Barriers and facilitators of implementation of shared decision-making in clinical practice; An umbrella review
Bibliographic record
Abstract
Abstract Background: Involving patients in their healthcare by means of shared decision-making (SDM) is promoted through policy and research. However, its implementation in routine practice has certain complexities and intricacies. This umbrella review was carried out to review literature exploring barriers and facilitators of the implementation of SDM and provide a comparative view of these factors in different settings. Methods: The search strategy was focused on peer-reviewed systematic reviews on the implementation of SDM with the primary aim of identifying as many as possible of facilitators and barriers. We systematically searched PubMed, Embase, and Web of Science from date of conception to August 2022. We included studies that reported providers' perspectives, while those focusing solely on patients' perspectives or mixing both patients' and providers' perspectives were excluded. We limited our focus to the studies published in the English language. Quality assessment of studies was performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Results: Of the 5415 records found, titles and abstracts of 1932 articles were screened and 53 were reviewed in full text. 15 articles met the inclusion criteria. We then created a map of all items against the included papers and sorted them into 7 general categories and 4 specialized categories. Our categorization was devised to represent a root cause and solution-based approach and provide a clear and meaningful framework for understanding the factors influencing the successful integration of SDM in healthcare settings. The general categories include: 1) Provider-related: Encompasses factors such as provider attitude and skill of SDM. 2) Patient-related: Includes patient factors, such as literacy, and willingness to engage in decision-making. 3) Environmental context: Addresses the physical clinical setting and personnel. 4) Disease-related: Addresses the impact of clinical nature of diseases on SDM adoption. 5) Administrative: Involves logistical and organizational factors, workflow alignment. 6) Access and trust to knowledge or tools: Focuses on accessibility to reliable resources and decision aids. 7) Social interaction and cultural: Emphasizes social influences and cultural norms. The specialized categories include: 1) pediatrics 2) screening (which mostly comprises the screening carried out in primary care) 3) end-of-life ICU care 4) mental healthcare. Overall, the most cited items were time constraints, physicians’ knowledge, and physicians’ skills in SDM. Conclusion: The implementation of SDM is still comparatively young; many studies have been conducted yet there is limited research focusing on specific settings and specialties. Furthermore, quantitative research on the subject is very scarce. Organizations and health policymakers aiming to implement SDM can benefit from considering factors gathered in our study for better planning. Registration: The protocol for this study was registered to Tabriz University of Medical Sciences research vice as a thesis proposal with the following number: 64957. The English version has been registered in Open Science Framework. (DOI: 10.17605/OSF.IO/GZNA4) Keywords: Shared Decision making, Implementation, Barriers and Facilitators
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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 | 0.059 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.033 | 0.025 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| 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".