Impact of Detailed Versus Generic Instructions on Fine-Tuned Language Models for Patient Discharge Instructions Generation: Comparative Statistical Analysis
Bibliographic record
Abstract
BACKGROUND: Discharge instructions are essential for patients after hospital care but are time-consuming to write. With the rise of large language models (LLMs), there is a strong potential to automate this process. This study explores the use of open-source LLMs for generating discharge instructions. OBJECTIVE: We investigated whether a Mistral model can reliably generate patient-oriented discharge instructions. Two distinct instruction-tuning paradigms were compared, each using a different mechanism for embedding guidance during fine-tuning. METHODS: In our experiment, we applied Mistral-NeMo-Instruct, an LLM, in combination with 2 distinct instruction strategies for fine-tuning. The first were detailed instructions tailored to the task of discharge instruction generation. The second was a basic instruction with minimal guidance and no task-specific detail. The independent variable in this study is the instruction strategy (detailed vs generic), while the dependent variables are the evaluation scores of the generated discharge instructions. The generated discharge instructions were evaluated against 3621 ground-truth references. We used Bilingual Evaluation Understudy (BLEU-1) to BLEU-4, Recall-Oriented Understudy for Gisting Evaluation (ROUGE-1, ROUGE-2, and Recall-Oriented Understudy for Gisting Evaluation-Longest Common Subsequence), SentenceTransformer similarity, and Bidirectional Encoder Representations From Transformers Score as evaluation metrics to assess the quality of the generated outputs in comparison to the corresponding ground-truth instructions for the same discharge summaries. RESULTS: The detailed instruction model demonstrated superior performance across all automated evaluation metrics compared with the generic instruction model. Bidirectional Encoder Representations From Transformers Score increased from 78.92% to 87.05%, while structural alignment measured by Recall-Oriented Understudy for Gisting Evaluation-Longest Common Subsequence improved from 8.59% to 26.52%. N-gram precision (BLEU-4) increased from 0.81% to 21.24%, and the Metric for Evaluation of Translation With Explicit Ordering scores rose from 15.33% to 18.47%. Additional metrics showed consistent gains: ROUGE-1 improved from 16.59% to 42.72%, and ROUGE-2 increased from 1.97% to 45.84%. All improvements were statistically significant (P<.001), indicating that detailed, task-specific instruction design substantially enhances model performance. CONCLUSIONS: The use of detailed, task-specific instruction strategies significantly enhances the effectiveness of open-source LLMs in generating discharge instructions. These findings indicate that carefully designed instructions during fine-tuning substantially improve model performance.
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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 | 0.035 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".