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Background Medical information management for stroke patients is currently a very time-consuming endeavor. There are clear guidelines and procedures to treat patients having acute stroke, but it is not known how well these established practices are reflected in patient documentation. Objective This study compares a variety of documentation processes regarding stroke. The main objective of this work is to provide an overview of the most commonly occurring medical concepts in stroke documentation and identify overlaps between different documentation contexts to allow for the definition of a core data set that could be used in potential data interfaces. Methods Medical source documentation forms from different documentation contexts, including hospitals, clinical trials, registries, and international standards, regarding stroke treatment followed by rehabilitation were digitized in the operational data model. Each source data element was semantically annotated using the Unified Medical Language System. The concept codes were analyzed for semantic overlaps. A concept was considered common if it appeared in at least two documentation contexts. The resulting common concepts were extended with implementation details, including data types and permissible values based on frequent patterns of source data elements, using an established expert-based and semiautomatic approach. Results In total, 3287 data elements were identified, and 1051 of these emerged as unique medical concepts. The 100 most frequent medical concepts cover 9.51% (100/1051) of all concept occurrences in stroke documentation, and the 50 most frequent concepts cover 4.75% (50/1051). A list of common data elements was implemented in different standardized machine-readable formats on a public metadata repository for interoperable reuse. Conclusions Standardization of medical documentation is a prerequisite for data exchange as well as the transferability and reuse of data. In the long run, standardization would save time and money and extend the capabilities for which such data could be used. In the context of this work, a lack of standardization was observed regarding current information management. Free-form text fields and intricate questions complicate automated data access and transfer between institutions. This work also revealed the potential of a unified documentation process as a core data set of the 50 most frequent common data elements, accounting for 34% of the documentation in medical information management. Such a data set offers a starting point for standardized and interoperable data collection in routine care, quality management, and clinical research.

StudyPortal was implemented as the first multilingual search platform for geographic visualization of clinical trials and scientific articles. The platform queries information from ClinicalTrials.gov, PubMed, a geodatabase and geographic maps to enable geospatial study search and real-time rendering of study locations or research networks on a map. Thus, disease-specific clinical studies or whole research networks can be shown in a geographic proximity. Moreover, a semantic layer enables multilingual disease input and autosuggestion of medical terms based on the Unified Medical Language System. The portal is accessible on https://studyportal.uni-muenster.de. This paper presents details on implementation of the novel search platform, its search evaluation and future work.

The Portal of Medical Data Models is an open-access platform for medical forms and data models. Annotation with UMLS codes enables semantic interoperability and secondary use of data. The number of forms and users are growing. The site has been updated and two analyzing tools have been added.

Background For cancer domains such as acute myeloid leukemia (AML), a large set of data elements is obtained from different institutions with heterogeneous data definitions within one patient course. The lack of clinical data harmonization impedes cross-institutional electronic data exchange and future meta-analyses. Objective This study aimed to identify and harmonize a semantic core of common data elements (CDEs) in clinical routine and research documentation, based on a systematic metadata analysis of existing documentation models. Methods Lists of relevant data items were collected and reviewed by hematologists from two university hospitals regarding routine documentation and several case report forms of clinical trials for AML. In addition, existing registries and international recommendations were included. Data items were coded to medical concepts via the Unified Medical Language System (UMLS) by a physician and reviewed by another physician. On the basis of the coded concepts, the data sources were analyzed for concept overlaps and identification of most frequent concepts. The most frequent concepts were then implemented as data elements in the standardized format of the Operational Data Model by the Clinical Data Interchange Standards Consortium. Results A total of 3265 medical concepts were identified, of which 1414 were unique. Among the 1414 unique medical concepts, the 50 most frequent ones cover 26.98% of all concept occurrences within the collected AML documentation. The top 100 concepts represent 39.48% of all concepts’ occurrences. Implementation of CDEs is available on a European research infrastructure and can be downloaded in different formats for reuse in different electronic data capture systems. Conclusions Information management is a complex process for research-intense disease entities as AML that is associated with a large set of lab-based diagnostics and different treatment options. Our systematic UMLS-based analysis revealed the existence of a core data set and an exemplary reusable implementation for harmonized data capture is available on an established metadata repository.

Julian Varghese,1 Michael Fujarski,2 Stefan Hegselmann,1 Philipp Neuhaus,1 Martin Dugas1,3 1Institute of Medical Informatics, University of Münster, 2Faculty of Mathematics and Computer Sciences, University of Münster, 3Institute of Medical Informatics, European Research Center for Information Systems (ERCIS), Münster, Germany Objective: Best-practice data models harmonize semantics and data structure of medical variables in clinical or epidemiological studies. While there exist several published data sets, it remains challenging to find and reuse published eligibility criteria or other data items that match specific needs of a newly planned study or registry. A novel Internet-based method for rapid comparison of published data models was implemented to enable reuse, customization, and harmonization of item catalogs for the early planning and development phase of research databases. Methods: Based on prior work, a European information infrastructure with a large collection of medical data models was established. A newly developed analysis module called CDEGenerator provides systematic comparison of selected data models and user-tailored creation of minimum data sets or harmonized item catalogs. Usability was assessed by eight external medical documentation experts in a workshop by the umbrella organization for networked medical research in Germany with the System Usability Scale. Results: The analysis and item-tailoring module provides multilingual comparisons of semantically complex eligibility criteria of clinical trials. The System Usability Scale yielded “good usability” (mean 75.0, range 65.0–92.5). User-tailored models can be exported to several data formats, such as XLS, REDCap or Operational Data Model by the Clinical Data Interchange Standards Consortium, which is supported by the US Food and Drug Administration and European Medicines Agency for metadata exchange of clinical studies. Conclusion: The online tool provides user-friendly methods to reuse, compare, and thus learn from data items of standardized or published models to design a blueprint for a harmonized research database. Keywords: common data elements, semantic interoperability, metadata repositories, Unified Medical Language System

BACKGROUND Medical coding is essential for standardized communication and integration of clinical data. The Unified Medical Language System by the National Library of Medicine is the largest clinical terminology system for medical coders and natural language processing tools. However, abundance of ambiguous codes leads to low rates of uniform coding among different coders. OBJECTIVE To measure uniform coding among different medical experts in terms of inter-rater reliability (IR) and analyze the effect on IR by using an expert-based online code suggestion system. METHODS A quasi-experimental study was conducted. Six medical experts coded 602 medical items from structured quality assurance forms (QA) or free-text eligibility criteria (EC) of 20 different clinical trials. Medical item content was selected based on mortality-leading diseases according to WHO data. The intervention consisted of using a semi-automatic code suggestion tool that is linked to a European information infrastructure providing a large medical text corpus of more than 300,000 medical form items with expert-assigned semantic codes. Krippendorff’s alpha (Kalpha) with bootstrap analysis was used for IR analysis and coding times were measured before and after intervention. RESULTS The intervention improved IR in structured QA form items (from Kalpha= 0.50, 95%-CI [0.43-0.57] to Kalpha = 0.62 [0.55-0.69]) and free-text eligibility criteria (from Kalpha = 0.19 [0.14-0.24] to Kalpha = 0.43 [0.37-0.50]) while preserving or slightly reducing mean coding time per item for all six coders. Regardless of intervention, pre-coordination and structured items were associated with significant higher IR, but the proportion of items that were pre-coordinated significantly increased after intervention (EC: Odds ratio: 4.92 [2.78 - 8.72]; QA: Odds ratio: 1.96 [1.19-3.25]). CONCLUSIONS The online code suggestion mechanism improved IR towards moderate or even substantial inter coder agreement. Pre-coordination and use of structured vs. free-text data elements are key drivers for higher IR.

Summary Background: Eligibility criteria (EC) of clinical trials play a key role in selecting appropriate study candidates and the validity of the outcome of a clinical trial. However, in most cases EC are provided in unstandardised ways such as free text, which raises significant challenges for machine-readability. Objectives: To establish a list of most frequent medical concepts in clinical trials with semantic annotations. This concept list contributes to standardisation of EC and identifies relevant data items in electronic health records (EHRs) for clinical research. The coverage of the list in two major clinical vocabularies, MeSH and SNOMED-CT, will be assessed. Methods: Four hundred and twenty-fivec linical trials conducted between 2000 and 2011 at a German university hospital were analysed. 6671 EC were manually annotated by a medical coder using Concept Unique Identifiers (CUIs) provided by the Unified Medical Language System. Two physicians performed a semi-automatic CUI code revision. Concept frequency was analysed and clusters of concepts were manually identified.A binomial significance test was applied to quantify coverage differences of the most frequent concepts in MeSH and SNOMED-CT. Results: Based on manual medical coding of 425 clinical trials, 7588 concepts were identified, of which 5236 were distinct. A top 100 list containing 101 most frequent medical concepts was established. The concepts of this list cover 25 % of all concept occur-rences in all analysed clinical trials. This list reveals six missing entries in SNOMED-CT, 12 in MeSH. The median of EC frequency per trial has increased throughout the trial years (2000 –2005: 8 EC/trial, 2011: 14 EC/ trial). Conclusions: Relatively few concepts cover one quarter of concept occurrences that represent EC in recent studies. Therefore, these concepts can serve as candidate data elements for integration into EHRs to optimise patient recruitment in clinical research.

The volume and complexity of patient data – especially in personalised medicine – is steadily increasing, both regarding clinical data and genomic profiles: Typically more than 1,000 items (e.g., laboratory values, vital signs, diagnostic tests etc.) are collected per patient in clinical trials. In oncology hundreds of mutations can potentially be detected for each patient by genomic profiling. Therefore data integration from multiple sources constitutes a key challenge for medical research and healthcare. Semantic annotation of data elements can facilitate to identify matching data elements in different sources and thereby supports data integration. Millions of different annotations are required due to the semantic richness of patient data. These annotations should be uniform, i.e., two matching data elements shall contain the same annotations. However, large terminologies like SNOMED CT or UMLS don’t provide uniform coding. It is proposed to develop semantic annotations of medical data elements based on a large-scale public metadata repository. To achieve uniform codes, semantic annotations shall be re-used if a matching data element is available in the metadata repository. A web-based tool called ODMedit ( https://odmeditor.uni-muenster.de/ ) was developed to create data models with uniform semantic annotations. It contains ~800,000 terms with semantic annotations which were derived from ~5,800 models from the portal of medical data models (MDM). The tool was successfully applied to manually annotate 22 forms with 292 data items from CDISC and to update 1,495 data models of the MDM portal. Uniform manual semantic annotation of data models is feasible in principle, but requires a large-scale collaborative effort due to the semantic richness of patient data. A web-based tool for these annotations is available, which is linked to a public metadata repository.

Standardized quality assurance (QA) plays an import role to maintain and develop success of cardiovascular procedures (CP). Well-established QA models from Germany could be shared in a form repository for world-wide reuse and exchange. Therefore, we collected the complete set of all quality QA forms for CP, which is obligatory to be filled out by all German health service providers. Original forms were converted into standardized study forms according to ODM (Operational Data Model) and translated into English. Common medical concepts and clusters of medical concepts were identified based on UMLS coding of form items. All forms are available on the web as multilingual ODM documents. UMLS concept coverage analysis indicates 88% coverage with few but critically important definition gaps, which need to be addressed by UMLS.

Quality assurance (QA) is a key factor to evaluate success of organ transplantations. In Germany QA documentation is progressively developed and enforced by law. Our objective is to share QA models from Germany in a standardized format within a form repository for world-wide reuse and exchange. Original QA forms were converted into standardized study forms according to the Operational Data Model (ODM) and shared for open access in an international forms repository. Form elements were translated into English and semantically enriched with Concept Unique Identifiers from the Unified Medical Language System (UMLS) based on medical expert decision. All forms are available on the web as multilingual ODM documents. UMLS concept coverage analysis indicates 92% coverage with few but critically important definition gaps. New content and infrastructure for harmonized documentation forms is provided in the domain of organ transplantations enabling world-wide reuse and exchange.

Medical forms are frequently used to document patient data or to collect relevant data for clinical trials. It is crucial to harmonize medical forms in order to improve interoperability and data integration between medical applications. Here we propose a (semi-) automatic annotation of medical forms with concepts of the Unified Medical Language System (UMLS). Our annotation workflow encompasses a novel semantic blocking, sophisticated match techniques and post-processing steps to select reasonable annotations. We evaluate our methods based on reference mappings between medical forms and UMLS, and further manually validate the recommended annotations.

Introduction: Information systems are a key success factor for medical research and healthcare. Currently, most of these systems apply heterogeneous and proprietary data models, which impede data exchange and integrated data analysis for scientific purposes. Due to the complexity of medical terminology, the overall number of medical data models is very high. At present, the vast majority of these models are not available to the scientific community. The objective of the Portal of Medical Data Models (MDM, https://medical-data-models.org) is to foster sharing of medical data models. Methods: MDM is a registered European information infrastructure. It provides a multilingual platform for exchange and discussion of data models in medicine, both for medical research and healthcare. The system is developed in collaboration with the University Library of Munster to ensure sustainability. A web front-end enables users to search, view, download and discuss data models. Eleven different export formats are available (ODM, PDF, CDA, CSV, MACRO-XML, REDCap, SQL, SPSS, ADL, R, XLSX). MDM contents were analysed with descriptive statistics. Results: MDM contains 4387 current versions of data models (in total 10 963 versions). 2475 of these models belong to oncology trials. The most common keyword (n = 3826) is ‘Clinical Trial’; most frequent diseases are breast cancer, leukemia, lung and colorectal neoplasms. Most common languages of data elements are English (n = 328 557) and German (n = 68 738). Semantic annotations (UMLS codes) are available for 108 412 data items, 2453 item groups and 35 361 code list items. Overall 335 087 UMLS codes are assigned with 21 847 unique codes. Few UMLS codes are used several thousand times, but there is a long tail of rarely used codes in the frequency distribution. Discussion: Expected benefits of the MDM portal are improved and accelerated design of medical data models by sharing best practice, more standardised data models with semantic annotation and better information exchange between information systems, in particular Electronic Data Capture (EDC) and Electronic Health Records (EHR) systems. Contents of the MDM portal need to be further expanded to reach broad coverage of all relevant medical domains. Database URL: https://medical-data-models.org