Elkin Peter L, Trusko Brett E, Koppel Ross, Speroff Ted, Mohrer Daniel, Sakji Saoussen, Gurewitz Inna, Tuttle Mark, Brown Steven H
Mount Sinai School of Medicine, New York, NY 10029, USA.
Stud Health Technol Inform. 2010;155:14-29.
Clinicians involved in clinical care generate daily volumes of important data. This data is important for continuity of care, referrals to specialists and back to the patient's medical home. The same data can be used to generate alerts to improve the practice and to generate care activities to ensure that all appropriate care services are provided for the patient given their known medical histories using electronic quality (eQuality) monitoring. For many years we have used patient records as a data source for human abstraction of clinical research data. With the advent of electronic health record (EHR) data we can now make use of computable EHR data that can perform retrospective research studies more rapidly and lower the activation energy necessary to ask the next important question using electronic studies (eStudies). Barriers to these eStudies include: the lack of interoperable data between and among practices, the lack of computable definitions of measures, the lack of training of health professionals to use Ontology based Informatics tools that allow the execution of this type of logic, common methods need to be developed to distribute computable best practice rules to ensure rapid dissemination of evidence, better translating research into practice.
参与临床护理的医生每天都会产生大量重要数据。这些数据对于护理的连续性、转诊至专科医生以及转回患者的医疗之家至关重要。同样的数据可用于生成警报以改进医疗实践，并生成护理活动，以确保根据患者已知的病史，利用电子质量（eQuality）监测为患者提供所有适当的护理服务。多年来，我们一直将患者记录用作临床研究数据人工提取的数据源。随着电子健康记录（EHR）数据的出现，我们现在可以利用可计算的EHR数据，这些数据能够更快速地进行回顾性研究，并降低使用电子研究（eStudies）提出下一个重要问题所需的活化能。这些eStudies的障碍包括：各医疗机构之间缺乏可互操作的数据、缺乏可计算的测量定义、卫生专业人员缺乏使用基于本体的信息学工具的培训，而这些工具可执行此类逻辑，需要开发通用方法来分发可计算的最佳实践规则，以确保证据的快速传播，更好地将研究转化为实践。
