Talking About Notes: Using a Design-Based Research Approach to Develop a Discharge Summary Template on a Geriatric Inpatient Unit
Bibliographic record
Abstract
Background: Discharge summaries are important educational tools, guiding trainees in their collection and documentation of data. As geriatric competencies are integrated in medical curricula, documentation on in-patient geriatric rotations should represent the unique care and education provided, yet often follow generic templates. What content should be included in a geriatric discharge summary has not previously been explored and was the purpose of this study. Methods: A mixed-methods, designed-based research approach was used to assess note quality on a geriatric in-patient unit and iteratively co-develop a template with examples through three phases: 1) needs assessment, 2) consensus building, and 3) template development. Results: Sixty-eight discharge summaries were assessed by five geriatricians, with 14 gaps identified. Many of these reflected elements that were present but addressed generically without attention to the specificity required from a geriatric perspective. In response, the team developed a geriatric-specific template with explicit examples. Through the consensus process three barriers to quality notes and trainee education were identified: the chronic state of low-quality notes being accepted as the norm, time limitations due to the high volume of patients, and high volume of clinical documents. Conclusions: The identification of gaps in geriatric discharge summaries allowed for the co-development of an instructional template and examples that goes beyond simple headings and highlights the importance of applying and documenting geriatric competencies. Although we encourage others to take up and modify the tools for trainees in their local context, more importantly, we encourage them to take up the dialogue about note quality.
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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 | 0.203 | 0.263 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".