Piloting the Extension for Community Healthcare Outcomes (ECHO) Pediatric Oncology Telehealth Education Program in Western Kenya: Implementation Study
Bibliographic record
Abstract
Background: Childhood cancer has an annual incidence of 150-160 cases per million children worldwide but remains vastly underdiagnosed in low- to middle-income countries such as those in Sub-Saharan Africa. The Moi Teaching and Referral Hospital (MTRH) serves a population of 25 million people, including 10 million children. The average number of pediatric cancer diagnoses was 216 cases annually in 2017-2019, which was well below the anticipated 1500 cases based on epidemiology data. The remaining 75%-80% of pediatric cancer cases remain undiagnosed, and these patients are not likely to survive. Prior outreach and needs assessments demonstrated a lack of medical knowledge related to pediatric cancer as a primary barrier to improved referrals, diagnoses, and ultimately, cure. Objective: This study aimed to address disparities in medical knowledge contributing to low diagnostic rates of cancer in children. We implemented Project ECHO (Extension for Community Healthcare Outcomes)-a validated virtual guided practice and telementoring model-to connect multidisciplinary specialists at MTRH with staff in medically underserved communities in western Kenya for training, technical assistance, and mentorship. Methods: Sessions were freely available on Zoom twice monthly and featured an expert-led didactic topic followed by a learner-led, case-based discussion. The discussion used dialogue education to promote learning and engagement among participants, with mentorship from the expert team. Information on ECHO participation was tracked, and electronic surveys were sent to the participants at the end of the pilot year. The ECHO program was run in parallel with the pediatric oncology cancer registry to monitor trends in diagnostic rates within the referral region. Results: The ECHO program launched successfully in January 2020 with a curriculum focused on pediatric oncology for health care providers. A total of 22 sessions were conducted, with an average of 23 learners per session. A total of 148 participants attended at least one session, with the majority (n=80, 54.1%) attending multiple sessions. The year-end analysis in January 2021 demonstrated that 286 new pediatric patients were diagnosed with cancer at MTRH, representing a 33% increase over the 3-year average. Conclusions: The Project ECHO platform created a dynamic virtual platform to continue to engage stakeholders across western Kenya. The implementation of this telehealth education platform in Kenya represents an effective model for increasing the recognition and earlier referral of childhood cancer in low- to middle-income countries.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".