Case Management and Telehealth: A Scoping Review
Bibliographic record
Abstract
Background: Case management (CM) is an intervention adapted to the needs of patients with chronic conditions or complex needs. Factors associated with effectiveness of CM, such as high intervention intensity, can represent challenges to its implementation. Telehealth has the potential to help overcome these challenges, but little work has been done to synthesize available evidence on telehealth CM. The purpose of this scoping review was thus to fill this gap and document which telehealth modalities have been used, summarize perspectives of key users, and discuss evidence on effectiveness of telehealth-delivered CM. Methods: A search in MEDLINE, Scopus, and CINAHL for articles published between January 2005 and January 2021 was done. Studies in which telehealth was used for patient-case manager interaction and conducted in a population with complex health needs and/or chronic conditions were included. Articles selected for full-text review were independently screened by two reviewers. Data extraction was conducted once and validated by a second reviewer. Results: Of 3,108 articles, 22 were retained for data extraction. A narrative synthesis was conducted. Most studies evaluated CM interventions delivered over telephone, yet, literature suggests that face-to-face contact is essential to CM success. Results also indicate that telehealth CM is acceptable and effective, associated with better utilization of health services and favorable clinical outcomes. Conclusions: Lack of research evaluating telehealth CM delivered using modalities other than telephone. Further research should evaluate CM interventions that integrate platforms enabling visual information exchange.
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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 | 0.017 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.032 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".