Industry
Life Sciences
Patient-centered Care Models Taking Center Stage in Life Sciences
HIGHLIGHTS
- Medical affairs is an important function in pharma, serving as a bridge between research and development (R&D), and product commercialization.
- It, however, faces several challenges related to stakeholder demands, regulatory compliance, data privacy, scientific integrity, and resource constraints, besides the needs of a digital-first world.
- Artificial intelligence (AI) and generative AI (GenAI) can revolutionize medical affairs by enhancing efficiency, effectiveness, and patient care; streamlining processes, uncovering insights from data, and improving decision-making.
On this page
Bridging the gap
Medical affairs (MA) is a critical function in pharma as it helps bridge the gap between research and development (R&D) and commercialization of a product.
R&D innovations are successful when they become reality by utilizing the core capabilities of medical affairs that align scientific and clinical information. Post-drug marketing experiences in the real world also get translated into various opportunities for R&D to achieve better health outcomes and support public health.
The MA function leverages insights generated in the R&D and commercialization processes to:
- design ideal product profiles that address unmet needs gathered from patient types and product effects.
- design trials to meet the objectives of product profiles based on product types and trial designs.
- define a product’s value based on the value generated by products off-label.
- Collect product-related data and generate subsequent insights using safety profiles from product safety database develop corresponding medical and commercial strategies eg, strategy to advertise, focusing on geo, and population.
Today, medical affairs function’s role is becoming strategic. Its efficacy is being measured based on its contribution to scientific excellence and credibility, which can be enhanced through scientific thought leadership, by managing medical and scientific education from early development to the post-marketing phase, and by deriving scientific and medical insights. The result is that the complexity of MA function is increasing. With patients actively taking part in their healthcare decisions, medical affairs has to help them take the right measures with accurate and user-friendly information. Health care professionals also want to be empowered with knowledge about diseases and products to prescribe the right medicines.
Understanding unmet patient needs and coming up patient-centric solutions require an effective mechanism that gathers the right insights from patients. The significant increase in scientific and clinical information, patient experiences, and real cases needs to be exploited to generate insights for crafting rational hypothesis to bring better treatment options. All this calls for a transformation of various key aspects of medical affairs to fulfill the expectations of all stakeholders for better health outcomes.
Existing challenges
The MA staff are overburdened with ever-increasing loads of scientific and clinical information.
This results in challenges like delays, non-compliance, lack of visibility, and high cost of operations.
To get quality, tailor-made information, the MA team faces iterative review cycles, increased turnaround time (TAT), and high fatigue levels, affecting their overall efficiency.
Typically, MA organizations face the following operational challenges:
- High time and effort spent on finding right and precise information from the ever-growing volume of data.
- Compliance with regulations (marketing and commercial separation) and risk management.
- Staying up to date with medical insights, changing product profiles, use, safety, and label claims for better health outcomes.
- Use of the right channels and fit-to-purpose information for the right stakeholders.
- Inadequate investment in tools to analyze and generate insights to make a difference in patient outcome metrics.
- Challenges in using real-world evidence (RWE) - mining, analysis and insight generation from the vast amount of real-world data is complex.
- Patient engagement and understanding their needs to bring the right treatment approaches for them.
Hence, there is a need for the transformation of medical affairs to be:
a) Customer-centric:
- Understanding customers’ needs and providing the right knowledge at the right time to physicians, other stakeholders.
- Leveraging human-centric artificial intelligence (AI) approaches to query internal and external data sets in a conversational format and receive timely, accurate, and concise intelligence on their customers.
b) Collaborative:
- Collaboration between clinical development and medical affairs to ensure drugs and medical devices meet the needs of patients and healthcare providers.
- Achieve synergistic strengths and drive toward a shared goal to develop, differentiate, and deliver products that impact patient outcomes.
c) Knowledge-driven:
- Knowledge creation and collection: Broaden content knowledge, with a deep focus on product technicalities by providing greater contextual insights.
- Knowledge management: Internal management of a biopharma company’s medical knowledge.
- Knowledge dissemination: Interactions with external audiences, focused on key opinion leaders (KOLs) and use of digital channels and formats available.
d) Value-focused:
- Metrics to evaluate the performance and effectiveness of MA stakeholders, eg, creating qualitative metrics measuring the impact of actions taken.
- Evaluating KOLs based on ‘reach, relevance, and resonance’.
- Monitoring metrics and implementing feedback.
- Aligning metrics based on the audience.
Overhauling MA skills
New-age technologies such as AI and generative AI (GenAI) should be leveraged to bolster key capabilities to engage and empower the stakeholders (patients, HCPs, regulatory authorities) in medical affairs.
AI and GenAI can bring substantial business benefits by:
- Enabling scientific excellence: Typically, pharma companies strive for scientific excellence by providing credible scientific information for better medical treatments to improve health outcomes.With right inputs from experts and right LLMs, huge scientific data from various sources can be reviewed and compiled with faster TAT. Further, by eliminating curation and simulations, robust scientific hypotheses and correlations can be generated, enabling scientific excellence in bringing better health outcomes. Example: An LLM such as OpenAI’s GPT-41 and Google’s Bard2.
- Extracting evidence generation: Data from real-world experiences is critical to support regulatory submission. Rich data on patient experiences from the real world, from various sources and in different formats can be extracted and synthesized to produce meaningful data outcomes for faster regulatory approvals. Example: Meta’s localized learning and model aggregation (LLaMA).
- Managing scientific and medical information: To enable accurate, concise, and tailor-made information for various stakeholders, scientific, medical, and clinical knowledge has to be managed smartly so that it is updated, easily searchable, and ready for querying. Faster responses to queries using AI and GenI can empower stakeholders to conduct their jobs better and provide value. Example: Google’s MedPaLM.
- Engaging with stakeholders: Empower the right stakeholder with the right information at the right time through the right channel. AI and GenAI enable faster identification of stakeholders, contextualization with the right intelligence to capture the ask of the stakeholder, appropriate response with language accessibility, and personalization based on the profile, role, and orchestration of the preferred channel. Example: AI-powered tools and super prompters.
- Generating medical business insights: Extraction of insights from various interactions between stakeholders to generate business intelligence or gathering insights from a 360-degree view of patients, their diseases, conditions, co-morbidities, sentiments, and preferences, can elicit medical insights to provide better options.
Click here to explore various medical affairs use cases in detail for Gen AI implementation.
Various capabilities of AI/GenAI can enable transformation of medical affairs (see Figure 1). Here is a heatmap of use cases under MA capabilities that can be impacted by Gen AI. where the dark blue colour represents high, light blue represents medium and faded one represents low.
How AI, GenAI benefits medical affairs
How AI, GenAI benefits medical affairs
Technologies like AI and GenAI can enhance collaboration, engagement, and stakeholder empowerment. The offer businesses the following benefits:
- Improved health outcomes: Rich and effective evidence generation for promising new treatments.
- Quality information: Smart content generation and delivery for stakeholder empowerment.
- Improved compliance: Better clinical decisions and effective physician education for better patient outcomes.
Optimized MA outcomes
Technologies like AI, GenAI can significantly enhance various MA elements.
They will accelerate MA outcomes by bringing agility in information management, providing the right information to the right stakeholders through the right channel, and all of it measurable through metrics management.
AI and GenAI will strengthen businesses through medical insights management, by helping identify new opportunities, and generating new evidence from a vast amount of data from the real world with minimal human effort.
Business operating model may be redefined as most activities currently outsourced will be insourced. Roles and responsibilities will change due to elimination of many tasks like searching, mining, pattern finding, creation and transformation of content.
