Implementation of a learning healthcare system for sickle cell disease.
Implementation of a learning healthcare system for sickle cell disease.
复制标题
DOI：
10.1093/jamiaopen/ooaa024
复制
发表时间：
2020-10
期刊：
影响因子：
2.1
通讯作者：
Kolb EA
中科院分区：
文献类型：
--
作者：
Miller R;Coyne E;Crowgey EL;Eckrich D;Myers JC;Villanueva R;Wadman J;Jacobs-Allen S;Gresh R;Volchenboum SL;Kolb EA
本研究以镰状细胞病（SCD）为模型，旨在建立一个全面的学习型医疗保健系统，以支持疾病管理和研究。一个多学科团队开发了一个SCD临床数据字典，以标准化床边数据输入，并告知一个可扩展的环境，能够将复杂的电子医疗记录（EHRs）转换为实时可访问的知识。临床医生专家在SCD护理开发了一个数据字典来描述重要的SCD相关的健康维护和不良事件。SCD数据字典使用EPIC SmartForms（一种高效的床边数据输入工具）部署在EHR中。使用Pentaho data Integration从EHR数据库（Clarity）中提取其他数据元素，并将其存储在数据分析数据库（SQL）中。开发了一个自定义应用程序，镰状细胞知识库，以改进数据分析和可视化。评估数据输入的利用率、准确性和完整性。SCD知识库有助于生成患者级和汇总数据可视化，推动将数据转化为影响护理的知识。可以选择单个患者来监测健康维持、合并症、不良事件频率和严重程度以及药物剂量/依从性。在床边使用的疾病特定数据字典最终将增加电子病历数据集的有意义使用，以推动一致的临床数据输入，提高数据准确性，并支持有助于质量改进和研究的分析。
Using sickle cell disease (SCD) as a model, the objective of this study was to create a comprehensive learning healthcare system to support disease management and research. A multidisciplinary team developed a SCD clinical data dictionary to standardize bedside data entry and inform a scalable environment capable of converting complex electronic healthcare records (EHRs) into knowledge accessible in real time. Clinicians expert in SCD care developed a data dictionary to describe important SCD-associated health maintenance and adverse events. The SCD data dictionary was deployed in the EHR using EPIC SmartForms, an efficient bedside data entry tool. Additional data elements were extracted from the EHR database (Clarity) using Pentaho Data Integration and stored in a data analytics database (SQL). A custom application, the Sickle Cell Knowledgebase, was developed to improve data analysis and visualization. Utilization, accuracy, and completeness of data entry were assessed. The SCD Knowledgebase facilitates generation of patient-level and aggregate data visualization, driving the translation of data into knowledge that can impact care. A single patient can be selected to monitor health maintenance, comorbidities, adverse event frequency and severity, and medication dosing/adherence. Disease-specific data dictionaries used at the bedside will ultimately increase the meaningful use of EHR datasets to drive consistent clinical data entry, improve data accuracy, and support analytics that will facilitate quality improvement and research.
登录
查看更多内容
影响因子：
12.8
作者：
Steinberg, Martin H.;McCarthy, William F.;Castro, Oswaldo;Ballas, Samir K.;Armstrong, F. Danny;Smith, Wally;Ataga, Kenneth;Swerdlow, Paul;Kutlar, Abdullah;DeCastro, Laura;Waclawiw, Myron A.
通讯作者：
Waclawiw, Myron A.
影响因子：
3.1
作者：
Morain, Stephanie R.;Kass, Nancy E.;Grossmann, Claudia
通讯作者：
Grossmann, Claudia
影响因子：
5.8
作者：
Saraf, Santosh L.;Molokie, Robert E.;Nouraie, Mehdi;Sable, Craig A.;Luchtman-Jones, Lori;Ensing, Gregory J.;Campbell, Andrew D.;Rana, Sohail R.;Niu, Xiao M.;Machado, Roberto F.;Gladwin, Mark T.;Gordeuk, Victor R.
通讯作者：
Gordeuk, Victor R.
影响因子：
158.5
作者：
Adams, RJ;McKie, VC;Wiznitzer, M
通讯作者：
Wiznitzer, M
影响因子：
39.2
作者：
Brawley, Otis W.;Cornelius, Llewellyn J.;Schori, Melissa
通讯作者：
Schori, Melissa