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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".