The Impact of Accreditation on Patient Safety and Quality of Care Indicators at King Abdulaziz University Hospital in Saudi Arabia
Bibliographic record
Abstract
This study aimed to determine if the accreditation process has a positive impact on patient safety and quality of care. A 4 year retrospective and prospective study design was used. A total of 119 performance indicators were collected through various processes and were lately transformed into 81 patient safety and quality indicators. The numbers and rates of hospital mortality, Healthcare-Associated Infections (HAI), medication errors, cardiopulmonary resucutation codes, surgeries and invasive procedures, blood transfusion reaction and adverse events were the main outcome measures. The following areas had the corresponding number of indicators that were found to be sensitive to Canadian accreditation and that significantly improved post-accreditation: Four indicators of perioperative mortality and rates of neonatal mortality per 100 NICU admissions (p<0.05). Healthcare-associated Infections: sixteen out of twenty-six measured indicators (p<0.05). Blood utilization: one out of two measured indicators, i.e. total number of blood transfusion reactions (p<0.05). Surgeries and invasive procedure: two out of seven measured indicators, i.e. total number of unplanned returns to surgery within 48 h and rate of unplanned returns to surgery per 100 operations (p<0.05). Two out of eight measured indicators, i.e. total number of patients who survived after the first CPR and rate of survival after first CPRper 100 coded patients (p<0.05). Two out of eighteen measured indicators, i.e. rate of pressure ulcers per 1000 admissions and total number of the occurrence variance reports (p<0.05). Accreditation has a positive impact on patient safety and quality of care indicators. © Medwell Journals, 2011.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".