An Economic–Business Approach to Clinical Risk Management
Bibliographic record
Abstract
This paper introduces risk factors in the field of healthcare and discusses the clinical risks, identification, risk management methods, and tools as well as the analysis of specific situations. Based on documentary analysis, an efficient and coherent methodological choice of an informative and non-interpretative approach, it relies on “unobtrusive” and “non-reactive” information sources, such that the research results are not influenced by the research process itself. To ensure objective and systematical analysis, our research involved three macro-phases: (a) the first involved a skimming (a superficial examination) of the documents collected; (b) the second reading (a thorough examination) allowed a selection of useful information; (c) the third phase involved classification and evaluation of the collected data. This iterative process combined the elements of content and thematic analysis that categorised the information into different categories which were related to the central issues for research purposes. Finally, from the perspective of safety analysis and risk management, we suggest that comprehensive control and operation should be conducted in a holistic way, including patient safety, cost consumption, and organizational responsibility. An organizational strategy that revolves around a constant and gradual risk management process is an important factor in clinical governance which focuses on the safety of patients, operators, and organizations.
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 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 | 0.025 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".