Effectiveness of case management interventions for frequent users of healthcare services: a scoping review
Bibliographic record
Abstract
OBJECTIVE: Frequent users of healthcare services are a vulnerable population, often socioeconomically disadvantaged, who can present multiple chronic conditions as well as mental health problems. Case management (CM) is the most frequently performed intervention to reduce healthcare use and cost. This study aimed to examine the evidence of the effectiveness of CM interventions for frequent users of healthcare services. DESIGN: Scoping review. DATA SOURCES: An electronic literature search was conducted using the MEDLINE, Scopus and CINAHL databases covering January 2004 to December 2015. A specific search strategy was developed for each database using keywords 'case management' and 'frequent use'. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: To be included in the review, studies had to report effects of a CM intervention on healthcare use and cost or patient outcomes. Eligible designs included randomised and non-randomised controlled trials and controlled and non-controlled before-after studies. Studies limited to specific groups of patients or targeting a single disease were excluded. Three reviewers screened abstracts, screened each full-text article and extracted data, and discrepancies were resolved by consensus. RESULTS: The final review included 11 articles evaluating the effectiveness of CM interventions among frequent users of healthcare services. Two non-randomised controlled studies and 4 before-after studies reported positives outcomes on healthcare use or cost. Two randomised controlled trials, 2 before-after studies and 1 non-randomised controlled study presented mitigated results. Patient outcomes such as drug and alcohol use, health locus of control, patient satisfaction and psychological functioning were evaluated in 3 studies, but no change was reported. CONCLUSIONS: Many studies suggest that CM could reduce emergency department visits and hospitalisations as well as cost. However, pragmatic randomised controlled trials of adequate power that recruit the most frequent users of healthcare services are still needed to clearly confirm its effectiveness.
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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 | 0.029 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".