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.     

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.     

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.     

The iTHRIV Commons: a cross-institution information and health research data sharing architecture and web application

ObjectiveThe integrated Translational Health Research Institute of Virginia (iTHRIV) aims to develop an information architecture to support data workflows throughout the research lifecycle for cross-state teams of translational researchers.Materials and MethodsThe iTHRIV Commons is a cross-state harmonized infrastructure supporting resource discovery, targeted consultations, and research data workflows. As the front end to the iTHRIV Commons, the iTHRIV Research Concierge Portal supports federated login, personalized views, and secure interactions with objects in the ITHRIV Commons federation. The canonical use-case for the iTHRIV Commons involves an authenticated user connected to their respective high-security institutional network, accessing the iTHRIV Research Concierge Portal web application on their browser, and interfacing with multi-component iTHRIV Commons Landing Services installed behind the firewall at each participating institution.ResultsThe iTHRIV Commons provides a technical framework, including both hardware and software resources located in the cloud and across partner institutions, that establishes standard representation of research objects, and applies local data governance rules to enable access to resources from a variety of stakeholders, both contributing and consuming.DiscussionThe launch of the Commons API service at partner sites and the addition of a public view of nonrestricted objects will remove barriers to data access for cross-state research teams while supporting compliance and the secure use of data.ConclusionsThe secure architecture, distributed APIs, and harmonized metadata of the iTHRIV Commons provide a methodology for compliant information and data sharing that can advance research productivity at Hub sites across the CTSA network.     

The technology behind TB DEPOT: a novel public analytics platform integrating tuberculosis clinical,genomic, and radiological data for visual and statistical exploration

ObjectiveClinical research informatics tools are necessary to support comprehensive studies of infectious diseases. The National Institute of Allergy and Infectious Diseases (NIAID) developed the publicly accessible Tuberculosis Data Exploration Portal (TB DEPOT) to address the complex etiology of tuberculosis (TB).Materials and MethodsTB DEPOT displays deidentified patient case data and facilitates analyses across a wide range of clinical, socioeconomic, genomic, and radiological factors. The solution is built using Amazon Web Services cloud-based infrastructure, .NET Core, Angular, Highcharts, R, PLINK, and other custom-developed services. Structured patient data, pathogen genomic variants, and medical images are integrated into the solution to allow seamless filtering across data domains.ResultsResearchers can use TB DEPOT to query TB patient cases, create and save patient cohorts, and execute comparative statistical analyses on demand. The tool supports user-driven data exploration and fulfills the National Institute of Health’s Findable, Accessible, Interoperable, and Reusable (FAIR) principles.DiscussionTB DEPOT is the first tool of its kind in the field of TB research to integrate multidimensional data from TB patient cases. Its scalable and flexible architectural design has accommodated growth in the data, organizations, types of data, feature requests, and usage. Use of client-side technologies over server-side technologies and prioritizing maintenance have been important lessons learned. Future directions are dynamically prioritized and key functionality is shared through an application programming interface.ConclusionThis paper describes the platform development methodology, resulting functionality, benefits, and technical considerations of a clinical research informatics application to support increased understanding of TB.     

临床研究数据管理策略

 临床研究中产生的大量医学数据使临床科研人员逐渐认识到数据长期保存和共享的重要性,进而在实践中形成了研究数据管理的理念和方法。临床研究数据管理,可以促进临床研究的准确和高效进行,满足研究人员、机构、研究资助者的预期和要求。本文通过单个临床研究数据管理和机构临床研究数据管理两个层面,讨论了临床研究数据管理的关键事项及相关策略,帮助临床科研人员及科研管理人员了解临床研究数据管理流程,促进临床科研活动规范执行和机构数据管理的发展。     

Brief communication: Launching PCORnet,a national patient-centered clinical research network

The Patient-Centered Outcomes Research Institute (PCORI) has launched PCORnet, a major initiative to support an effective, sustainable national research infrastructure that will advance the use of electronic health data in comparative effectiveness research (CER) and other types of research. In December 2013, PCORI''s board of governors funded 11 clinical data research networks (CDRNs) and 18 patient-powered research networks (PPRNs) for a period of 18 months. CDRNs are based on the electronic health records and other electronic sources of very large populations receiving healthcare within integrated or networked delivery systems. PPRNs are built primarily by communities of motivated patients, forming partnerships with researchers. These patients intend to participate in clinical research, by generating questions, sharing data, volunteering for interventional trials, and interpreting and disseminating results. Rapidly building a new national resource to facilitate a large-scale, patient-centered CER is associated with a number of technical, regulatory, and organizational challenges, which are described here.     

Brief communication: Patient-powered research networks: building capacity for conducting patient-centered clinical outcomes research

The Patient-Centered Outcomes Research Institute (PCORI) recently launched PCORnet to establish a single inter-operable multicenter data research network that will support observational research and randomized clinical trials. This paper provides an overview of the patient-powered research networks (PPRNs), networks of patient organizations focused on a particular health condition that are interested in sharing health information and engaging in research. PPRNs will build on their foundation of trust within the patient communities and draw on their expertise, working with participants to identify true patient-centered outcomes and direct a patient-centered research agenda. The PPRNs will overcome common challenges including enrolling a diverse and representative patient population; engaging patients in governance; designing the data infrastructure; sharing data securely while protecting privacy; prioritizing research questions; scaling small networks into a larger network; and identifying pathways to sustainability. PCORnet will be the first distributed research network to bring PCOR to national scale.     

癌症基因组学相关数据管理与应用探析

目的：梳理美国癌症基因组图谱计划（The Cancer Genome Atlas,TCGA）相关数据的收集、整理、组织、共享及应用情况,为建立及完善国家级大型的开放癌症基因组学相关数据资源提供参考。方法：系统调研TCGA计划的数据管理相关机构工作流程、数据共享利用等方面的解决方案和最佳实践。 结果：TCGA计划通过多中心合作,建立了组织样本采集、处理、质量控制、序列测定、特征分析、数据共享与研究应用等全链条的癌症基因组图谱数据管理流程,从所属癌症、数据类型、处理水平等角度对数据进行精细分类,并针对汇总数据和个体数据分别采取开放存取和受控访问两种共享机制,研究者们利用其共享数据开展了相关癌症特征基因的突变、扩增和缺失、以及受影响的信号通路等多方面研究。 结论：癌症基因组图谱计划的实践探索可为大规模癌症基因组学相关研究计划的实施提供数据管理方面的经验借鉴与参考。     

Enabling realistic health data re-identification risk assessment through adversarial modeling

ObjectiveRe-identification risk methods for biomedical data often assume a worst case, in which attackers know all identifiable features (eg, age and race) about a subject. Yet, worst-case adversarial modeling can overestimate risk and induce heavy editing of shared data. The objective of this study is to introduce a framework for assessing the risk considering the attacker’s resources and capabilities.Materials and MethodsWe integrate 3 established risk measures (ie, prosecutor, journalist, and marketer risks) and compute re-identification probabilities for data subjects. This probability is dependent on an attacker’s capabilities (eg, ability to obtain external identified resources) and the subject’s decision on whether to reveal their participation in a dataset. We illustrate the framework through case studies using data from over 1 000 000 patients from Vanderbilt University Medical Center and show how re-identification risk changes when attackers are pragmatic and use 2 known resources for attack: (1) voter registration lists and (2) social media posts.ResultsOur framework illustrates that the risk is substantially smaller in the pragmatic scenarios than in the worst case. Our experiments yield a median worst-case risk of 0.987 (where 0 is least risky and 1 is most risky); however, the median reduction in risk was 90.1% in the voter registration scenario and 100% in the social media posts scenario. Notably, these observations hold true for a wide range of adversarial capabilities.ConclusionsThis research illustrates that re-identification risk is situationally dependent and that appropriate adversarial modeling may permit biomedical data sharing on a wider scale than is currently the case.     

Rasch analysis on OSCE data : An illustrative example

Background

The Objective Structured Clinical Examination (OSCE) is a widely used tool for the assessment of clinical competence in health professional education. The goal of the OSCE is to make reproducible decisions on pass/fail status as well as students'' levels of clinical competence according to their demonstrated abilities based on the scores. This paper explores the use of the polytomous Rasch model in evaluating the psychometric properties of OSCE scores through a case study.

Method

The authors analysed an OSCE data set (comprised of 11 stations) for 80 fourth year medical students based on the polytomous Rasch model in an effort to answer two research questions: (1) Do the clinical tasks assessed in the 11 OSCE stations map on to a common underlying construct, namely clinical competence? (2) What other insights can Rasch analysis offer in terms of scaling, item analysis and instrument validation over and above the conventional analysis based on classical test theory?

Results

The OSCE data set has demonstrated a sufficient degree of fit to the Rasch model (Χ² = 17.060, DF=22, p=0.76) indicating that the 11 OSCE station scores have sufficient psychometric properties to form a measure for a common underlying construct, i.e. clinical competence. Individual OSCE station scores with good fit to the Rasch model (p > 0.1 for all Χ² statistics) further corroborated the characteristic of unidimensionality of the OSCE scale for clinical competence. A Person Separation Index (PSI) of 0.704 indicates sufficient level of reliability for the OSCE scores. Other useful findings from the Rasch analysis that provide insights, over and above the analysis based on classical test theory, are also exemplified using the data set.

Conclusion

The polytomous Rasch model provides a useful and supplementary approach to the calibration and analysis of OSCE examination data.     

Brief communication: Developing a data infrastructure for a learning health system: the PORTAL network

The Kaiser Permanente & Strategic Partners Patient Outcomes Research To Advance Learning (PORTAL) network engages four healthcare delivery systems (Kaiser Permanente, Group Health Cooperative, HealthPartners, and Denver Health) and their affiliated research centers to create a new national network infrastructure that builds on existing relationships among these institutions. PORTAL is enhancing its current capabilities by expanding the scope of the common data model, paying particular attention to incorporating patient-reported data more systematically, implementing new multi-site data governance procedures, and integrating the PCORnet PopMedNet platform across our research centers. PORTAL is partnering with clinical research and patient experts to create cohorts of patients with a common diagnosis (colorectal cancer), a rare diagnosis (adolescents and adults with severe congenital heart disease), and adults who are overweight or obese, including those with pre-diabetes or diabetes, to conduct large-scale observational comparative effectiveness research and pragmatic clinical trials across diverse clinical care settings.     

Brief communication: PaTH: towards a learning health system in the Mid-Atlantic region

The PaTH (University of Pittsburgh/UPMC, Penn State College of Medicine, Temple University Hospital, and Johns Hopkins University) clinical data research network initiative is a collaborative effort among four academic health centers in the Mid-Atlantic region. PaTH will provide robust infrastructure to conduct research, explore clinical outcomes, link with biospecimens, and improve methods for sharing and analyzing data across our diverse populations. Our disease foci are idiopathic pulmonary fibrosis, atrial fibrillation, and obesity. The four network sites have extensive experience in using data from electronic health records and have devised robust methods for patient outreach and recruitment. The network will adopt best practices by using the open-source data-sharing tool, Informatics for Integrating Biology and the Bedside (i2b2), at each site to enhance data sharing using centrally defined common data elements, and will use the Shared Health Research Information Network (SHRINE) for distributed queries across the network.     

Brief communication: Changing the research landscape: the New York City Clinical Data Research Network

The New York City Clinical Data Research Network (NYC-CDRN), funded by the Patient-Centered Outcomes Research Institute (PCORI), brings together 22 organizations including seven independent health systems to enable patient-centered clinical research, support a national network, and facilitate learning healthcare systems. The NYC-CDRN includes a robust, collaborative governance and organizational infrastructure, which takes advantage of its participants’ experience, expertise, and history of collaboration. The technical design will employ an information model to document and manage the collection and transformation of clinical data, local institutional staging areas to transform and validate data, a centralized data processing facility to aggregate and share data, and use of common standards and tools. We strive to ensure that our project is patient-centered; nurtures collaboration among all stakeholders; develops scalable solutions facilitating growth and connections; chooses simple, elegant solutions wherever possible; and explores ways to streamline the administrative and regulatory approval process across sites.     

NIH生物医学数据共享仓储分析

目的：对美国国立卫生研究院（NIH）的共享仓储进行分析,为生物医学科学数据领域相关研究和我国医学科学数据共享仓储建设提供参考和借鉴。方法：对UniProt、Protein Data Bank、GenBank等10个典型的数据共享仓储进行对比分析,总结其在数据获取方式、数据管理及共享模式、服务方式等方面的经验。结果：各仓储根据自身特性,设计了符合自身特点的数据管理链条及流程规范。结论：我国可借鉴美国国立卫生研究院生物医学数据共享仓储的建设经验,设计数据服务工具、开展半人工半自动模式的数据审核,收集尽可能详尽的元数据,并制定符合自身仓储特点的引用规范等。     

An i2b2-based,generalizable, open source,self-scaling chronic disease registry

Objective

Registries are a well-established mechanism for obtaining high quality, disease-specific data, but are often highly project-specific in their design, implementation, and policies for data use. In contrast to the conventional model of centralized data contribution, warehousing, and control, we design a self-scaling registry technology for collaborative data sharing, based upon the widely adopted Integrating Biology & the Bedside (i2b2) data warehousing framework and the Shared Health Research Information Network (SHRINE) peer-to-peer networking software.

Materials and methods

Focusing our design around creation of a scalable solution for collaboration within multi-site disease registries, we leverage the i2b2 and SHRINE open source software to create a modular, ontology-based, federated infrastructure that provides research investigators full ownership and access to their contributed data while supporting permissioned yet robust data sharing. We accomplish these objectives via web services supporting peer-group overlays, group-aware data aggregation, and administrative functions.

Results

The 56-site Childhood Arthritis & Rheumatology Research Alliance (CARRA) Registry and 3-site Harvard Inflammatory Bowel Diseases Longitudinal Data Repository now utilize i2b2 self-scaling registry technology (i2b2-SSR). This platform, extensible to federation of multiple projects within and between research networks, encompasses >6000 subjects at sites throughout the USA.

Discussion

We utilize the i2b2-SSR platform to minimize technical barriers to collaboration while enabling fine-grained control over data sharing.

Conclusions

The implementation of i2b2-SSR for the multi-site, multi-stakeholder CARRA Registry has established a digital infrastructure for community-driven research data sharing in pediatric rheumatology in the USA. We envision i2b2-SSR as a scalable, reusable solution facilitating interdisciplinary research across diseases.     

Dynamically adjusting case reporting policy to maximize privacy and public health utility in the face of a pandemic

ObjectiveSupporting public health research and the public’s situational awareness during a pandemic requires continuous dissemination of infectious disease surveillance data. Legislation, such as the Health Insurance Portability and Accountability Act of 1996 and recent state-level regulations, permits sharing deidentified person-level data; however, current deidentification approaches are limited. Namely, they are inefficient, relying on retrospective disclosure risk assessments, and do not flex with changes in infection rates or population demographics over time. In this paper, we introduce a framework to dynamically adapt deidentification for near-real time sharing of person-level surveillance data.Materials and MethodsThe framework leverages a simulation mechanism, capable of application at any geographic level, to forecast the reidentification risk of sharing the data under a wide range of generalization policies. The estimates inform weekly, prospective policy selection to maintain the proportion of records corresponding to a group size less than 11 (PK11) at or below 0.1. Fixing the policy at the start of each week facilitates timely dataset updates and supports sharing granular date information. We use August 2020 through October 2021 case data from Johns Hopkins University and the Centers for Disease Control and Prevention to demonstrate the framework’s effectiveness in maintaining the PK11 threshold of 0.01.ResultsWhen sharing COVID-19 county-level case data across all US counties, the framework’s approach meets the threshold for 96.2% of daily data releases, while a policy based on current deidentification techniques meets the threshold for 32.3%.ConclusionPeriodically adapting the data publication policies preserves privacy while enhancing public health utility through timely updates and sharing epidemiologically critical features.     

老年血液透析患者临床资料分析

目的研究老年血液透析（HD）患者的临床资料，探讨如何控制老年血透患者的合并症，提高生活质量和生存率，降低死亡率。方法采用回顾性方法对比分析84例老年血液透析患者（≥60岁）与92例非老年患者（〈60岁）的临床资料。结果年龄是透析患者的死亡危险因素；国内老年血透患者慢性肾小球肾炎是首要病因，糖尿病肾病所占比例逐步提升；心脑血管疾惠、贫血、感染仍是老年血透患者最主要的合并症及并发症；老年患者的营养状况和透析充分性欠佳；心血管疾患仍是老年血透患者的首位致死原因。结论控制心脑血管疾患、贫血、感染为主的并发症和合并症，加强透析充分性，改善患者的营养状况可使老年透析患者生活质量提高，病死率下降。糖尿病肾病在老年血透患者中比例逐年增加，应引起重视。     

Blueprint for aligned data exchange for research and public health

Making EHR Data More Available for Research and Public Health (MedMorph) is a Centers for Disease Control and Prevention-led initiative developing and demonstrating a reference architecture (RA) and implementation, including Health Level Seven International Fast Healthcare Interoperability Resources (HL7 FHIR) implementation guides (IGs), describing how to leverage FHIR for aligned research and public health access to clinical data for automated data exchange. MedMorph engaged a technical expert panel of more than 100 members to model representative use cases, develop IGs (architectural and content), align with existing efforts in the FHIR community, and demonstrate the RA in research and public health uses. The RA IG documents common workflows needed to automatically send research data to Research Patient Data Repositories for multiple use cases. Sharing a common RA and canonical data model will improve data sharing for research and public health needs and generate evidence. MedMorph delivers a robust, reusable method to utilize data from electronic health records addressing multiple research and public health needs.