Streamlining surgical instrument counting: a matrixed multiple case study on the fidelity of weighing systems in the operating room
Bibliographic record
Abstract
BACKGROUND: Many technologies have been developed to aid in surgical instrument counting, but wide adoption is rare. A technology that has been widely adopted around 20 years ago is the weighing scale. Lessons can be extracted from its sustainment and fidelity, and applied to the development and implementation of new laboursaving technologies in healthcare. METHODS: We conducted semi-structured interviews with experienced staff in four hospitals that use weighing systems in their surgical instrument cycle, which we analysed according to the Matrixed Multiple Case Study (MMCS) methodology. Hospitals were designated a low, medium, or high sustainment and fidelity score, after which influencing factors were identified. These factors were categorised according to the i-PARIHS domains of Innovation, Recipient, Context, and Facilitation. Within-site analysis and cross-site analysis was performed to identify influencing factors associated with a high or low level of sustainment or fidelity. RESULTS: All hospitals showed a high sustainment. Two hospitals showed low fidelity, and two showed high fidelity. Twenty-one total influencing factors were identified, divided among all i-PARIHS domains. All hospitals experienced similar limitations of the technology, and all hospitals showed signs of facilitation efforts during the implementation phase. In low-fidelity hospitals, interdepartmental coordination and trust in technology were limited, in contrast to high-fidelity hospitals. A large and/or complex surgical instrument inventory hindered fidelity of the weighing system. CONCLUSIONS: 20 years after implementation, there is varying success concerning the fidelity of weighing systems for surgical instrument counting. All participating hospitals have adapted their workflow to the limitations of the technology in different ways. Given the relative straight-forwardness of weighing scales as a technology, our findings underline the complexity of implementation processes, regardless of the complexity of the innovation.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 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".