Sources of unsafe primary care for older adults: a mixed-methods analysis of patient safety incident reports
Bibliographic record
Abstract
Background: older adults are frequent users of primary healthcare services, but are at increased risk of healthcare-related harm in this setting. Objectives: to describe the factors associated with actual or potential harm to patients aged 65 years and older, treated in primary care, to identify action to produce safer care. Design and Setting: a cross-sectional mixed-methods analysis of a national (England and Wales) database of patient safety incident reports from 2005 to 2013. Subjects: 1,591 primary care patient safety incident reports regarding patients aged 65 years and older. Methods: we developed a classification system for the analysis of patient safety incident reports to describe: the incident and preceding chain of incidents; other contributory factors; and patient harm outcome. We combined findings from exploratory descriptive and thematic analyses to identify key sources of unsafe care. Results: the main sources of unsafe care in our weighted sample were due to: medication-related incidents e.g. prescribing, dispensing and administering (n = 486, 31%; 15% serious patient harm); communication-related incidents e.g. incomplete or non-transfer of information across care boundaries (n = 390, 25%; 12% serious patient harm); and clinical decision-making incidents which led to the most serious patient harm outcomes (n = 203, 13%; 41% serious patient harm). Conclusion: priority areas for further research to determine the burden and preventability of unsafe primary care for older adults, include: the timely electronic tools for prescribing, dispensing and administering medication in the community; electronic transfer of information between healthcare settings; and, better clinical decision-making support and guidance.
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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 | 0.001 |
| 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".