Community-based Personalized Care and QOL

Secondary research questions include:

1. what is the feasibility of obtaining study measures through routinely collected program participant data and what design adjustments are needed to balance pragmatism and high quality data collection?
2. What are the analytic approaches to be developed to aggregate data from individual participants into meaningful units (e.g. care-team level, organizational level, social network, and geographic area) to inform ongoing adaptation of the components of the intervention?
3. what are the perceptions of knowledge users on the usefulness of comparative effectiveness pragmatic trial evidence?

The project involves using administrative data from the ICES to create outcome measures and feedback systems for communities, and the INSPIRE PHC Unit for expertise in health system integration, care in the community, and knowledge translation.

Inclusion criteria: Seniors (65+) and people who self-identify as having a functional disability. Pilot study sites: 7 municipalities and the city of Windsor in Windsor-Essex County in Ontario. The combined total eligible population in all 8 sites is about 60,000 people, from which a minimum sample of 3000 registered patients and 1000 registered caregivers will be drawn

In Year 1, planned enrolment includes a minimum of 1100 intervention clients from all recruitment methods in the care model intervention, with similar numbers of control and intention to treat clients.

The primary outcomes are quality of life (QOL); perceived health; experience of care, and perceived social connection.

The primary outcomes for caregivers will be quality of life; perceived burden; caregiver perceptions of care; and perceived social connection.

Secondary outcomes for both will include goal attainment, distress management, symptom management, places of care, and health care utilization and costs. In terms of health equity, the investigators will measure the difference between the average/median population quality of life and cost outcomes compared to patients at the bottom income quartiles stratified by risk level. At a systems level, algorithms will be developed and applied to the data collected from participants to provide aggregate organization and system-level reports, co-designed with end users to support them as learning organizations.