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Real-World Integration of a Sepsis Deep Learning Technology Into Routine Clinical Care: Implementation Study

机译：败血症深入学习技术的真实融合进入常规临床护理：实施研究

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Background Successful integrations of machine learning into routine clinical care are exceedingly rare, and barriers to its adoption are poorly characterized in the literature. Objective This study aims to report a quality improvement effort to integrate a deep learning sepsis detection and management platform, Sepsis Watch, into routine clinical care. Methods In 2016, a multidisciplinary team consisting of statisticians, data scientists, data engineers, and clinicians was assembled by the leadership of an academic health system to radically improve the detection and treatment of sepsis. This report of the quality improvement effort follows the learning health system framework to describe the problem assessment, design, development, implementation, and evaluation plan of Sepsis Watch. Results Sepsis Watch was successfully integrated into routine clinical care and reshaped how local machine learning projects are executed. Frontline clinical staff were highly engaged in the design and development of the workflow, machine learning model, and application. Novel machine learning methods were developed to detect sepsis early, and implementation of the model required robust infrastructure. Significant investment was required to align stakeholders, develop trusting relationships, define roles and responsibilities, and to train frontline staff, leading to the establishment of 3 partnerships with internal and external research groups to evaluate Sepsis Watch. Conclusions Machine learning models are commonly developed to enhance clinical decision making, but successful integrations of machine learning into routine clinical care are rare. Although there is no playbook for integrating deep learning into clinical care, learnings from the Sepsis Watch integration can inform efforts to develop machine learning technologies at other health care delivery systems.
机译：背景技术机器学习进入常规临床护理的成功整合非常罕见，并且其采用的障碍在文献中具有较差的特征。目的本研究旨在报告一项质量改进努力，将深度学习脓毒症检测和管理平台，败血症观看融入常规临床护理。方法在2016年，由学术卫生系统的领导地位组装了由统计学人员，数据科学家，数据工程师和临床医生组成的多学科团队，从而改善败血症的检测和治疗。本报告质量改进措施遵循学习卫生系统框架来描述败血症观察的问题评估，设计，开发，实施和评估计划。结果SEPSIS WATION成功融入了常规临床护理，并重塑了局部机器学习项目的执行方式。前线临床工作人员高度从事工作流程，机器学习模型和应用的设计和开发。开发了新型机器学习方法以早期检测败血症，并实施所需的鲁棒基础设施的模型。需要大量的投资来对准利益攸关方，制定信任关系，定义角色和责任，以及培训前线员工，导致建立与内外研究小组的3个合作伙伴关系，以评估败血症观察。结论机器学习模型通常开发，以提高临床决策，但机器学习成功结合常规临床护理是罕见的。虽然没有将深度学习融入临床护理的比赛，但败血症观看整合的学习可以为在其他医疗保健交付系统中开发机器学习技术的努力。

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