Current Implementation Outcomes of Digital Surgical Simulation in Low- and Middle-Income Countries: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Digital surgical simulation and telecommunication provides an attractive option for improving surgical skills, widening access to training, and improving patient outcomes; however, it is unclear whether sufficient simulations and telecommunications are accessible, effective, or feasible in low- and middle-income countries (LMICs). OBJECTIVE: This study aims to determine which types of surgical simulation tools have been most widely used in LMICs, how surgical simulation technology is being implemented, and what the outcomes of these efforts have been. We also offer recommendations for the future development of digital surgical simulation implementation in LMICs. METHODS: We searched PubMed, MEDLINE, Embase, Web of Science, Cochrane Database of Systematic Reviews, and the Central Register of Controlled Trials to look for qualitative studies in published literature discussing implementation and outcomes of surgical simulation training in LMICs. Eligible papers involved surgical trainees or practitioners who were based in LMICs. Papers that include allied health care professionals involved in task sharing were excluded. We focused specifically on digital surgical innovations and excluded flipped classroom models and 3D models. Implementation outcome had to be reported according to Proctor's taxonomy. RESULTS: This scoping review examined the outcomes of digital surgical simulation implementation in LMICs for 7 papers. The majority of participants were medical students and residents who were identified as male. Participants rated surgical simulators and telecommunications devices highly for acceptability and usefulness, and they believed that the simulators increased their anatomical and procedural knowledge. However, limitations such as image distortion, excessive light exposure, and video stream latency were frequently reported. Depending on the product, the implementation cost varied between US $25 and US $6990. Penetration and sustainability are understudied implementation outcomes, as all papers lacked long-term monitoring of the digital surgical simulations. Most authors are from high-income countries, suggesting that innovations are being proposed without a clear understanding of how they can be incorporated into surgeons' practical training. Overall, the study indicates that digital surgical simulation is a promising tool for medical education in LMICs; however, additional research is required to address some of the limitations in order to achieve successful implementation, unless scaling efforts prove futile. CONCLUSIONS: This study indicates that digital surgical simulation is a promising tool for medical education in LMICs, but further research is necessary to address some of the limitations and ensure successful implementation. We urge more consistent reporting and understanding of implementation of science approaches in the development of digital surgical tools, as this is the critical factor that will determine whether we are able to meet the 2030 goals for surgical training in LMICs. Sustainability of implemented digital surgical tools is a pain point that must be focused on if we are to deliver digital surgical simulation tools to the populations that demand them the most.
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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 | 0.026 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".