2 Sub-optimal care for patients in the out of hours primary care setting at the end of life: a mixed methods study
Bibliographic record
Abstract
Background Patients receiving palliative care are often at increased risk of unsafe care, (Mazzocato and Stiefel 1997; Dietz et al. 2014) and the identification of improved ways of delivering palliative care outside working hours is a priority area. (Best et al. 2015) Aim To explore the nature and causes of unsafe care delivered to patients receiving palliative care from primary care services outside normal working hours. Methods The National Reporting and Learning System (NRLS) collates patient safety incident reports written by healthcare professionals in England and Wales. We characterised reports, identified by keyword searches, using codes to describe what happened, underlying causes, harm outcome, and severity. Exploratory descriptive and thematic analyses identified factors underpinning unsafe care. Findings We identified 1072 incidents of suboptimal care, which included: medication-related issues (n=613); access to timely care (n=123); and or non-medication related treatment such as pressure ulcer relief or catheter care. (n=102). Almost two thirds of reports (n=695) described harm with outcomes including increased pain, emotional and psychological distress and dying in a place not of their choosing. Commonly identified contributory factors to these incidents were a failure to follow protocol (n=282); lack of skills/confidence of staff (n=156) and patients requiring medication delivered via a syringe driver (n=80). Conclusions This study is the largest characterisation of unsafe care for patients requiring palliative care in the community, outside working hours. Possible targets for organisations looking to improve care include improved communication between providers; better knowledge of commonly used medications and routes and easier access to medications and equipment. References 1. Mazzocato C, Stiefel F. How safe are opioids in palliative care?Supportive care in cancer: official journal of the Multinational Association of Supportive Care in Cancer 1997;5(6):427–427. 2. Dietz I, et al. “Please describe from your point of view a typical case of an error in palliative care”: Qualitative data from an exploratory cross-sectional survey study among palliative care professionals. Journal of palliative medicine2014;17(3):331–337. 3. Best S, et al. Research priority setting in palliative and end of life care: the James Lind Alliance approach consulting patinets carers and clinicians. BMJ supportive & palliative care2015;5(1):102.1–102.
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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 | 0.034 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".