Assessing the Quality of Teleconsultations in a Store-And-Forward Telemedicine Network – Long-Term Monitoring Taking into Account Differences between Cases
Bibliographic record
Abstract
We have previously proposed a method for assessing the quality of individual teleconsultation cases; this paper proposes an additional step to allow the long-term monitoring of quality. The basic scenario is a teleconsultation system (aka an e-referral system or a tele-expertise system) where the referrer posts a question about a clinical case, the question is relayed to an appropriate expert, and the chosen expert provides an answer. The people running this system want assurances that it is stable, i.e., they want routine quality assurance information about the "output" from the "process." This requires two things. It needs a method of assessing the quality of individual patient consultations. And it needs a method for taking into account differences between patients, so that these quality assessments can be compared longitudinally. Building on the previously proposed methodology, the present paper proposes two techniques for measuring the difficulty posed by a particular teleconsultation. The first is an indirect method, similar to a willingness to pay economic estimation. The second is a direct method. Using these two methods with real data from a telemedicine network showed that the first method was feasible, but did not produce useful results in a pilot trial. The second method, while more laborious, was also feasible and did produce useful results. Thus, when output quality is measured, an allowance can be made for the characteristics of the case submitted. This means that fluctuations in output quality can be attributed to variations in the process (network) or to variations in the raw materials (queries submitted to the network). Long-term quality assurance should assist those providing telemedicine services in low-resource settings to ensure that the services are operated effectively and efficiently, despite the constraints and complexities of the environment.
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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 | 0.027 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".