Post-Implementation Evidence-Based Decision Making: The Telehealth Assessment Tool For Health (TeATH)
Bibliographic record
Abstract
Few telehealth implementations survive the initial pilot phase, and any anticipated Return on Investment seldom materialises. Within South Africa a reason is the lack of post-implementation monitoring and assessment. To address this practice gap we developed a simple and practical tool to allow decision- and policy-makers to assess the post-implementation state of current telehealth applications. Recognised management approaches were reviewed, and elements adopted or adapted to develop the new decision support tool. A systems-based approach, applying a revised People, Process, and Technology methodology (incorporating Infrastructure), and Balanced Score Card and e-Readiness principles, was applied. This allowed development of the Telehealth Assessment Tool for Health (TeATH), whose utility was demonstrated by assessing the current performance of existing teleradiology implementations in three Provincial hospitals in Mpumalanga Province. Expected results were achieved, with TeATH revealing fair performance in the Technology dimension, but poor performance across People, Process, and Infrastructure for all three hospitals. TeATH is a simple and generic tool that provides decision support and guidance to health planners, differentiating weak or lagging implementations for which remedial action can be introduced. The tool has been adopted by the Provincial Department of Health, and has already influenced recent policy decisions. Broad application of TeATH would reduce wasteful expenditure, and facilitate implementation and uptake of telehealth in South Africa and elsewhere.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.185 | 0.314 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".