Abstract
Along with the increasing adoption of electronic health records (EHRs) are expectations that data collected within EHRs will be readily available for outcomes and comparative effectiveness research. Yet the ability to effectively share and reuse data depends on implementing and configuring EHRs with these goals in mind from the beginning. Data sharing and integration must be planned both locally as well as nationally. The rich data transmission and semantic infrastructure developed by the National Cancer Institute (NCI) for research provides an excellent example of moving beyond paper-based paradigms and exploiting the power of semantically robust, network-based systems, and engaging both domain and informatics expertise. Similar efforts are required to address current challenges in sharing EHR data.
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This project was supported (in part) by the National Institutes of Health through the University of Michigan’s Cancer Center Support Grant (5 P30 CA46592), and by the National Center for Research Resources (Award Number UL1RR024986). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the National Center for Research Resources.
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Appendix: Definitions
Appendix: Definitions
caDSR is a cancer-specific set of common data elements and the metadata for cancer research developed through the caBIG project. Examples include preferred terms, definitions, map** to reference coding systems etc. [15, 19, 20].
Common Data Elements (CDEs) are data elements and annotations defined as standards across research or clinical projects. CDEs and relationships with other data elements are typically maintained in a metadata repository. Effective CDEs are typically developed by multi-disciplinary groups and validated in successive rounds of critical analysis [14].
Comparative effectiveness research (CER) is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat and monitor a clinical condition, or to improve the delivery of care. CER aims to answer questions such as “What works best, for whom, and under what conditions?” [40, 41].
Data sharing refers to the set of technologies, standards, regulations, and trust factors that make data collected for one purpose electronically available for other purposes. Data reuse and secondary use of data are often used synonymously with data sharing.
Electronic health records (EHRs) are longitudinal electronic records of patients’ health information, generated by one or more encounters in any care delivery setting, and commonly include patient demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data and radiology reports [42].
Meaningful use (MU) refers to the Centers for Medicare and Medicaid Services (CMS) program that provides a financial incentive for the “meaningful use” of certified EHR technology. The American Recovery and Reinvestment Act of 2009 specifies three main components: (a) use of a certified EHR in a meaningful manner; (b) use of certified EHR technology for electronic exchange of health information; and (c) use of certified EHR technology to submit clinical quality and other measures [43].
Outcomes research encompasses a set of methodologies long used by the health services research community to study aspects of health care delivery [44]. Outcomes and effectiveness research are facilitated by the integration of large-scale, multi-institutional data from EHRs, clinical trial management systems, pharmacy, radiology, and disease registries such as SEER.
Structured data refers to data that are organized into a structure such as fixed fields, and often stored in a structured database organized by columns and rows. Structured data are often coded according to some agreed upon coding system such as ICD-9, SNOMED-CT, or AJCC TNM standards for coding tumor stage. Coded structured data are can be made available for data sharing through a variety of interfaces including Web browsers, database query languages, application-specific interfaces, or data exchange formats.
Unstructured data refers to data such as free text that is not captured and stored in fixed fields, and not readily stored in rows and columns in databases. EHR documents such as physician notes and discharge summaries are largely composed of unstructured data. Increasingly, computer assisted techniques such as natural language processing (NLP) are being used to convert unstructured data to structured data—greatly reducing the need for manual chart review and subsequent data entry to convert unstructured to structured data.
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Manion, F.J., Harris, M.R., Buyuktur, A.G. et al. Leveraging EHR Data for Outcomes and Comparative Effectiveness Research in Oncology. Curr Oncol Rep 14, 494–501 (2012). https://doi.org/10.1007/s11912-012-0272-6
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DOI: https://doi.org/10.1007/s11912-012-0272-6