British journal of anaesthesia
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Review Meta Analysis
Machine learning-augmented interventions in perioperative care: a systematic review and meta-analysis.
We lack evidence on the cumulative effectiveness of machine learning (ML)-driven interventions in perioperative settings. Therefore, we conducted a systematic review to appraise the evidence on the impact of ML-driven interventions on perioperative outcomes. ⋯ CRD42023433163 (PROSPERO).
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Operating theatres consume large amounts of energy and consumables and produce large amounts of waste. There is an increasing evidence base for reducing the climate impacts of healthcare that could be enacted into routine practice; yet, healthcare-associated emissions increase annually. Implementation science aims to improve the systematic uptake of evidence-based care into practice and could, therefore, assist in addressing the environmental impacts of healthcare. ⋯ This review demonstrates a gap between evidence for reducing environmental impacts and uptake of proposed practice changes to deliver low-carbon healthcare. Future research into 'greening' healthcare should use implementation research methods to establish a solid implementation evidence base. SYSTEMATIC REVIEW PROTOCOL: PROSPERO CRD42022342786.