Characteristics of Case Management in Primary Care Associated With Positive Outcomes for Frequent Users of Health Care: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Case management (CM) interventions are effective for frequent users of health care services, but little is known about which intervention characteristics lead to positive outcomes. We sought to identify characteristics of CM that yield positive outcomes among frequent users with chronic disease in primary care. METHODS: For this systematic review of both quantitative and qualitative studies, we searched MEDLINE, CINAHL, Embase, and PsycINFO (1996 to September 2017) and included articles meeting the following criteria: (1)population: adult frequent users with chronic disease, (2)intervention: CM in a primary care setting with a postintervention evaluation, and (3)primary outcomes: integration of services, health care system use, cost, and patient outcome measures. Independent reviewers screened abstracts, read full texts, appraised methodologic quality (Mixed Methods Appraisal Tool), and extracted data from the included studies. Sufficient and necessary CM intervention characteristics were identified using configurational comparative methods. RESULTS: Of the 10,687 records retrieved, 20 studies were included; 17 quantitative, 2 qualitative, and 1 mixed methods study. Analyses revealed that it is necessary to identify patients most likely to benefit from a CM intervention for CM to produce positive outcomes. High-intensity intervention or the presence of a multidisciplinary/interorganizational care plan was also associated with positive outcomes. CONCLUSIONS: Policy makers and clinicians should focus on their case-finding processes because this is the essential characteristic of CM effectiveness. In addition, value should be placed on high-intensity CM interventions and developing care plans with multiple types of care providers to help improve patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".