Case Management Models and Continuing Care: A Literature Review across nations, settings, approaches, and assessments
Bibliographic record
Abstract
Older adults accessing continuing care often have multiple chronic conditions. Research suggests that case management is a promising approach to reduce health care expenditure and improve patient outcomes. To optimize healthcare delivery, an examination of existing case management models and their effectiveness is essential. This literature review was conducted using Joanna Briggs Institute (JBI) methods to explore case management models for older adults accessing continuing care services. Searches were conducted in PubMed and CINAHL from 2010 to 2018. A total of 37 articles were included in this review. Approaches to case management are diverse with respect to composition of care providers, method of care provision, and location of care. Findings from 27 quantitative studies demonstrated that nurse-led and interdisciplinary team case management models that include home visits can effectively reduce hospital admission/readmission while lowering costs. Mixed results were found on the impact of case management on patient satisfaction, ED visits, quality of life, length of stay, self-efficacy, social integration and caregiver burden. Among 10 qualitative studies, 3 facilitators for quality case management were identified that include receiving care at home, building trusting relationships, and improving self-efficacy. Based on these findings, we conclude that nurse-led and interdisciplinary team case management can effectively reduce hospital admission of frail older adults while lowering costs, particularly within home care settings.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".