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Background: Due to the nature of information generation in health care, clinical documents contain duplicate and sometimes conflicting information. Recent implementation of Health Information Exchange (HIE) mechanisms in which clinical summary documents are exchanged among disparate health care organizations can proliferate duplicate and conflicting information., Materials and Methods: To reduce information overload, a system to automatically consolidate information across multiple clinical summary documents was developed for an HIE network. The system receives any number of Continuity of Care Documents (CCDs) and outputs a single, consolidated record. To test the system, a randomly sampled corpus of 522 CCDs representing 50 unique patients was extracted from a large HIE network. The automated methods were compared to manual consolidation of information for three key sections of the CCD: problems, allergies, and medications., Results: Manual consolidation of 11,631 entries was completed in approximately 150h. The same data were automatically consolidated in 3.3min. The system successfully consolidated 99.1% of problems, 87.0% of allergies, and 91.7% of medications. Almost all of the inaccuracies were caused by issues involving the use of standardized terminologies within the documents to represent individual information entries., Conclusion: This study represents a novel, tested tool for de-duplication and consolidation of CDA documents, which is a major step toward improving information access and the interoperability among information systems. While more work is necessary, automated systems like the one evaluated in this study will be necessary to meet the informatics needs of providers and health systems in the future., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Proliferation of health information technologies creates opportunities to improve clinical and public health, including high quality, safer care and lower costs. To maximize such potential benefits, health information technologies must readily and reliably exchange information with other systems. However, evidence from public health surveillance programs in two states suggests that operational clinical information systems often fail to use available standards, a barrier to semantic interoperability. Furthermore, analysis of existing policies incentivizing semantic interoperability suggests they have limited impact and are fragmented. In this essay, we discuss three approaches for increasing semantic interoperability to support national goals for using health information technologies. A clear, comprehensive strategy requiring collaborative efforts by clinical and public health stakeholders is suggested as a guide for the long road towards better population health data and outcomes., (Published by Elsevier Inc.)

Interoperable health information exchange depends on adoption of terminology standards, but international use of such standards can be challenging because of language differences between local concept names and the standard terminology. To address this important barrier, we describe the evolution of an efficient process for constructing translations of LOINC terms names, the foreign language functions in RELMA, and the current state of translations in LOINC. We also present the development of the Italian translation to illustrate how translation is enabling adoption in international contexts. We built a tool that finds the unique list of LOINC Parts that make up a given set of LOINC terms. This list enables translation of smaller pieces like the core component "hepatitis c virus" separately from all the suffixes that could appear with it, such "Ab.IgG", "DNA", and "RNA". We built another tool that generates a translation of a full LOINC name from all of these atomic pieces. As of version 2.36 (June 2011), LOINC terms have been translated into nine languages from 15 linguistic variants other than its native English. The five largest linguistic variants have all used the Part-based translation mechanism. However, even with efficient tools and processes, translation of standard terminology is a complex undertaking. Two of the prominent linguistic challenges that translators have faced include: the approach to handling acronyms and abbreviations, and the differences in linguistic syntax (e.g. word order) between languages. LOINC's open and customizable approach has enabled many different groups to create translations that met their needs and matched their resources. Distributing the standard and its many language translations at no cost worldwide accelerates LOINC adoption globally, and is an important enabler of interoperable health information exchange., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Objectives: We wanted to develop a method for evaluating the consistency and usefulness of LOINC code use across different institutions, and to evaluate the degree of interoperability that can be attained when using LOINC codes for laboratory data exchange. Our specific goals were to: (1) Determine if any contradictory knowledge exists in LOINC. (2) Determine how many LOINC codes were used in a truly interoperable fashion between systems. (3) Provide suggestions for improving the semantic interoperability of LOINC., Methods: We collected Extensional Definitions (EDs) of LOINC usage from three institutions. The version space approach was used to divide LOINC codes into small sets, which made auditing of LOINC use across the institutions feasible. We then compared pairings of LOINC codes from the three institutions for consistency and usefulness., Results: The number of LOINC codes evaluated were 1917, 1267 and 1693 as obtained from ARUP, Intermountain and Regenstrief respectively. There were 2022, 2030, and 2301 version spaces among ARUP and Intermountain, Intermountain and Regenstrief and ARUP and Regenstrief respectively. Using the EDs as the gold standard, there were 104, 109 and 112 pairs containing contradictory knowledge and there were 1165, 765 and 1121 semantically interoperable pairs. The interoperable pairs were classified into three levels: (1) Level I - No loss of meaning, complete information was exchanged by identical codes. (2) Level II - No loss of meaning, but processing of data was needed to make the data completely comparable. (3) Level III - Some loss of meaning. For example, tests with a specific 'method' could be rolled-up with tests that were 'methodless'., Conclusions: There are variations in the way LOINC is used for data exchange that result in some data not being truly interoperable across different enterprises. To improve its semantic interoperability, we need to detect and correct any contradictory knowledge within LOINC and add computable relationships that can be used for making reliable inferences about the data. The LOINC committee should also provide detailed guidance on best practices for mapping from local codes to LOINC codes and for using LOINC codes in data exchange., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Despite the existence of multiple standards for the coding of biomedical data and the known benefits of doing so, there remain a myriad of biomedical information domain spaces that are essentially un-coded and unstandardized. Perhaps a worse situation is when the same or similar information in a given domain is coded to a variety of different standards. Such is the case with cephalometrics - standardized measurements of angles and distances between specified landmarks on X-ray film used for orthodontic treatment planning and a variety of research applications. We describe how we unified the existing cephalometric definitions from 10 existing cephalometric standards to one unifying terminology set using an existing standard (LOINC). Using our example of an open and web-based orthodontic case file system, we describe how this work benefited our project and discuss how adopting or expanding established standards can benefit other similar projects in specialized domains., (Copyright © 2011 Elsevier Inc. All rights reserved.)