Authors: Stephanie Roy and Ban Tawfik
In the rapidly evolving landscape of the pharmaceutical industry, artificial intelligence (AI) and machine learning (ML) tools have emerged as transformative, revolutionizing commercial outreach strategies and maximizing their impact. By streamlining operations, reducing costs, and providing valuable insights, these technologies are now instrumental in overcoming the challenges of new product launches, intensified competition for smaller patient populations, and the proliferation of innovative therapies.
Commercial teams face mounting pressure to swiftly establish their products in the market, requiring a deep understanding of market dynamics, competitive landscapes, and the patient journey. Access to non-identified data that encompasses comprehensive patient journeys, healthcare provider (HCP) preferences, and patient treatment-seeking behaviors makes this possible. Commercial teams have already leveraged AI/ML advancements and predictive analytics to identify an entire treatable population for specific diseases and pinpoint physicians likely to prescribe treatments. Adopting this personalized, data-driven approach amplifies current strategies and provides real-time insights into diverse patient profiles, intricate journey mapping, and HCP interactions with remarkable speed and precision. Consequently, sales and marketing efforts become more agile and laser-focused on expanding market share.
Advantages and challenges of utilizing real-world patient data for improved commercial strategies and patient outcomes
Life science companies increasingly use real-world patient data (RWD) to improve outcomes and enhance their market strategies, helping the right products reach the right patients at the right time. This data provides valuable insights for budget impact models, impactful brand messaging, and effective outreach plans, as well as a comprehensive understanding of the disease landscape, patient characteristics, and unmet needs.
The integration of RWD into clinical trial approaches and commercial strategies improves outcomes, but the implementation of RWD is a complex process. To be successful, organizations need access to technology and local domain expertise, including a better understanding of the challenges related to data privacy regulations, the decentralized decision-making processes, and fragmented data in European markets. Despite the abundance of structured and unstructured data sources available, extracting meaningful insights from them for swift decision-making remains a challenge.
By capitalizing on real-world patient data, life science companies can align their research and commercialization efforts with the goal of enhancing patients’ lives through effective treatments. This is particularly important in niche indications with limited patient populations, empowering industry stakeholders to more precisely navigate this changing landscape.
Unleashing the potential of AI/ML: Transforming personalized strategies for enhanced healthcare outcomes
Pharmaceutical sales and marketing teams face the complex task of capturing the attention of time-constrained healthcare professionals, requiring a shift from generic pitches to personalized approaches. AI/ML technologies provide specific physician and patient-level insights teams need to build precise marketing and sales strategies that deliver better results through a suite of purpose-built algorithms including early disease detection, disease progression, non-adherence patterns, and more.
Aligning brand value and sales potential with market needs can be achieved through AI/ML optimization, allowing organizations to engage customers effectively and identify market gaps. A comprehensive view of patients and providers facilitates the identification of market gaps, while a deeper understanding of patient journeys and treatment pathways unveils opportunities for personalized interventions. Predictive analytics empower market development by identifying new patients, and profiling and segmenting HCPs quickly and accurately enables targeted engagement with key decision makers. Embracing these data-driven approaches help to optimize brand performance, maximize resources, and drive commercial success. These approaches have had a measurable impact on life science companies, such as generating a 35% RX uplift and 27% increase in brand conversions as well as identifying 95x more undiagnosed patients for ultra-rare diseases than traditional approaches.
Finding the right partner
This transformative era presents an unprecedented opportunity to swiftly address intricate inquiries in real-time. However, the development and utilization of groundbreaking algorithms necessitate a deep understanding of the patient data landscape and the intricacies involved with global data collection. To fully realize the potential of AI/ML capabilities, organizations will need partners with access to advanced ML and analytics technology, integrated global industry data, deep healthcare industry knowledge, and technical expertise in building insightful algorithms.
Each organization has unique needs and finding the right kind of partnership will depend on the domain of interest. For example, if an organization is operating across different global regions, a partner can provide AI insights that will help navigate the complex landscape of global data regulations and determine which standards apply to a given initiative. For organizations that need to reach niche patient populations, partners can help elucidate unique patient needs and help shape personalized engagement for better intervention outcomes.
Equipped with data, advanced technology, and industry expertise, companies can forge ahead and create AI/ML-driven solutions that provide invaluable insights for medical, commercial, and health system decisions. As they look toward the future, strategic partnerships will be the catalyst for innovation, propelling the life sciences industry into uncharted territories of discovery and success.
Bios:
Stephanie Roy
Stephanie leads the Global Commercial and Medical Affairs segment within IQVIA’s Analytics and AI Solutions team. Her team helps design and deliver innovative, data-driven analytical solutions, providing patient and HCP insights to Pharma clients. She has been with IQVIA for ten years in a variety of delivery, consulting, and business development roles. Ms. Roy completed an MBA/MPH degree with a focus on healthcare policy and administration from UC Berkeley Haas School of Business and a BA in Biological Science from the University of Chicago.
Ban Tawfik
Ban leads the product design, technical delivery, and commercial strategy of IQVIA’s Analytics Solutions and AI solutions in the xUS markets. She’s been with IQVIA for 6 years helping clients realize value from utilizing predictions and advance RWD insights. Before IQVIA, Ban has worked as Senior Product Manager at several global software providers, and as Head of Patient Engagement Solutions for one of the UK’s leading providers of global point-of-care systems in healthcare.
