Addressing the Issue of Complex Drug Launches
The biopharmaceutical (biopharma) industry is facing a growing challenge in launching new drugs due to rising development costs, demands for faster time-to-market, complex patient needs, and increased market competition. In addition, new care models and the growing influence of payers add further complexity to the challenge of launching a successful drug. The value chains have become more intricate and there is a greater need for innovative solutions to address these mounting challenges.
Although many biopharmaceutical companies have adopted advanced AI technologies for drug discovery and clinical development phases, the implementation of AI in the drug launch phase has been slower. AI can provide critical insights that allow for early and effective engagement with stakeholders such as patients, healthcare professionals, and payers, which are crucial factors in the development of a successful drug launch strategy.
Pathway to Improvement with AI Technologies
Biopharma companies have access to an enormous amount of data from various sources, and AI technologies can help unlock the potential of this data, particularly real-world data, to deliver key business insights. These technologies include machine learning (ML), deep learning (DL), natural language processing (NLP), computer vision, supervised learning, unsupervised learning, and more. These technologies can help explore previously overlooked data sources to optimize marketing strategies. By providing effective implementation of these AI technologies, we can enable biopharma companies to reveal elaborate interactions in data and help in the recognition of patterns that might have been previously missed with traditional statistical methods. Such results and insights can help support key decision-making in a drug launch process.
Real-World Data Utilization
Real-world data can play a crucial role in supporting a successful drug launch, both in the pre-launch and post-launch phases. Real-world data can be used to monitor drug product performance post-launch, identify patient needs, and collect feedback from stakeholders to improve post-launch support and marketing. This presents opportunities to engage with patients and ensure their needs are met, while also maintaining product satisfaction. Moreover, the use of real-world data can help meet regulatory and compliance demands, as monitoring data can identify adverse events in patients. For instance, an NLP approach can be implemented to monitor and scan clinical patient feedback data for a drug, enabling early detection of adverse event recurrence. This approach can help address potential regulatory concerns while utilizing patient feedback to identify drug issues and increase engagement.
Predicting Pricing in an Evolving Industry
The biopharmaceutical industry is becoming increasingly complex, with a growing number of care models and healthcare delivery options available. In this industry, health outcomes have become a key revenue metric, particularly as value-based care models become more prevalent. Analytics tools can provide support for drug pricing, which is often scrutinized by stakeholders. ML and DL tools can be used to develop predictive pricing models, which continuously adapt to market changes and justify the value of the drug to stakeholders. For instance, a Prophet model for predictive pricing could theoretically use historical pricing and volume data to produce accurate insights for future pricing trends. Such models can also be used to optimize launch prices and adaptive pricing in the post-launch cycle.
Intelligent Forecasting Solutions to Plan for Future
Biopharma companies have access to a vast amount of data that can be used to develop accurate forecast models to navigate the challenges of an increasingly demanding market. This allows companies to effectively plan for the future and be less susceptible to adverse market changes, as companies are better prepared for different scenarios. Analytics solutions that utilize different algorithms, such as neural networks, Bayesian networks, and genetic algorithms, can be integrated to address various challenges in different areas of the value chain. These algorithms can perform tasks like automating complex workflows, forecasting modeling, and conducting trade-off analyses in "what if" scenarios. By integrating these capabilities, biopharma companies can future-proof their operations.
AI-Driven Marketing Strategies and Segmentation
AI technologies can enhance marketing approaches for drug launches, both in pre-launch and post-launch cycles, by optimizing stakeholder engagement methods. In a post-pandemic world, engagement methods have evolved, and companies must utilize online and offline channels to meet stakeholder needs. Data platforms equipped with AI capabilities can utilize safeguarded patient data to optimize marketing campaigns, tailoring them to patient populations and healthcare professionals. Social media analytics enhanced with NLP and sentiment analysis can be used to gauge patient trends, and sentiment, and increase engagement. ML-based chatbots can be used to effectively communicate with stakeholders and enhance patients' drug experience. Social media analytics can also be implemented in predictive pricing models, in addition to marketing purposes.
ML methods can identify relevant stakeholder segments as targets, maximize customer reach, and improve a biopharma company's patient engagement strategies. Learning algorithms can observe changes in patient behavior and habits over time, informing strategic decision-making processes for companies.
Integrating AI in Launch and Compliance
AI capabilities have the potential to transform launch and marketing strategies for biopharma companies. By accurately identifying market segments, marketing content can be tailored to patient populations, providing value-driven messaging and information. This fosters greater transparency in communication between companies and stakeholders, earning more trust with stakeholders. Implementation of AI technologies in marketing and commercial teams also enables greater collaboration between those teams and enables aligned thinking on maximizing product value.
AI technologies can play a pivotal role in data privacy and security. As companies obtain an ever-increasing amount of data, the privacy and security of the data are essential, from an ethical perspective to satisfy government legislation and regulatory compliance. AI technologies can help monitor government laws and regulations, ensuring compliance, while also helping improve the security of the data by identifying potential vulnerabilities in the data exchange. Successful integration of AI in the data infrastructure can ensure ethical compliance with stakeholders, and foster greater trust with patients.
The Benefits of AI-Driven Future
We have discussed how AI technologies can help mitigate and solve many of the complex issues being faced in the biopharma industry. The launch of a new drug product is an increasingly costly endeavor, and the utilization of AI technologies helps companies make informed decisions to maximize the value of their product. AI utilization also allows for increased targeted engagement with key stakeholders such as patients and healthcare professionals, forming greater trust and communication with stakeholders.
With accurate predictive pricing models and intelligent forecasting, companies are better equipped to deal with the shifting market landscape and gain more sustainability for success over the post-launch period of a product. Integrating AI capabilities for teams at different phases of the value chain also allows for product feedback to be incorporated, which can improve stakeholder engagement and the quality of the product. With a multitude of possibilities, AI technologies present a comprehensive pathway to success for the biopharma industry in supporting drug launches and maximizing patient engagement.
At Visionet, we understand the challenges that biopharma companies face in launching new drugs. That's why we offer a unique AI-driven solution – Intelligent Sensor Data Platform (ISDP) solutions to help you maximize the value of your drug discovery and launches. Visionet's ISDP is a comprehensive AI-powered solution that enables clinical sponsors to track patients’ conditions 24/7, captures objective drug outcomes, and ensures protocol adherence during trials. With our AI-enabled analytics tools and innovative approaches, we can help you navigate the complex landscape of the biopharma industry and achieve greater success. Get in touch with Visionet today to learn more and take your drug launches to the next level!