Multiple “market conditions” are forcing manufacturing companies to rethink global launch strategies, as well as local market access strategies such as interaction with payers, prescribers and authorities in order to achieve the expected Return on Investment and achieve market share. In a sense, the “market conditions” have changed and “traditional” product global launch strategies no longer yield the expected results.
The future of pharmaceutical and healthcare is reshaping, and industry trends require a more holistic approach and thinking, to increase market share via analytics using Big Data with Artificial Intelligence (AI), not just physician Customer Relationship Management (CRM) systems and patient survey data, as had been done in the past.
What are the industry trends and market conditions?
- Health payer pressures to reduce costs despite earlier reductions already achieved with the use of reference-pricing.
- Endorsing pricing and reimbursement legislation by governments and other payers to minimize pharmaceutical spending growth (e.g., Tender Management).
- Consolidation of pharma’s target audience requiring new marketing models for specialty drugs (e.g., the average number of patients per launch brand in the first year went from approximately 180,000 in 2007, to 42,000 in 2016, and is estimated to drop to 28,000 by 2027).
- The reimbursement approach is shifting from traditional fee-for-service to “Value-Based Healthcare System” (quality/cost) via patient outcome measurements.
- Physician prescribing decisions influenced by “Formulary” protocols defined by healthcare policy makers and payers (e.g., Managed Care Organizations (MCOs) and Pharmacy Benefit Managers (PBMs) increasingly using formulary exclusion policies including innovative and specialty medicines).
Let’s focus on “Value-Based Healthcare System” and “Formulary” implications and how these trends are affecting market share?
The influence and complexity of value-based frameworks, including value-payment models and innovative agreements, is expected to increase:
- Drug prices are influenced by value-based agreements and, as derived, payment models are more rigorous with the evolution of strategies used by payers and policy makers. These strategies will define required data and utilization tracking
- The multiple structured value “frameworks” being developed and will influence the pricing landscape (e.g. Therapeutic Area, Indication).
- The indication “framework” structures and payment models are influenced by the entry of cell and gene therapies.
- “Value Frameworks” (Cost/Value) are influencing the clinical decision-making criteria in the United States (e.g., National Comprehensive Cancer Network (NCCN), American College of Cardiology (ACC) and American Heart Association (AHA) guidelines).
Formulary exclusions are the most striking trend because of the perceived clinical equivalence and rise of generic drugs:
- Formulary decisions include real-world evidence and comparative effectiveness of drugs.
- The comparisons and real-world evidence require a definition of “Value attributes” to measure outcomes, efficacy and performance.
- The “Value Attributes” are collected, measured, valued, aggregated and converted (using a decision rule) to evaluate whether the value metric was achieved.
The trends need a consensus from a large volume of evidence data collection and fast processing analysis, typically initiated early in the commercial lifecycle, is a must.
How can AI help overcome this environment?
To minimize the effect, the pharmaceutical industry needs to invest more in providing robust evidence of the clinical and cost effectiveness of products to negotiate directly with providers and payers for access. Manufacturers will need to show innovation and differentiation to make a case for new drug prices scrutinized by payers, providers and policymakers.
The goal is not just set prices to recoup research investment and successfully hit profit goals as was traditionally done with International Reference Pricing (IRP) and Launch Sequence approaches.
Analytics, supported by AI, must include the effect of pricing on increasingly cost-cognizant private and public payers, as well as the value of the product for manufactures to prove that its drugs provide value that justifies cost and more efficacy than competitor’s products.
AI can accelerate the gathering and analysis of medical evidence, market access information and the clinical trial results supporting the case which could take prolonged time if done by traditional means (i.e., human intensive analysis).
In addition, AI data analysis can improve product launches by identifying patterns in several key areas:
- Local Market Trends – Identification of standardized protocols and guidelines for treating patients in local markets as payers and providers increasingly align their approaches.
- Key Opinion Leaders (KOL) – Identify the trending KOLs for better focus on effort and resources within various areas of the business (e.g., clinical, medical affairs, commercial, sales and marketing) and detect missing influencers, and identify opportunities for focused sales and marketing action.
- Patients – Integration of multiple patient’s data patterns for examination of unmet needs and physician/patient segmentation.
- Products potential – Identify opportunity for the brand after assessing patient’s unmet needs and defining the optimal value brand proposition (i.e., Optimal pricing to demonstrate superior patient outcomes and value for payers).
As seen, the healthcare industry is well suited for implementation of AI due to large amounts of data available and the need to better understand the landscape to predict patterns and take action. Healthcare is trending to value-based care which has terabytes of data for analysis, resulting in high appreciation for what AI can achieve because value-based care requires multiple data sources to integrate with multiple structures.
How does Business Intelligence (BI) differ from AI? The answer is simple: Traditional BI environments are great for repeatable reporting process, while AI allows for a predictive approach by analyzing multiple internal and external data points.
So, how do we move forward? How do you select an AI solution for your organization? Here are a few key points:
- Start by identifying the core processes and business strategy, including organizational roles and interdependencies.
- Identify and assess the internal and external data sources that compile patient and customer data for analytics inclusion into the AI solution.
- Move forward with evaluating AI solutions by executing pilots.
Achieving the results
A combination of Global Price Management processes and AI pricing analytics can assist the organization in establishing value-based pricing for best results on product launch and gain market share.
The transformation from the traditional human-centered “diagnostic” approach — in which we could respond to questions such as “What happened?” and “Why did it happen?” — to a more “predictive” approach that answers: “What will happen?” and “What should I do?” results in effective decision-making.