Economic evaluations of patient-navigation programs in cancer care.
Bibliographic record
Abstract
14 Background: Lack of coordination is challenging our health care systems. This is especially true in cancer care, which is based on multiple treatment alternatives and several types of patient-professional interactions. One recommendation is to develop patient navigation programs based on telemedicine in order to avoid costs that are due to under-coordination among providers and/or between patients and providers. The objective of our study was to identify the evidence on the economic impact of such programs in oncology, and to develop a methodological framework to conduct economic evaluations. Methods: We conducted a literature review, exploring articles indexed in Medline (2005-2015), and focusing on economic evaluations of navigation programs in oncology, with particular attention to the use of telemedicine. Results: Of the 14 studies included, nine were randomized controlled trials. Four studies adopted a societal perspective. Every study computed the direct costs of the program. Six studies included indirect costs in the total costs associated with the program, mainly based on patient productivity loss and travel cost. Only two papers included indirect costs associated with informal care. Two studies showed that patient navigation programs were less costly than standard care. Most of the total cost of patient navigation is attributable to direct medical costs (i.e. patient admission, diagnostic follow-up and medical intervention). Conclusions: More evidence is needed regarding the economic impact of navigation programs in oncology. This review provides some guidance for the design of economic evaluations. If these programs are funded through public resources, a societal perspective should be adopted since it covers the direct, indirect and intangible costs of the program. Furthermore, a key strategy will be to identify the most common situations of under-coordination occurring alongside the usual care pathway and measure avoidable costs. This advocates for an extended use of economic evaluations based on randomized controlled trials.
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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 | 0.046 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".