面向标准化数据整合的医学通用数据模型探析

美国观察性医疗结果合作组织（Observational Medical Outcomes Partnership,OMOP）建立的通用数据模型（Common Data Model,CDM）提供了数据结构和内容的标准化研究方法,目前已广泛地应用于各类科学研究。深入分析了CDM的主要模块架构,梳理和总结了多源数据向CDM转换的流程、每一步的实现方法和主要思路,介绍了模型的应用,探讨了模型应用中的关键问题,并提出了相关建议。     

盆底功能障碍性疾病专病数据库的建设与应用

目的：以盆底功能障碍性疾病专病队列临床诊疗数据为基础建立临床科研平台,实现临床业务数据到科研数据的自动转化,加速成果产出及转化。方法：结合临床诊疗实际,制定针对盆底功能障碍性疾病的专病队列研究的核心数据指标,利用提取—转化—加载（ETL）技术,统一清洗并存储临床业务数据,对原业务系统存在的结构化指标进行映射和归一,对非结构化内容进行人工标注及机器学习训练,利用自然语言处理技术进行后结构化治理,建立基于Elasticsearch搜索分析引擎的专病数据模型,构建盆底功能障碍性疾病临床科研平台。结果：专病队列临床科研平台基于底层全结构化标准数据库,可提供全量病历信息全息展示,实现了临床诊疗信息到科学研究数据的自动转换,提高了科研队列纳排入组的效率,有利于挖掘提升临床诊断治疗的深度信息。结论：基于盆底功能障碍性疾病专病队列的临床科研平台可实现临床研究试验病例快速配置,加速临床科研成果产出及转化,可为后续拓展基于数据库的专科管理及辅助决策提供支持。     

省级健康医疗大数据平台设计与应用实践

阐述甘肃省健康医疗大数据平台建设目标、业务需求、数据来源、业务内容、系统架构及部署,介绍平台应用实践,包括跨地区患者流向分析、构建人工智能决策引擎等方面,对进一步完善平台、建立健康医疗大数据模型等进行探讨。     

医疗全量数据模型管理工具建设与实践

介绍建立数据模型工具的意义、数据模型构成与功能,阐述医疗全量数据的数据模型管理工具系统设计及功能、应用效果,指出其应用可实现对数据模型全生命周期管理,为后续数据治理奠定良好基础。     

基于医院数据中心的临床科研平台构建

目的 基于医院数据中心构建科学数据平台,实现一体化临床科研工作。方法 从医院数据中心采集数据,随后基于科研通用数据模型和专病数据集进行数据标准化治理,形成科研数据平台数据中心,并在其上层构建科研应用以满足临床研究的需求。结果 该临床科研平台在多个临床科室投入使用,导入了数个科研项目,相较于传统科研路径,科研立项、病例筛选、病例变量采集、数据清洗和数据统计效率均有大幅度提升,差异有统计学意义（P<0.001）,临床科研整体效率显著提升。结论 以医院数据中心为基础构建的临床科研数据平台,为医院医生的科研工作提供了强有力的支撑,成功搭建了多个专病数据库,同时通过信息技术手段为医院的科研管理工作提供了有效的科研服务,进而提高了院内各科室的科研效率。     

基于通用数据模型的健康医疗大数据平台数据治理研究

以健康医疗大数据平台建设过程中的数据治理实践为例,从数据抽取与清洗、文本数据结构化和数据映射等方面探讨基于通用数据模型的多中心健康医疗大数据质量提升方法和技术,总结相关实践问题与经验,为跨机构、跨部门的健康医疗大数据治理提供参考。     

临床科研共享系统质量控制体系升级

以临床科研共享系统升级过程中建立的研究平台为基础,进行科研数据结构优化,分析原有数据质量的问题,对原有存储方式进行优化,建立新的数据质量控制体系,使临床科研共享系统与医疗大数据时代、精准医疗的未来发展方向接驳。     

医疗机构自主可控大数据科研平台建设方案

以多组学数据分析场景为例,从基础硬件设施、基础软件、应用系统3方面阐述医疗机构自主可控大数据科研平台国产化建设方案,为生物医学数据科研平台建设积累经验,为国内医疗科研机构提供参考。     

临床科研数据库平台关键技术研究与实现

虽然医疗信息化发展提供了丰富的临床诊疗数据资源,但目前临床科研仍然经常遇到数据可及性和可用性低的问题,建立临床科研数据库平台是解决该问题的有效途径.在分析问题的基础上,提出了临床科研数据库平台的系统架构,并结合实践对提高数据可及性和可用性的关键技术及其实现方案进行了介绍.     

利用Cloud P2P构建健康医疗大数据平台

分析健康医疗行业集成研究现状和健康医疗大数据平台建设存在的难点,采用Cloud P2P网络构建健康医疗大数据平台,提出包含资源层、感知/接入层、传输层、服务层和应用层5个层次的平台框架。     

Validation of a common data model for active safety surveillance research

OBJECTIVE: Systematic analysis of observational medical databases for active safety surveillance is hindered by the variation in data models and coding systems. Data analysts often find robust clinical data models difficult to understand and ill suited to support their analytic approaches. Further, some models do not facilitate the computations required for systematic analysis across many interventions and outcomes for large datasets. Translating the data from these idiosyncratic data models to a common data model (CDM) could facilitate both the analysts' understanding and the suitability for large-scale systematic analysis. In addition to facilitating analysis, a suitable CDM has to faithfully represent the source observational database. Before beginning to use the Observational Medical Outcomes Partnership (OMOP) CDM and a related dictionary of standardized terminologies for a study of large-scale systematic active safety surveillance, the authors validated the model's suitability for this use by example. VALIDATION BY EXAMPLE: To validate the OMOP CDM, the model was instantiated into a relational database, data from 10 different observational healthcare databases were loaded into separate instances, a comprehensive array of analytic methods that operate on the data model was created, and these methods were executed against the databases to measure performance. CONCLUSION: There was acceptable representation of the data from 10 observational databases in the OMOP CDM using the standardized terminologies selected, and a range of analytic methods was developed and executed with sufficient performance to be useful for active safety surveillance.     

Analysis of treatment pathways for three chronic diseases using OMOP CDM

The present study examined treatment pathways (the ordered sequence of medications that a patient is prescribed) for three chronic diseases (hypertension, type 2 diabetes, and depression), compared the pathways with recommendations from guidelines, discussed differences and standardization of medications in different medical institutions, explored population diversification and changes of clinical treatment, and provided clinical big data analysis-based data support for the development and study of drugs in China. In order to run the “Treatment Pathways in Chronic Disease” protocol in Chinese data sources,we have built a large data research and analysis platform for Chinese clinical medical data. Data sourced from the Clinical Data Repository (CDR) of the First Affiliated Hospital of Nanjing Medical University was extracted, transformed, and loaded into an observational medical outcomes partnership common data model (OMOP CDM) Ver. 5.0. Diagnosis and medication information for patients with hypertension, type 2 diabetes, and depression from 2005 to 2015 were extracted for observational research to obtain treatment pathways for the three diseases. The most common medications used to treat diabetes and hypertension were metformin and acarbose, respectively, at 28.5 and 20.9% as first-line medication. New drugs were emerging for depression; therefore, the favorite medication changed accordingly. Most patients with these three diseases had different treatment pathways from other patients with the same diseases. The proportions of monotherapy increased for the three diseases, especially in recent years. The recommendations presented in guidelines show some predominance. High-quality, effective guidelines incorporating domestic facts should be established to further guide medication and improve therapy at local hospitals. Medical institutions at all levels could improve the quality of medical services, and further standardize medications in the future. This research is the first application of the CDM model and OHDSI software in China, which were used to study, treatment pathways for three chronic diseases (hypertension, type 2 diabetes and depression), compare the pathways with recommendations from guidelines, discuss differences and standardization of medications in different medical institutions, demonstrate the urgent need for quality national guidelines, explores population diversification and changes of clinical treatment, and provide clinical big data analysis-based data support for the development and study of drugs in China.     

Harmonizing units and values of quantitative data elements in a very large nationally pooled electronic health record (EHR) dataset

ObjectiveThe goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing.Materials and MethodsThe National COVID Cohort Collaborative (N3C) table of laboratory measurement data—over 3.1 billion patient records and over 19 000 unique measurement concepts in the Observational Medical Outcomes Partnership (OMOP) common-data-model format from 55 data partners. We grouped ontologically similar OMOP concepts together for 52 variables relevant to COVID-19 research, and developed a unit-harmonization pipeline comprised of (1) selecting a canonical unit for each measurement variable, (2) arriving at a formula for conversion, (3) obtaining clinical review of each formula, (4) applying the formula to convert data values in each unit into the target canonical unit, and (5) removing any harmonized value that fell outside of accepted value ranges for the variable. For data with missing units for all the results within a lab test for a data partner, we compared values with pooled values of all data partners, using the Kolmogorov-Smirnov test.ResultsOf the concepts without missing values, we harmonized 88.1% of the values, and imputed units for 78.2% of records where units were absent (41% of contributors’ records lacked units).DiscussionThe harmonization and inference methods developed herein can serve as a resource for initiatives aiming to extract insight from heterogeneous EHR collections. Unique properties of centralized data are harnessed to enable unit inference.ConclusionThe pipeline we developed for the pooled N3C data enables use of measurements that would otherwise be unavailable for analysis.     

临床科研数据库建设中的数据标准化问题探讨

临床科研数据库的应用可以使积累的诊疗数据转变成丰富的临床研究资源.介绍了国内临床科研数据库的现状,并对其发展趋势进行了分析,总结了临床科研数据库建设面临的困难,提出引入数据标准是解决临床科研数据库数据整合、交换和共享从而挖掘数据潜在价值的根本方法.以OHDSI作为建立并推广数据标准的成功案例,介绍了其医学标准术语集和通...     

The OneFlorida Data Trust: a centralized,translational research data infrastructure of statewide scope

The OneFlorida Data Trust is a centralized research patient data repository created and managed by the OneFlorida Clinical Research Consortium (“OneFlorida”). It comprises structured electronic health record (EHR), administrative claims, tumor registry, death, and other data on 17.2 million individuals who received healthcare in Florida between January 2012 and the present. Ten healthcare systems in Miami, Orlando, Tampa, Jacksonville, Tallahassee, Gainesville, and rural areas of Florida contribute EHR data, covering the major metropolitan regions in Florida. Deduplication of patients is accomplished via privacy-preserving entity resolution (precision 0.97–0.99, recall 0.75), thereby linking patients’ EHR, claims, and death data. Another unique feature is the establishment of mother-baby relationships via Florida vital statistics data. Research usage has been significant, including major studies launched in the National Patient-Centered Clinical Research Network (“PCORnet”), where OneFlorida is 1 of 9 clinical research networks. The Data Trust’s robust, centralized, statewide data are a valuable and relatively unique research resource.     

论基于信息标准的临床数据中心构建

结合国内外医疗信息化的现状，介绍了临床数据中心的发展历程和构建框架，该框架基于HL7RIM模型表达，同时通过各种医学信息标准和HIT技术的实现，构建基于信息标准的临床数据中心体系结构和基础架构，用以实现具备统一和开放性的系统集成框架，推动医院内异构医疗信息系统的交互，并对临床数据中心在医疗信息化过程中发挥的作用和未来发展趋势进行了展望。