Implementing and Evaluating a Model of Care Coordination in Primary Care for Older Adults using a Co-Design Approach
Bibliographic record
Abstract
Background: Older Canadians with chronic diseases are the biggest users of the health care system. Primary health care (PHC) could play a central, coordinating role in assessing and managing older adults, but at present lacks specific strategies to fulfil this role. Priorities for enhanced care coordination in PHC include: 1) consistent processes to identify and assess older persons and create individual care plans aligned with risk levels; 2) improved care coordination and system navigation; 3) improved access to appropriate services; and 4) improved patient and caregiver engagement (Heckman et al., 2013; World Health Organization, 2008; Wagner, 2000; Goodwin et al., 2013).Aims: This study aims to understand how a model of risk-stratified care coordination for older adults can be developed and implemented in primary care. Information gathered will provide an in-depth understanding of: (1) the local context a region in Ontario, Canada (2) what referral pathways can link older patients to services appropriate for their level of risk, and (3) providers, patients and caregivers experiences to understand how the model could be modified and what factors are important for implementation in future primary care sites.Methods: This study used mixed methods, within a developmental evaluation approach (Patton, 2011). Ongoing focus group (n=6) and key informant interviews were conducted with patients (n=15), families (n=4), and primary care and community care providers (n=15) in three locations (rural and urban) in Ontario, Canada. Data were coded using a line by line, emergent approach. Risk-screening data (n=600) and service utilization were also collected and analyzed at the study sites.Results: A model of care coordination was developed through engagement of patients, families, and health care providers. Components of the model include: a) consistent screening (interRAI Assessment Urgency Algorithm) and referral processes; b) coordination of care through individualized care plans; and c) patient and caregiver engagement in decision-making. Implementation resulted in patient and provider awareness of resources for self-management, stronger linkages between PHC teams and community resources; improved patient and caregiver experiences and engagement in decision-making.Conclusions: A model of care coordination was developed and implemented in primary care through an ongoing, iterative process with older adults, caregivers, and health care providers.This process resulted in key principles necessary for improving care coordination in primary care for older adults and their caregivers.Limitations: This project was conducted in one Ontario region. Strategies may need to be tailored to the specific needs and resources of other communities.Suggestions for Future Research: The next phases of our work will involve implementing and evaluating this model in primary care sites that are not team-based settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".