Harnessing AI for clinical data registries: A guided approach

Amid a flurry of activity in the advanced analytics space – including OpenAI’s rollout of customizable versions of ChatGPT, the launch of Microsoft’s AI-powered Microsoft Fabric and Humane’s recent release of the AI Pin – industries are looking for ways to integrate natural language processing (NLP), artificial intelligence (AI) and machine learning (ML) into their business operations.

Healthcare is no exception. With the average hospital producing approximately 137 terabytes of data every day, stakeholders across the healthcare ecosystem are interested in using tools like NLP, AI and ML to streamline the extraction and analysis of this data. Potential use cases include developing clinical decision support tools that can improve patient care, augmenting clinical trial platforms to streamline trial operations and enhance trial diversity, and automating clinical workflows to reduce administrative burden and mitigate physician burnout.

While NLP, AI and ML (a collection of tools that will be generally referred to as AI in this piece) have the potential to transform and improve healthcare, their use does present certain risks. President Biden’s recent Executive Order on the use of AI notes that “irresponsible use could exacerbate societal harms such as fraud, discrimination, bias, and disinformation; displace and disempower workers; stifle competition; and pose risks to national security.”

In a similar vein, the World Health Organization recently published guidance for regulating the use of AI in healthcare. As such, businesses in heavily regulated industries like healthcare must tread cautiously with their use of AI.

Based on our substantial experience in leveraging AI, we see potential to derive value from healthcare datasets across the industry. This includes collaborating with various medical societies to build next-generation registries integrating AI. Additionally, AI applications can help streamline healthcare operations, such as reducing waiting lists in hospital settings.

Read the full article in Health Data Management