People with diabetes who use subcutaneous insulin are at risk of harm during Transfer of Care (ToC) due to the challenges of moving between different care settings. Safety is maintained by healthcare resilience, where people and organisations respond, anticipate, monitor, and learn in response to challenges. To support healthcare resilience and improve safety, real-time data within digital systems has the potential to be used to support monitoring current issues and anticipation of future risks. These data are potentially new leading indicators for safety. The availability and use of a wider array of leading indicators will facilitate proactive safety improvement and enhance current measurement regimes primarily focused on collecting lagging indicators. This research explored the use of the Functional Resonance Analysis Method (FRAM) to model insulin management during ToC. By examining variability in care processes across ToC linked to outcomes, potential areas where leading indicators could be developed and applied were identified. The findings from the FRAM model were explored in a collaborative way to identify potential leading indicators. These were classified as active leading indicators that could provide real-time information to people with diabetes who use insulin (PWDI), caregivers and staff or passive leading indicators that provide information to organisations about the structural capacity for safe outcomes. This approach could be adapted and applied to other areas of healthcare to promote safety improvement. Further research will be needed to understand the impact of leading safety indicators on outcomes, workflows and resources.