Digitizing operating theater data in resource‐limited settings: Understanding surgical care delivery post‐implementation at Tanzanian referral hospital
Bibliographic record
Abstract
BACKGROUND: Digitizing surgical data infrastructure is critical for policymakers to make informed decisions. The implementation of the first web-based operating theater (OT) recordings at Muhimbili Orthopedic Institute (MOI) represents significant advancements in data management for Tanzania. This study aims to share post-platform implementation outcomes, challenges, and insights gained offering guidance to settings facing similar data repository challenges. METHODS: In July 2023, after training clinicians, the platform was deployed at MOI operating theaters (OTs) to facilitate prospective data entry following procedures, ensuring timely updates of perioperative outcomes. Semi-structured interviews were conducted with key stakeholders to gather insights into the platform's functionality and efficient data management systems. We presented data from August 2023 to February 2024 along with platform insights. RESULTS: Over 4449 procedures were conducted, comprising 1321 emergencies and 3128 electives, with orthopedics/trauma accounting for the majority (3606). Trauma-related emergencies (921) predominate among interventions. General anesthesia was prevalent; 60.56% in emergencies and 44.51% in electives. Orthopedics/trauma utilized 90.91% of assigned operating days in electives, while neurosurgery utilized 93.39% (p < 0.011). The cancellation rate was 7.5%, primarily due to emergency interferences (32%). Of procedures, 96.76% were discharged, while 2.81% died. Challenges encountered during platform implementation included securing local support, integrating technology, and navigating administrative adjustments. Lessons learned emphasized continuous communication for stakeholder buy-in and training for platform familiarity. CONCLUSION: The web-based OT recordings at MOI succeeded with local support and showed promise for wider scalability. To ensure sustainability, ongoing follow-up, monitoring of platform functionality, local funding establishment, and strengthening global partnerships are recommended.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".