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Background Routine collection of patient-reported outcome measures is important to illustrate the effectiveness of care delivered, particularly in the context of rapidly growing population need for palliative care within constraints of limited resources and workforce (Etkind, Daveson, Kwok, et al. J Pain Symptom Manage. 2015;49(3):611-24).Aim To illustrate how outcome measures have been successfully introduced to a specialist palliative care team, and to demonstrate meaningful use of the data for different audiences.Method Implementation of outcome measures was led and facilitated by an organisational steering group throughout. Palliative Phase of Illness, Australia-modified Karnofsky Performance Scale (AKPS), Integrated Palliative Outcome Scale (IPOS), Barthel index and a Quality of life question were used for patients under the care of hospice inpatient and community teams. Ongoing training for all multidisciplinary team members was developed. Collection of outcome measures was integrated into the electronic patient record. Case presentations at handovers, board rounds, multidisciplinary meetings routinely embedded use of outcome measures. Secondary analysis of data was led by external colleagues, with co-production of full reports and summary infographics.Results For the 4-year period ending April 2024, data for over 2,000 inpatient episodes and almost 15,000 community episodes have been collated. This data was used in annual reports to show the impact of care and as evidence for the Care Quality Commission of effective interventions. It has been shared in quality boards within the hospice to showcase the work to team members, patients and carers. The data has been used to identify areas of high impact, areas to improve and to demonstrate areas of inequities, such as outcomes according to socio-economic status.Conclusion Routine use of outcome measures can be successfully embedded in clinical care and data used to share messages with different audiences. Ongoing work is under way to interrogate the data further, explore case mix complexity, and demonstrate cost effective use of resources.