PDF(Pubmed)
Abstract：
BACKGROUND: A coordinated care system helps provide timely access to treatment for suspected acute stroke. In Northwestern Ontario (NWO), Canada, communities are widespread with several hospitals offering various diagnostic equipment and services. Thus, resources are limited, and health care providers must often transfer patients with stroke to different hospital locations to ensure the most appropriate care access within recommended time frames. However, health care providers frequently situated temporarily (locum) in NWO or providing care remotely from other areas of Ontario may lack sufficient information and experience in the region to access care for a patient with a time-sensitive condition. Suboptimal decision-making may lead to multiple transfers before definitive stroke care is obtained, resulting in poor outcomes and additional health care system costs.
OBJECTIVE: We aimed to develop a tool to inform and assist NWO health care providers in determining the best transfer options for patients with stroke to provide the most efficient care access. We aimed to develop an app using a comprehensive geomapping navigation and estimation system based on machine learning algorithms. This app uses key stroke-related timelines including the last time the patient was known to be well, patient location, treatment options, and imaging availability at different health care facilities.
METHODS: Using historical data (2008-2020), an accurate prediction model using machine learning methods was developed and incorporated into a mobile app. These data contained parameters regarding air (Ornge) and land medical transport (3 services), which were preprocessed and cleaned. For cases in which Ornge air services and land ambulance medical transport were both involved in a patient transport process, data were merged and time intervals of the transport journey were determined. The data were distributed for training (35%), testing (35%), and validation (30%) of the prediction model.
RESULTS: In total, 70,623 records were collected in the data set from Ornge and land medical transport services to develop a prediction model. Various learning models were analyzed; all learning models perform better than the simple average of all points in predicting output variables. The decision tree model provided more accurate results than the other models. The decision tree model performed remarkably well, with the values from testing, validation, and the model within a close range. This model was used to develop the \"NWO Navigate Stroke\" system. The system provides accurate results and demonstrates that a mobile app can be a significant tool for health care providers navigating stroke care in NWO, potentially impacting patient care and outcomes.
CONCLUSIONS: The NWO Navigate Stroke system uses a data-driven, reliable, accurate prediction model while considering all variations and is simultaneously linked to all required acute stroke management pathways and tools. It was tested using historical data, and the next step will to involve usability testing with end users.
OBJECTIVE: We aimed to develop a tool to inform and assist NWO health care providers in determining the best transfer options for patients with stroke to provide the most efficient care access. We aimed to develop an app using a comprehensive geomapping navigation and estimation system based on machine learning algorithms. This app uses key stroke-related timelines including the last time the patient was known to be well, patient location, treatment options, and imaging availability at different health care facilities.
METHODS: Using historical data (2008-2020), an accurate prediction model using machine learning methods was developed and incorporated into a mobile app. These data contained parameters regarding air (Ornge) and land medical transport (3 services), which were preprocessed and cleaned. For cases in which Ornge air services and land ambulance medical transport were both involved in a patient transport process, data were merged and time intervals of the transport journey were determined. The data were distributed for training (35%), testing (35%), and validation (30%) of the prediction model.
RESULTS: In total, 70,623 records were collected in the data set from Ornge and land medical transport services to develop a prediction model. Various learning models were analyzed; all learning models perform better than the simple average of all points in predicting output variables. The decision tree model provided more accurate results than the other models. The decision tree model performed remarkably well, with the values from testing, validation, and the model within a close range. This model was used to develop the \"NWO Navigate Stroke\" system. The system provides accurate results and demonstrates that a mobile app can be a significant tool for health care providers navigating stroke care in NWO, potentially impacting patient care and outcomes.
CONCLUSIONS: The NWO Navigate Stroke system uses a data-driven, reliable, accurate prediction model while considering all variations and is simultaneously linked to all required acute stroke management pathways and tools. It was tested using historical data, and the next step will to involve usability testing with end users.
摘要：
背景：协调的护理系统有助于为疑似急性中风提供及时的治疗。在安大略省西北部（NWO），加拿大,社区分布广泛，几家医院提供各种诊断设备和服务。因此，资源有限,医疗保健提供者必须经常将中风患者转移到不同的医院，以确保在建议的时间范围内获得最适当的护理。然而,经常位于NWO的临时（locum）或在安大略省其他地区远程提供护理的医疗保健提供者可能在该地区缺乏足够的信息和经验，无法为具有时间敏感性的患者提供护理。次优决策可能会导致在获得明确的中风护理之前进行多次转移，导致不良结果和额外的医疗保健系统成本。
目的：我们旨在开发一种工具来告知和协助NWO医疗保健提供者确定中风患者的最佳转移选择，以提供最有效的护理服务。我们旨在使用基于机器学习算法的综合地理映射导航和估计系统开发应用程序。这个应用程序使用与中风相关的关键时间线，包括患者最后一次被认为是好的，患者位置,治疗方案,以及不同医疗机构的成像可用性。
方法：使用历史数据（2008-2020年），开发了一种使用机器学习方法的准确预测模型，并将其集成到移动应用程序中。这些数据包含有关空中（Ornge）和陆地医疗运输（3种服务）的参数，经过预处理和清洁。对于Ornge航空服务和陆地救护车医疗运输都涉及患者运输过程的情况，合并数据并确定运输旅程的时间间隔。数据被分发用于训练(35%)，测试（35%），并对预测模型进行验证(30%)。
结果：总计，从Ornge和陆地医疗运输服务的数据集中收集了70,623条记录，以开发预测模型。分析了各种学习模型；在预测输出变量方面，所有学习模型的性能均优于所有点的简单平均值。决策树模型提供了比其他模型更准确的结果。决策树模型表现非常好，根据测试的值，验证,和近距离内的模型。该模型用于开发“NWO导航中风”系统。该系统提供了准确的结果，并证明了移动应用程序可以成为医疗保健提供者在NWO中导航中风护理的重要工具，可能影响患者护理和结果。
结论：NWO导航中风系统使用数据驱动，可靠,准确的预测模型，同时考虑所有变化，并同时与所有必需的急性卒中管理途径和工具相关联。使用历史数据进行了测试，下一步将涉及最终用户的可用性测试。
目的：我们旨在开发一种工具来告知和协助NWO医疗保健提供者确定中风患者的最佳转移选择，以提供最有效的护理服务。我们旨在使用基于机器学习算法的综合地理映射导航和估计系统开发应用程序。这个应用程序使用与中风相关的关键时间线，包括患者最后一次被认为是好的，患者位置,治疗方案,以及不同医疗机构的成像可用性。
方法：使用历史数据（2008-2020年），开发了一种使用机器学习方法的准确预测模型，并将其集成到移动应用程序中。这些数据包含有关空中（Ornge）和陆地医疗运输（3种服务）的参数，经过预处理和清洁。对于Ornge航空服务和陆地救护车医疗运输都涉及患者运输过程的情况，合并数据并确定运输旅程的时间间隔。数据被分发用于训练(35%)，测试（35%），并对预测模型进行验证(30%)。
结果：总计，从Ornge和陆地医疗运输服务的数据集中收集了70,623条记录，以开发预测模型。分析了各种学习模型；在预测输出变量方面，所有学习模型的性能均优于所有点的简单平均值。决策树模型提供了比其他模型更准确的结果。决策树模型表现非常好，根据测试的值，验证,和近距离内的模型。该模型用于开发“NWO导航中风”系统。该系统提供了准确的结果，并证明了移动应用程序可以成为医疗保健提供者在NWO中导航中风护理的重要工具，可能影响患者护理和结果。
结论：NWO导航中风系统使用数据驱动，可靠,准确的预测模型，同时考虑所有变化，并同时与所有必需的急性卒中管理途径和工具相关联。使用历史数据进行了测试，下一步将涉及最终用户的可用性测试。
