in Healthcare
From tradition to transformation
The pharma sector has historically been slow to adopt new technologies but has recently started realizing the potential benefits of integrating Generative AI (GenAI). Fractal’s engagement with the industry provides a firsthand look at this evolution. When we first spoke with leaders in the pharmaceutical sector, the conversations were grounded in curiosity: “What’s your perspective on large language models (LLMs)? How can it assist in refining our processes?”
These dialogues have matured considerably in just 18 months, reflecting the rapid pace at which the industry is adopting GenAI. Bridging this rapid evolution in understanding AI’s capabilities, one of the most pivotal applications of these advanced tools emerges in addressing the complexities of the Medical Legal Review process.
Navigating pharma’s biggest challenge: medical legal reviews
The Medical Legal Review (MLR) process is a cornerstone within the pharmaceutical sector, acting as a filter to ensure that content disseminated by pharma companies aligns with the stringent guidelines set by regulatory authorities such as the FDA in the US and the EMA in Europe.
However, while the MLR process is crucial, it’s also fraught with inefficiencies, which can delay vital sales and marketing drives, thereby affecting revenue. To alleviate this, we are introducing GenAI-based MLR solutions that aspire to transform the MLR paradigm, condensing a process that traditionally took days into mere hours. This surge in efficiency not only trims costs but also facilitates quicker launches of sales campaigns. Additionally, the time conserved allows companies to innovate and fine-tune their marketing endeavors.
Tailored global strategies
Cross-border regulations present another challenge for pharmaceutical enterprises, with each country having a unique regulatory climate. For instance, FDA stipulations in the US vary markedly from European directives, while emerging markets may have another set of guidelines.
Assisting a multinational pharmaceutical client with their oncology product’s global promotion strategy illuminated GenAI’s potential. GenAI’s capacity to be trained on broad data sets spanning multiple countries allows it to synthesize insights cohesively, abiding by region-specific business rules. For this project, GenAI collated data from diverse territories such as the APAC, US, and Europe, offering tailored solutions specific to each market’s needs. Furthermore, the utilization of GenAI has halved solution development timeframes.
Real-time physician insights
Patient safety is critical in pharmaceutical evaluations, sometimes overshadowing even the benefits of a drug. As a result, physicians prioritize the safety profile of a medication before considering its clinical merits. GenAI, with its expansive training across diverse documents and data sets, seamlessly addresses this demand. It can provide real-time insights into a drug’s safety metrics when queried by healthcare professionals. Moreover, GenAI’s dynamic framework can corroborate real-world observations against its vast database, ensuring that potential side effects, even those not documented during trials, are promptly identified, and evaluated, ensuring a patient’s well-being.
As the pharmaceutical industry leverages GenAI for challenges like MLR, cross-border regulations, and patient safety, it is vital to recognize that the success of these applications hinges on understanding user intent and crafting user-focused solutions.
A Design-thinking-led disruption
GenAI is not just about processing vast amounts of data; it is about discerning the real intent behind a user’s query. For instance, if a physician wants to uncover the top five side effects of a drug, the AI model is trained to precisely extract that from the extensive collection of documents. It prioritizes user intent and prevents misleading data or “hallucinations.” When faced with unfamiliar queries, the AI design must ensure an appropriate answer or provide a seamless transition to human assistance.
This concept can be illustrated by the challenge of digital fatigue, especially in the case of doctors: they are often inundated with vast amounts of information, much of which may not be immediately relevant. This constant barrage, typically from pharmaceutical representatives, can lead to reluctance from doctors to engage. The key to solving this problem lies in addressing the core issue by synthesizing design thinking, engineering, and AI.
● Design thinking: Pinpointing the real challenge is the first step, done by reframing the problem. Digital fatigue may seem like the primary concern, but upon closer inspection, it becomes clear that it is merely a symptom of the overflow of potentially irrelevant content.
● Engineering: Effective problem-solving requires the proper data foundation. Engineering ensures that the most relevant data, be it structured, unstructured, or a combination of the two, feeds into the solution. This foundation is crucial for AI’s subsequent role.
● AI tools: With this foundation, GenAI can formulate the solution. In the context of the digital fatigue example, the solution developed by Fractal is a “digital rep equivalent.” This tool is designed to act as a physician’s assistant, presenting precise information at the exact moment it is needed. This combats the standard practice of overwhelming doctors with unnecessary information and thus alleviates digital fatigue.
As we explore the profound transformative capabilities of GenAI, its multifaceted impact on the pharmaceutical landscape becomes evident, addressing everything from individualized insights to broader industry growth potentials.
The impact across industry facets
The needs and requirements of pharmaceutical companies can currently be distilled into three primary categories:
Faster insights and streamlined productivity
“Time to insight” is crucial for enterprise efficiency, and traditional dashboards with generic Key Performance Indicators (KPIs) often miss the mark in catering to specific user needs. GenAI is transforming this landscape with a focus on personalization. It goes beyond standard metrics, enabling users to tailor their decision-making dashboards based on their unique priorities.
The breakthrough lies in shifting from static Business Intelligence (BI) tools to conversational BI platforms. GenAI lets users interact with the interface conversationally, facilitating immediate insights. While foundational KPIs remain essential, the emphasis is on providing a platform attuned to individual requirements without compromising on consistent data sources.
For instance, a sales representative could quickly query the number of prospects in a targeted territory for an upcoming visit instead of waiting for analyst feedback. GenAI ensures instant access to reliable data, streamlining decision-making and boosting productivity, particularly in the pharma sector.
More customer-centric experiences
The pharma sector recognizes the pressing need to elevate the customer experience for its diverse clientele, from medical professionals to end patients. GenAI stands at the forefront of this transformation. To cater to adept users such as physicians, GenAI’s conversational tools, trained extensively on varied data, assure the provision of pertinent, compliant, and easily understandable information, countering the digital exhaustion prevalent among many. Large Language Models, integral to GenAI, not only adhere to MLR standards but also set clear “guardrails” for data credibility and compliance.
On the other hand, for end patients, Fractal has identified GenAI’s power to enhance patient experiences when combined with analytics, engineering, and design. This integration promises reduced errors and improved efficiency in repetitive tasks. At its core, Fractal emphasizes the importance of patient-centricity, aiming to revolutionize patient experiences in the pharma sector.
Increased growth opportunities
GenAI is instrumental in uncovering growth avenues within the complex pharmaceutical landscape. Amidst a strict regulatory environment, real-world observations of drug combinations hold potential value. For instance, an FDA-approved oncology drug, “product X,” might show enhanced patient outcomes when combined with “product Y,” even though such combinations cannot be promoted without FDA endorsement. GenAI enables pharmaceutical companies to harness these observations, guiding more focused research. If combined usage shows promise in clinical trials, companies can approach the FDA for approval, potentially broadening a drug’s application and significantly improving patient outcomes. Essentially, insights from real-world patient data, when harnessed with Gen AI, can profoundly shape better healthcare pathways.
Catalyzing a new era in healthcare
The pharmaceutical industry, once hesitant in embracing new technologies, is now swiftly recognizing the transformative power of GenAI. As these innovations take root, the industry stands on the cusp of a brighter, more efficient future. The horizon looks promising as the synergy between GenAI and the pharmaceutical sector unfolds, setting the stage for unprecedented advancements in the healthcare sector.