Koscielniak Nikolas, Jenkins Diane, Hassani Sahar, Buckon Cathleen, Tucker Joshua S, Sienko Susan, Tucker Carole A
Clinical and Translational Science Institute Wake Forest School of Medicine Winston-Salem North Carolina USA.
Quality Measurement & Performance Improvement Shriners Hospitals for Children Tampa Florida USA.
Learn Health Syst. 2022 Feb 15;6(3):e10305. doi: 10.1002/lrh2.10305. eCollection 2022 Jul.
To describe the development and implementation of learning health system (LHS) infrastructure for a pediatric specialty care health system to support LHS research in pediatric rehabilitation settings.
An existing pediatric common data model (eg, PEDSnet) of standardized medical terminologies for research was expanded and leveraged for this stud, and applied to SHOnet, a clinical research data resource consisting of deidentified data extracted from the electronic health record (EHR) from the Shriners Hospitals for Children speacialty pediatric health care system. We mapped EHR data for laboratory, procedures, drugs, and conditions to standardized vocabularies including ICD-10, CPT, RxNorm, and LOINC to the common data model using an established extraction-transformation-loading process. Rigorous quality checks were conducted to ensure a high degree of data conformance, completeness, and plausibility. SHOnet data elements from all sources are de-identified and the server is managed by the SHC Information Systems Department. SHOnet data are refreshed monthly and data elements are continually expanded based on new research endeavors.
Not applicable.
The Shriners Health Outcomes Network (SHOnet) includes data for over 10 000 distinct observational data elements based on over two million patient encounters between 2011 and present.
The systematic process to develop SHOnet is replicable and flexible for other pediatric rehabilitation research settings interested in building out their LHS capabilities. Challenges and facilitators may arise for building such LHS infrastructure for rehabilitation in areas of (a) data capture, curation, query, and governance, (b) generating knowledge from data, and (c) dissemination and implementation of new institutional knowledge. Further research studies are needed to evaluate these data resources for scalable system-learning endeavors.SHOnet is an exemplar of an LHS for rehabilitation and specialty care settings. The success of an LHS is dependent on engagement of multiple stakeholders, shared governance, effective knowledge translation, and deep commitment to long-term strategies for engaging clinicians, administration, and families in leveraging knowledge to improve clinical outcomes.
描述为儿科专科护理卫生系统开发和实施学习型卫生系统（LHS）基础设施，以支持儿科康复环境中的LHS研究。
将现有的用于研究的标准化医学术语儿科通用数据模型（如PEDSnet）进行扩展并用于本研究，并应用于SHOnet，这是一个临床研究数据资源，由从施莱宁儿童医院专科儿科卫生保健系统的电子健康记录（EHR）中提取的去识别化数据组成。我们使用既定的提取-转换-加载过程，将实验室、程序、药物和病症的EHR数据映射到包括ICD-10、CPT、RxNorm和LOINC在内的标准化词汇表，以形成通用数据模型。进行了严格的质量检查，以确保高度的数据一致性、完整性和合理性。来自所有来源的SHOnet数据元素均经过去识别化处理，服务器由SHC信息系统部管理。SHOnet数据每月更新，数据元素根据新的研究工作不断扩展。
不适用。
施莱宁健康结果网络（SHOnet）包括基于2011年至今超过两百万次患者就诊的超过10000个不同的观察数据元素的数据。
开发SHOnet的系统过程对于其他有兴趣建立其LHS能力的儿科康复研究环境来说是可复制且灵活的。在为康复建立这样的LHS基础设施时，可能会在以下方面出现挑战和促进因素：（a）数据捕获、管理、查询和治理；（b）从数据中生成知识；（c）新机构知识的传播和实施。需要进一步的研究来评估这些数据资源用于可扩展的系统学习工作的情况。SHOnet是康复和专科护理环境中LHS的一个范例。LHS的成功取决于多个利益相关者的参与、共享治理、有效的知识转化，以及对让临床医生、管理人员和家庭参与利用知识改善临床结果的长期战略的坚定承诺。
