Bridging the Data Gap

Jim Streeter
President of technology
Chief technology officer
Envision Pharma Group

The pharmaceutical industry is awash in data, from clinical trial results and patient records to market research and real-world evidence. The potential of this data to revolutionize drug development, enhance patient care, and improve commercial outcomes is immense. However, the vast volume and variety of this data, along with concerns about quality, privacy, and integration, present significant challenges. Unlocking this potential amidst these challenges requires a strategic approach, one that leverages the power of interoperability and clean data to fuel Artificial intelligence (AI) driven insights.

The future of AI in pharma: Embracing interoperability

AI-driven ecosystems, powered by interoperable systems and standardized data, can transform the landscape by accelerating drug development, optimizing clinical trials, and streamlining commercialization efforts.

Consider the vast amounts of data generated throughout the drug development process. Historically, much of this information remained siloed, limiting its potential impact. With interoperable systems using standardized data, AI algorithms can analyze and integrate intelligence housed on previously separate platforms, uncovering hidden patterns, correlations and insights that were once unattainable.

The true game changer of interoperable systems is the fact that we do not have to re-learn insights that have already been gleaned historically in siloed platforms. By leveraging standardized data and interoperable systems, AI eliminates the need for time-consuming knowledge re-acquisition, providing instant access to a wealth of historical data and insights used in other areas of a business. This reduces the need for repetitive tasks and redundant trials, avoiding duplicated efforts and allows pharmaceutical companies to allocate more time and resources to addressing complex challenges and on innovative research and development.

Imagine having access to all relevant data surrounding a drug program, from discovery to market launch and beyond. If this data is integrated into existing processes and systems, the result is a collaborative environment where humans and AI work in tandem. Users can pose complex questions about drug efficacy, patient responses, or market trends, and AI can rapidly analyze vast datasets to provide actionable insights.

Ultimately, this leads to faster time-to-market for new therapies, improved patient outcomes, and more efficient resource allocation across the industry. However, this potential is not without its challenges.

Overcoming challenges: Clean data and the avoidance of hallucinations

While the potential of AI in pharma is immense, its success hinges on the quality of the data it uses. We know that data often contains gaps and inconsistencies; data sets are seldom complete, and it is common to find data in the wrong fields. Clean, standardized data is essential for training reliable and accurate AI models. Reliable insights comes from clean data. Without it, AI will “hallucinate” or not use the data at all, generating outputs that are inaccurate, misleading and therefore unusable. However, clean data doesn’t happen by accident. It requires discipline and diligence, leveraging advanced algorithms to ensure that AI is fueled by high-quality and complete data.

This is no different from the early days of analytics a decade ago, when ensuring data integrity was paramount to prevent faulty insights. Just as inaccurate data could derail analytics projects, leading to misleading patterns and incorrect conclusions, so too can it hinder the effectiveness of AI. However, AI possesses a certain resilience, capable of identifying and working around data inconsistencies to a degree that previous analytics tools could not. This doesn't mean AI is infallible, but it does highlight the importance of robust data cleaning processes to maximize the potential of AI in driving accurate and valuable insights.

Training AI models on clean, well-structured data enables outputs to be transparent and explainable. This means that users understand how the model arrived at its conclusions. By prioritizing clean data and transparent AI, we can ensure that AI is a reliable and trustworthy tool that supports, rather than hinders, the pursuit of groundbreaking medical advancements.

Humanizing data: making data accessible and usable for all stakeholders

While it's great to have access to vast data, it amounts to nothing more than static numbers if we don’t humanize it. This means making the data accessible and understandable so that stakeholders can easily utilize it in their daily workflow. It's about presenting data in a way that is intuitive and usable for the people interacting with it. AI shouldn't be a separate, specialized tool reserved for data scientists; it should be woven into the fabric of everyday operations, accessible and usable by everyone.

Think of it like the integration of AI assistants into our daily lives, like Microsoft's Copilot. These tools are designed for the general public, not just tech experts, and they make complex tasks easier without requiring specialized knowledge. The same principle applies to AI in pharma. By embedding AI directly into the systems and tools that we use every day, we remove the barriers to adoption and empower everyone to leverage its benefits. This approach also democratizes access to powerful insights, making them accessible to everyone who needs them. This facilitates more informed decision-making and accelerating drug development and improving patient outcomes.

Embracing the AI revolution in pharma

In the midst of a data-driven revolution, the strategic implementation of AI has the potential to reshape the delivery of life-changing therapies to patients. By embracing interoperable AI-driven ecosystems, clean data and transparent AI, and championing people-centric implementation, pharma companies can unlock the true value of their data, leading to faster innovation and improved patient outcomes. Partnering with experienced organizations specializing in AI integration can significantly streamline this process. These partners offer expertise in data management, AI model development, and change management, ensuring a seamless transition and maximizing the return on investment. By leveraging their knowledge and experience, pharmaceutical companies can navigate the complexities of AI adoption more efficiently and effectively.

The journey towards a fully realized AI-driven future in pharma may be complex, but the rewards for those who successfully navigate this path will be transformative. With the right strategies and partnerships in place, the pharmaceutical industry is poised to usher in a new era of innovation, efficiency, and ultimately, improved patient care.