Introduction
When it comes to generative artificial intelligence (gen AI), it is natural for healthcare and pharma leaders to feel excited about the ability to make major productivity gains. But the possibilities go beyond that. Productivity is important, yet real success will come when generative AI begins providing deep insights that support making smart decisions. In addition, very soon, most organizations will use gen AI to promote efficiency, but the real game-changer will be that it will apply to generating insights and direct, important options. Beyond the automatic tasks, there are many areas where AI is ready to create a meaningful effect.
What is Real-World Evidence?
Real-world evidence is information about how healthcare interventions work in real-life conditions outside controlled clinical trials. It comes from various sources, such as:
- Electronic health records (EHRs)
- Health insurance claims
- Patient registries
- Wearable devices and mobile apps
- Surveys and patient-reported outcomes
While traditional clinical trials provide controlled, precise data, they often involve small groups of patients under strict conditions. RWE fills in the gaps by showing how treatments perform across broader populations in everyday healthcare settings. This makes RWE an essential tool for understanding treatment effectiveness, safety, and long-term outcomes.
Key Ways Real-World Evidence is Changing Healthcare
1. Improving Drug Development
Developing new drugs is expensive and time-consuming. Real-world evidence is transforming the process by:
- Helping identify patient populations who are most likely to benefit from a new treatment
- Providing safety and efficacy data faster than traditional trials alone
- Supporting regulatory approvals and post-marketing surveillance
Pharmaceutical companies can use RWE to make smarter decisions, reduce development costs, and bring new treatments to patients more quickly.
2. Supporting Personalized Medicine
Every patient is unique, and one-size-fits-all treatments don’t always work. RWE helps healthcare providers tailor therapies based on real-world patient experiences. By analyzing data from patients with similar conditions, doctors can:
- Choose treatments with the highest likelihood of success
- Minimize side effects
- Adjust therapies based on patient lifestyle and comorbidities
This personalized approach improves patient satisfaction and outcomes, making healthcare more effective and efficient.
3. Enhancing Patient Safety
RWE allows healthcare providers and regulators to monitor the safety of medications and medical devices after they hit the market. By analyzing data from thousands of patients, real-world evidence can:
- Detect rare or long-term side effects
- Identify interactions between drugs
- Inform recalls or safety warnings quickly
This proactive approach keeps patients safer and helps healthcare systems respond faster to potential risks.
4. Improving Clinical Decision-Making
Doctors and healthcare professionals can use RWE to make informed decisions backed by actual patient data. This helps in:
- Determining which treatments are most effective for specific patient populations
- Reducing trial-and-error in prescribing medications
- Optimizing treatment plans for chronic conditions
By relying on real-world data, clinicians can provide care that is evidence-based, practical, and more likely to succeed.
5. Supporting Health Policy and Guidelines
Health authorities and policymakers can use RWE to develop guidelines and policies that reflect real-world patient experiences. For example, RWE can:
- Inform reimbursement decisions for expensive therapies
- Guide public health initiatives and disease management programs
- Shape clinical guidelines to improve population health
By using real-world evidence, policymakers can ensure that healthcare resources are used efficiently and benefit as many patients as possible.
6. Accelerating Digital Health Innovation
With the rise of wearable devices, telehealth, and mobile apps, healthcare is generating more real-world data than ever before. RWE leverages this data to:
- Evaluate the effectiveness of digital health solutions
- Track patient engagement and adherence to treatments
- Identify trends that can guide innovation in healthcare technologies
This creates a cycle of continuous improvement, where data from everyday life informs better solutions for patient care.
Identifying drug molecules
In pharmaceutical research and development (R&D), a lot of effort is required to identify the correct molecules. Generative AI has the ability to reduce this burden by streamlining the process and making it more efficient.
- Drug discovery: Another amazing possibility that gen AI can help realize is the ability to extract and link between T-cell phenotypes and their respective genetic markers, which can open new avenues for research.
- Clinical trials: Developing a clinical testing protocol is often time-consuming. AI integration can help streamline the planning and operations of clinical trials and revolutionize clinical research, making it more efficient, cost-effective and patient-centric.
- Regulatory submissions: Generative AI and large language AI models are already gaining popularity for their ability to generate text, and their capabilities are improving steadily. Over the coming years, it will be worth observing how these tools can support healthcare consulting companies in producing the extensive documentation required by regulatory authorities, like the U.S. Food and Drug Administration.
- Data moats: Another valuable application of gen AI lies in building data moats. Generative AI enables organizations to work with large, diverse datasets in ways that produce sharper insights and help establish a competitive edge.
- Predicting patient drop-off: There is also scope for generative AI in pharma for creating synthetic data that can be used to anticipate when patients might discontinue participation in a clinical trial, allowing for better planning and interventions.
How to mitigate the risk of AI in pharma
It is impossible to talk about the potential of generative AI without acknowledging the risks that come with it. While every industry faces certain challenges, the risks are especially significant for pharmaceutical companies. This sector operates under strict regulatory frameworks and must also navigate sensitive issues, such as intellectual property protection and data privacy. Given that the outcomes often relate directly to human health, it is critical for life sciences organizations to evaluate these risks carefully and establish strong policies and controls to minimize them. Generic, off-the-shelf AI solutions are unlikely to meet these stringent needs, making it essential to develop customized systems equipped with proper safeguards and active human supervision.
Pharmaceutical firms also need to recognize that not all risks are the same and that each use case or domain carries its own level of exposure. For example, in medical affairs, where AI-generated insights could influence patient outcomes, the margin for error is extremely small. In contrast, within research environments, a model’s inaccuracy or “hallucination” might have lower consequences and, in some cases, could even inspire new ideas, such as suggesting an unexplored chemical compound with therapeutic potential. For pharma, there are certain key risk areas that must be kept in mind:
- Model inaccuracies: If gen AI is trained on incomplete data or low-quality data, it may produce inaccurate or misleading information. To mitigate this, organizations should implement robust review mechanisms to ensure that humans validate AI-generated outputs before they reach healthcare professionals or patients. This means that AI should serve as a decision-support tool rather than the final authority.
- Intellectual property and data privacy: Since most fundamental models are trained on vast amounts of publicly available data, issues such as copyright, plagiarism or IP infringement may occur. For pharmaceutical companies, this concern is heightened by the strict rules governing the storage and use of patient information jurisdictions, for example, require sensitive data to reside on in-house servers. To protect against these risks, organizations should rely on internally obtained data for model training and include explicit IP protection clauses in agreements with third-party vendors.
The key to developing generative AI for the long run
Looking at the bigger picture, the question becomes: what does it take to build a lasting generative AI capability? Cloud infrastructure is certainly a key element, but just as important is the development of orchestration–and agent-based architectures that can scale effectively. Laying down the right foundation models will be critical for enabling generative AI at scale. In addition, working with a technology services partner can help organizations set up a robust tech stack of generative AI services and machine learning tools designed to unlock the full value of their data.
Getting started with generative AI begins with choosing the type of model that best suits your needs. One approach to this is working with publicly available models that are already well-known and then optimizing them to suit specific organizational requirements. Another option is to create a custom AI model from scratch and optimizing it to meet unique needs. Whichever approach is selected, there are several important factors to keep in mind when planning to adopt generative AI:
- Ensuring that the right cloud infrastructure is in place.
- Developing AI capabilities and models that can be scaled effectively.
- Establishing clear processes for writing prompts and deciding who should oversee them.
- Having the right mix of people with the skills needed to manage and grow AI capabilities.
- Maintaining strict privacy and security for sensitive data.
- Tracking impact through well-defined performance indicators
- Integrating generative AI alongside existing traditional AI systems
Some organizations are beginning to experiment with insight agents, which are essentially question and answer (Q&A) interfaces that enable insight leads and marketers to ask questions about the data directly. Instead of letting valuable information sit unused in old market research files, dashboards or secondary data sets, these tools help teams pull out insights more quickly, driving efficiency. In some cases, generative AI is also being used to analyze call center transcripts from patients and providers, uncovering insights that can guide real-time strategic adjustments and approaches that are already familiar in several other industries.
