Establishing a tele-clinic service for kidney transplant recipients through a patient-codesigned quality improvement project
Bibliographic record
Abstract
Kidney transplant patients in our regional centre travel long distances to attend routine hospital follow-up appointments. Patients incur travel costs and productivity losses as well as adverse environmental impacts. A significant proportion of these patients, who may not require physical examination, could potentially be managed through telephone consultations (tele-clinic). We adopted a Quality Improvement approach with iterative Plan–Do–Study–Act (PDSA) cycles to test the introduction of a tele-clinic service. We codesigned the service with patients and developed a prototype delivery model that we then tested over two PDSA improvement ramps containing multiple PDSA cycles to embed the model into routine service delivery. Nineteen tele-clinics were held involving 168 kidney transplant patients (202 tele-consultations). 2.9% of tele-clinic patients did not attend compared with 6.9% for face-to-face appointments. Improving both blood test quality and availability for the tele-clinic was a major focus of activity during the project. Blood test quality for tele-clinics improved from 25% to 90.9%. 97.9% of survey respondents were satisfied overall with their tele-clinic, and 96.9% of the patients would recommend this to other patients. The tele-clinic saved 3527 miles of motorised travel in total. This equates to a saving of 1035 kgCO2. There were no unplanned admissions within 30 days of the tele-clinic appointment. The service provided an immediate saving of £6060 for commissioners due to reduced tele-clinic tariff negotiated locally (£30 less than face-to-face tariff). The project has shown that tele-clinics for kidney transplant patients are deliverable and well received by patients with a positive environmental impact and modest financial savings. It has the potential to be rolled out to other renal centres if a national tele-clinic tariff can be negotiated, and an integrated, appropriately reimbursed community phlebotomy system can be developed to facilitate remote monitoring of patients.
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".