Assessment of Clinical Risk Management System in Hospitals: An Approach for Quality Improvement
Bibliographic record
Abstract
BACKGROUND: Clinical risks have created major problems in healthcare system such as serious adverse effects on patient safety and enhancing the financial burden for the healthcare. Thus, clinical risk management (CRM) system has been introduced for improving the quality and safety of services to health care. The aim of this study was to assess the status of CRM in the hospitals. METHODS: A cross-sectional study was conducted on 200 nursing staff from three teaching hospitals affiliated with the Kerman University of Medical Sciences in southeast of Iran. Data were collected from the participants using questionnaire and observational checklist in quality improvement offices and selected wards. The data were analyzed using SPSS version 20. RESULTS: Almost, 57% of persons participated in at least one of training sessions on CRM. The status of CRM system was rated from weak to moderate (2.93±0.72- 3.18±0.66). Among the six domains of CRM system, the highest mean belonged to domain the monitoring of analysis, evaluation and risk control (3.18±0.72); the lowest mean belonged to domain the staff's knowledge, recognition and understanding of CRM (2.93±0.66). There were no integrated electronic systems for recording and analyzing clinical risks and incidents in the hospitals. CONCLUSION: Attempts have been made to establish CRM through improvement quality approach such as clinical governance and accreditation, but not enough, however, health care should move toward quality improvement and safe practice through the effective integration of CRM in organizational process.
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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 | 0.023 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".