Engaging medical staff in clinical governance: introducing new technologies and clinical practice into public hospitals
Bibliographic record
Abstract
Introduction. To enhance patient care, medical staff at major tertiary teaching hospitals are encouraged to innovate through introducing new technologies and clinical practices. However, such introduction must be safe, efficient, effective and appropriate for patients and the organisation, and actively lead by engage medical staff. Method. This study outlines the development, implementation and evaluation of a framework for introducing new technologies and clinical practice to a major tertiary health service. Evaluation includes survey of medical Heads of Units (HOUs) for framework’s effectiveness, and comparison of level of medical staff engagement against a best-practice model. Results. Over 2-year period: 19 applications, 7 approved. Successful external funding of $1.993 million achieved. Survey of HOUs in June 2009: response rate 59% (25 of 42 HOUs), with 11 of 25 respondents utilised the committee. Of those 14 of 25 who had not utilised the committee, low awareness of the committee’s existence (2 respondents). Most elements of the best-practice model for engaging medical staff were achieved. Recommendations include improvements to committee process and raising profile with medical staff. Discussion. This study demonstrates an effective and successful clinical governance process for introducing new technologies and clinical practice into a major tertiary teaching hospital, supported by moderate levels of medical staff engagement. What is known about the topic? To enhance patient care in an innovative research and teaching environment, medical staff at major tertiary teaching hospitals are encouraged to innovate and introduce new technologies and clinical practices. However, such introduction needs to be safe, efficient, effective and appropriate for patients and the organisation, and actively engage medical staff in overseeing such responsibility. What does this paper add? This study demonstrates an effective and successful clinical governance process for introducing new technologies and clinical practice into a major tertiary teaching hospital, supported by moderate levels of medical staff engagement. What are the implications for practitioners? All health services or hospitals with a focus for medical research and innovation, that incorporate new technologies into their clinical practice, should ensure governance processes similar to those outlined, to ensure best-practice evidence-based clinical and corporate governance. Effective engagement of medical staff in such processes is essential.
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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 | 0.029 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".