Building and training AI models for pharmacovigilance

There’s no escaping the rise of AI adoption and integration. It’s not so much a question of whether you will adopt AI into your business practices, but how.

Health IT Analytics shared in a published survey that healthcare and life sciences organisations are increasingly investing in generative artificial intelligence (GenAI) projects. In fact, 3% of respondents in leadership positions reported a 100-300% rise for AI budgets, and 8% witnessed increases of over 300%.

With this explosion of investment, it is not surprising that, alongside the innovation, there are growing calls for setting guidelines for responsible AI and considering what it means to establish best practices, limit bias, and train AI models for pharmacovigilance. Gartner predicts that by 2026, 50% of governments worldwide will enforce use of responsible AI through regulations, policies, and the need for data privacy.

Moving forward, there will be a need to balance innovation alongside responsible AI. This starts with how AI is built and trained. That intentionality leads to reliable detection of adverse events, streamlined processes, and increased patient safety and industry compliance.

Utilising AI in pharmacovigilance

According to the World Health Organization (WHO), pharmacovigilance is described as “the science and activities relating to the detection, assessment, understanding, and prevention of adverse effects or any other medicine/vaccine related problem.” When preventing adverse events, it is important for the AI model to help organisations remain pharmacovigilant, or to create a pharmacovigilance in AI.

Insights from conversation data offer numerous use cases and opportunities throughout the healthcare industry when considering utilising AI in pharmacovigilance. AI can help detect patient risk factors that may increase the likelihood of an adverse event by analysing large datasets, such as patient conversation data in hubs, contact submissions, chats, emails, and more.

To be effective in identifying adverse events, AI algorithms need a reliable and diverse source of data to learn and train from. That data source already exists inside your organisation with recorded conversations between HCPs, pharma companies, patients, and others. AI enables analysis of these conversations and the compilation of large datasets to inform better decision-making, identify personnel training needs, and pinpoint pain points patients encounter along their journey.

Responding to bias in AI

AI relies on the validity of the datasets from which it pulls information. Algorithmic biases occur when data scientists use incomplete data lacking full representation of a specific patient population. Since it’s being interpreted and trained into models based on the data that is fed into it, models have the potential to display bias. Whatever the data inputs are, the AI models take them and synthesise the data into a mental map of the world before responding based on those inputs.

Another part of training AI is labelling, which is a large part of enabling the data set that the model learns from. This is why humans are a critical piece of the puzzle to listen, engage, and then label the conversation with additional context to help AI be as objective as possible.

Ways AI helps identify adverse events

When used with intention — with the proper safety precautions in place — AI becomes more than a tool with its ability to augment and support human judgment to enhance the patient experience within healthcare.

Pharmacovigilance is a crucial aspect of patient safety and medical development and advancements and, with the help of AI, it has become even more effective in helping prevent adverse events for pharma organisations. As you think about your own AI investment and strategy, consider these ways AI can be utilised to identify and address adverse events through pharmacovigilance:

• Predictive analytics and automation: AI can help predict adverse events before they occur, allowing healthcare professionals to take preventative and proactive measures - rather than ones that reactively harm patient health and employee experiences.
• Reliable reporting with real-time feedback: AI can provide real-time feedback to pharma and compliance leaders, helping them to build comprehensive reports and train their teams to respond appropriately when a compliance event occurs.
• Compliance monitoring at scale: AI can monitor conversations at scale, ensuring that regulations are being followed by healthcare organisations.
• Limiting bias through human review: AI can help limit bias by having humans (experts in healthcare and AI) review the AI output for any invalid or biased results, which leads to reliable, training data sets.
• Establish best practices for accountability and trust: As AI is used and the sourced data is trained and informs the outputs, the evolution of best practices will continue.

AI in data analytics is revolutionising the healthcare industry by providing insights that were previously thought to be impossible to obtain, and doing so at scale. By leveraging AI, organisations unlock opportunities to proactively support their work of creating safe and effective treatments for their patients.