Key factors of case management interventions for frequent users of healthcare services: a thematic analysis review
Bibliographic record
Abstract
OBJECTIVE: The aim of this paper was to identify the key factors of case management (CM) interventions among frequent users of healthcare services found in empirical studies of effectiveness. DESIGN: Thematic analysis review of CM studies. METHODS: We built on a previously published review that aimed to report the effectiveness of CM interventions for frequent users of healthcare services, using the Medline, Scopus and CINAHL databases covering the January 2004-December 2015 period, then updated to July 2017, with the keywords 'CM' and 'frequent use'. We extracted factors of successful (n=7) and unsuccessful (n=6) CM interventions and conducted a mixed thematic analysis to synthesise findings. Chaudoir's implementation of health innovations framework was used to organise results into four broad levels of factors: (1) ,environmental/organisational level, (2) practitioner level, (3) patient level and (4) programme level. RESULTS: Access to, and close partnerships with, healthcare providers and community services resources were key factors of successful CM interventions that should target patients with the greatest needs and promote frequent contacts with the healthcare team. The selection and training of the case manager was also an important factor to foster patient engagement in CM. Coordination of care, self-management support and assistance with care navigation were key CM activities. The main issues reported by unsuccessful CM interventions were problems with case finding or lack of care integration. CONCLUSIONS: CM interventions for frequent users of healthcare services should ensure adequate case finding processes, rigorous selection and training of the case manager, sufficient intensity of the intervention, as well as good care integration among all partners. Other studies could further evaluate the influence of contextual factors on intervention impacts.
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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 | 0.094 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".