PDF(Pubmed)
Abstract：
BACKGROUND: The rapidly increasing availability of medical data in electronic health records (EHRs) may contribute to the concept of learning health systems, allowing for better personalized care. Type 2 diabetes mellitus was chosen as the use case in this study.
OBJECTIVE: This study aims to explore the applicability of a recently developed patient similarity-based analytics approach based on EHRs as a candidate data analytical decision support tool.
METHODS: A previously published precision cohort analytics workflow was adapted for the Dutch primary care setting using EHR data from the Nivel Primary Care Database. The workflow consisted of extracting patient data from the Nivel Primary Care Database to retrospectively generate decision points for treatment change, training a similarity model, generating a precision cohort of the most similar patients, and analyzing treatment options. This analysis showed the treatment options that led to a better outcome for the precision cohort in terms of clinical readouts for glycemic control.
RESULTS: Data from 11,490 registered patients diagnosed with type 2 diabetes mellitus were extracted from the database. Treatment-specific filter cohorts of patient groups were generated, and the effect of past treatment choices in these cohorts was assessed separately for glycated hemoglobin and fasting glucose as clinical outcome variables. Precision cohorts were generated for several individual patients from the filter cohorts. Treatment options and outcome analyses were technically well feasible but in general had a lack of statistical power to demonstrate statistical significance for treatment options with better outcomes.
CONCLUSIONS: The precision cohort analytics workflow was successfully adapted for the Dutch primary care setting, proving its potential for use as a learning health system component. Although the approach proved technically well feasible, data size limitations need to be overcome before application for clinical decision support becomes realistically possible.
OBJECTIVE: This study aims to explore the applicability of a recently developed patient similarity-based analytics approach based on EHRs as a candidate data analytical decision support tool.
METHODS: A previously published precision cohort analytics workflow was adapted for the Dutch primary care setting using EHR data from the Nivel Primary Care Database. The workflow consisted of extracting patient data from the Nivel Primary Care Database to retrospectively generate decision points for treatment change, training a similarity model, generating a precision cohort of the most similar patients, and analyzing treatment options. This analysis showed the treatment options that led to a better outcome for the precision cohort in terms of clinical readouts for glycemic control.
RESULTS: Data from 11,490 registered patients diagnosed with type 2 diabetes mellitus were extracted from the database. Treatment-specific filter cohorts of patient groups were generated, and the effect of past treatment choices in these cohorts was assessed separately for glycated hemoglobin and fasting glucose as clinical outcome variables. Precision cohorts were generated for several individual patients from the filter cohorts. Treatment options and outcome analyses were technically well feasible but in general had a lack of statistical power to demonstrate statistical significance for treatment options with better outcomes.
CONCLUSIONS: The precision cohort analytics workflow was successfully adapted for the Dutch primary care setting, proving its potential for use as a learning health system component. Although the approach proved technically well feasible, data size limitations need to be overcome before application for clinical decision support becomes realistically possible.
摘要：
背景：电子健康记录（EHR）中医疗数据的可用性迅速增加可能有助于学习卫生系统的概念，允许更好的个性化护理。选择2型糖尿病作为本研究的用例。
目的：本研究旨在探索最近开发的基于EHR的患者相似性分析方法作为候选数据分析决策支持工具的适用性。
方法：使用Nivel初级保健数据库中的EHR数据，针对荷兰初级保健环境调整了先前发布的精确队列分析工作流程。工作流程包括从Nivel初级保健数据库中提取患者数据，以回顾性地生成治疗变更的决策点。训练相似性模型，生成一个最相似患者的精确队列，并分析治疗方案。该分析显示了在血糖控制的临床读数方面导致精确队列更好的结果的治疗选择。
结果：从数据库中提取了11,490名被诊断为2型糖尿病的注册患者的数据。生成患者组的特定治疗过滤器队列，我们将糖化血红蛋白和空腹血糖作为临床结局变量,分别评估了这些队列中过去治疗选择的效果.从过滤器队列为几个个体患者生成精确队列。治疗方案和结果分析在技术上是可行的，但通常缺乏统计能力来证明治疗方案具有更好结果的统计学意义。
结论：精确队列分析工作流程已成功适应荷兰初级保健环境，证明其用作学习卫生系统组件的潜力。尽管该方法在技术上证明是可行的，在临床决策支持的应用成为现实可能之前，需要克服数据大小的限制。
目的：本研究旨在探索最近开发的基于EHR的患者相似性分析方法作为候选数据分析决策支持工具的适用性。
方法：使用Nivel初级保健数据库中的EHR数据，针对荷兰初级保健环境调整了先前发布的精确队列分析工作流程。工作流程包括从Nivel初级保健数据库中提取患者数据，以回顾性地生成治疗变更的决策点。训练相似性模型，生成一个最相似患者的精确队列，并分析治疗方案。该分析显示了在血糖控制的临床读数方面导致精确队列更好的结果的治疗选择。
结果：从数据库中提取了11,490名被诊断为2型糖尿病的注册患者的数据。生成患者组的特定治疗过滤器队列，我们将糖化血红蛋白和空腹血糖作为临床结局变量,分别评估了这些队列中过去治疗选择的效果.从过滤器队列为几个个体患者生成精确队列。治疗方案和结果分析在技术上是可行的，但通常缺乏统计能力来证明治疗方案具有更好结果的统计学意义。
结论：精确队列分析工作流程已成功适应荷兰初级保健环境，证明其用作学习卫生系统组件的潜力。尽管该方法在技术上证明是可行的，在临床决策支持的应用成为现实可能之前，需要克服数据大小的限制。
