OP04.12: Tele‐ultrasound for redesigning specialty healthcare delivery to remote communities
Bibliographic record
Abstract
We sought to establish a program of Maternal-Fetal Medicine (MFM) Telemedicine that is safe, sustainable, acceptable and of high quality to all areas in Alberta that need it. Since December 2019, a multi-disciplinary team has been dedicated to planning this quality improvement system design project. After an initial data review of a subset of MFM consults we performed a pilot study to investigate the feasibility of using telemedicine and tele-ultrasound to enable prompt maternal-fetal medicine consultations for patients in remote locations. We began with training of sonographers followed by implementation in a small pilot. Interim analysis of the acceptability and feasibility of the program was done through patient surveys, images audit and review of neonatal outcome. Initially date review from 2018 to 2020 that 20% of patients who came to the urban centres eventually delivered in their rural communities suggesting they could have had some or all of their scans in their community with MFM supervision. During the subsequent pilot, interim image review showed more consistent improvement in image quality and completeness between September 2020 (prior to training) to Summer 2021 (3 months after training). Pretraining, ∼40% of images were considered inadequate or incomplete for preliminary MFM use. Post training ∼80-90% of images were considered adequate for preliminary MFM use. As at January 2022, 66% of patients seen in the program delivered in their community while 33 % were appropriately triaged to delivery in the urban centre based on tele-ultrasound diagnosis/suspicion such as placenta increta, IUGR, partial agenesis of the corpus callosum or maternal co-morbidities. Patient surveys showed that 85% of respondents agreed that the program reduced stress/indirect costs and 100% said they would use the program again. MFM Telemedicine is feasible and can be safe with adequate supervision. Additional support and resources are needed to scale and spread this quality improvement initiative in.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".