Opportunities for machine learning to improve surgical ward safety.
Opportunities for machine learning to improve surgical ward safety.
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DOI：
10.1016/j.amjsurg.2020.02.037
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发表时间：
2020-10
影响因子：
3
通讯作者：
Bihorac A
中科院分区：
文献类型：
--
作者：
Loftus TJ;Tighe PJ;Filiberto AC;Balch J;Upchurch GR Jr;Rashidi P;Bihorac A
对失代偿的延迟认识和外科病房的抢救失败是可预防伤害的主要来源。本次审查吸收并严格评估了现有证据，并确定了改善外科病房安全的机会。其中包括来自 Cochrane 图书馆、EMBASE 和 PubMed 数据库的 58 篇文章。只有 15-20% 被拘留的患者能够幸存。在大多数情况下，不稳定的微妙迹象通常出现在危重疾病和逮捕之前，并且潜在的病理是可逆的。粗略的风险评估导致对病房的高风险患者进行分类不足，其中并发症的监测依赖于耗时的健康记录人工审查、不频繁的患者评估、缺乏准确性和自主性的预测模型以及有偏见且容易出错的决策。流式电子健康记录数据、可穿戴连续监测器以及深度学习和强化学习的最新进展可以促进高效、准确的风险评估、对不稳定性的早期识别以及有关可逆基础病理的诊断和治疗的更好决策。对失代偿的延迟认识和外科病房的抢救失败是可预防伤害的主要来源。粗略的风险评估会损害病房的安全，导致高风险术后患者未能分类到病房，人手不足的医疗服务提供者很少对患者进行评估并使用认知捷径，从而导致偏见和容易出错的决策。流式电子健康记录数据、可穿戴连续监测器和最新的机器学习进展可以促进高效、准确的风险评估、对不稳定性的早期识别以及有关可逆基础病理的诊断和治疗的更好决策。
Delayed recognition of decompensation and failure-to-rescue on surgical wards are major sources of preventable harm. This review assimilates and critically evaluates available evidence and identifies opportunities to improve surgical ward safety. Fifty-eight articles from Cochrane Library, EMBASE, and PubMed databases were included. Only 15–20% of patients suffering ward arrest survive. In most cases, subtle signs of instability often occur prior to critical illness and arrest, and underlying pathology is reversible. Coarse risk assessments lead to under-triage of high-risk patients to wards, where surveillance for complications depends on time-consuming manual review of health records, infrequent patient assessments, prediction models that lack accuracy and autonomy, and biased, error-prone decision-making. Streaming electronic heath record data, wearable continuous monitors, and recent advances in deep learning and reinforcement learning can promote efficient and accurate risk assessments, earlier recognition of instability, and better decisions regarding diagnosis and treatment of reversible underlying pathology. Delayed recognition of decompensation and failure-to-rescue on surgical wards are major sources of preventable harm. Ward safety is compromised by coarse risk assessments leading to under-triage of high-risk postoperative patients to wards, where understaffed providers make infrequent patient assessments and use cognitive shortcuts, leading to bias and error-prone decision-making. Streaming electronic heath record data, wearable continuous monitors, and recent machine learning advances can promote efficient and accurate risk assessments, earlier recognition of instability, and better decisions regarding diagnosis and treatment of reversible underlying pathology.
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DOI：
10.1186/s13054-015-0950-5
发表时间：
2015-05-20
期刊：
Critical care (London, England)
影响因子：
--
作者：
Douw G;Schoonhoven L;Holwerda T;Huisman-de Waal G;van Zanten AR;van Achterberg T;van der Hoeven JG
通讯作者：
van der Hoeven JG
影响因子：
8.8
作者：
FRANKLIN, C;MATHEW, J
通讯作者：
MATHEW, J
影响因子：
3.3
作者：
Green M;Marzano V;Leditschke IA;Mitchell I;Bissett B
通讯作者：
Bissett B
影响因子：
168.9
作者：
Dybowski, R;Weller, P;Gant, V
通讯作者：
Gant, V
影响因子：
3.7
作者：
ChangLai, SP;Hung, WT;Liao, KK
通讯作者：
Liao, KK