Positive outcomes and limitations of a case management intervention in primary care for patients with complex care needs
Bibliographic record
Abstract
Context: Growing evidence suggests that case management (CM) is an effective intervention to improve the care of patients with chronic conditions and complex care needs who are at risk for poorer health outcomes. While positive outcomes have been associated with CM in a range of settings and for a variety of patient populations, less is known about CMs potential when implemented in Canadian primary care settings for patients with complex care needs. Objective: To identify positive outcomes and limitations of a CM program in primary care for patients with complex care needs. Study design: Secondary data analysis of realist evaluation data. Setting: Seven primary care clinics across four provinces in Canada. Population studied: People with complex care needs. Intervention: Twelve-month CM intervention led by nurse case managers (NCMs) in partnership with patients and other healthcare professionals consisting of four core components: 1) Patient needs assessment; 2) Care planning, including individualized services plan; 3) Care coordination; 4) Self-management support. Outcome measures: Program experiences and outcomes reported via realist interviews with patients (15), relatives (1), NCMs (6), providers (4), clinic managers (3) and other healthcare professionals (4) and analyzed thematically. Results: Five themes represent the positive outcomes associated with the CM program: more appropriate and efficient use of health services (e.g., fewer emergency department visits), improved patient health and well-being (e.g., improved physical and mental health and health management), enhanced professional collaboration (e.g., communication between NCMs and providers), expanded professional practice (e.g., increased NCM knowledge and networks), and greater satisfaction for all stakeholders. CM limitations include limited availability of appropriate services and patient circumstances that impede their readiness to participate. Conclusions: CM can positively affect patient satisfaction, health, and collaborative care in primary care settings by focusing on patient needs and goals and dedicating time and resources to coordinate care for patients with complex care needs. Patient circumstances and wider health system challenges may limit the effectiveness of CM for some.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".