The Impact of Project ECHO on Participant and Patient Outcomes: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Project Extension for Community Healthcare Outcomes (ECHO) uses tele-education to bridge knowledge gaps between specialists at academic health centers and primary care providers from remote areas. It has been implemented to address multiple medical conditions. The authors examined evidence of the impact of all Project ECHO programs on participant and patient outcomes. METHOD: The authors searched PubMed, MEDLINE, EMBASE, PsycINFO, and ProQuest from January 2000 to August 2015 and the reference lists of identified reviews. Included studies were limited to those published in English, peer-reviewed articles or indexed abstracts, and those that primarily focused on Project ECHO. Editorials, commentaries, gray literature, and non-peer-reviewed articles were excluded. The authors used Moore's evaluation framework to organize study outcomes for quality assessment. RESULTS: The authors identified 39 studies describing Project ECHO's involvement in addressing 17 medical conditions. Evaluations of Project ECHO programs generally were limited to outcomes from Levels 1 (number of participants) to 4 (providers' competence) of Moore's framework (n = 22 studies, with some containing data from multiple levels). Studies also suggested that Project ECHO changed provider behavior (n = 1), changed patient outcomes (n = 6), and can be cost-effective (n = 2). CONCLUSIONS: Project ECHO is an effective and potentially cost-saving model that increases participant knowledge and patient access to health care in remote locations, but further research examining its efficacy is needed. Identifying and addressing potential barriers to Project ECHO's implementation will support the dissemination of this model as an education and practice improvement initiative.
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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 | 0.056 | 0.184 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| 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".