A Survey on the Electronic Discharge Summary Process in an Acute Inpatient Ward
Bibliographic record
Abstract
Aims The study aimed to assess staff understanding of the discharge process in an Elmdale ward, Halifax and improve the promptness of discharge reports to other primary care professionals. Methods Initially, the discharge process was reviewed in March 2023 to establish a baseline, focusing on completion time and personnel involved in the process. An online survey was conducted using Survey Monkey with 20 responses from the staff, including nurses, pharmacists, and doctors, to gather insights into their comprehension of the discharge process. Electronic data for EPMA (electronic prescribing and medication administration) discharge form from SystmOne was analyzed to determine the percentage of completed discharge summaries and identify any incomplete or absent summaries among patients discharged from Elmdale ward (an acute inpatient ward) between March 1st and March 31st, 2023. Results The data showed that 76.9% of discharges were completed within 24 hours, with weekend discharge completion at 4 and only 25% after 5 pm. Half of the discharge summaries were closed by nurses, 46% by doctors, and one by the ward clerk. The median time taken to complete the discharge process was 25.83 hours, slightly exceeding the 24-hour target. Survey results indicated that 60% of staff were aware of the 24-hour timeline, but there were gaps in communication between staff members. Additionally, only 40% of staff had received formal EPMA discharge summary training, with nursing staff being the majority. Eighty percent of survey respondents expressed challenges with the discharge summary process, particularly regarding communication with the pharmacy team and closing the discharge summary. Weekend discharge data revealed gaps in responsibilities when the ward clerk was unavailable to send letters. Overall, the findings suggest a need for improved communication and training to enhance the efficiency and effectiveness of the discharge process, ensuring timely and accurate transmission of discharge reports to primary care physicians and other professionals. Conclusion More than half of the staff understood the discharge process however communication between staff in regard to the discharge process impacted on the timeliness of the summaries completed.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 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".