How Echos Amplify: The Power of ECHO Rheumatology for Group Practice to Manage Inflammatory Arthritis & Autoimmune Disease
Bibliographic record
Abstract
<h3>CONTEXT:</h3> Project ECHO is a guided practice model that increases workforce capacity to provide best-practice specialty care and reduce health disparities. It is well-suited to rheumatology care delivery in Ontario, as the number of rheumatologists in the province has remained static despite the growing prevalence and burden of rheumatic disease. Consequently, there remain striking disparities in access to care, particularly in rural and remote areas across the province. ECHO Rheumatology (‘ECHO’) began in Ontario, Canada in 2017. ECHO fosters and supports rheumatic disease care champions within a Family Health Team (FHT), a type of primary care group practice in Ontario, Canada. <h3>OBJECTIVES:</h3> To present experiences of ECHO PCPs from FHTs that demonstrate how ECHO supports complex rheumatic disease management; to present metrics regarding impact of the program. <h3>STUDY DESIGN:</h3> We conducted a multi-method study including PCP questionnaires and focus groups. Focus groups were conducted to explore PCP experiences participating in ECHO, practice change, and interprofessional collaboration. <h3>ANALYSIS:</h3> Paired samples t-tests were calculated to examine pre-post changes. Focus group transcripts were analyzed using thematic content analysis. <h3>SETTING:</h3> Ontario, Canada <h3>STUDY POPULATION: ECHO PCPs OUTCOME MEASURES:</h3> Self-efficacy, knowledge, satisfaction, practice change. <h3>RESULTS:</h3> PCPs are increasing their interest and collaboration between other health professionals as a result of participating in ECHO. ECHO was also noted to foster Virtual Communities of Practice (VCoP) through weekly sessions and ongoing mentorship regarding rheumatic patient cases. PCPs were also highly satisfied with the program. One focus group participant said ““A fantastic program for us rural providers because of limited access to specialists.” In terms of questionnaire outcomes, all 16 items of self-efficacy questionnaire increased significantly (p<0.005). Participants’ knowledge scores increased from 59% (SD 15%) pre-ECHO to 66% (SD 19%) post-ECHO (p=0.045). <h3>CONCLUSION:</h3> The prevalence and burden of rheumatic disease is rising. ECHO is a program in Ontario, Canada that has promise to build capacity within primary care, especially those practicing in group practices like Family Health Teams. In addition to increasing PCP self-efficacy and knowledge, ECHO has the potential to develop virtual communities of practice and increase interprofessional collaboration.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".