Brief communication: The ADVANCE network: accelerating data value across a national community health center network

The ADVANCE (Accelerating Data Value Across a National Community Health Center Network) clinical data research network (CDRN) is led by the OCHIN Community Health Information Network in partnership with Health Choice Network and Fenway Health. The ADVANCE CDRN will ‘horizontally’ integrate outpatient electronic health record data for over one million federally qualified health center patients, and ‘vertically’ integrate hospital, health plan, and community data for these patients, often under-represented in research studies. Patient investigators, community investigators, and academic investigators with diverse expertise will work together to meet project goals related to data integration, patient engagement and recruitment, and the development of streamlined regulatory policies. By enhancing the data and research infrastructure of participating organizations, the ADVANCE CDRN will serve as a ‘community laboratory’ for including disadvantaged and vulnerable patients in patient-centered outcomes research that is aligned with the priorities of patients, clinics, and communities in our network.     

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.     

Brief communication: CAPriCORN: Chicago Area Patient-Centered Outcomes Research Network

The Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) represents an unprecedented collaboration across diverse healthcare institutions including private, county, and state hospitals and health systems, a consortium of Federally Qualified Health Centers, and two Department of Veterans Affairs hospitals. CAPriCORN builds on the strengths of our institutions to develop a cross-cutting infrastructure for sustainable and patient-centered comparative effectiveness research in Chicago. Unique aspects include collaboration with the University HealthSystem Consortium to aggregate data across sites, a centralized communication center to integrate patient recruitment with the data infrastructure, and a centralized institutional review board to ensure a strong and efficient human subject protection program. With coordination by the Chicago Community Trust and the Illinois Medical District Commission, CAPriCORN will model how healthcare institutions can overcome barriers of data integration, marketplace competition, and care fragmentation to develop, test, and implement strategies to improve care for diverse populations and reduce health disparities.     

Framework for monitoring equity in access and health systems issues in antiretroviral therapy Programmes in southern Africa

Universal provision of antiretroviral therapy (ART), while feasible, is expensive. In light of this limitation, the World Health Organisation (WHO) has launched the 3 × 5 initiative, to provide ART to 3 million people by the end of the year 2005. In Southern Africa, large-scale provision of ART will likely be achieved through fragile public health systems. ART programmes should therefore be developed and expanded in ways that will not aggravate inequities or result in the inappropriate withdrawal of resources from other health interventions or from other parts of the health system. This paper, proposes a framework for monitoring equity in access and health systems issues in ART programmes in Southern Africa. It proposes that an equity monitoring system should comprise seven thematic areas. These thematic areas encompass a national monitoring system which extends beyond one agency or single data collection method. Together with monitoring of targets in terms of numbers treated, there should also be monitoring of health systems impacts and issues in ART expansion, with reporting both nationally and to a regional body.     

Metadata-driven Ad Hoc Query of Patient Data: Meeting the Needs of Clinical Studies

Clinical study data management systems (CSDMSs) have many similarities to clinical patient record systems (CPRSs) in their focus on recording clinical parameters. Requirements for ad hoc query interfaces for both systems would therefore appear to be highly similar. However, a clinical study is concerned primarily with collective responses of groups of subjects to standardized therapeutic interventions for the same underlying clinical condition. The parameters that are recorded in CSDMSs tend to be more diverse than those required for patient management in non-research settings, because of the greater emphasis on questionnaires for which responses to each question are recorded separately. The differences between CSDMSs and CPRSs are reflected in the metadata that support the respective systems'' operation, and need to be reflected in the query interfaces. The authors describe major revisions of their previously described CSDMS ad hoc query interface to meet CSDMS needs more fully, as well as its porting to a Web-based platform.Clinical study data management systems (CSDMSs) have many similarities to clinical patient record systems (CPRSs). Both systems model much of their data using an entity-attribute-value (EAV) representation, which is discussed shortly. It is highly desirable to provide ad hoc query capability in a CSDMS/CPRS. Existing systems allow patient-centric query—retrieval of data from a single patient. However, the task of attribute-centric query, identifying sets of patients based on a Boolean combination of parameters, is technically more challenging, even if it is less important for real-time patient management.In a previous paper describing an attribute-centric query tool,¹ we discussed reasons that generic query tools for attribute-centric query of EAV-based systems are preferable to tedious, error-prone hand coding. Such tools need to be built from scratch, however, because commercial query tools are not currently designed for EAV data. This paper describes major extensions of our previous work, with an architectural overhaul based on lessons learned in the interim. We describe a Web-based query tool for TrialDB (formerly ACT/DB²), a CSDMS implemented at Yale and in use at the Yale Cancer Center and Yale General and Children''s Clinical Research Centers, the Vanderbilt University Cancer Center, and the National Cancer Institute–supported Cancer Genetics Network.     

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.     

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.     

Brief communication: pSCANNER: patient-centered Scalable National Network for Effectiveness Research

This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration''s 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses.     

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.     

72例急性胰腺炎的临床护理体会

目的探讨急性胰腺炎的护理措施,以供临床参考。方法回顾性分析2009年1月～201o年11月收治的72例急性胰腺炎患者的临床资料,总结临床护理方法,提出护理注意事项。结果72例患者经过积极治疗与优质护理服务,所有患者均得到有效救治。72例患者平均住院时间为（39．27±8．66）天,患者治愈59例,好转10例,死亡3例。护理方法包括病情观察、引流管护理、营养支持、防止并发症及心理护理和出院时的健康教育。结论对急性胰腺炎患者进行优质护理干预,能够有效缩短住院时间,防止并发症的发生。     

Patient characteristics associated with venous thromboembolic events: a cohort study using pooled electronic health record data

Objective

To demonstrate the potential of de-identified clinical data from multiple healthcare systems using different electronic health records (EHR) to be efficiently used for very large retrospective cohort studies.

Materials and methods

Data of 959 030 patients, pooled from multiple different healthcare systems with distinct EHR, were obtained. Data were standardized and normalized using common ontologies, searchable through a HIPAA-compliant, patient de-identified web application (Explore; Explorys Inc). Patients were 26 years or older seen in multiple healthcare systems from 1999 to 2011 with data from EHR.

Results

Comparing obese, tall subjects with normal body mass index, short subjects, the venous thromboembolic events (VTE) OR was 1.83 (95% CI 1.76 to 1.91) for women and 1.21 (1.10 to 1.32) for men. Weight had more effect then height on VTE. Compared with Caucasian, Hispanic/Latino subjects had a much lower risk of VTE (female OR 0.47, 0.41 to 0.55; male OR 0.24, 0.20 to 0.28) and African-Americans a substantially higher risk (female OR 1.83, 1.76 to 1.91; male OR 1.58, 1.50 to 1.66). This 13-year retrospective study of almost one million patients was performed over approximately 125 h in 11 weeks, part time by the five authors.

Discussion

As research informatics tools develop and more clinical data become available in EHR, it is important to study and understand unique opportunities for clinical research informatics to transform the scale and resources needed to perform certain types of clinical research.

Conclusions

With the right clinical research informatics tools and EHR data, some types of very large cohort studies can be completed with minimal resources.     

Family medicine research. Status at the end of the first decade

In a national survey of family medicine university units and residencies, 549 MD and 135 PhD faculty pursuing family medicine research were identified. Resources available for research were assessed, as were practice data system characteristics. The practice base nationally of programs pursuing research included 2.6 million patients from 1 million families, making 5.1 million visits per year. Common major impediments to research reported by programs included lack of faculty time (78%), lack of funding for faculty (61%) or staff, equipment and supplies (48%), and lack of research skill (45%) and role models (43%). The annual amount of all research grants received for calendar year 1979 was $3.4 million, of which $2.6 million was from federal government sources. This represented 0.06% of the federal health research effort. Continued development of family medicine research will require increased funding support both for research and research training.     

Informatics for public health and health system collaboration: Applications for the control of the current COVID-19 pandemic and the next one

Public health faces unprecedented challenges in its efforts to control COVID-19 through a national vaccination campaign. Addressing these challenges will require fundamental changes to public health data systems. For example, of the core data systems for immunization campaigns is the immunization information system (IIS); however, IISs were designed for tracking the vaccinated, not finding the patients who are high risk and need to be vaccinated. Health systems have this data in their electronic health records (EHR) systems and often have a greater capacity for outreach. Clearly, a partnership is needed. However, successful collaborations will require public health to change from its historical hierarchical information supply chain model to an ecosystem model with a peer-to-peer exchange with population health providers. Examples of the types of informatics innovations necessary to support such an ecosystem include a national patient identifier, population-level data exchange for immunization data, and computable electronic quality measures. Rather than think of these components individually, a comprehensive approach to rapidly adaptable tools for collaboration is needed.     

We can’t mitigate what we don’t monitor: using informatics to measure and improve healthcare systems’ climate impact and environmental footprint

Climate change, human health, and healthcare systems are inextricably linked. As the climate warms due to greenhouse gas (GHG) emissions, extreme weather events, such as floods, fires, and heatwaves, will drive up demand for healthcare. Delivering healthcare also contributes to climate change, accounting for ∼5% of the global carbon emissions. To rein in healthcare’s carbon footprint, clinicians and health policy makers must be able to measure the GHG contributions of healthcare systems and clinical practices. Herein, we scope potential informatics solutions to monitor the carbon footprint of healthcare systems and to support climate-change decision-making for clinicians, and healthcare policy makers. We discuss the importance of methods and tools that can link environmental, economic, and healthcare data, and outline challenges to the sustainability of monitoring efforts. A greater understanding of these connections will only be possible through further development and usage of models and tools that integrate diverse data sources.     

Using the CER Hub to ensure data quality in a multi-institution smoking cessation study

Comparative effectiveness research (CER) studies involving multiple institutions with diverse electronic health records (EHRs) depend on high quality data. To ensure uniformity of data derived from different EHR systems and implementations, the CER Hub informatics platform developed a quality assurance (QA) process using tools and data formats available through the CER Hub. The QA process, implemented here in a study of smoking cessation services in primary care, used the ‘emrAdapter’ tool programmed with a set of quality checks to query large samples of primary care encounter records extracted in accord with the CER Hub common data framework. The tool, deployed to each study site, generated error reports indicating data problems to be fixed locally and aggregate data sharable with the central site for quality review. Across the CER Hub network of six health systems, data completeness and correctness issues were prevalent in the first iteration and were considerably improved after three iterations of the QA process. A common issue encountered was incomplete mapping of local EHR data values to those defined by the common data framework. A highly automated and distributed QA process helped to ensure the correctness and completeness of patient care data extracted from EHRs for a multi-institution CER study in smoking cessation.     

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.     

Health and GIS: Toward Spatial Statistical Analyses

This study examined the extent to which health studies, mostly in public health and epidemiology, used geographical information systems (GIS). We identified a wide range of tools they used—ranging from geocoding through simple buffer/overlay functions to spatial query functions. However, studies tend to rely on tools outside of GIS for spatial statistical analyses. This may reflect a lack of spatial statistical tools that are suitable for health researchers whose data are rather geographically aggregated count data than continuous data. Implementation within GIS of spatial analytical tools suitable for aggregated data over a region will increase the use of GIS beyond simple GIS operations in health studies.     

The Australian e-Health Research Centre: enabling the health care information and communication technology revolution

The CSIRO (Commonwealth Scientific and Industrial Research Organisation) and the Queensland Government have jointly established the Australian e-Health Research Centre (AEHRC) with the aim of developing innovative information and communication technologies (ICT) for a sustainable health care system. The AEHRC, as part of the CSIRO ICT Centre, has access to new technologies in information processing, wireless and networking technologies, and autonomous systems. The AEHRC's 50 researchers, software engineers and PhD students, in partnership with the CSIRO and clinicians, are developing and applying new technologies for improving patients' experience, building a more rewarding workplace for the health workforce, and improving the efficiency of delivering health care. The capabilities of the AEHRC fall into four broad areas: smart methods for using medical data; advanced medical imaging technologies; new models for clinical and health care interventions; and tools for medical skills development. Since its founding in 2004, new technology from the AEHRC has been adopted within Queensland (eg, a mobile phone-based cardiac rehabilitation program), around Australia (eg, medical imaging technologies) and internationally (eg, our clinical terminology tools).     

Using electronic health record alerts to provide public health situational awareness to clinicians

Alerting providers to public health situations requires timeliness and context-relevance, both lacking in current systems. Incorporating decision support tools into electronic health records may provide a way to deploy public health alerts to clinicians at the point of care. A timely process for responding to Health Alert Network messages sent by the New York City Department of Health and Mental Hygiene was developed by a network of community health centers. Alerts with order sets and recommended actions were created to notify primary care providers of local disease outbreaks. The process, effect, and lessons learned from alerts for Legionella, toxogenic E coli, and measles outbreaks are described. Electronic alerts have the potential to improve management of diseases during an outbreak, including appropriate laboratory testing, management guidance, and diagnostic assistance as well as to enhance bi-directional data exchange between clinical and public health organizations.