A multi-stage process to develop quality indicators for community-based palliative care using interRAI data

Background Individuals receiving palliative care (PC) are generally thought to prefer to receive care and die in their homes, yet little research has assessed the quality of home- and community-based PC. This project developed a set of valid and reliable quality indicators (QIs) that can be generated using data that are already gathered with interRAI assessments-an internationally validated set of tools commonly used in North America for home care clients. The QIs can serve as decision-support measures to assist providers and decision makers in delivering optimal care to individuals and their families. Methods The development efforts took part in multiple stages, between 2017-2021, including a workshop with clinicians and decision-makers working in PC, qualitative interviews with individuals receiving PC, families and decision makers and a modified Delphi panel, based on the RAND/ULCA appropriateness method. Results Based on the workshop results, and qualitative interviews, a set of 27 candidate QIs were defined. They capture issues such as caregiver burden, pain, breathlessness, falls, constipation, nausea/vomiting and loneliness. These QIs were further evaluated by clinicians/decision makers working in PC, through the modified Delphi panel, and five were removed from further consideration, resulting in 22 QIs. Conclusions Through in-depth and multiple-stakeholder consultations we developed a set of QIs generated with data already collected with interRAI assessments. These indicators provide a feasible basis for quality benchmarking and improvement systems for care providers aiming to optimize PC to individuals and their families.

Automated summary

Through this project, we have developed a set of valid quality indicators for home- and community-based palliative care. These indicators can be generated using existing data and serve as decision-support measures for providers and decision makers in delivering optimal care.