Developing a process of risk-stratified care coordination for older adults in primary care
Bibliographic record
Abstract
BACKGROUND \nOlder Canadians with chronic diseases are the highest users of the health care system. Primary health care (PHC) could play a central, coordinating role in assessing older adults and managing their care, 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). This dissertation project aims to understand how a process of risk-stratified care coordination for older adults can be developed and implemented in primary care. Information gathered to answer this question will provide an in-depth understanding of: i) the local context where the process is implemented, including available health and support services; ii) the process of implementing a screening and referral process in primary care, and iii) the experiences of providers, patients and caregivers with implementation to see how the process might be modified and to understand what factors are important for future spread. \nMETHODS \nThe Chronic Care Model (Wagner et al., 1999), a framework to guide care improvements and a multi-level (environmental, organizational, patient, provider, and program) framework for implementation of health innovations (Chaudoir et al., 2013) were used to guide the three study phases. Overalldata collection and analysis followed a mixed methods design, within a developmental evaluation approach. Data were collected using ethnographic observations (phases 1,2,3), informal feedback (phase 2), individual and focus group interviews (phases 1 and 3), and survey (phases 1 and 3) and tracking forms (phase 3). Data were analyzed using appropriate qualitative and quantitative techniques. Patients, family caregivers, and health care providers were purposefully sampled from two Family Health Teams in Ontario (rural and urban). \nRESULTS \nThrough focus group interviews with health care providers, lack of care coordination, information sharing, patient engagement, and service awareness were identified. To address these concerns, a process of risk-screening and care coordination for patients 70 + years of age was developed and implemented through an iterative process, in two primary care clinics. 512 patients were screened for level of risk using the interRAI Assessment Urgency Algorithm (AUA) and care was coordinated for individuals based on level of need. Among those screened, 70% of individuals screened as low risk, 25% were screened as moderate risk, and 5% were screened as high risk. As a result, service referrals were made to self-management, community programs, and specialized geriatric services using an online referral mechanism. Although the screening and referral process is time consuming, health care providers, patients and caregivers identified many benefits including early identification of service need, greater awareness of services available in the community, and improved relationships between patients and providers. \nCONCLUSIONS \nA process of risk-stratified care coordination was developed and implemented in primary care through an ongoing, iterative process with older adults, caregivers, and health care providers. Future research activities should focus on testing these findings in other models of care (e.g. solo-physician practice) and in other regions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.079 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".