The automated patient discharge summary: improving communication at transfers of care after completion of radiotherapy
Bibliographic record
Abstract
Abstract Aim To develop an auto-generated patient discharge summary for all patients being treated in the Radiation Therapy Department. Materials and methods A patient discharge summary was developed using auto-generated data for all patients being treated in the Radiation Therapy Department. This ensures information relevant to the care of the patient is communicated effectively during transitions of care following radiation treatment, and provides a record of the treatment site(s), dose delivered, start/completion dates and contact information for Radiation Oncologists. The eScribe feature in MosaiQTM is utilised to auto-generate the patient discharge summary in less than one minute, and then printed and given to patients on the last day of treatment. This was piloted with palliative radiotherapy patients (n=22), who also completed a telephone survey. Results Results revealed patients had passed this document onto other healthcare providers and appreciated having a record of their treatments. Feedback was obtained from radiation therapy staff and the Patient and Family Advisory Committee. Subsequently, the language of the patient discharge summary was simplified and a disclaimer was added, indicating the document is not a complete radiation therapy treatment record. This initiative was then rolled out to all radiotherapy patients. Findings Overall, the patient discharge summary allows for a quick, automated and standardised approach for transfer of information during care transitions without significant impact to the Radiation Therapy Departmental workflow.
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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 | 0.013 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".