Automating cancer registries: Pearls and pitfalls

Clinical registries are essential in oncology for monitoring the quality of patient care and supporting research. However, maintaining these registries is resource-intensive and can burden clinical staff. Technologies such as artificial intelligence (AI) now offer the ability to automatically extract data from electronic medical records into registries, with the potential to lower costs and improve efficiency.

To examine the practical opportunities and challenges of automating oncology registries, using key lessons from the partial automation of the Australian Brain Cancer Registry (ABCR).The innovation:This analysis draws on the ABCR project experience, detailing the use of technologies ranging from discrete data extraction to advanced AI. It outlines the multidisciplinary approach required and discusses key factors relevant to registry automation.What can be learnt from this case?Successful registry automation relies on close collaboration between clinicians, researchers and programmers. Human oversight remains essential, particularly when the AI is uncertain about specific data points. Key factors for effective automation include clearly defined data elements, strong communication among stakeholders, robust safeguards for patient privacy and planning for long-term sustainability and interoperability of the registry. It is also important to avoid introducing bias by over-prioritising data that are easiest to extract automatically.

Automating cancer registries can reduce costs but requires thorough planning. The optimal approach may involve humans and machines working together.Implications for health information management practice:Giving early attention to data accuracy, patient privacy and the long-term sustainability of the registry is critical for long-term success.