Oral Presentation Clinical Oncology Society of Australia Annual Scientific Meeting 2017

Development of a clinically embedded patient-reported outcome framework - lessons learnt (#119)

Mei Krishnasamy 1
  1. University of Melbourne, Melbourne, VIC, Australia

Patients’ reports of their quality of life, symptoms and functional status can provide important data on the impact of health conditions and medical treatments. However the ability to collect reliable and relevant patient-reported outcome (PRO) data that can inform health service improvements is challenging. This two-phase study set out to develop a lung cancer PRO framework to collect longitudinal patient-reported outcome data. Phase 1 focused on the development of the data collection framework and phase 2 includes a 12-month implementation and evaluation component. This paper will report on development of the framework and consider lessons learnt to date 

Methods: A Delphi process was used with multidisciplinary lung cancer clinicians to achieve consensus with regard to the PRO items to be collected as part of the initiative, the schedule of assessments, and feasibility of PRO administration. Four patients provided feedback on the PRO measures chosen by clinicians and the proposed data collection schema and methods.

Results: The EORTC QLQ-C30 and the lung cancer-specific module (QLQ LC13) were administered at baseline and at two, six and 12 months, and a brief social isolation measure (PROMIS) was administered at baseline only. A subset of patients about to commence chemo-radiation treatment was chosen for the implementation phase. Since the initiative started in October 2016, 14 of 19 (74%) eligible patients have been recruited. Preliminary data indicate high adherence to baseline assessments (100%), but this dropped to 50% at two months, with 38% of patients citing side effects or worsening ill health as reasons for non-participation.

Conclusion: Lessons learnt to date include; deciding which PRO measures to collect requires careful attention to clinical relevance and patient burden; ability to ensure standardised data collection and maximum data completeness is time consuming