The use of positive deviance approach to improve health service delivery and quality of care: a scoping review
Bibliographic record
Abstract
BACKGROUND: Quality has been a persistent challenge in the healthcare system, particularly in resource-limited settings. As a result, the utilization of innovative approaches is required to help countries in their efforts to enhance the quality of healthcare. The positive deviance (PD) approach is an innovative approach that can be utilized to improve healthcare quality. The approach assumes that solutions to problems are already available within the community and identifying and sharing those solutions can help others to resolve existing issues. Therefore, this scoping review aimed to synthesize the evidence regarding the use of the PD approach in healthcare system service delivery and quality improvement programs. METHODS: Articles were retrieved from six international databases. The last date for article search was June 02, 2023, and no date restriction was applied. All articles were assessed for inclusion through a title and/or abstract read. Then, articles that passed the title and abstract review were screened by reading their full texts. In case of duplication, only the full-text published articles were retained. A descriptive mapping and evidence synthesis was done to present data with the guide of the Preferred Reporting Items for Systematic Reviews and Meta-analysis extension for Scoping Reviews checklist and the results are presented in text, table, and figure formats. RESULTS: A total of 125 articles were included in this scoping review. More than half, 66 (52.8%), of the articles were from the United States, 11(8.8%) from multinational studies, 10 (8%) from Canada, 8 (6.4%) from the United Kingdom and the remaining, 30 (24%) are from other nations around the world. The scoping review indicates that several types of study designs can be applied in utilizing the PD approach for healthcare service and quality improvement programs. However, although validated performance measures are utilized to identify positive deviants (PDs) in many of the articles, some of the selection criteria utilized by authors lack clarity and are subject to potential bias. In addition, several limitations have been mentioned in the articles including issues in operationalizing PD, focus on leaders and senior managers and limited staff involvement, bias, lack of comparison, limited setting, and issues in generalizability/transferability of results from prospects perspective. Nevertheless, the limitations identified are potentially manageable and can be contextually resolved depending on the nature of the study. Furthermore, PD has been successfully employed in healthcare service and quality improvement programs including in increasing surgical care quality, hand hygiene practice, and reducing healthcare-associated infections. CONCLUSION: The scoping review findings have indicated that healthcare systems have been able to enhance quality, reduce errors, and improve patient outcomes by identifying lessons from those who exhibit exceptional practices and implementing successful strategies in their practice. All the outcomes of PD-based research, however, are dependent on the first step of identifying true PDs. Hence, it is critical that PDs are identified using objective and validated measures of performance as failure to identify true PDs can subsequently lead to failure in identifying best practices for learning and dissemination to other contextually similar settings.
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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 | 0.053 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.006 |
| 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".