Clinical Pharmaceutical Safety and Healthcare Systems Management- An Updated Review Article For Pharmacists, Health Securities, and Medical Maintenance Specialists
Bibliographic record
Abstract
Background: Healthcare is a safety-critical industry where preventable harm remains a major public health concern. Learning from high-risk sectors like aviation and nuclear power, safety management systems (SMS) offer a proactive, systematic framework for managing safety through organizational structures, risk management, and continuous improvement. Aim: This review was commissioned to inform the development of the NHS England's patient safety policy. It aims to synthesis evidence on SMS in healthcare to address three key questions: the attributes of a successful NHS SMS, the links between an SMS and quality management, and the next steps for safety management in the NHS. Methods: The study conducted a comparative review of national patient safety approaches, analyzing systems in the Netherlands, Australia, Canada, Ireland, and New Zealand, with a focus on the integration of SMS principles. Results: The Netherlands was the only country with a mandatory, certified SMS for hospitals, which was associated with a reduction in preventable adverse events. Other countries embedded core SMS components—such as leadership, risk management, and safety assurance—within national standards and accreditation frameworks but did not mandate a formal SMS. Evidence from the Dutch programme showed improvements, though outcomes were influenced by contextual factors like implementation support and concurrent initiatives. Conclusion: Core SMS principles are transferable to healthcare and can contribute to improved safety outcomes. However, successful implementation requires significant contextual adaptation, strong leadership, and robust supporting infrastructure, rather than simply adopting a generic model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".