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The pharmaceutical industry generated total global revenue of US $1.4 trillion in 2021, which is expected to touch US $1.77 trillion by 2026. North America is expected to contribute US $700 billion to the industry in 2025, the EU and Southeast Asia US$ 250–300 billion, and China US $200 billion.
The US leads the pharmaceutical market with 49.1 per cent share, and introducing a new drug to the market costs on average around US $2 billion. Despite the significant investment involved in launching new drugs, almost one-third of them do not meet the expectations of stakeholders, i.e., healthcare professionals, patients, and payers. Hence, the design and execution of a market access strategy are vital to the success or failure of a drug launch.
New-age specialty drugs (such as antibodies) and cell and gene therapies are gaining traction, but very few could be considered as hits. Some of the successful specialty drugs are Humira – an antibody drug by Abbott for treating rheumatoid arthritis that has generated over US $20 billion annually over the past few years – and Keytruda by Merck (oncotherapy) which is expected to generate US $27 billion in 2027. As more specialty drugs, cell and gene therapies, and drugs targeting rare indications are developed, designing and executing market access strategies become more important than ever.
The classical approach of cost-based pricing is detrimental to pharmaceutical companies, mainly due to non-specific communication of relevant information to stakeholders, inability to differentiate a product from the competition, or just lower price points of similar products in the market. Regeneron faced such an issue in 2012 when oncologists at New York’s Sloan-Kettering Cancer Center publicly refused to prescribe the company’s colon cancer drug, Zaltrap (ziv-aflibercept), as it was priced at twice the cost of similar drugs available on the market.
The healthcare industry is shifting its focus to a value-based model where value would determine the price of a drug given to patients. This shift is partly rooted in the economics and accessibility of advanced life-saving therapies being developed. Consolidation and organization of the target audience of pharmaceutical manufacturers in the form of healthcare policymakers and payers is another reason for the shift. In developed countries, healthcare policymakers and payers have a strong say in what physicians can prescribe as the system is moving toward therapy protocols, as opposed to the conventional model where physicians could decide and prescribe the line of treatment.
Value-based care focuses on the patient and creates therapies and treatments that combat illness and improve the quality of life. This high-quality and cost-effective care is achieved through constant interaction with the patient. The traditional model, known as fee-for-service, assigns reimbursements to healthcare organizations based on services rendered. In value-based care, however, reimbursement is contingent upon the quality of care rendered and is tied to patient outcomes. This ostensibly straightforward shift in emphasis requires major adjustments from healthcare providers.
Numerous value-based initiatives implemented by providers, payers, life sciences firms, and others in the healthcare ecosystem share a common objective: high-quality care, better patient experience, and substantial reduction in unnecessary costs. The combination of healthcare delivery, community consciousness, and new technological developments promise to help value-based healthcare yield substantial returns.
The concept of value-based pricing has always existed, but it is only in recent times that sincere efforts have been expended toward its industry adoption, and as with any change, it is fraught with difficulties Quantifying the value provided is a difficult task even for general medicine targeting indications such as diabetes and cardiovascular/respiratory diseases. The value provided to a patient must be measured against subjective parameters that determine post-treatment quality of life, whereas the value provided to a payer may be quantified more readily with objective measures such as reduced hospitalization.
Artificial intelligence (AI) is instrumental in the successful implementation of value-based pricing in the pharmaceutical industry, where large amounts of data need to be analyzed to recognize patterns and predict successful outcomes. AI is capable of identifying meaningful relationships in raw data and can be utilized to aid with diagnosis, treatment, and outcome prediction in various medical circumstances. This technology can be used in nearly every field of medicine, including drug development and patient monitoring. It has already been used at the drug discovery and design stage for in silico analysis of thousands of molecules and possible outcomes to predict candidate molecules likely to succeed.
A market access strategy for a new therapy is usually designed by analyzing tons of data from myriad sources including historical pricing data, submissions to regulatory bodies and evaluations by said bodies, clinical trials, and real-world data. Artificial intelligence can not only rapidly analyze large volumes of data but also design value-based programs with inputs on numerous points (listed below) based on meaningful relationships identified in the data. Artificial intelligence can:
• Identify eligible populations – artificial intelligence can be used to determine which patient populations and subpopulations should be included in this type of arrangement.
• Check efficiency - artificial intelligence can help remove some uncertainty regarding the efficacy of a therapy in specific populations.
• Disease or product selection – by analyzing current treatment options, artificial intelligence can determine the optimal placement of a product in a treatment algorithm.
• Identifying, measuring, and tracking outcomes – artificial intelligence can be used to identify appropriate metrics for assessing therapy outcomes.
Natural language processing, context-aware processing, querying, and deep learning are the primary types of AI used in the pharmaceutical industry.
Next steps for pharma industry
• Develop a consumer-centric strategy – the initial step for the industry is to implement a strategy that enables and encourages consumers to make healthy lifestyle choices. This includes deploying web portals and mobile applications that facilitate access to exhaustive details (administrative and clinical), communication with patients or members, and access to medically curated resources. A corporation's technical and digital strategies must be integrated with consumer policy.
• Implement individualized programmes – the next step should be to design, implement, and promote health and health programs for behavioral alterations in particular individuals in clinical social environments. To this end, organizations must ensure they have a procedure in place for soliciting patient input or members regarding the satisfaction and ease of participation across multiple touchpoints.
• Develop and evaluate multiple data sources – applications such as chatbots of the next generation can personalize dialogue for health conditions, potential plan benefits, and patient preferences and improve patient/consumer participation. Ideally, this should result in improved patient health and contentment, in addition to cost savings for both services and administration.
• Use data insight - Lastly, any consumer-centric strategy must consider and respond to what data may reveal through analytic examination of the results for various consumer subgroups and their caregiver's metrics, including patient satisfaction and net promoter scores, in conjunction with clinical quality. Moreover, healthcare expenditure should be generated for each initiative step.
The value-based care is the future of the pharmaceutical industry. The modern consumer is more health-conscious and wants to be involved in their healthcare. Hence, the industry would have to redesign its strategies to ensure customer involvement from the beginning and ascertain their feedback is incorporated.
(Author is senior manager, Technology Research and Advisory, Aranca)
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