outcome of this complex, often qualitative, assessment process. The final price and reimbursement potential are no longer just a function of clinical data but of a societal negotiation about the value of treating a rare and devastating disease. The forecast, therefore, must attempt to quantify these “softer” inputs, a radical departure from traditional modeling.

Cell and Gene Therapies: These one-time, potentially curative treatments represent the apex of forecasting difficulty. Their multi-million-dollar upfront costs create extreme “sticker shock” for payers, making traditional reimbursement models untenable. Forecasting for these therapies requires modeling entirely new payment structures, like the staggered, annuity-based payments that spread the cost over many years and are often tied to the long-term durability of the cure.²⁷ The forecast is not for a product sale, but for a long-term, performance-contingent financial arrangement, looking more like a financial instrument than a traditional sales projection.

Part IV – The Forecaster’s New Toolkit: Advanced Analytics and Data Integration

To navigate the new variables and profound uncertainties of value-based reimbursement, biopharma forecasters require a new generation of analytical tools. The era of relying on deterministic spreadsheets and simple trend lines is over. The modern forecasting toolkit must be probabilistic, adaptive, and deeply integrated with diverse data streams to provide a resilient and realistic view of the future.

Embracing Uncertainty: Probabilistic Forecasting with Monte Carlo Simulations

The single greatest challenge introduced by value-based contracts is uncertainty. The final revenue realized from an Outcomes-Based Contract is, by definition, unknown at the time of forecasting because it depends on future real-world performance. Traditional forecasting models that produce a single-point estimate (e.g., “Year 5 revenue will be $1.2 billion”) are fundamentally dishonest in this environment, as they present a false sense of precision.

Monte Carlo simulation offers a powerful solution by embracing uncertainty rather than ignoring it. Instead of using a single value for each input variable, a Monte Carlo-based forecast uses a probability distribution. For example, instead of assuming the “Outcome Achievement Rate” will be exactly 65%, the forecaster can define it as a normal distribution with a mean of 65% and a standard deviation of 10%, reflecting the uncertainty around that estimate. For variables with very high uncertainty, like the timing of a competitor launch, a uniform distribution might be used.

The model then runs thousands or even millions of iterations, each time randomly sampling a value from the defined distribution for each input variable. The result is not a single revenue number but a full probability distribution of potential revenue outcomes. This output allows leaders to move beyond a single, likely-to-be-wrong number and understand the full spectrum of possibilities. For instance, the output might show:

There is a 90% probability of achieving at least $800 million in revenue.
The most likely outcome (the mode of the distribution) is $1.1 billion.
There is only a 15% probability of exceeding $1.5 billion.

This probabilistic approach transforms uncertainty from an unmanageable problem into a quantifiable risk. It allows for sophisticated scenario planning, where the impact of discrete events (e.g., a competitor launching early, a Phase III trial disappointing) can be modeled by changing the input distributions.⁴⁹ This provides leadership with a clear-eyed view of the risks and opportunities, enabling them to set realistic action standards (e.g., “We will only proceed with this launch if the Monte Carlo simulation shows at least a 75% probability of achieving a $500 million net present value”) and make more robust strategic decisions in the face of ambiguity.

The AI and Machine Learning Imperative: Building Adaptive Forecasting Engines

While Monte Carlo simulations help manage uncertainty, artificial intelligence (AI) and machine learning (ML) provide the engine to generate more accurate inputs for those simulations and to make the entire forecasting process dynamic and adaptive. The integration of AI/ML represents a paradigm shift from static, reactive forecasting to proactive, learning systems.⁵¹

Predictive Analytics for Better Inputs:

AI and ML algorithms excel at analyzing vast, complex, and disparate datasets to uncover hidden patterns and relationships that human analysts would miss. By feeding RWE, claims data, clinical trial data, and market trends into ML models, companies can generate more accurate predictions for the key variables in a value-based forecast.13 For example, ML models can:

Predict patient adherence rates based on demographic and clinical profiles.
Identify which patients are most likely to respond to a therapy, enabling more precise cohort selection for contracts.
Forecast real-world event rates to set more accurate baselines for OBCs.
Case studies demonstrate the power of this approach. One McKinsey analysis found that using generative AI for supply chain management could increase demand forecast accuracy by 15%. Another case showed that leveraging predictive analytics could reduce the duration of clinical trials by 20%, a critical input for long-range financial modeling.

Adaptive and Reinforcement Learning for Dynamic Markets:

Perhaps the most revolutionary application of AI is the creation of adaptive forecasting models. Traditional models are static; when market conditions change, they must be manually updated or completely rebuilt. Adaptive models, in contrast, are designed to learn and evolve in real time.

A cutting-edge approach combines Multi-Task Learning (MTL) and Reinforcement Learning (RL).

MTL allows a model to learn simultaneously across multiple related tasks (e.g., forecasting sales for different drugs or in different regions). This enables the model to capture shared patterns and generalize its learnings, improving overall accuracy.
RL introduces a feedback loop. The model (the “agent”) makes a forecast (an “action”). It then compares this forecast to the actual sales data that comes in (the “environment”) and receives a “reward” or “penalty” based on its accuracy. Through this process, the agent continuously adjusts its internal parameters to make better forecasts in the future.

An MTL-RL model can adapt to sudden market shocks—a new competitor launch, a change in payer policy, a new clinical guideline—without human intervention. It learns from its own errors and the real-time flow of data, making it uniquely suited to the dynamic and unpredictable nature of value-based contracting.⁵⁵ This leads to the concept of “driverless forecasting,” an automated, AI-driven solution that can generate accurate short-term forecasts at scale across numerous brands, countries, and payer channels, freeing up human analysts to focus on high-level strategy.

The Long View: Integrating Patent Intelligence for Lifecycle Forecasting

A forecast is only as good as its long-term assumptions, and in the biopharma industry, no variable is more impactful to the long-term view than patent exclusivity. The moment a blockbuster drug loses its patent protection and faces generic or biosimilar competition—the so-called “patent cliff”—its revenue trajectory changes irrevocably. Accurately forecasting this event and its aftermath is essential for any credible long-range plan.

This is where the strategic integration of patent intelligence becomes indispensable. Platforms like DrugPatentWatch provide a comprehensive, integrated database of drug patents, pending applications, litigation, Paragraph IV challenges, clinical trial data, and biosimilar development pipelines.⁵⁸ This is not just a legal tool; it is a critical forecasting and competitive intelligence asset.

Integrating patent intelligence allows forecasters to:

Accurately Predict Loss of Exclusivity (LOE): By monitoring patent expiration dates, ongoing litigation, and the status of generic challengers, forecasters can move from a rough estimate to a data-driven prediction of when a product will face competition. This allows for proactive planning and resource allocation in the years leading up to LOE.⁵⁸
Model the “Patent Cliff 2.0”: The post-LOE landscape has become more complex. The last patent cliff was dominated by small molecules facing rapid generic substitution. The current cliff involves numerous biologics, which face a slower, more nuanced erosion from biosimilars. Furthermore, as the case of Humira demonstrates, a brand can maintain significant volume share post-LOE but still lose over 60% of its net sales due to the steep discounting required to compete. Forecasters must model these new dynamics, including scenarios where the company employs defensive strategies like launching an “authorized generic” (AG) or engaging in deep “brand-for-generic” contracting with payers to retain volume.
Anticipate Competitive Disruption: Patent intelligence is a powerful leading indicator of future competition. By analyzing the patent filings of rival companies, an organization can discern their R&D strategies, identify emerging technologies, and anticipate the launch of new products that could become the new standard of care.⁵⁹ In a value-based world, a new, more effective competitor doesn’t just erode market share; it can completely undermine the value proposition of an existing product, rendering its OBCs untenable. This forward-looking intelligence is vital for assessing the long-term risk to a product’s revenue stream.

The orchestration of these advanced tools creates a powerful, modern “forecasting ecosystem.” It is not a matter of choosing one tool over another, but of integrating them into a continuous, learning process. AI/ML engines ingest vast amounts of RWE and patent data from platforms like DrugPatentWatch. This analysis informs the probability distributions and event likelihoods that are fed into a Monte Carlo simulation. The simulation, in turn, generates a range of potential revenue scenarios that guide strategic decision-making. As real-world data on contract performance and rebate payments becomes available, a reinforcement learning loop automatically fine-tunes the AI models, which then refines the inputs for the next round of simulations. This creates a closed-loop, self-correcting forecasting system that is robust, resilient, and built to thrive in an environment of constant change and uncertainty.

Part V – Building a Value-Ready Organization: Strategy, Structure, and Collaboration

The most sophisticated forecasting models and advanced analytical tools will ultimately fail if they are implemented within an organization that is not structured and culturally aligned to operate in a value-based world. The transition to value-based forecasting is as much a challenge of organizational design and change management as it is one of data science. Building a “value-ready” organization requires breaking down traditional silos, redesigning reporting structures, and leading a fundamental shift in mindset from volume to value.

The Cross-Functional Command Center: Breaking Down Silos for Value-Based Success

Accurate value-based forecasting is fundamentally a team sport; it is impossible to achieve in a siloed organization. The complexity of the inputs and the interdependencies between clinical, economic, and commercial factors demand deep and continuous collaboration across functions. A study found that 60% of underperforming sales teams cited poor collaboration as their key challenge, highlighting the critical nature of this integration.

A successful value-based forecasting process requires a “cross-functional command center” where key stakeholders come together to share data, align on assumptions, and build a unified view of the market. The essential functions at this table include ⁶³:

Market Access: This team is the voice of the payer. They understand the intricacies of formulary design, reimbursement policies, and payer requirements. Their input is crucial for defining the “contract-eligible patient cohort” and the likely terms of any value-based agreement.
Health Economics and Outcomes Research (HEOR): The HEOR team are the value quantifiers. They design and execute the RWE studies that provide the evidence needed to populate the forecasting model, from predicting outcome achievement rates to conducting cost-effectiveness analyses.
Medical Affairs: This function provides the deep clinical and scientific expertise. They offer insights into clinical trial data, disease pathways, and therapeutic efficacy, ensuring that all assumptions are medically sound and that the value story is evidence-based.
Research & Development (R&D): R&D’s involvement is critical for long-range forecasting. They provide updates on the clinical development pipeline—both internal and competitor—which informs lifecycle management and competitive landscape scenarios.
Commercial (Sales and Marketing): The commercial team brings the on-the-ground perspective. They provide insights into prescriber behavior, patient needs, and the practical realities of executing commercial strategies in the field.
Finance: The finance team ensures that all forecasting models are financially rigorous, aligned with corporate budgeting and planning processes, and that the potential ROI of different strategies is accurately assessed.

Crucially, this collaboration cannot begin after a product is launched. To be effective, it must start as early as Phase II of clinical development.⁶⁴ Early engagement ensures that clinical trials are designed from the outset to generate the specific clinical, economic, and patient-reported outcomes data that payers will demand. This “payer-rationalized trial design” is the foundation of a strong value proposition and, by extension, a credible long-range forecast. By building the evidence package payers require from the beginning, companies can accelerate time to market and secure better reimbursement conditions.⁶⁴

Structuring for Success: The Optimal HEOR and Market Access Org Design

Effective collaboration is enabled or hindered by organizational structure. As the importance of demonstrating value has grown, biopharma companies have grappled with the question of where the key value-demonstration functions—Pricing and Market Access (P&MA) and HEOR—should sit within the organization. There is no single “right” answer, but the chosen structure has significant implications for the effectiveness of the forecasting process.⁶⁸

Two dominant models have emerged :

HEOR within Medical Affairs: In this structure, the HEOR function reports up through the Chief Medical Officer. The primary advantage of this model is that it fosters strong alignment between the evidence generation strategy and the overall scientific and medical strategy for a product. It helps ensure scientific rigor and objectivity. The potential disadvantage is that the HEOR team may become disconnected from the commercial realities and urgent needs of the market access team, leading to evidence that is scientifically interesting but not commercially impactful.
HEOR within Market Access (Commercial): In this model, HEOR reports up through the commercial pillar, often alongside the P&MA team. This structure ensures a singular point of accountability for demonstrating value to payers. It provides a sharp commercial focus, ensuring that HEOR activities are directly tied to the strategic goal of securing and maintaining access and reimbursement. The risk, however, is a potential loss of scientific independence, where the pressure to meet commercial goals could compromise the objectivity of the research.

Regardless of the specific reporting lines, several structural principles are key to success:

Senior-Level Representation: The market access function must have a powerful voice at the highest levels of the organization. Best-in-class companies are establishing Executive Vice President (EVP) level roles for P&MA, ensuring that access and value considerations are embedded in all major corporate strategic decisions.⁶⁸ This senior leadership is critical for securing resources, driving cross-functional alignment, and elevating the importance of value demonstration across the enterprise.
Formalized Cross-Functional Collaboration: If HEOR and P&MA are in separate pillars, it is essential to institutionalize their collaboration through formal processes, shared governance bodies, and joint accountability for outcomes. The structure must not allow separate reporting lines to create strategic misalignment.
Integration with the Broader Organization: The ideal market access structure is one that mirrors and integrates seamlessly with the company’s overall operating model, whether that is organized by therapeutic area, geography, or lifecycle stage. Many companies use a matrix structure, where individuals have both functional (vertical) and cross-functional brand team (horizontal) responsibilities. While challenging to manage, this structure is designed to break down silos and ensure that specialized expertise is applied effectively across the portfolio.

Leading the Charge: A Change Management Framework for Value-Based Transformation

Ultimately, adapting to the world of value-based care is a profound exercise in change management. It involves shifting the core mindset, skills, processes, and culture of an entire organization. Research shows that nearly two-thirds of all major change projects fail, often due to poor planning, ineffective communication, and resistance from staff. Leaders who underestimate the human and cultural dimensions of this transformation do so at their peril. The best forecasting model in the world is useless if the organization is unable or unwilling to execute the value-based strategies it informs.

Established change management models provide proven frameworks for guiding this transition. Leaders can adapt principles from models such as:

Kurt Lewin’s Change Model: A simple, three-stage process of Unfreezing (creating the motivation for change), Changing (implementing new processes and behaviors), and Refreezing (stabilizing the new state and embedding it in the culture).⁷¹
John Kotter’s 8-Step Model: A more detailed, sequential process that emphasizes building momentum: 1) Create a sense of urgency, 2) Build a guiding coalition, 3) Form a strategic vision, 4) Enlist a volunteer army, 5) Enable action by removing barriers, 6) Generate short-term wins, 7) Sustain acceleration, and 8) Institute change.⁷¹

Applying these frameworks to the value-based transformation requires a set of deliberate, concrete actions from leadership:

Champion Leadership Buy-In: Change must be driven from the top. When C-suite executives actively champion and participate in collaborative initiatives, success rates increase dramatically.
Develop and Communicate a Shared Vision: All departments must be aligned around a common definition of “value” and a shared goal. This fosters unity and ensures all teams are pulling in the same direction.⁶³
Invest in New Capabilities and Training: The organization must invest in developing the skills needed for the new world. This includes training in HEOR, RWE analytics, data science, and the art of negotiating complex value-based contracts.
Build Inclusive Communication Channels: Establish regular cross-functional meetings, huddles, and digital collaboration platforms to ensure transparent and continuous communication. This breaks down silos and allows for rapid problem-solving.
Realign Incentives: This is perhaps the most critical and difficult step. The organization’s incentive structures must be reoriented from volume to value. If sales representatives are compensated solely based on prescription volume, they have no incentive to support patient adherence programs or other activities crucial for success in an OBC. Physician compensation models must also evolve to reward value-based behaviors. Without this fundamental realignment of incentives, any behavioral change will be superficial and short-lived.

The biggest barrier to adopting new forecasting models is not a lack of technology or methodology; it is organizational inertia and the powerful gravitational pull of misaligned incentives. A company can build a perfect Monte Carlo model that shows its success is contingent on high patient adherence. But if the commercial team is not equipped, resourced, or incentivized to execute an adherence support program, the forecast is guaranteed to fail because its underlying assumptions cannot be met in the real world. Therefore, change management is not an afterthought in this process. It is the essential enabling condition that connects the strategic plan (the forecast) to operational reality (the execution). The success of the forecast is ultimately dependent on the success of the organizational transformation.

Industry Insight

Key Takeaways

The Forecasting Crisis is a Strategic Crisis: Traditional, volume-based sales forecasting models are becoming obsolete as the healthcare market shifts irreversibly to value-based reimbursement. The resulting inaccuracies are not just technical errors but symptoms of a deeper misalignment between how many biopharma companies operate and how the market now pays.
Value-Based Contracts Redefine Revenue: New reimbursement models like Outcomes-Based Contracts (OBCs), Risk-Sharing Agreements (RSAs), and subscription models fundamentally change the forecasting equation. Revenue is no longer a simple function of price times volume; it is a complex, performance-contingent variable dependent on real-world clinical and economic outcomes.
RWE and HEOR are the New Foundational Inputs: To forecast in a value-based world, companies must master the generation and application of Real-World Evidence (RWE) and Health Economics and Outcomes Research (HEOR). These disciplines provide the data needed to model new variables like “outcome achievement rates” and “performance-adjusted uptake.”
Advanced Analytics are Non-Negotiable: The uncertainty and complexity of new reimbursement models demand a more sophisticated analytical toolkit. Probabilistic methods like Monte Carlo simulations are required to quantify risk, while AI and Machine Learning, particularly adaptive models using Reinforcement Learning, are needed to create dynamic forecasts that can learn and adjust to real-time market changes.
Patent Intelligence is Critical for the Long View: Long-range forecasting must be grounded in a robust understanding of the intellectual property landscape. Integrating patent intelligence from platforms like DrugPatentWatch is essential for accurately predicting Loss of Exclusivity (LOE), modeling the increasingly complex “patent cliff,” and anticipating competitive disruptions that could undermine a product’s value proposition.
Organizational Transformation is the Ultimate Enabler: Success is impossible without profound organizational change. This requires breaking down functional silos to foster deep collaboration between Market Access, HEOR, Medical Affairs, and Commercial teams. It also demands a deliberate, leader-led change management strategy to shift the corporate culture, build new capabilities, and, most importantly, realign incentives from volume to value.

Frequently Asked Questions (FAQ)

1. Our organization is just beginning to grapple with this shift. What is the most critical first step to take in adapting our forecasting capabilities for value-based care?

The most critical first step is not to immediately buy a new software tool, but to establish a cross-functional governance team or “command center.” This team should include senior leaders from Market Access, HEOR, Medical Affairs, Commercial, and Finance.⁶³ The initial mandate of this team should be to create a shared understanding of the problem and develop a common language around value. They must conduct a thorough current-state assessment to identify the biggest gaps in data, capabilities, and processes. This foundational step of breaking down silos and fostering collaboration is essential because no single department has the complete picture required for value-based forecasting. Attempting to implement new models without this cross-functional alignment will inevitably lead to forecasts built on incomplete assumptions and an inability to execute the strategies they inform.

2. How can we justify the significant investment in new data sources (like RWE) and advanced analytics platforms (like AI/ML) to our leadership?

The justification should be framed around risk mitigation and strategic advantage, not just incremental forecast accuracy. The cost of not investing is a continued reliance on obsolete models that produce wildly inaccurate forecasts, leading to multi-billion-dollar errors in resource allocation and strategic planning. The ROI can be demonstrated in several ways:

De-risking Development: Advanced analytics applied to RWE and clinical data can help de-risk assets earlier in the development cycle, preventing investment in programs with a low probability of demonstrating real-world value.³⁶
Optimizing Contract Negotiation: A robust, data-driven forecast provides immense leverage in negotiations with payers. It allows you to confidently propose and accept terms for an OBC because you have a probabilistic understanding of the likely financial outcomes.
Improving Capital Efficiency: Accurate forecasts tied to value allow for more precise allocation of commercial resources, focusing spending on activities that will actually drive the outcomes that matter for reimbursement. McKinsey estimates that gen AI alone can unlock $4 billion to $7 billion in annual value in biopharma operations, including improved demand forecasting.
Competitive Advantage: In a “boom-or-bust” launch environment, the ability to accurately forecast and navigate market access complexities is a primary differentiator. Companies that master this will be the ones that succeed.

3. What is the single biggest mistake companies make when trying to implement Outcomes-Based Contracts (OBCs)?

The biggest mistake is underestimating the operational complexity and the importance of a true partnership with the payer. Many companies focus heavily on the financial and clinical terms of the contract but fail to adequately plan for the “how.” This includes defining, in granular detail, how outcomes data will be collected, who is responsible for analysis, how disputes will be adjudicated, and how patient privacy will be protected.²⁰ A second, related mistake is viewing the payer as an adversary rather than a partner. OBCs require a high degree of mutual trust and transparency to succeed. Without a collaborative spirit and a commitment to transparent data sharing—even when the data is unfavorable—the contract will likely collapse under the weight of mistrust and operational friction.

4. How does the rise of value-based care impact forecasting for products approaching Loss of Exclusivity (LOE)?

Value-based care adds a new layer of complexity to LOE forecasting. Traditionally, the focus was on predicting the erosion of volume and price due to generic entry. Now, forecasters must also consider:

The Impact on “Brand-for-Generic” Contracting: A key post-LOE strategy is to offer deep rebates to payers in exchange for maintaining preferred formulary status for the brand over the generic. The “value” of the brand (e.g., established trust, patient support services) becomes a key negotiating lever. The forecast must model the probability of securing these contracts and the level of discounting required.
Value of Wraparound Services: A branded product can differentiate itself from a generic by offering value-added services like patient education, adherence support, or dedicated nurse lines. The forecast must quantify the impact of these services on patient and prescriber loyalty, which can slow the erosion curve.
The Stickiness of Value-Based Arrangements: If a product is deeply embedded in a payer’s value-based care programs (e.g., its outcomes are a key metric in an ACO’s performance), the payer may have a disincentive to switch to a generic, as it could disrupt their quality scores. This “stickiness” can be a powerful defensive moat that needs to be modeled.

5. How can smaller biopharma companies with limited resources compete in this new environment?

Smaller companies can turn their size into an advantage by being more agile and focused. While they may not have the resources for massive data infrastructure, they can succeed by:

Targeting Niche Opportunities: Focus on rare diseases or specific patient subpopulations where they can build deep expertise and a compelling value story. Payers are often more willing to engage in innovative contracting for orphan drugs with high unmet need.⁴⁵
Excelling at Partnership: Smaller companies can partner strategically with academic centers to generate RWE, with technology vendors for specific analytical capabilities, and with payers who are willing to experiment with novel contracts. Success is about building an ecosystem, not owning every capability.
Leveraging External Expertise: Outsource complex modeling and analytics to specialized consulting firms that have the necessary experience and platforms, allowing the company to access top-tier capabilities without the massive overhead.
Being Nimble in Negotiation: As IQVIA notes, emerging biopharma has been disproportionately successful in the recent “boom-or-bust” launch environment, suggesting that their agility and focus can be a significant advantage over larger, more bureaucratic competitors.