through Patient Analytics, AI, and Human Intelligence
Pharma is becoming more intertwined with patient needs and behaviors, marking a pivotal shift toward a more patient-centric approach. AI is spearheading this transformation, influencing each step of the pharma value chain to ensure more personalized and effective patient care.
Organizations are using AI-driven tools to reorient themselves to be more patient-centric.
The impact on pharma’s value chain
At the front end of the value chain, clinical trials have shifted from generic methodologies to individualized approaches, where pharmaceutical companies prioritize patient-centric factors, including social determinants, genetics, and demographics. However, the actual value for pharma companies lies in applications beyond mere trials.
On the commercial side, patient support programs have extended to more than just financial assistance. Pharma companies are becoming more proactive in reaching out to patients through promotional means (a shift from earlier restrictions) and educational initiatives. Television campaigns and other methods now focus on educating patients on drug choices and the importance of adherence in reducing overall costs.
Data-driven value
This shift to becoming more patient-centric is being powered by data analytics. Where previously the focus was strictly on patient prescription data, now, with access to third-party sources like IQVIA, Symphony, and companies like Optum, pharma companies can process actual claims data. This information provides invaluable insights into patient journeys, treatment pathways, and drug performance metrics in the real world. Such analytics paint a clearer picture of patient behaviors and drug efficacy, bridging the gap between pharmaceutical companies and the individuals they serve.
Patient journey insights help us identify which physicians attend to specific patient demographics. With that knowledge, sales representatives receive precise recommendations on which doctors to approach, armed with data suggesting a high likelihood of a drug being prescribed by that physician. We’ve even optimized communication methods based on a doctor’s preference. If a physician prefers emails, we use that channel. Otherwise, we might opt for a more personalized touch.
The way we connect with them has evolved into an omni-channel approach. It’s no longer just about sales representatives physically visiting doctors. There are diverse channels of communication in place, each tailored to understanding doctors’ specific needs.
AI is carving pathways in marketing, sales strategies, and product positioning and providing value across the full spectrum of healthcare, from researchers to healthcare professionals and patients. This is being achieved through a range of AI-driven tools and applications.
Tools driving the value-based approach
AI tools have emerged to refine and optimize functions as the industry transitions to a more values-based approach. They include the following:
| Tool | Application | Description |
|---|---|---|
| Smart AI chatbots | Customer support & education | Chatbots provide real-time answers, simplifying complex data and policy documents for patients and professionals. |
| Generative AI & conversational AI | Marketing and user experience | Facilitates content creation for marketing and enhances user interactions, offering a tailored experience. |
| Data analytics through AI | Research and development | Spots trends in large data sets, predicts outcomes, and provides insights, aiding decision-making around user needs and preferences. |
| AI models for commercial promotions | Commercial and sales | Assists in segmenting and analyzing physicians’ tendencies to prescribe specific drugs, ensuring targeted promotions. |
| Summarization tools | Information management and dissemination | Beyond chatbots, these tools condense vast amounts of information into succinct summaries for easier understanding. |
Patient Jarvis
Developed by Fractal, Patient Jarvis is a transformative tool for sales strategies, offering detailed insights into patient treatments and enabling tailored solutions. It harnesses medical claims data, providing pharmaceutical companies an in-depth view of patient treatment trajectories, including brand transitions and prescribing patterns. Additionally, Patient Jarvis addresses data interoperability challenges by standardizing various data formats. This streamlines data analysis and offers detailed metrics such as brand market shares and patient demographics. Moreover, the tool’s adaptability allows companies to customize it to their unique needs swiftly.
Balancing the power of AI with the human touch
As with most industries, AI has its share of challenges that must be overcome. In emotionally charged fields like oncology, there is a need for a balance between AI interventions and genuine human interactions.
While AI offers efficiency, specific patient interactions demand empathy and emotional understanding that automated responses cannot replicate. For instance, a patient grappling with emotional concerns would benefit more from human guidance than an AI chatbot. It is crucial, therefore, to discern where AI tools are appropriate and where a human touch is indispensable.
Another area requiring human intervention is ensuring AI models adhere to increasingly stringent regulatory requirements.
Data privacy, ethical, and regulatory challenges
Many pharmaceutical companies are turning to the concept of responsible AI as a solution in the face of AI’s potential risks. This approach emphasizes the elimination of biases throughout data processes, from collection to dissemination, to mitigate risks like privacy violations and misleading interpretations. It is vital to adhere to strict regulatory guidelines, such as GDPR in Europe and FDA regulations in the U.S. To ensure AI models remain ethical, they must be compliant, be committed to bias reduction, and undergo real-time monitoring.
Therefore, implementing a “responsible AI framework” is critical—a comprehensive approach encompassing everything from policy creation and compliance to stringent model testing. This will aid in producing content that meets responsible AI standards while acting as an educational resource for internal teams, guaranteeing that AI is utilized responsibly and ethically.
The AI-powered commercial future in pharma
Fractal is spearheading this transition. Fractal emphasizes the commercial facet by embedding AI into various stages, from drug discovery to market penetration. Collaborations within the industry guide the evolution of AI tools, with a clear focus on commercially viable, patient-centric solutions.
The synergy between AI and the commercial realm in pharma offers an unprecedented opportunity. While patient-centricity remains the north star, commercial success is increasingly defined by the astute adoption of AI.
The future of pharma AI
Moving toward the future, we noticeably move from traditional off-the-shelf products to more customizable, targeted solutions. This results in pharma companies building solutions in their environment rather than purchasing ready-to-use products: this approach accelerates the implementation process, ensuring a seamless fit with the company’s existing systems.
Fractal has adopted a hands-on approach by collaborating with pharma companies, providing invaluable feedback, and helping shape the roadmap of future features we plan to introduce. The goal is continually enhancing the product, ensuring we remain at the forefront of AI’s role in the pharmaceutical landscape.