The Impact of Patients’ Engagement in the Prevention of Surgical Site Infections: A Systematic Review
Bibliographic record
Abstract
Background: Infections at surgical sites are a significant risk to patients undergoing surgery, increasing hospitalization and health care costs, morbidity, and mortality. Participation of patients in healthcare decision-making helps identify community needs and preferences, resulting in more patient-centered and effective care. To achieve positive surgical outcomes, patients must cooperate and participate in preoperative and postoperative care, including postoperative wound care. Aim: This systematic review intends to determine what the outcomes are of patient engagement in improving health outcomes with surgical site infection prevention method. Methods: Up to April 20, 2024, the following prime databases were searched: Pub Med, Science Direct, Scopus, web of sciences and Google Scholar. A flow chart adapted from PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) was used to present the process of studying patient engagement in surgical site infections. Newcastle Ottawa and the Joanna Briggs Institute's (JBI) critical evaluation tools were used to assess bias risk and quality of included studies. Results: Our review indicates patient engagement in surgical sites helps prevent infections. Involving patients in healthcare decision-making has several benefits. For example, a more engaged patient adheres to treatment plans more frequently, and this leads to improvement in health outcomes. It has been reported that the rate of SSIs before implementation was 16.4 and decreased significantly to 4.7%. SSI rates decreased in all three surgical specialties in colorectal surgery from 3.2% to 2.7%, plastic surgery from 1.2% to 0.5%, and general surgery from 0.86% to 0.33%. Conclusion: Healthcare providers should engage patients in treatment plans reduce infection, and improve healthcare outcomes, and create a more patient-centred healthcare system. Engaging them at each stage of the healthcare process may enhance their experience and result in better outcomes for them.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".