Virtual multidisciplinary preoperative assessments: A multi-site formative evaluation and evidence-based guide for implementing change
Bibliographic record
Abstract
Introduction: Virtual care has recently gained momentum yet remains underutilized for preoperative assessment. We applied implementation science methodology to the development of a virtual preoperative assessment pathway. Methods: We conducted a two-phase formative evaluation of a multidisciplinary virtual preoperative assessment. In phase 1, we conducted semi-structured interviews with patients, family members, health care providers, and decision makers to explore their experiences and perceptions of virtual care and preoperative assessment. We performed thematic analysis using the Promoting Action on Research Implementation in Health Services (PARIHS) framework to identify factors influencing the implementation of virtual preoperative assessments. In phase 2, evidence-based strategies from the Expert Recommendations for Implementing Change (ERIC) were matched to PARIHS themes and rated for importance and feasibility by stakeholders using Go-Zone analysis. Results: Forty stakeholders were interviewed, including 12 patients or family members, 18 health care providers, and 10 decision makers. Eight themes and 49 subthemes were identified to focus the implementation of virtual preoperative assessment. Twelve implementation strategies were judged to be most important and feasible by stakeholders: develop a formal implementation blueprint, identify early adopters, identify and prepare champions, involve patients and family members, conduct local consensus discussions, build a coalition, develop educational materials, distribute educational materials, prepare patients to be active participants, revise professional roles, re-examine the implementation, and stage implementation scaleup. Discussion: We identified 12 evidence-based strategies to guide the implementation of virtual multidisciplinary preoperative assessments. Our findings can be used to guide implementation of this care innovation in other settings.
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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 | 0.419 | 0.290 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".