70 Improving the Documentation of DNACPR Decisions Following the Transition to Electronic Record Keeping
Bibliographic record
Abstract
Abstract Background This quality improvement project was based at The Royal Bolton Hospital across our four Complex Care wards. Introduction We have recently transferred to electronic record keeping. At these points of transition there may be an adverse impact on the quality of patient care and safety. We recognised on our own ward there were inaccuracies between the required paper form and electronic documentation of DNACPR decisions. Consequently, we wanted to review and improve the accuracy of our DNACPR documentation to ensure safe and effective patient care. Methods To gauge the scope of the problem we audited 87 patient’s electronic and paper notes, with no exclusion criteria. We reviewed whether each patient had a formal resuscitation decision, and if a DNACPR decision had been made whether we met our hospital policy by having: 93% of the 87 patient’s had an active decision regarding resuscitation, with a DNACPR decision documented for 50 patients. Of these 50 patients only 11 had all three forms of documentation. More worryingly, there were discrepancies in the documented DNACPR decisions for 11 patients across paper and electronic records. Interventions We escalated our concerns to the Clinical Governance team who sent out a trust wide SBAR highlighting this as an urgent clinical issue. On a directorate level we incorporated DNACPR decision documentation into our afternoon safety huddle and arranged informal teaching for medical, nursing and administrative staff. Results Reassuringly, the subsequent re-audit of 90 patient’s notes showed only one patient to have a discrepancy between paper and electronic documentation. We saw an improvement to 98% having paper forms in the right bedside notes and 100% having a documented electronic DNACPR decision. Conclusion Through local education and trust-wide dissemination of our expected standards we have seen some improvement. We recognise the importance of maintaining this, and importantly that there is still work to be done. The electronic “Resuscitation and treatment escalation plan” is still rarely completed and provides important information on escalation of care and thus will be the focus of a further educational intervention.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".