32 * IMPROVING THE ELECTRONIC DISCHARGE LETTER (EDL) ON AN OLDER PERSONS' UNIT
Bibliographic record
Abstract
Evidence base: NHS Connecting for health has developed a discharge summary toolkit (2008) to assist care providers in meeting national EDL standards. A comprehensive EDL correlates with reduced re-admissions, safe transitions and quality of care after discharge. A recent study into the quality of written communication about older adults was found to be insufficient and represented a potential health hazard for older patients with no information on social and physical abilities; including ADL knowledge and home care services (Garesen & Johnsen 2007 BMC Health Research). Change strategies: Our pilot study in Jan 2012 (n = 20) identified that the accuracy and quality of information conveyed in EDL's by our medical colleagues regarding physical condition was either missing (60%) or inaccurate (30%). Our aim was to improve the quality of information about the physical and social condition in the EDL. Five standards were set for inclusion of specific information by the physiotherapists; 1. Accommodation Information 2. Mobility Status 3. Elderly Mobility Scale and Gait Speed 4.Onward therapy referrals 5.Use of abbreviations. An audit cycle was undertaken in June (Cycle 1) and following root-cause-analysis and change implementation a re-audit in Dec (Cycle 2). A proposal was submitted through the Trust's Clinical Governance Forum. Change effects: Total compliance and individual standard compliance of EDL improved between cycle 1 and 2 for the above 5 standards; see Table 1 below. Proportional Compliance Comparisons Conclusions: Our audit provides evidence of a change in practice between cycle 1 and 2 contributing to improvements in the quality of the EDL supporting NHS Connecting for health toolkit and National Standards. Subjective feedback from Physiotherapists, Geriatricians and acute teams suggest that access to accurate and complete patient information in the EDL improved quality and efficiency.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".