Health Services Research and Data Linkages: Issues, Methods, and Directions for the Future

Bradley, Cathy J.; Penberthy, Lynne; Devers, Kelly J.; Holden, Debra J

doi:10.1111/j.1475-6773.2010.01142.x

Cited by 124 publications

(111 citation statements)

References 24 publications

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“…Standardization enables data collected from different studies, centers, or hospitals to be comparable, understandable, and exchangeable (interoperable). By standardizing terms, clinicians and researchers can combine results from their research efforts with those of other studies and networks, as well as with clinical care records, or link to subject-level data with population-based environmental 23 or public health data, 24 Figure 1 illustrates an "is-a" hierarchy (albuterol is a bronchodilator agent). Another common structure is a "partof" hierarchy, which is often used to organize anatomic concepts.…”

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confidence: 99%

Building a Common Pediatric Research Terminology for Accelerating Child Health Research

et al. 2014

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Longitudinal observational clinical data on pediatric patients in electronic format is becoming widely available. A new era of multiinstitutional data networks that study pediatric diseases and outcomes across disparate health delivery models and care settings are also enabling an innovative collaborative rapid improvement paradigm called the Learning Health System. However, the potential alignment of routine clinical care, observational clinical research, pragmatic clinical trials, and health systems improvement requires a data infrastructure capable of combining information from systems and workflows that historically have been isolated from each other. Removing barriers to integrating and reusing data collected in different settings will permit new opportunities to develop a more complete picture of a patient' s care and to leverage data from related research studies. One key barrier is the lack of a common terminology that provides uniform definitions and descriptions of clinical observations and data. A well-characterized terminology ensures a common meaning and supports data reuse and integration. A common terminology allows studies to build upon previous findings and to reuse data collection tools and data management processes. We present the current state of terminology harmonization and describe a governance structure and mechanism for coordinating the development of a common pediatric research terminology that links to clinical terminologies and can be used to align existing terminologies. By reducing the barriers between clinical care and clinical research, a Learning Health System can leverage and reuse not only its own

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Building a Common Pediatric Research Terminology for Accelerating Child Health Research

et al. 2014

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“…for cancer and acute coronary syndromes) [12]. Similar linkages are also available between databases in the USA [23]. The availability of linked data depends greatly on the data provider, data infrastructure and, in the USA, healthcare provider.…”

Section: The Promise Of Ehr Datamentioning

confidence: 99%

Promises and pitfalls of electronic health record analysis

et al. 2017

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Routinely collected electronic health records (EHRs) are increasingly used for research. With their use comes the opportunity for large-scale, high-quality studies that can address questions not easily answered by randomised clinical trials or classical cohort studies involving bespoke data collection. However, the use of EHRs generates challenges in terms of ensuring methodological rigour, a potential problem when studying complex chronic diseases such as diabetes. This review describes the promises and potential of EHRs in the context of diabetes research and outlines key areas for caution with examples. We consider the difficulties in identifying and classifying diabetes patients, in distinguishing between prevalent and incident cases and in dealing with the complexities of diabetes progression and treatment. We also discuss the dangers of introducing time-related biases and describe the problems of inconsistent data recording, missing data and confounding. Throughout, we provide practical recommendations for good practice in conducting EHR studies and interpreting their results.

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“…It has been shown that infrastructure to support data linkages (disease registries, clinical data from EMRs, etc) could significantly impact translational research and patient outcomes, yet gaps in capacity across research and medical settings exist [12]. The biggest data linkage issue between sites is entity resolution: it is difficult to tell whether or not two patients are identical or not.…”

Section: Challengesmentioning

confidence: 99%

Informatics-based challenges of building collaborative healthcare research and analysis networks from rural community health centers

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et al. 2016

2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)

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We discuss informatics-based challenges of constructing large-scale collaborative networks for healthcare research and analysis from rural community health centers. These types of networks provide data access and analytic insights across multiple heterogeneous health centers for both healthcare professionals and biomedical researchers. Challenges fall into three general categories: data access, data integration, and technical infrastructure. Data access issues arise in balancing patient privacy, security, and utility; data integration issues persist from each site independently operating its desired electronic medical record; technical infrastructure challenges include creating an analysis and reporting hub capable of scaling across a large collaborative network. Other challenges, such as the difficulty of site recruitment, are important to discuss, but cannot be solved directly through informatics alone. We discuss these challenges and their potential solutions in the context of our implementation of the Kentucky Diabetes and Obesity Collaborative (KDOC). KDOC is a network of Federally-Qualified Community Health Centers (FQHCs) that established a collaborative infrastructure for research and analysis of obesity and diabetes in rural and under-served communities.

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Health Services Research and Data Linkages: Issues, Methods, and Directions for the Future

Abstract: The current environment is characterized by budget and technical challenges, but investments in data infrastructure are arguably cost-effective given the need to reform our health care system and to monitor the impact of health reform initiatives.

Cited by 124 publications

References 24 publications

Building a Common Pediatric Research Terminology for Accelerating Child Health Research

Building a Common Pediatric Research Terminology for Accelerating Child Health Research

Promises and pitfalls of electronic health record analysis

Informatics-based challenges of building collaborative healthcare research and analysis networks from rural community health centers

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