麻醉信息管理系统在围术期质量控制中的作用
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摘要
目的探讨运用麻醉信息管理系统(AIMS)实现围术期麻醉质量控制数字化,提高麻醉科医疗安全与质量管理水平。方法利用AIMS对患者围术期相关信息包括国家卫计委17项麻醉质量控制指标进行自动采集、整理、存储并统计分析,采取环节控制、终末控制及全程控制相结合的方法,对存在问题提出持续改进措施;并探讨人工智能(AI)在麻醉质控领域的应用。结果围术期信息化麻醉管理数据及17项麻醉质量控制指标数据收集及时、高效、精准,大数据分析处理促进了麻醉质控管理水平持续提高; AI能推进麻醉智能辅助技术的发展,使麻醉质量控制管理更加简化和精确。结论通过AIMS信息化围术期麻醉质量控制,有助于提高临床麻醉医疗水平,且有望实现麻醉质控标准化、医疗服务同质化的目标。
Objective After the implementation of anesthesia information management system( AIMS) in our hospital,how to improve the patient's quality care and the management of anesthesia department in perioperative period is discussed. Methods After instituted the use of AIMS,patient's perioperative vital anesthesia data was collected,processed and stored through the automated electronic anesthetic records. The accurate anesthesia data could also be confirmed by the quality controlling team. The quality control metrics were being continually refined during the whole process. The application of artificial intelligence( AI) was being incorporated into the quality control of anesthesia effort. Results After five years of construction and improvement,the quality control metrics and the management of anesthesia department are becoming more precise and efficient. And the level of the anesthesia management has been elevated. AI can promote the development of intelligence associate technology of anesthesia and make the quality control management of anesthesia more transparent and precise. Conclusion With the support of AIMS,we can improve the quality control metrics and quality management of department. Furthermore,the anesthesia care quality standardization and all medical services to pursue the same quality care goal can be achieved quickly.
Objective After the implementation of anesthesia information management system( AIMS) in our hospital,how to improve the patient's quality care and the management of anesthesia department in perioperative period is discussed. Methods After instituted the use of AIMS,patient's perioperative vital anesthesia data was collected,processed and stored through the automated electronic anesthetic records. The accurate anesthesia data could also be confirmed by the quality controlling team. The quality control metrics were being continually refined during the whole process. The application of artificial intelligence( AI) was being incorporated into the quality control of anesthesia effort. Results After five years of construction and improvement,the quality control metrics and the management of anesthesia department are becoming more precise and efficient. And the level of the anesthesia management has been elevated. AI can promote the development of intelligence associate technology of anesthesia and make the quality control management of anesthesia more transparent and precise. Conclusion With the support of AIMS,we can improve the quality control metrics and quality management of department. Furthermore,the anesthesia care quality standardization and all medical services to pursue the same quality care goal can be achieved quickly.
引文
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[9]聂曦明,王龙,刘丽萍,等.人工智能技术在神经重症监护中的应用[J].临床荟萃,2018,33(4):277-281.DOI:10.3969/j.issn.1004-583X.2018.04.001.
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[11]GAMBUS P,SHAFER S L.Artificial intelligence for everyone[J].Anesthesiology,2018,128(3):431-433.DOI:10.1097/ALN.0000000000001984.
[12]刁琰,张民,单清,等.利用人工智能技术实现临床输血管控与决策分析智能化的应用研究[J/OL].中华肺部疾病杂志(电子版),2015,8(4):115-116.DOI:10.3877/cma.j.issn.1674-6902.2015.04.042.
[13]LEE C K,HOFER I,GABEL E,et al.Development and validation of a deep neural network model for prediction of postoperative in-hospital mortality[J].Anesthesiology,2018,129(4):649-662.DOI:10.1097/ALN.0000000000002186.
[14]KENDALE S,KULKARNI P,ROSENBERG A,et al.Supervised machine-learning predictive analytics for prediction of postinductionhypotension[J].Anesthesiology,2018,129(4):675-688.DOI:10.1097/ALN.0000000000002374.
[15]HATIB F,JIAN Z,BUDDI S,et al.Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis[J].Anesthesiology,2018,129(4):663-674.DOI:10.1097/ALN.0000000000002300.
[16]MENDEZ J A,LEON A,MARRERO A,et al.Improving the anesthetic process by a fuzzy rule based medical decision system[J].Artif Intell Med,2018,84:159-170.DOI:10.1016/j.artmed.2017.12.005.
[17]MATHIS M R,KHETERPAL S,NAJARIAN K.Artificial intelligence for anesthesia:what the practicing clinician needs to know:more than black magic for the art of the dark[J].Anesthesiology,2018,129(4):619-622.DOI:10.1097/ALN.0000000000002384.
[2]RODRIQUEZ L I,SMAKA T J,MAHLA M,et al.Default drug doses in anesthesia information management systems[J].Anesth Analg,2017,125(1):255-260.DOI:10.1213/ANE.0000000000001611.
[3]王润玲,粱昌勇,顾东晓.安徽3家三级综合医院HIS使用效果影响因素分析[J].中华医院管理杂志,2015,31(1):55-58.DOI:10.3760/cma.j.issn.1000-6672.2015.01.017.
[4]STOL I S,EHRENFELD J M,EPSTEIN R H.Technology diffusion of anesthesia information management systems into academic anesthesia departments in the United States[J].Anesth Analg,2014,118(3):644-650.DOI:10.1213/ANE.0000000000000055.
[5]KADRY B,FEASTER W W,MACARIO A,et al.Anesthesia information management systems:past,present,and future of anesthesia records[J].Mt Sinai J Med,2012,79(1):154-165.DOI:10.1002/msj.21281.
[6]SINCLAIR D R.Gaining acceptance for anesthesia information management systems among anesthesiologists[J].Can J Anaesth,2013,60(7):730-732.DOI:10.1007/s12630-013-9926-6.
[7]屠伟峰,赵霞,耿茜.信息技术在麻醉闭环质量管理体系中的作用[J].现代医院,2017,17(1):58-61.DOI:10.3969/j.issn.1671-332X.2017.01.020.
[8]贺佳彦.麻醉临床信息系统的建设及在麻醉质量控制中的应用[J].医疗装备,2015,28(17):120-122.DOI:10.3969/j.issn.1002-2376.2015.17.080.
[9]聂曦明,王龙,刘丽萍,等.人工智能技术在神经重症监护中的应用[J].临床荟萃,2018,33(4):277-281.DOI:10.3969/j.issn.1004-583X.2018.04.001.
[10]LEE H C,RYU H G,CHUNG E J,et al.Prediction of bispectral index during target-controlled infusion of propofol and remifentanil:a deep learning approach[J].Anesthesiology,2018,128(3):492-501.DOI:10.1097/ALN.0000000000001892.
[11]GAMBUS P,SHAFER S L.Artificial intelligence for everyone[J].Anesthesiology,2018,128(3):431-433.DOI:10.1097/ALN.0000000000001984.
[12]刁琰,张民,单清,等.利用人工智能技术实现临床输血管控与决策分析智能化的应用研究[J/OL].中华肺部疾病杂志(电子版),2015,8(4):115-116.DOI:10.3877/cma.j.issn.1674-6902.2015.04.042.
[13]LEE C K,HOFER I,GABEL E,et al.Development and validation of a deep neural network model for prediction of postoperative in-hospital mortality[J].Anesthesiology,2018,129(4):649-662.DOI:10.1097/ALN.0000000000002186.
[14]KENDALE S,KULKARNI P,ROSENBERG A,et al.Supervised machine-learning predictive analytics for prediction of postinductionhypotension[J].Anesthesiology,2018,129(4):675-688.DOI:10.1097/ALN.0000000000002374.
[15]HATIB F,JIAN Z,BUDDI S,et al.Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis[J].Anesthesiology,2018,129(4):663-674.DOI:10.1097/ALN.0000000000002300.
[16]MENDEZ J A,LEON A,MARRERO A,et al.Improving the anesthetic process by a fuzzy rule based medical decision system[J].Artif Intell Med,2018,84:159-170.DOI:10.1016/j.artmed.2017.12.005.
[17]MATHIS M R,KHETERPAL S,NAJARIAN K.Artificial intelligence for anesthesia:what the practicing clinician needs to know:more than black magic for the art of the dark[J].Anesthesiology,2018,129(4):619-622.DOI:10.1097/ALN.0000000000002384.