Transforming Pharma: Leveraging AI for Advanced Market Access and Pricing Dynamics

Published on 12 March 2024 at 17:13

AI in Pharmaceutical Strategy

  • AI is transforming pharmaceutical strategy and can be utilized in pricing and market access decision-making.
  • The use of AI can help improve strategies for new and existing products and generate better market access decision-making.

Industry Trends and Market Conditions

  • Dynamic market conditions are pressuring pharma manufacturers to rethink global launch and market access strategies.
  • Health payer pressure, reimbursement legislation, and changing physician prescribing decisions are driving industry trends.

Role of AI in Decision-Making

  • Manufacturers need to show innovation and differentiation in making a case for new drug prices scrutinized by payers, providers, and policymakers.
  • AI can accelerate the gathering and analysis of medical evidence, market access information, and clinical trial results.

Selecting an AI Solution

  • Start by identifying core processes, business strategy, and data sources for analytics inclusion into the AI solution.
  • Evaluate AI solutions by executing pilots to ensure their effectiveness in market access-related applications.

AI in Pricing & Reimbursement and Health Technology Assessment (HTA)

  • AI brings advantages in HTA evaluation, pricing, and negotiations by helping companies build value propositions and differentiate products.
  • AI can analyze large amounts of data and predict prices and outcomes of negotiations with HTA agencies with over 90% accuracy.

AI in Payers’ Informed Decisions

  • AI-enabled virtual assistant aims to improve and personalize member engagement related to healthcare spending and benefits information.
  • AI can also help reduce members’ healthcare spending by predicting their healthcare costs.

AI in Outcome-based Contracting (OBC) & Value-Based Programs

  • AI can help in designing OBCs by identifying the population, outcomes, and metrics, and predicting costs.
  • AI can also broaden the evidence base and find and examine unstructured patient data for value-based care reimbursement.

AI for Field Reimbursement Managers (FRMs)

  • AI and machine learning can develop risk score models to proactively intervene on behalf of patients likely to experience roadblocks in their healthcare journey.
  • AI can predict best pricing with measures such as Predictive Acquisition Cost (PAC).

Impact of AI on Healthcare

  • AI has significant potential to streamline member engagement in healthcare.
  • It also optimizes delivery of value-based care and outcomes-based contracting.

Market Access Challenges in Health-Tech

  • There are unsolved market access challenges for promising health-tech, despite the power of AI in eHealth.
  • Predictive analytics for payer contracting is also an area of focus in AI.

Pricing and Market Access in Pharma

  • AI-based drug price predictions claim to be 90% accurate.
  • Transition to predictive data analytics is crucial for pricing pharmaceuticals.

Top 10 insights from Market Access Strategist GPT

  1. Transformative Role of AI in Pharma Strategy: AI is revolutionizing the pharmaceutical industry by enhancing decision-making processes, especially in pricing and market access. This transformation is pivotal in both launching new products and managing existing ones.

  2. Adapting to Dynamic Market Conditions: Pharmaceutical companies are facing changing market dynamics, such as health payer pressures, evolving reimbursement legislations, and shifts in physician prescribing habits. These factors necessitate a reassessment of global launch and market access strategies.

  3. AI in Evidence and Analysis: AI significantly accelerates the gathering and analysis of crucial data like medical evidence, market access information, and clinical trial results. This speed and efficiency can be a game-changer for manufacturers in demonstrating innovation and differentiation when negotiating drug prices.

  4. Choosing the Right AI Solution: Implementing AI begins with identifying core business processes and data sources. Effective integration of AI into market access strategy requires piloting and evaluating AI solutions to ensure they meet specific needs.

  5. AI’s Role in HTA, Pricing, and Negotiation: AI is instrumental in Health Technology Assessment (HTA), pricing strategies, and negotiation processes. It aids in building compelling value propositions and differentiating products by analyzing large datasets and accurately predicting negotiation outcomes.

  6. Enhancing Payer Decision-Making with AI: AI-enabled tools can improve member engagement by providing personalized healthcare spending and benefits information. Additionally, AI can predict healthcare costs, aiding members in reducing their healthcare expenditure.

  7. AI in Outcome-based Contracting and Value-Based Programs: AI facilitates the design of outcome-based contracts (OBCs) by identifying relevant populations, outcomes, metrics, and cost predictions. It also expands the evidence base and analyzes unstructured patient data, crucial for value-based care reimbursement.

  8. AI for Field Reimbursement Managers (FRMs): AI and machine learning technologies are employed to create risk score models, helping FRMs proactively address patient healthcare access issues and predict optimal pricing strategies.

  9. Streamlining Healthcare with AI: AI has a substantial impact on optimizing member engagement in healthcare and the effective delivery of value-based and outcomes-based contracting.

  10. Market Access Challenges and Opportunities in Health-Tech: Despite AI’s advancements in eHealth, there remain unresolved market access challenges. AI's predictive analytics in payer contracting is a growing focus area, especially with claims of AI-based drug price predictions being 90% accurate. The transition to predictive data analytics is becoming increasingly crucial in pharmaceutical pricing.

Top 10 advisory points from HEOR advisor GPT

  1. Integration of AI in Pharma Strategies: AI is increasingly becoming a critical tool for pharmaceutical companies to model business processes and improve strategies for both new and existing products, especially in market access decision-making.

  2. Need for AI in Global Pricing and Market Access: Dynamic market conditions and the evolving landscape of healthcare demand that pharmaceutical companies adopt a more holistic approach, utilizing Big Data and AI to refine global launch and local market access strategies.

  3. Emerging Industry Trends: Trends include increasing pressure from health payers to reduce costs, governmental legislation on pricing and reimbursement to control pharmaceutical spending, consolidation of target markets, shifts towards Value-Based Healthcare Systems, and the influence of healthcare policy on physician prescribing decisions.

  4. AI's Role in Decision Making: AI aids in the rapid gathering and analysis of medical evidence, market access information, and clinical trial results, which traditionally require extensive time and manual analysis. This acceleration supports better-informed product launch decisions.

  5. Applications in Local Market Trends and Key Opinion Leaders (KOLs): AI assists in identifying local market treatment protocols, trending KOLs for targeted marketing efforts, integrating patient data to uncover unmet needs, and determining optimal pricing strategies based on product potential and value for payers.

  6. Selection of AI Solutions: Companies are encouraged to start by identifying core processes and strategies, assess available data sources for AI analytics, and evaluate AI solutions through pilot projects to enhance decision-making in market access and pricing.

  7. AI in Pricing, Reimbursement, and HTA: AI offers advantages in HTA evaluations and negotiations by enabling companies to build compelling value propositions, differentiate products, and rapidly respond to HTA bodies’ inquiries.

  8. AI for Payer Informed Decisions: Partnerships, like that between Humana and IBM Watson Health, highlight how AI can improve member engagement by providing accurate benefits, costs, and provider information, ultimately aiming to reduce healthcare spending.

  9. AI in Outcome-based Contracting (OBC) and Value-Based Programs: AI facilitates the design of OBCs by identifying appropriate populations, outcomes, metrics, and predicting costs, which supports the shift towards value-based reimbursement models.

  10. Ethical, Regulatory, and Technical Challenges: While AI's adoption in healthcare and pharma is on the rise, companies must navigate ethical, regulatory, and technical hurdles. Successful exploitation of AI will grant companies a competitive edge through rapid insights generation, which traditional methods cannot match.

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