Prospective economic evaluation of an electronic discharge communication tool: analysis of a randomised controlled trial
Bibliographic record
Abstract
OBJECTIVE: To complete an economic evaluation within a randomised controlled trial (RCT) comparing the use of an electronic discharge communication tool (eDCT) compared with usual care. SETTING: Patients being discharged from a single tertiary care centre's internal medicine Medical Teaching Units. PARTICIPANTS: Between January 2012 and December 2013, 1399 patients were randomised to a discharge mechanism. Forty-five patients were excluded from the economic evaluation as they did not have data for the index hospitalisation cost; 1354 patients contributed to the economic evaluation. INTERVENTION: eDCT generated at discharge containing structured content on reason for admission, details of the hospital stay, treatments received and follow-up care required. The control group was discharged via traditional dictation methods. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary economic outcome was the cost per quality-adjusted life year (QALY) gained. Secondary outcomes included the cost per death avoided and the cost per readmission avoided. RESULTS: The average transcription cost was $C22.28 per patient, whereas the estimated cost of the eDCT was $C13.33 per patient. The cost per QALY gained was $C239 933 in the eDCT arm compared with usual care due to the very small gains in effectiveness and approximately $C800difference in resource utilisation costs. The bootstrap analyses resulted in eDCT being more effective and more costly in 29.2% of samples, less costly and more effective in 29.2% of samples, less effective and more costly in 23.9% of samples and finally, less costly and less effective in 17.7% of samples. CONCLUSIONS: The eDCT reduced per patient costs of the generation of discharge summaries. The bootstrap estimates demonstrate considerable uncertainty supporting the finding of neutrality reported in the clinical component of the RCT. The immediate transcription cost savings and previously documented provider and patient satisfaction may increase the impetus for organisations to invest in such systems, provided they have a foundation of eHealth infrastructure and readiness. TRIAL REGISTRATION NUMBER: NCT01402609.
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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 | 0.066 | 0.143 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.019 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".