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Simple Summary: To obtain new insights in prostate cancer care, multiple clinics and research institutions must jointly analyse their data (by some, referred to as "big data analysis"). For this purpose, a common "data language" should be used. The OMOP CDM (common data model) is such a data language that can be used by different data holders. The aim of this article is to describe how a large dataset from a prostate cancer study with almost 50,000 prostate cancer cases was successfully transferred to the OMOP CDM standard. Challenges and their solutions during this process are discussed, and the importance of using data standards within prostate cancer research are reported from a scientific data holder's perspective. To enhance international and joint research collaborations in prostate cancer research, data from different sources should use a common data model (CDM) that enables researchers to share their analysis scripts and merge results. The OMOP CDM maintained by OHDSI is such a data model developed for a federated data analysis with partners from different institutions that want to jointly investigate research questions using clinical care data. The German Cancer Society as the scientific lead of the Prostate Cancer Outcomes (PCO) study gathers data from prostate cancer care including routine oncological care data and survey data (incl. patient-reported outcomes) and uses a common data specification (called OncoBox Research Prostate) for this purpose. To further enhance research collaborations outside the PCO study, the purpose of this article is to describe the process of transferring the PCO study data to the internationally well-established OMOP CDM. This process was carried out together with an IT company that specialised in supporting research institutions to transfer their data to OMOP CDM. Of n = 49,692 prostate cancer cases with 318 data fields each, n = 392 had to be excluded during the OMOPing process, and n = 247 of the data fields could be mapped to OMOP CDM. The resulting PostgreSQL database with OMOPed PCO study data is now ready to use within larger research collaborations such as the EU-funded EHDEN and OPTIMA consortium. [ABSTRACT FROM AUTHOR]

Interoperability between research management systems, especially digital libraries or repositories, has been a central theme in the community for the past years, with the discussion focused on means of enriching, linking, and disseminating outputs. This paper considers a frequently overlooked aspect, namely the migration of records across systems, by introducing the Stateful Library Analysis and Migration system (SLAM) and presenting practical experiences with migrating records from DSpace and Digital Commons repositories to Figshare. [ABSTRACT FROM AUTHOR]

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Context.--Biomedical terminologies such as Logical Observation Identifiers, Names, and Codes (LOINC) were developed to enable interoperability of health care data between disparate health information systems to improve patient outcomes, public health, and research activities. Objective.--To ascertain the utilization rate and accuracy of LOINC terminology mapping to 10 commonly ordered tests by participants of the College of American Pathologists (CAP) Proficiency Testing program. Design.--Questionnaires were sent to 1916 US and Canadian laboratories participating in the 2018 CAP coagulation (CGL) and/or cardiac markers (CRT) surveys requesting information on practice setting, instrument(s) and test method(s), and LOINC code selection and usage in the laboratory and electronic health records. Results.--Ninety of 1916 CGL and/or CRT participants (4.7%) responded to the questionnaire. Of the 275 LOINC codes reported, 54 (19.6%) were incorrect: 2 codes (5934-2 and 12345-1) (0.7%) did not exist in the LOINC database and the highest error rates were observed in the property (27 of 275, 9.8%), system (27 of 275, 9.8%), and component (22 of 275, 8.0%) LOINC axes. Errors in LOINC code selection included selection of the incorrect component (eg, activated clotting time instead of activated partial thromboplastin time); selection of panels that can never be used to obtain an individual analyte (eg, prothrombin time panel instead of international normalized ratio); and selection of an incorrect specimen type. Conclusions.--These findings of real-world LOINC code implementation across a spectrum of laboratory settings should raise concern about the reliability and utility of using LOINC for clinical research or to aggregate data. [ABSTRACT FROM AUTHOR]

Context.--Clinical laboratories are obligated to implement Logical Observation Identifier Names and Codes (LOINC), an informatics standard used to uniquely identify laboratory tests. The historical progress of laboratories in achieving this goal is unknown. Objective.--To evaluate the implementation of LOINC by clinical laboratories with attention to LOINC's adoption, diversity, and correctness over time. Design.--We aggregated data from 130 facilities within the Veterans Health Administration (VA), an early adopter of LOINC, during a 20-year period (1999-2018). To assess the adoption of LOINC, we calculated the annual proportion of tests and results without a LOINC. To assess the diversity of LOINC, we counted the yearly number of distinct LOINCs in active use. To assess the correctness of LOINC over time, we compared the assigned LOINCs to a manually reviewed gold standard for each year. Results.--We reviewed a total of 586 000 tests and 9.162 billion results. LOINC adoption, measured as a proportion of both tests and results, improved over time (P < .001). In the final year reviewed, 85% (172 142 of 202 125) of laboratory tests and 99% (547 229 066 of 551 205 087) of results had LOINCs. The number of distinct LOINCs in active use from 1999 to 2018 increased 2.78-fold from 4502 to 12 503 (P < .001). Correctness generally improved but varied considerably by test and across time. Conclusions.--The adoption of LOINC has improved during the past 2 decades. More diverse LOINCs were associated with increased adoption and were a challenge to keep up-to-date. The correctness of LOINCs has improved but remains an issue that likely necessitates supplemental review for most applications. [ABSTRACT FROM AUTHOR]

Context.-- The Logical Observation Identifiers Names and Codes (LOINC) system is supposed to facilitate interoperability, and it is the federally required code for exchanging laboratory data. Objective.-- To provide an overview of LOINC, emerging issues related to its use, and areas relevant to the pathology laboratory, including the subtleties of test code selection and importance of mapping the correct codes to local test menus. Data Sources.-- This review is based on peer-reviewed literature, federal regulations, working group reports, the LOINC database (version 2.65), experience using LOINC in the laboratory at several large health care systems, and insight from laboratory information system vendors. Conclusions.-- The current LOINC database contains more than 55 000 numeric codes specific for laboratory tests. Each record in the LOINC database includes 6 major axes/parts for the unique specification of each individual observation or measurement. Assigning LOINC codes to a laboratory's test menu should be a defined process. In some cases, LOINC can aid in distinguishing laboratory data among different information systems, whereby such benefits are not achievable by relying on the laboratory test name alone. Criticisms of LOINC include the complexity and resource-intensive process of selecting the most correct code for each laboratory test, the real-world experience that these codes are not uniformly assigned across laboratories, and that 2 tests that may have the same appropriately assigned LOINC code may not necessarily have equivalency to permit interoperability of their result data. The coding system's limitations, which subsequently reduce the potential utility of LOINC, are poorly understood outside of the laboratory. [ABSTRACT FROM AUTHOR]

Twenty-five years of LOINC (Logical Observation Identifier Names and Codes) and almost 20 years of experience with the implementation of LOINC in Germany – without having so far achieved a binding national definition of or a relevant routine use of LOINC in laboratory data communication. This article sketches the development of LOINC use in Germany since the year 2000 on the basis of grey literature. For the first time, the use of LOINC in Germany is experiencing a significant impetus at the national level: On the one hand, the current health legislation with its stipulations for a legally defined electronic patient record provides the necessary framework for nationwide stipulations; on the other hand, there is a significant impulse from the German Medical Informatics Initiative (MII) out of the medical research field for implementing a uniform LOINC subset. In recognition of the 25th anniversary of the LOINC nomenclature (1995–2019), the article traces the emergence of LOINC – which is characterized by interactions between European (EUCLIDES, READ, NPU) and US (HL7, LOINC, SNOMED CT) developments and the interplay of various standardization initiatives. Different national definitions and e-health strategies resulting from this history will be a challenge for the future e-health harmonization in the EU. The concerns of medical research and biobanking must be taken into account here, since the standardization of lab data according to international nomenclatures is of utmost importance for them. [ABSTRACT FROM AUTHOR]

Disease management requires the use of mixed languages when discussing etiology, diagnosis, treatment, and follow-up. All phases require data management, and, in the optimal case, such data are interdisciplinary and uniform and clear to all those involved. Such semantic data interoperability is one of the technical building blocks that support emerging digital medicine, e-health, and P4-medicine (predictive, preventive, personalized, and participatory). In a world where infectious diseases are on a trend to become hard-to-treat threats due to antimicrobial resistance, semantic data interoperability is part of the toolbox to fight more efficiently against those threats. In this review, we will introduce semantic data interoperability, summarize its added value, and analyze the technical foundation supporting the standardized healthcare system interoperability that will allow moving forward to e-health. We will also review current usage of those foundational standards and advocate for their uptake by all infectious disease-related actors. [ABSTRACT FROM AUTHOR]

With the implementation of the Austrian electronic health record (ELGA), laboratory results are formally standardized for the first time throughout Austria. The nomenclature of the lab analyses, their sequence and structure as well as the display of laboratory results are unified with ELGA. One of the most significant steps is using Logical Observation Identifiers Names and Codes (LOINC) as a reference terminology for lab analyses. Thereby laboratory results can be reused semantically interoperable all over Austria. [ABSTRACT FROM AUTHOR]

Laboratory data is a treasure chest for personalized medicine: it is – in general – electronically available, highly structured, quality controlled and indicative for many diseases. However, it is also a box with (probably more than) seven locks: laboratories use their own internal coding systems, results are reported in different languages (four official languages plus English with very distinct features in Switzerland), report formats are not uniform, standard nomenclature (e.g. Logical Observation Identifiers Names and Codes [LOINC]) is not routinely used and even these coding systems lack important information, including data, for example, about the specific "kit" used for testing or preanalytical procedures affecting the sample quality and result interpretability. Visualization of complex laboratory and reporting "-omics" data are additional challenges. Currently, there is no "passepartout" key for all these locks available, and also newer concepts such as Fast Health Interoperability Resources (FHIR) might not provide enough plasticity to unconditionally render laboratory data interoperable. In this short overview, we present current approaches in Switzerland with a specific focus on the exemplary Bernese implementations. [ABSTRACT FROM AUTHOR]

Background: Terminologies facilitate data exchange and enable laboratories to assist in patient care even if complex treatment pathways involve multiple stakeholders. This paper examines the three common terminologies Nomenclature for Properties and Units (NPU), Logical Observation Identifiers Names and Codes (LOINC), and SNOMED Clinical Terms (SNOMED CT). Methods: The potential of each terminology to encode five exemplary laboratory results is assessed. The terminologies are evaluated according to scope, correctness, formal representations, and ease of use. Results: NPU is based on metrological concepts with strict rules regarding the coding of the measurand and the result value. Clinically equivalent results are regularly mapped to the same code but there is little support to differentiate results from non-standardized measurements. LOINC encodes analyses as offered by the laboratory. Its large number of entries allows different mappings for the same analysis. SNOMED CT contains few analyses natively, but its formal composition mechanism allows representing measurements by post-coordinated expressions that are equivalent to LOINC codes. SNOMED CT's strength lies in its support of many non-numerical result values. Implicit code hierarchies exist in NPU and LOINC. SNOMED CT has explicit, elaborate axioms that elucidate the meaning of its content. Its complexity and its license conditions, however, impede a more widespread use. Interpretive comments, a crucial part of laboratory results, are still difficult to encode with any of the terminologies. Conclusions: All three terminologies have distinct potentials and limitations, but the approximation of SNOMED CT and LOINC suggests using them together. Terminologies need to be expanded to also cover interpretive comments. [ABSTRACT FROM AUTHOR]

Since the 1970s, computer supported data processing has been implemented in the laboratory and laboratory information systems (LIS) are being developed. In the following years, the programs were expanded and new laboratory requirements were inserted to the LIS. In the last few years, the requirements have grown more and more. The current tasks of the LIS are not only the management of laboratory requirements but also management of processes, data security and data transfer and they have become very important. Therefore, the current monolithic architecture of LIS has reached its limits. New methodologies like service oriented architecture, e.g. microservices, should be implemented. Thereby different specialized manufacturers provide software for one or a few tasks. These tasks can be more easily actualized like in the new field of agile software development. This new concept has been designed to provide updates and customer requirements according to its new organization structure in program development in a short time. For efficient data transfer, new interfaces and a standardization of master data like logical observation identifier names and codes (LOINC®) are advisable. With the growing data transfer, data security plays an increasingly important role. New concepts like blockchain programming (e.g. Medrec) are currently tested in (laboratory) medicine. To get an overview of the requirements of the own LIS, an Ishikawa diagram should be created. The main points of an Ishikawa diagram are shown and discussed. Based on the today-collected data, expert systems will be developed. For this kind of data mining, a structured data exchange is necessary. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to compare the content of the falls prevention guideline developed by Korean Hospital Nurses' Association (KHNA) in 2008 with the seven guidelines recommended by The Joint Commission. Methods: The contents of the eight guidelines were categorized into eight assessment, eight intervention, and two outcome elements that were considered core concepts of individual nursing activities. Concept-level comparisons were conducted using the International Classification for Nursing Practice, and the Logical Observation Identifiers, Names, and Codes. Two researchers independently examined the content coverage of each guideline and assigned 1 point to included content, 0 to excluded content, and 0.5 to similar content that was ambiguous in terms of a broader expression. The scores were compared, with discordance among raters resolved through consensus-based discussions. Results: The communication element in the assessment area was missing from the KHNA guideline, while five elements of pathophysiologic, mental/cognitive, therapeutic, and post-fall assessments showed lack of content. Although there were no missing elements in the intervention area, the three elements of staff communication, risk-targeted, and post-fall interventions showed considerable lack of content compared to the other guidelines. In terms of outcomes, the severity of fall-related injury was omitted, and the facility's policy on falls prevention was not addressed. Conclusion: Many nursing elements and key concepts were missing from the KHNA guideline, and its content was not comprehensive. This suggests that the current guideline needs to be revised urgently, and the points identified in this study should be considered a high priority. [ABSTRACT FROM AUTHOR]

The LOINC-RSNA Radiology Playbook represents the future direction of standardization for radiology procedure names. We developed a software solution (“RadMatch”) utilizing Python 2.7 and FuzzyWuzzy, an open-source fuzzy string matching algorithm created by SeatGeek, to implement the LOINC-RSNA Radiology Playbook for adult abdomen and pelvis CT and MR procedures performed at our institution. Execution of this semi-automated method resulted in the assignment of appropriate LOINC numbers to 86% of local CT procedures. For local MR procedures, appropriate LOINC numbers were assigned to 75% of these procedures whereas 12.5% of local MR procedures could only be partially mapped. For the standardized local procedures, only 63% of CT and 71% of MR procedures had corresponding RadLex Playbook identifier (RPID) codes in the LOINC-RSNA Radiology Playbook, which limited the utility of RPID codes. RadMatch is a semi-automated open-source software tool that can assist radiology departments seeking to standardize their radiology procedures via implementation of the LOINC-RSNA Radiology Playbook. [ABSTRACT FROM AUTHOR]

The eSource Data Interchange Group, part of the Clinical Data Interchange Standards Consortium, proposed five scenarios to guide stakeholders in the development of solutions for the capture of eSource data. The fifth scenario was subdivided into four tiers to adapt the functionality of electronic health records to support clinical research. In order to develop a system belonging to the “Interoperable” Tier, the authors decided to adopt the service-oriented architecture paradigm to support technical interoperability, Health Level Seven Version 3 messages combined with LOINC (Logical Observation Identifiers Names and Codes) vocabulary to ensure semantic interoperability, and Healthcare Services Specification Project standards to provide process interoperability. The developed architecture enhances the integration between patient-care practice and medical research, allowing clinical data sharing between two hospital information systems and four clinical data management systems/clinical registries. The core is formed by a set of standardized cloud services connected through standardized interfaces, involving client applications. The system was approved by a medical staff, since it reduces the workload for the management of clinical trials. Although this architecture can realize the “Interoperable” Tier, the current solution actually covers the “Connected” Tier, due to local hospital policy restrictions. [ABSTRACT FROM AUTHOR]

To meet the need of the computational power, most of the users may go for a cloud based services for its scalability, flexibility and reliability. Cloud services have become an integral part of IT and analytical enterprises. Owing to increase in necessity of commercial cloud products being readily available, it has become extremely difficult for users to identify suitable cloud services. This paper proposes the recommender system precisely designed for the discovery of cloud services. Though there is an exponential increase in demand for cloud services, the amount of research done in this particular field is abysmal. Cloud Service Identification System (CSIS) crawls through Internet, identifies cloud services and stores them in a database. The user's search query is processed and recommends cloud services accurately. [ABSTRACT FROM AUTHOR]

Disparate data must be represented in a common format to enable comparison across multiple institutions and facilitate Big Data science. Nursing assessments represent a rich source of information. However, a lack of agreement regarding essential concepts and standardized terminology prevent their use for Big Data science in the current state. The purpose of this study was to align a minimum set of physiological nursing assessment data elements with national standardized coding systems. Six institutions shared their 100 most common electronic health record nursing assessment data elements. From these, a set of distinct elements was mapped to nationally recognized Logical Observations Identifiers Names and Codes (LOINC®) and Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT®) standards. We identified 137 observation names (55% new to LOINC), and 348 observation values (20% new to SNOMED CT) organized into 16 panels (72% new LOINC). This reference set can support the exchange of nursing information, facilitate multi-site research, and provide a framework for nursing data analysis. [ABSTRACT FROM AUTHOR]

Background: Manual mapping of laboratory data to Logical Observation Identifiers Names and Codes (LOINC) requires a major effort. Application of the LOINC mapping assistant RELMA V.6.6 can reduce the effort required for mapping. The goal of the paper is to perform a semi-automated mapping of Russian laboratory terms to LOINC.Methods: A semi-automated mapping of the 2563 terms from two clinics in Russia was performed. The first step was automatic mapping using RELMA V.6.6 and LOINC V.2.48 Russian translation by Yaroslavl state medical academy. The second step was a manual expert mapping. To evaluate the correctness of mapping all the mapped terms were reviewed by two experts.Results: The paper presents the results of semi-automatic mapping of Russian laboratory terms to LOINC. Two clinics (A and B) and a laboratory service participated in the project. The following results were achieved: mapping of 86% terms from Clinic A and 87% from Clinic B. It has to be mentioned that 99% of terms used in 2014 were mapped. In total 2398 out of 2563 were mapped.Discussion: The required effort was reasonable and the price of mapping and maintenance was considered as relatively low in comparison to manual methods.Conclusion: RELMA V.6.6 and LOINC V.2.48 offer the opportunity of a low effort LOINC mapping even for non-English languages. The study proved that the mapping effort is acceptable and mapping results are on the same level as the manual mapping. [ABSTRACT FROM AUTHOR]

There is now widespread recognition of the powerful potential of electronic health record (EHR) systems to improve the health-care delivery system. The benefits of EHRs grow even larger when the health data within their purview are seamlessly shared, aggregated and processed across different providers, settings and institutions. Yet, the plethora of idiosyncratic conventions for identifying the same clinical content in different information systems is a fundamental barrier to fully leveraging the potential of EHRs. Only by adopting vocabulary standards that provide the lingua franca across these local dialects can computers efficiently move, aggregate and use health data for decision support, outcomes management, quality reporting, research and many other purposes. In this regard, the International Classification of Functioning, Disability, and Health (ICF) is an important standard for physiotherapists because it provides a framework and standard language for describing health and health-related states. However, physiotherapists and other health-care professionals capture a wide range of data such as patient histories, clinical findings, tests and measurements, procedures, and so on, for which other vocabulary standards such as Logical Observation Identifiers Names and Codes and Systematized Nomenclature Of Medicine Clinical Terms are crucial for interoperable communication between different electronic systems. In this paper, we describe how the ICF and other internationally accepted vocabulary standards could advance physiotherapy practise and research by enabling data sharing and reuse by EHRs. We highlight how these different vocabulary standards fit together within a comprehensive record system, and how EHRs can make use of them, with a particular focus on enhancing decision-making. By incorporating the ICF and other internationally accepted vocabulary standards into our clinical information systems, physiotherapists will be able to leverage the potent capabilities of EHRs and contribute our unique clinical perspective to other health-care providers within the emerging electronic health information infrastructure. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An enormous amount of data exists in unstructured diagnostic and interventional radiology reports. Free text or non-standardized terminologies limit the ability to parse, extract, and analyze these report data elements. Medical lexicons and ontologies contain standardized terms for relevant concepts including disease entities, radiographic technique, and findings. The use of standardized terms offers the potential to improve reporting consistency and facilitate computer analysis. The purpose of this project was to implement an interface to aid in the creation of standards-compliant reporting templates for use in interventional radiology. Non-standardized procedure report text was analyzed and referenced to RadLex, SNOMED-CT, and LOINC. Using JavaScript, a web application was developed which determined whether exact terms or synonyms in reports existed within these three reference resources. The NCBO BioPortal Annotator web service was used to map terms, and output from this application was used to create an interactive annotated version of the original report. The application was successfully used to analyze and modify five distinct reports for the Society of Interventional Radiology's standardized reporting project. [ABSTRACT FROM AUTHOR]

National policies in the United States require the use of standard terminology for data exchange between clinical information systems. However, most electronic health record systems continue to use local and idiosyncratic ways of representing clinical observations. To improve mappings between local terms and standard vocabularies, we sought to make existing mappings (wisdom) from health care organizations (the Crowd) available to individuals engaged in mapping processes. We developed new functionality to display counts of local terms and organizations that had previously mapped to a given Logical Observation Identifiers Names and Codes (LOINC) code. Further, we enabled users to view the details of those mappings, including local term names and the organizations that create the mappings. Users also would have the capacity to contribute their local mappings to a shared mapping repository. In this article, we describe the new functionality and its availability to implementers who desire resources to make mapping more efficient and effective. [ABSTRACT FROM AUTHOR]

In the era of the electronic health record, the success of laboratories and pathologists will depend on effective presentation and management of laboratory information, including test orders and results, and effective exchange of data between the laboratory information system and the electronic health record. In this third paper of a series that explores empowerment of pathology in the era of the electronic health record, we review key elements of managing laboratory information within the electronic health record and examine functional issues pertinent to pathologists and laboratories in the exchange of laboratory information between electronic health records and both anatomic and clinical pathology laboratory information systems. Issues with electronic order-entry and results-reporting interfaces are described, and considerations for setting up these interfaces are detailed in tables. The role of the laboratory medical director as mandated by the Clinical Laboratory Improvement Amendments of 1988 and the impacts of discordance between laboratory results and their display in the electronic health record are also discussed. [ABSTRACT FROM AUTHOR]

The article states that many clinicians find it hard to use accurate and compassionate language while talking to patients about routine code status. The Fast Fact and Fast Fact #365 suggest language which may be used by clinicians during such conversations. They are advised to normalize the discussions and provide basic definitions.

The article discusses the different types of semantic interoperability in the healthcare setting. Topics include the Healthcare Information and Management Systems Society's (HIMSS) definition of semantic interoperability between health information systems, standard vocabularies set by the Office of the National Coordinator for Health Information Technology, Interoperability Standards Advisory (ISA) as well as nursing terminologies recognized by the American Nurses Association (ANA).

Interfacing a hospital's laboratory information system with client information systems such as electronic medical or health records (EMRs/EHRs) is not a straight forward process. Many variables affect how to configure both the client's system and the laboratory's information system and in turn determine what is possible for passing data from one system to another. Information systems have different capabilities and have varying degrees of configurability. Laboratory testing varies due to instrument and differences in methodologies. Local definitions of what constitutes the appropriate parameters to report for a given analyte will mean that one laboratory's definition for a test will be different from another laboratory's definition. Other processes and work flows that need to be accounted for include test cancels, credits and reflex testing. How systems accommodate these exceptions is not standardized and will vary with the respective informatics environments. Alternate manual work flows may be needed to address extraordinary problems. [ABSTRACT FROM AUTHOR]

The article discusses a survey focusing on the nurses' perception of the American Nurses Association (ANA)-recognized, standardized, nursing terminologies in the U.S. Topics discussed include the demographics of our respondents in the survey; the users' confidence in using the terminologies; and the effects of documenting with standardized nursing terminologies.

Background: Structured information within patient medical records represents a largely untapped treasure trove of research data. In the United States, privacy issues notwithstanding, this has recently become more accessible thanks to the increasing adoption of electronic health records (EHR) and health care data standards fueled by the Meaningful Use legislation. The other side of the coin is that it is now becoming increasingly more difficult to navigate the profusion of many disparate clinical terminology standards, which often span millions of concepts. Objective: The objective of our study was to develop a methodology for integrating large amounts of structured clinical information that is both terminology agnostic and able to capture heterogeneous clinical phenotypes including problems, procedures, medications, and clinical results (such as laboratory tests and clinical observations). In this context, we define phenotyping as the extraction of all clinically relevant features contained in the EHR. Methods: The scope of the project was framed by the Common Meaningful Use (MU) Dataset terminology standards; the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), RxNorm, the Logical Observation Identifiers Names and Codes (LOINC), the Current Procedural Terminology (CPT), the Health care Common Procedure Coding System (HCPCS), the International Classification of Diseases Ninth Revision Clinical Modification (ICD-9-CM), and the International Classification of Diseases Tenth Revision Clinical Modification (ICD-10-CM). The Unified Medical Language System (UMLS) was used as a mapping layer among the MU ontologies. An extract, load, and transform approach separated original annotations in the EHR from the mapping process and allowed for continuous updates as the terminologies were updated. Additionally, we integrated all terminologies into a single UMLS derived ontology and further optimized it to make the relatively large concept graph manageable. Results: The initial evaluation was performed with simulated data from the Clinical Avatars project using 100,000 virtual patients undergoing a 90 day, genotype guided, warfarin dosing protocol. This dataset was annotated with standard MU terminologies, loaded, and transformed using the UMLS. We have deployed this methodology to scale in our in-house analytics platform using structured EHR data for 7931 patients (12 million clinical observations) treated at the Froedtert Hospital. A demonstration limited to Clinical Avatars data is available on the Internet using the credentials user "jmirdemo" and password "jmirdemo". Conclusions: Despite its inherent complexity, the UMLS can serve as an effective interface terminology for many of the clinical data standards currently used in the health care domain. [ABSTRACT FROM AUTHOR]