The large environmental impact (EI) of healthcare is of growing concern, especially given the increasing negative health effects of environmental deterioration.1,2 Paediatric intensive care units (PICU) are major contributors to this EI, partly due to the use of consumables and electricity.3–5 Environmental hotspots in clinical pathways (CP) must be identified to guide practical and effective interventions to lower the EI.5,6 Life cycle assessment (LCA) is currently the golden standard for EI assessment. However, LCA is time-consuming and highly complex. Thus, execution of LCAs on a large scale to analyse full CPs is not feasible in healthcare settings. Furthermore, the required data are often not available. Financial costs may be used as a proxy (spend-based LCA), but their representativeness is questionable.6,7 Furthermore, the CPs of patients in the PICU differ highly and cannot be represented by a single standard CP. Therefore, this research aimed to develop a process-based framework for environmental hotspot identification that requires less time and expertise compared to LCA and accounts for differences between patients, using a case study of six PICU post-cardiac surgery patients. Modules were designed as building blocks to represent medical events in the CP with the flexibility to deal with interpatient variation. Associated consumable and electricity use were allocated to each module based on medical protocols and discussions with clinical staff. From this iterative process, a set of allocation rules was established. The material composition of each product was analysed and recorded in a database. Per module, the median frequency of occurrence was calculated in the patient cohort data. The carbon emissions (kg CO2-equivalent) associated with each module per occurrence were calculated based on impact factors from an open-source database.8 The total of each module was defined as the EI per module occurrence (in kg material and in kg CO2-equivalent) multiplied by the median module frequency. This research was a first step towards an accurate and flexible approach to environmental hotspot identification within CPs related to consumable weight and bedside electricity consumption. The modules and the data analysis algorithm can be reused and expanded for other CPs, saving time and effort in future analyses. The allocation rules ensure standardised allocation of consumables and electricity across the current modules and when new modules are added. Challenges in this approach lie in the availability of product information from manufacturers and reliable, open-source impact factors, especially for pharmaceuticals.