Cancer Care Coordination: a Systematic Review and Meta-Analysis of Over 30 Years of Empirical Studies
Bibliographic record
Abstract
BACKGROUND: According to a landmark study by the Institute of Medicine, patients with cancer often receive poorly coordinated care in multiple settings from many providers. Lack of coordination is associated with poor symptom control, medical errors, and higher costs. PURPOSE: The aims of this systematic review and meta-analysis were to (1) synthesize the findings of studies addressing cancer care coordination, (2) describe study outcomes across the cancer continuum, and (3) obtain a quantitative estimate of the effect of interventions in cancer care coordination on service system processes and patient health outcomes. METHODS: Of 1241 abstracts identified through MEDLINE, EMBASE, CINAHL, and the Cochrane Library, 52 studies met the inclusion criteria. Each study had US or Canadian participants, comparison or control groups, measures, times, samples, and/or interventions. Two researchers independently applied a standardized search strategy, coding scheme, and online coding program to each study. Eleven studies met the additional criteria for the meta-analysis; a random effects estimation model was used for data analysis. RESULTS: Cancer care coordination approaches led to improvements in 81 % of outcomes, including screening, measures of patient experience with care, and quality of end-of-life care. Across the continuum of cancer care, patient navigation was the most frequent care coordination intervention, followed by home telehealth; nurse case management was third in frequency. The meta-analysis of a subset of the reviewed studies showed that the odds of appropriate health care utilization in cancer care coordination interventions were almost twice (OR = 1.9, 95 % CI = 1.5-3.5) that of comparison interventions. CONCLUSIONS: This review offers promising findings on the impact of cancer care coordination on increasing value and reducing healthcare costs in the USA.
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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 | 0.040 | 0.086 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.046 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".