Supporting research,protecting data: one institution’s approach to clinical data warehouse governance

Institutions must decide how to manage the use of clinical data to support research while ensuring appropriate protections are in place. Questions about data use and sharing often go beyond what the Health Insurance Portability and Accountability Act of 1996 (HIPAA) considers. In this article, we describe our institution’s governance model and approach. Common questions we consider include (1) Is a request limited to the minimum data necessary to carry the research forward? (2) What plans are there for sharing data externally?, and (3) What impact will the proposed use of data have on patients and the institution? In 2020, 302 of the 319 requests reviewed were approved. The majority of requests were approved in less than 2 weeks, with few or no stipulations. For the remaining requests, the governance committee works with researchers to find solutions to meet their needs while also addressing our collective goal of protecting patients.     

Data governance requirements for distributed clinical research networks: triangulating perspectives of diverse stakeholders

There is currently limited information on best practices for the development of governance requirements for distributed research networks (DRNs), an emerging model that promotes clinical data reuse and improves timeliness of comparative effectiveness research. Much of the existing information is based on a single type of stakeholder such as researchers or administrators. This paper reports on a triangulated approach to developing DRN data governance requirements based on a combination of policy analysis with experts, interviews with institutional leaders, and patient focus groups. This approach is illustrated with an example from the Scalable National Network for Effectiveness Research, which resulted in 91 requirements. These requirements were analyzed against the Fair Information Practice Principles (FIPPs) and Health Insurance Portability and Accountability Act (HIPAA) protected versus non-protected health information. The requirements addressed all FIPPs, showing how a DRN''s technical infrastructure is able to fulfill HIPAA regulations, protect privacy, and provide a trustworthy platform for research.     

数据仓库在医院应用的研究

目的：对医院信息进行全方位、多层次的查询和分析，为医院各类人员提供信息查询、数据分析和决策支持。方法：利用数据仓库(DW)技术，对“军字一号”医院信息系统中的数据进行提取、建模，并建立多维数据集。结果：建立了医院DW的构架，分析了各科室全费治愈患者平均住院费用和平均住院日。结论：DW技术是医院信思系统进一步的发展方向，将为医院决策支持提供最有用的信息。     

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.     

An atomic approach to the design and implementation of a research data warehouse

ObjectiveAs a long-standing Clinical and Translational Science Awards (CTSA) Program hub, the University of Pittsburgh and the University of Pittsburgh Medical Center (UPMC) developed and implemented a modern research data warehouse (RDW) to efficiently provision electronic patient data for clinical and translational research.Materials and MethodsWe designed and implemented an RDW named Neptune to serve the specific needs of our CTSA. Neptune uses an atomic design where data are stored at a high level of granularity as represented in source systems. Neptune contains robust patient identity management tailored for research; integrates patient data from multiple sources, including electronic health records (EHRs), health plans, and research studies; and includes knowledge for mapping to standard terminologies.ResultsNeptune contains data for more than 5 million patients longitudinally organized as Health Insurance Portability and Accountability Act (HIPAA) Limited Data with dates and includes structured EHR data, clinical documents, health insurance claims, and research data. Neptune is used as a source for patient data for hundreds of institutional review board-approved research projects by local investigators and for national projects.DiscussionThe design of Neptune was heavily influenced by the large size of UPMC, the varied data sources, and the rich partnership between the University and the healthcare system. It includes several unique aspects, including the physical warehouse straddling the University and UPMC networks and management under an HIPAA Business Associates Agreement.ConclusionWe describe the design and implementation of an RDW at a large academic healthcare system that uses a distinctive atomic design where data are stored at a high level of granularity.     

数据挖掘在医学科技查新中的应用

数据挖掘技术是基于关系数据库的一种有效的信息发掘工具。通过介绍数据挖掘技术在医学科技查新领域的应用情况，即在文献资料分析以及科研管理中的运用，分析探讨了数据挖掘技术在医学科技查新领域的应用前景和未来发展方向。     

军事数据治理框架下的主数据管理实施路径

阐述了军事数据治理的基本概念和军事主数据管理的意义,通过分析国外实际案例提出了包括7个核心环节的军事主数据管理实施路径,论述了实施军事主数据管理的可行性,为避免实践中出现盲目性和为军队实施主数据管理提供方法论的指导.     

BigMouth: a multi-institutional dental data repository

Few oral health databases are available for research and the advancement of evidence-based dentistry. In this work we developed a centralized data repository derived from electronic health records (EHRs) at four dental schools participating in the Consortium of Oral Health Research and Informatics. A multi-stakeholder committee developed a data governance framework that encouraged data sharing while allowing control of contributed data. We adopted the i2b2 data warehousing platform and mapped data from each institution to a common reference terminology. We realized that dental EHRs urgently need to adopt common terminologies. While all used the same treatment code set, only three of the four sites used a common diagnostic terminology, and there were wide discrepancies in how medical and dental histories were documented. BigMouth was successfully launched in August 2012 with data on 1.1 million patients, and made available to users at the contributing institutions.     

A systematic literature review of Native American and Pacific Islanders’ perspectives on health data privacy in the United States

BackgroundPrivacy-related concerns can prevent equitable participation in health research by US Indigenous communities. However, studies focused on these communities'' views regarding health data privacy, including systematic reviews, are lacking.MethodsWe conducted a systematic literature review analyzing empirical, US-based studies involving American Indian/Alaska Native (AI/AN) and Native Hawaiian or other Pacific Islander (NHPI) perspectives on health data privacy, which we define as the practice of maintaining the security and confidentiality of an individual’s personal health records and/or biological samples (including data derived from biological specimens, such as personal genetic information), as well as the secure and approved use of those data.ResultsTwenty-one studies involving 3234 AI/AN and NHPI participants were eligible for review. The results of this review suggest that concerns about the privacy of health data are both prevalent and complex in AI/AN and NHPI communities. Many respondents raised concerns about the potential for misuse of their health data, including discrimination or stigma, confidentiality breaches, and undesirable or unknown uses of biological specimens.ConclusionsParticipants cited a variety of individual and community-level concerns about the privacy of their health data, and indicated that these deter their willingness to participate in health research. Future investigations should explore in more depth which health data privacy concerns are most salient to specific AI/AN and NHPI communities, and identify the practices that will make the collection and use of health data more trustworthy and transparent for participants.     

Brief communication: Louisiana Clinical Data Research Network: establishing an infrastructure for efficient conduct of clinical research

The state of Louisiana, like the nation as a whole, is facing the salient challenge of improving population health and efficiency of healthcare delivery. Research to inform innovations in healthcare will best enhance this effort if it is timely, efficient, and patient-centered. The Louisiana Clinical Data Research Network (LACDRN) will increase the capacity to conduct robust comparative effectiveness research by building a health information technology infrastructure that provides access to comprehensive clinical data for more than 1 million patients statewide. To ensure that network-based research best serves its end-users, the project will actively engage patients and providers as key informants and decision-makers in the implementation of LACDRN. The network''s patient-centered research agenda will prioritize patients’ and clinicians’ needs and aim to support evidence-based decisions on the healthcare they receive and provide, to optimize patient outcomes and quality of life.     

The Biomedical Research Hub: a federated platform for patient research data

ObjectiveThe objective was to develop and operate a cloud-based federated system for managing, analyzing, and sharing patient data for research purposes, while allowing each resource sharing patient data to operate their component based upon their own governance rules. The federated system is called the Biomedical Research Hub (BRH).Materials and MethodsThe BRH is a cloud-based federated system built over a core set of software services called framework services. BRH framework services include authentication and authorization, services for generating and assessing findable, accessible, interoperable, and reusable (FAIR) data, and services for importing and exporting bulk clinical data. The BRH includes data resources providing data operated by different entities and workspaces that can access and analyze data from one or more of the data resources in the BRH.ResultsThe BRH contains multiple data commons that in aggregate provide access to over 6 PB of research data from over 400 000 research participants.Discussion and conclusionWith the growing acceptance of using public cloud computing platforms for biomedical research, and the growing use of opaque persistent digital identifiers for datasets, data objects, and other entities, there is now a foundation for systems that federate data from multiple independently operated data resources that expose FAIR application programming interfaces, each using a separate data model. Applications can be built that access data from one or more of the data resources.     

Research Integrated Network of Systems (RINS): a virtual data warehouse for the acceleration of translational research

ObjectiveIntegrated, real-time data are crucial to evaluate translational efforts to accelerate innovation into care. Too often, however, needed data are fragmented in disparate systems. The South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina (MUSC) developed and implemented a universal study identifier—the Research Master Identifier (RMID)—for tracking research studies across disparate systems and a data warehouse-inspired model—the Research Integrated Network of Systems (RINS)—for integrating data from those systems.Materials and MethodsIn 2017, MUSC began requiring the use of RMIDs in informatics systems that support human subject studies. We developed a web-based tool to create RMIDs and application programming interfaces to synchronize research records and visualize linkages to protocols across systems. Selected data from these disparate systems were extracted and merged nightly into an enterprise data mart, and performance dashboards were created to monitor key translational processes.ResultsWithin 4 years, 5513 RMIDs were created. Among these were 726 (13%) bridged systems needed to evaluate research study performance, and 982 (18%) linked to the electronic health records, enabling patient-level reporting.DiscussionBarriers posed by data fragmentation to assessment of program impact have largely been eliminated at MUSC through the requirement for an RMID, its distribution via RINS to disparate systems, and mapping of system-level data to a single integrated data mart.ConclusionBy applying data warehousing principles to federate data at the “study” level, the RINS project reduced data fragmentation and promoted research systems integration.     

The Omaha System: a systematic review of the recent literature

Background

The Omaha System (OS) is one of the oldest of the American Nurses Association recognized standardized terminologies describing and measuring the impact of healthcare services. This systematic review presents the state of science on the use of the OS in practice, research, and education.

Aims

(1) To identify, describe and evaluate the publications on the OS between 2004 and 2011, (2) to identify major trends in the use of the OS in research, practice, and education, and (3) to suggest areas for future research.

Methods

Systematic search in the largest online healthcare databases (PUBMED, CINAHL, Scopus, PsycINFO, Ovid) from 2004 to 2011. Methodological quality of the reviewed research studies was evaluated.

Results

56 publications on the OS were identified and analyzed. The methodological quality of the reviewed research studies was relatively high. Over time, publications’ focus shifted from describing clients’ problems toward outcomes research. There was an increasing application of advanced statistical methods and a significant portion of authors focused on classification and interoperability research. There was an increasing body of international literature on the OS. Little research focused on the theoretical aspects of the OS, the effective use of the OS in education, or cultural adaptations of the OS outside the USA.

Conclusions

The OS has a high potential to provide meaningful and high quality information about complex healthcare services. Further research on the OS should focus on its applicability in healthcare education, theoretical underpinnings and international validity. Researchers analyzing the OS data should address how they attempted to mitigate the effects of missing data in analyzing their results and clearly present the limitations of their studies.     

Development of a HIPAA-compliant environment for translational research data and analytics

High-performance computing centers (HPC) traditionally have far less restrictive privacy management policies than those encountered in healthcare. We show how an HPC can be re-engineered to accommodate clinical data while retaining its utility in computationally intensive tasks such as data mining, machine learning, and statistics. We also discuss deploying protected virtual machines. A critical planning step was to engage the university''s information security operations and the information security and privacy office. Access to the environment requires a double authentication mechanism. The first level of authentication requires access to the university''s virtual private network and the second requires that the users be listed in the HPC network information service directory. The physical hardware resides in a data center with controlled room access. All employees of the HPC and its users take the university''s local Health Insurance Portability and Accountability Act training series. In the first 3 years, researcher count has increased from 6 to 58.     

智慧校园数据治理与数据服务建设——以浙江中医药大学为例

智慧校园建设越来越受到高校的重视,校园内业务系统越建越多。由于各系统的数据字典差异,造成业务数据难以共享,数据价值未被发掘。文章以浙江中医药大学为例,采用统一规划和集中管理的方式进行智慧校园数据治理与数据服务建设,包括数据标准与规范的建设、数据清洗与交换的建设、共享数据库与主题数据库的建设、数据应用与服务的建设、数据安全建设。目前,该校所有新建业务系统均遵循统一的数据标准,并与共享数据库进行数据交换,缩短了新系统的建设周期。共享数据库为教师门户、学生门户和管理者决策分析提供准确的数据保障和服务。     

BigMouth: development and maintenance of a successful dental data repository

Few clinical datasets exist in dentistry to conduct secondary research. Hence, a novel dental data repository called BigMouth was developed, which has grown to include 11 academic institutions contributing Electronic Health Record data on over 4.5 million patients. The primary purpose for BigMouth is to serve as a high-quality resource for rapidly conducting oral health-related research. BigMouth allows for assessing the oral health status of a diverse US patient population; provides rationale and evidence for new oral health care delivery modes; and embraces the specific oral health research education mission. A data governance framework that encouraged data sharing while controlling contributed data was initially developed. This transformed over time into a mature framework, including a fee schedule for data requests and allowing access to researchers from noncontributing institutions. Adoption of BigMouth helps to foster new collaborations between clinical, epidemiological, statistical, and informatics experts and provides an additional venue for professional development.     

基于场景化的人口健康科学大数据安全治理体系构建

介绍数据安全治理基本理念和国内外人口健康科学大数据安全实践,指出我国开展人口健康科学数据安全治理的必要性,分析了人口健康科学大数据的特点及其在全生命周期各个场景动态流转的安全风险,从人员组织、制度规范、技术支撑等多个层面构建了基于场景化的人口健康科学大数据安全治理体系,为促进我国人口健康科学数据中心开展大数据安全治理提供理论支撑与应用参考。     

Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data

ObjectiveTo synthesize data quality (DQ) dimensions and assessment methods of real-world data, especially electronic health records, through a systematic scoping review and to assess the practice of DQ assessment in the national Patient-centered Clinical Research Network (PCORnet).Materials and MethodsWe started with 3 widely cited DQ literature—2 reviews from Chan et al (2010) and Weiskopf et al (2013a) and 1 DQ framework from Kahn et al (2016)—and expanded our review systematically to cover relevant articles published up to February 2020. We extracted DQ dimensions and assessment methods from these studies, mapped their relationships, and organized a synthesized summarization of existing DQ dimensions and assessment methods. We reviewed the data checks employed by the PCORnet and mapped them to the synthesized DQ dimensions and methods.ResultsWe analyzed a total of 3 reviews, 20 DQ frameworks, and 226 DQ studies and extracted 14 DQ dimensions and 10 assessment methods. We found that completeness, concordance, and correctness/accuracy were commonly assessed. Element presence, validity check, and conformance were commonly used DQ assessment methods and were the main focuses of the PCORnet data checks.DiscussionDefinitions of DQ dimensions and methods were not consistent in the literature, and the DQ assessment practice was not evenly distributed (eg, usability and ease-of-use were rarely discussed). Challenges in DQ assessments, given the complex and heterogeneous nature of real-world data, exist.ConclusionThe practice of DQ assessment is still limited in scope. Future work is warranted to generate understandable, executable, and reusable DQ measures.     

三种与文献相关的复杂网络的研究进展分析

首先总结了近年来引用网络、合著网络和共词网络这三类文献相关网络的基本属性分析和应用的研究进展,然后从构建网络的方法、粒度和研究的深入程度三方面对文献相关网络的研究现状进行了分析总结,并提出可以根据已构建成熟的论文相似性算法,构建论文相似性网络,并分析其基本属性和特征,探索新的论文评价指标。