A Technology Selection Tool Applying Multiple Criteria Decision Analysis for Virtual Care Implementation
Bibliographic record
Abstract
Objective: To develop and pilot a technology selection tool (TST) designed to evaluate and recommend virtual care technologies tailored to specific community clinical needs. Patients and Methods: Developed through collaborations among clinicians, software developers, technology experts, and health administrators, the TST uses a multiple criteria decision analysis framework to recommend technologies based on clinical relevance and technical quality. Its functionality was tested in a pilot project that assessed 5 technologies for their application in virtual wound care to support a remote community in Saskatchewan, Canada. The pilot study was completed March 7, 2025, through July 28, 2025. Results: The TST identified the TeleVU Glass View as the optimal technology for virtual wound care. The TST generated product scores for the TeleVU Glass View (71.67), Teladoc Xpress (70.10), 19 Labs GALE (50.67), and TytoCare TytoKit (47.00), whereas disqualifying the Teladoc Lite Cart for not meeting the pass-fail portability criterion. TeleVU's high product score resulted primarily from its technological attribute quality scores for Telestration (10), Audio (9), Video (9), and Share Content (9), which were all determined as clinically relevant for virtual wound care. The pilot enabled real-time wound care support by connecting local clinicians with virtual teams. Conclusion: The TST offers a practical and adaptable tool to support evidence-based decision making for selecting technologies for specific clinical applications.
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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 | 0.090 | 0.180 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.020 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".