Patient understanding of discharge instructions in the emergency department: do different patients need different approaches?
Bibliographic record
Abstract
BACKGROUND: Previous studies have demonstrated that patients have poor understanding of the discharge instructions provided from the emergency department (ED). The aims of this study are to determine if patient factors, such as income and level of education, correlate with patient understanding of discharge instructions and to explore if different patient populations prefer different resources for receiving discharge instructions. METHODS: We conducted live observations of physicians providing discharge instructions in the ED to 100 patients followed by a patient survey to determine their understanding in four domains (diagnosis, treatment plan, follow-up instructions, and return to ED (RTED) instructions) and collect patient demographics. We enrolled patients over the age of 18 being discharged home. We excluded non-English- or French-speaking patients and those with significant psychiatric history or cognitive impairment. We performed a two-way ANOVA analysis of patient factors and patient understanding. RESULTS: We found that patients had poor understanding of discharge instructions, ranging from 24.0% having poor understanding of their follow-up plan to 64.0% for RTED instructions. Almost half (42%) of patients did not receive complete discharge instructions. Lower income was correlated with a significant decrease in patient understanding of discharge diagnosis (p = 0.01) and RTED instructions (p = 0.04). Patients who did not complete high school trended towards lower levels of understanding of their diagnosis and treatment plan (p = 0.06). Lower income patients had a preference for receiving a follow-up phone call by a nurse, while higher income patients preferred online resources. CONCLUSIONS: Lower income patients and those who have not completed high school are at a higher risk of poor understanding discharge instructions. As new technological solutions emerge to aid patient understanding of discharge instructions, our study suggests they may not aid those who are at the highest risk of failing to understand their instructions.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".