ODMedit: uniform semantic annotation for data integration in medicine based on a public metadata repository

Objectives: The burden of asthma ranks among the highest for chronic diseases. Interoperable electronic health records (EHRs) can improve the management of chronic diseases such as asthma by facilitating sharing of data between health care settings along the continuum of care. Terminology such as SNOMED CT® (Systematized Nomenclature of Medicine-Clinical Terms) and LOINC® (Logistical Observation Identifier Names and Codes) are prerequisites for interoperability of EHRs. We sought to determine the extent to which data elements in a validated asthma care map (ACM) are congruent with these terminologies. Methods: A certified asthma educator entered all 169 elements in the ACM into the SNOMED CT® browser. Matched elements were assigned a concept name, an identification number, and classified into a hierarchy. LOINC® terminology was reviewed for asthma-related pulmonary function tests (PFTs). Results: Forty-two percent of the ACM elements were complete matches to existing SNOMED CT® concepts, 24% partial matches, and 34% unmatched. Specific asthma control parameters were either complete (n = 3) or partial (n = 4) matches, but overall “asthma control” was unmatched. There were 92% complete or partial matches for PFT elements to SNOMED CT® and 83% to LOINC®. Conclusions: The majority of ACM elements are congruent with standardized terminology, enabling EHR interoperability. Future requests for new concepts in SNOMED CT® and LOINC® should be pursued for asthma control parameters paramount to evidence-based practice.

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

Railway distributed system integration needs to realize information exchange, resources sharing and coordination process across fields, departments and application systems. And railway data integration is essential to implement this integration. In order to resolve the problem of heterogeneity of data models among data sources of different railway operation systems, this paper presents a novel integration data model of spatial structure, a XML-oriented 3-dimension common data model. The proposed model accommodates both the flexibility of level relationship and syntax expression in data integration. In this model, a spatial data pattern is used to describe and express the characteristic relationship of data items among all types of data. Based on the data model with rooted directed graph and the organization of level as well as the flexibility of the expression, the model can represent the mapping between different data models, including relationship model and object-oriented model. A consistent concept and algebraic description of the data set is given to function as the metadata in data integration, so that the algebraic manipulation of data integration is standardized to support the data integration of distributed system.

This article describes the challenges of implementing SNOMED CT into electronic clinical documentation systems for discharge summaries, synoptic operative notes and ambulatory documentation. Four significant implementation challenges were identified throughout these projects, which required collaboration between specialists across several disciplines to resolve. The challenges included: designing the graphical user interface for selecting SNOMED CT values, gathering and validating template specifications that use SNOMED CT subsets, handling SNOMED CT subsets and extensions, and, creating algorithms and the technological infrastructure to generate fast, meaningful, non-redundant search results. Our experiences suggest that, while the usage of SNOMED CT in tertiary care settings is promising, collaboration between specialists from multiple disciplines is needed to utilize their unique project management, data modeling, technical, and clinical skills in overcoming implementation challenges.

Unprincipled modeling decisions in large-domain ontologies, such as SNOMED CT, are problematic and might act as a barrier for their quality assurance and successful use in electronic health records. Most previous work has focused on clustering problematic concepts, which is helpful for quality control but faces difficulties in pinpointing the origin of those modeling problems. In this study, we examined the underlying structural patterns in SNOMED CT’s data model as such patterns directly reflect the modeling strategies of editors. Our results showed that 92% of all structural patterns found accumulated in the Procedure and Clinical finding sub-hierarchies, and pattern reuse was low; over 30% of patterns were only used once. A qualitative analysis of a sample of 50 such singleton patterns revealed modeling problems, including redundancy, omission, and inconsistency. The problems detected in the sample suggest that the analysis of structural patterns is a valuable technique for revealing problematic areas of SNOMED CT and modeling the styles of terminology editors. Furthermore, the patterns that describe the modeling of a large number of concepts could provide insights for template creation and refinement in SNOMED CT.

BackgroundTerminologies, such as Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Solor, assist with knowledge representation and management, data integration, and triggering clinical decision support (CDS) rules. Semantic relations in these terminologies provide explicit meaning in compositional expressions, which assist with many of the above-listed activities.ObjectiveThe aims of this research are to: (1) identify semantic relations that are not fully present in SNOMED CT and Solor and (2) use these identified semantic relations with terms that are currently present in SNOMED CT and Solor to form triples.MethodsWe identified relations that were not fully present in either SNOMED CT or Solor and were important for VA Knowledge Artifacts (KNARTS). These terms and the relations were formed into triples. The relations, terms, classifications, and sentences were used to implement the relations in the High Definition-Natural Language Processing (HD-NLP) program.ResultsThere are a total of 38 semantic relations. These had use cases built for each and were implemented in the Solor HD-NLP server for tagging of KNARTS.ConclusionsThese new SNOMED CT and Solor semantic relations will give clinicians the ability to add more detail and meaning to their clinical notes. This can improve our ability to trigger CDS rules, leading to improved CDS provided to clinicians during patient care.

Modern data-driven spoken language systems (SLS) require manual semantic annotation for training spoken language understanding parsers. Multilingual porting of SLS demands significant manual effort and language resources, as this manual annotation has to be replicated. Crowdsourcing is an accessible and cost-effective alternative to traditional methods of collecting and annotating data. The application of crowdsourcing to simple tasks has been well investigated. However, complex tasks, like cross-language semantic annotation transfer, may generate low judgment agreement and/or poor performance. The most serious issue in cross-language porting is the absence of reference annotations in the target language; thus, crowd quality control and the evaluation of the collected annotations is difficult. In this paper we investigate targeted crowdsourcing for semantic annotation transfer that delegates to crowds a complex task such as segmenting and labeling of concepts taken from a domain ontology; and evaluation using source language annotation. To test the applicability and effectiveness of the crowdsourced annotation transfer we have considered the case of close and distant language pairs: Italian–Spanish and Italian–Greek. The corpora annotated via crowdsourcing are evaluated against source and target language expert annotations. We demonstrate that the two evaluation references (source and target) highly correlate with each other; thus, drastically reduce the need for the target language reference annotations.

Precision oncology mandates developing standardized common data models (CDMs) to facilitate analyses and enable clinical decision making. Expert-opinion-based precision oncology initiatives are epitomized in Molecular Tumor Boards (MTBs), which process large volumes of clinical-genomic data to match genotypes with molecularly guided therapies. We used the Johns Hopkins University MTB as a use case and developed a precision oncology core data model (Precision-DM) to capture key clinical-genomic data elements. We leveraged existing CDMs, building upon the Minimal Common Oncology Data Elements model (mCODE). Our model was defined as a set of profiles with multiple data elements, focusing on next-generation sequencing and variant annotations. Most elements were mapped to terminologies or code sets and the Fast Healthcare Interoperability Resources (FHIR). We subsequently compared our Precision-DM with existing CDMs, including the National Cancer Institute's Genomic Data Commons (NCI GDC), mCODE, OSIRIS, the clinical Genome Data Model (cGDM), and the genomic CDM (gCDM). Precision-DM contained 16 profiles and 355 data elements. 39% of the elements derived values from selected terminologies or code sets, and 61% were mapped to FHIR. Although we used most elements contained in mCODE, we significantly expanded the profiles to include genomic annotations, resulting in a partial overlap of 50.7% between our core model and mCODE. Limited overlap was noted between Precision-DM and OSIRIS (33.2%), NCI GDC (21.4%), cGDM (9.3%), and gCDM (7.9%). Precision-DM covered most of the mCODE elements (87.7%), with less coverage for OSIRIS (35.8%), NCI GDC (11%), cGDM (26%) and gCDM (33.3%). Precision-DM supports clinical-genomic data standardization to support the MTB use case and may allow for harmonized data pulls across health care systems, academic institutions, and community medical centers.

OBJECTIVES/SPECIFIC AIMS: Dependence and abuse of prescription opioid pain medication has substantially increased over the last decade. The consistent rise in opioid dependence contributes to the rising prescription drug overdose deaths over the last decade. The study of the distribution and determinants of opioid dependence among patients who are treated with chronic pain medications prescribed by their healthcare providers would aid in answering some key questions about potential abuse and overdose on opioids. The descriptive epidemiology of opioid dependence would help in identifying the vulnerable age group, race, ethnicity, and type of opioid pain medications that more commonly result in dependence. METHODS/STUDY POPULATION: We implemented an Observational Medical Outcomes Partnership/Observational Health Data Sciences and Informatics (OMOP/OHDSI) database, to hold structured EHR data from our Allscripts patient records. We also created a high-throughput phenotyping, natural language processing system that can parse 7,000,000 clinical notes in 1.5 hours. This runs as a web service and provides a modular component based NLP system. After the full semantic parse, we match the content against any number of ontologies. For each match we tag it as either a positive, negative, or uncertain assertion. We then perform automated compositional expressions. The codes are stored in a Berkley database (BDB) NOSQL database and the compositional expressions are stored in Neo4J (a graph database) and Graph DB (a triple store). This flexibility allows rapid retrieval of complex questions in real time. The High-Throughput Phenotyping (HTP) Natural Language Processing (NLP) Subsystem (HTP-NLP) is software that produces, given biomedical text, semantic annotations of the text. The semantic annotations identify conceptual entities—their attributes, the relations they have with other entities and the events they participate in, as expressed in the input text. The conceptual entities, relations, attributes, and events identified are specified by various knowledge representations (KRs) as documented in Coding Sources. Examples of coding sources are medical terminologies [eg, SNOMED CT, RxNorm, LOINC and open biomedical ontologies (OBO) foundry ontologies, eg, gene ontology (GO), functional model of anatomy, OBI, and others]. The annotation results may be displayed or output in formats suitable for further processing. Entity identified is assigned a truth value from 0 to 1. Values from the text are assigned to entities from ontologies such as SNOMED CT. The retrospective analysis of EHR data from local clinic patients was performed using queries on the problem list, demographic data, and medication list of all the patients in the database. The OMOP/OHDSI database was collected from Allscripts EHRs from 2010 to 2015. This common data model helps in the systematic analysis of disparate observational databases of clinic records from the primary care and family medicine clinics in Western New York region. The database contained 212,343 patient records that were parsed and deidentified. Specific research IDs were assigned to each of the patient records and stored in a secure firewall device for data analytics. The entire 212,343 records were queried for opioid dependence from the ICD-9 and 10 diagnostic codes and SNOMED CT codes mapped to both the clinical notes and the problem list for each patient based on the mapped ICD and SNOMED CT codes. In total, 1356 patients were identified as to having opioid dependence. The records were stratified into 7 age groups from age 18 to 28 and ending with age 79–89 years. RESULTS/ANTICIPATED RESULTS: Of the 212,343 patients in the database 1356 patients revealed opioid dependence on the problem list, ICD9-10 codes and prescription opioid pain medication with or without Buprenorphine and Naloxone (Suboxone) in the medication list. The prevalence of opioid dependence in the clinic population was 0.64% (95% CI: 0.61%–0.67%) over a 5-year period. The 7,000,000 patient records generated 750,000,000 SNOMED CT codes (on average 107 codes per record). The highest numbers of opioid dependence were seen in the 29 to 38 years’ age group. That comprised 39.38% (95% CI: 36.78%–41.98%) of the total opioid dependent population but accounted for only 2.03% of whole clinic population in this age group (95% CI: 1.86% to 2.2%). The subjects were then stratified by race and ethnicity. There were 1005 patients with opioid dependence, in the non-Hispanic population (total number 108,402). Among the White non-Hispanic or Latino population with opioid dependence, 41.33% (95% CI: 38.27%–44.39%) were 29–38 years old. The next common age group among the White Non-Hispanic opioid dependent subjects was 19–28 years, comprising of 22.48% (95% CI: 19.88%–25.08%) of the total number of White non-Hispanic or Latino opioid dependent population. Among the total clinic population Hispanics comprise 51.24%, but they comprise only 2.58% (95% CI: 1.74%–3.42%) of the total opioid dependent population. The non-Hispanic population comprise 51.05% of total clinic population while the percent of people who are opioid dependent is 83.26% (95% CI: 83.04%–83.48%) of the total 1356 opioid dependent population. DISCUSSION/SIGNIFICANCE OF IMPACT: The trends of opioid dependence among the clinic population in the study indicate that the prevalence is more in a certain section of the population. The predominance is among the non-Hispanic White population in the 19–38 years of age. The prevalence in younger age implies that the complications related to opioid dependence would be there for a longer duration of time. The prevalence of dependence in this clinic population would be rising if this trend continues. Interventions at curbing prescription opioid dependence is necessary for the vulnerable population. The findings suggest that a broad based approach is necessary to address this problem. The distribution of opioid dependence in this patient population indicate the need for special attention to these specific age group and race ethnicities. The young age of many of the addicted patients demonstrate the risks of legitimate opioid prescriptions in leading this age group towards addiction and implies the need for routine screening for substance abuse. The evidence of complications of opioid overdose among long-term opioid users and risk of abuse with other agents including illicit agents makes the need for an approach that uses real-time interventions in addition to effect long-term improvement in addiction rates. A potentially cost-effective approach to implement monitoring programs and clinical decision support tools would be to develop inter operable linkage from the EHRs to the state Department of Healths’ prescription monitoring programs.

Semantic Web for Health Care and Life Sciences: a review of the state of the art

The huge amounts of biomedical data are stored in various formats and accessed through numerous interfaces. It is a crucial task for Data integration and exchange in cancer research. Data elements play an important role in data integration. The NCI supports a broad initiative to standardize the common data elements (CDEs) used in cancer research data capture and reporting. The Taiwan Cancer Registry (TCR), established in 1979, is organized and funded by the Health Department of the central government. The TCR's primary goal is to survey the incidence of cancer in Taiwan. The aim of the Taiwan Cancer Common Data Element Project (TCCDEP) is to facilitate convergence towards a common metadata standard in Taiwan cancer registry data. The project is implemented using a set of open source software and tools developed by the NCI, such as the caCORE SDK and caGrid. The experience of building, learning and using the open toolkit, Cancer Data Standards Repository (caDSR), developed by the National Cancer Institute's Center for Bioinformatics, NCICB, in the USA is reported. The caDSR is a metadata repository including CDEs used by NCI-sponsored organizations. The object of this work is to develop a database of metadata for medical data elements, referred to as the TCCDEP and to establish a common classification of data elements used in cancer registry. In this manuscript, we will be developing the common data elements using vocabulary standards, ontology and semantic modeling methodology. The CDEs included demographic data, clinical history, pathology data, and clinical outcome data including treatment, recurrence and vital status. These CDEs will be further enhanced to data sets across the participating cancer institutes to facilitate and supplement translational research. The Taiwan Cancer Registry (TCR) model and standard will be use as the basis for an electronic data standard repository to metadata or data descriptors. The TCCDEP developed 40 data elements to annotate the cancer registry data collected. In this project, we describe the process required to develop the model, the caDSR CDEs, and the results of the modeling effort. We address difficulties we encountered and modifications for solution. The caBIG (TM) grid project, gird model of Taiwan Cancer Registry (girdTCR), using the caCORE tools to define data elements for cancer registry has been shown to caBIG(TM)UML model project. Currently, the Taiwan cancer registry CDEs are released and available in CDE browser for reusing. Furthermore, we will extend our CDEs to daily clinical practice and trials, along with how the methods were used to fully implemented in hospitals and cancer research centers in Taiwan.

Adequacy of evolving national standardized terminologies for interdisciplinary coded concepts in an automated clinical pathway

So far, the portal for medical data models allows its users to download medical forms in a standardized format. Importing data models into electronic data capture software involved a manual step of downloading and importing the files. Now, the portal was enhanced with a web services interface to allow electronic data capture systems to automatically download the forms. This mechanism can be used in federated studies to ensure that all partners are working with identical definitions of study forms.

Applying artificial intelligence (AI) and machine learning (ML) techniques to biomedical problems is predicated on access to clean, high-quality data and reusable software tools. Data and tool repositories offer a vital infrastructure for centralizing, storing, and sharing such resources with the global research community of cross-disciplinary scientists. The Cancer Complexity Knowledge Portal (CCKP) is a NIH-listed domain-specific repository that makes oncology data findable and accessible. The CCKP is developed and maintained by the Multi-Consortia Coordinating (MC2) Center, led by Sage Bionetworks. The MC2 Center supports resource coordination among six cancer-focused interdisciplinary research consortia funded by the National Cancer Institute (NCI). Through this work, the CCKP has emerged as a significant repository for oncology data and tool sharing. To define the metadata standards for sharing resources on the CCKP, we host data models for various data modalities including genomics and imaging, and are developing new models for emerging multimodal data types like spatial transcriptomics. The data models undergo iterative development based on emerging community needs, with versioned releases maintained in a public GitHub repository. These data models power established data management tools developed by Sage Bionetworks, including the Schematic Python package and Data Curator App, to support annotation of data according to FAIR standards. The data models are designed to help researchers link research outputs (e.g., publications, datasets), thus helping the CCKP synthesize and highlight the activities and outputs of the NCI-funded cancer research programs in a meaningful way. The CCKP provides search and filtering capability to accelerate discovery and collaboration in the cancer research community. As of November 2024, the portal hosts curated information on 3, 786 publications, 904 datasets, and 292 computational tools emerging from over 140 research grants. Our data models also incorporate elements of the Cancer Research Data Commons (CRDC) Data Hub data model to support integration and reuse of datasets within the CRDC ecosystem. The recent advances in computational oncology showcase the strength of secondary use of research data and highlight an unmet need to ensure that data and tools available through repositories are reliable, and reusable. We are engaging with scientists, clinicians, and patient advocates, and leveraging user-centered design approaches and structured data models to make cancer data and tools more findable, accessible, and reusable. These ongoing improvements of the portal aim to increase its usability, bringing experimental labs generating data and computational labs using data closer together, to fuel the iterative cycle of scientific discovery. Citation Format: Orion Banks, Ashley Clayton, Aditi Gopalan, Amber Nelson, Stockard Simon, Verena Chung, Amy Heiser, Jay Hodgson, Aditya Nath, Adam Hindman, Milen Nikolov, Adam Taylor, James Eddy, Susheel Varma, Jineta Banerjee. Cancer Complexity Knowledge Portal: a centralized web portal for finding cancer related data, software tools, and other resources [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1062.

St. Louis, Missouri, sits astride the confluence of the Mississippi and Missouri Rivers, a position that made the city the “Gateway to the West.” Its most famous monument, the Gateway Arch, memorializes the millions of pioneers who traveled to the city seeking transportation to the frontier lands of the West, and who were outfitted with the tools and knowledge they needed on the journey to these new lands of opportunity. The Clinical & Translational Science Awards (CTSA) program has enabled Washington University (WU) and its regional partners to create a gateway of another sort, aimed at helping researchers toward new opportunities and discoveries in clinical and translational research. The Washington University Institute of Clinical and Translational Sciences (ICTS) was founded in September 2007 to serve as the intellectual and physical home for clinical and translational research and clinical research training at WU and its partner institutions. ICTS was founded on WU's 4 prior clinical research and training grants—GCRC, K12, K30, and T32—and on an existing university-wide strategic initiative in multidisciplinary collaborative research emphasizing genome sciences, biological imaging, and clinical investigation. The formation of the ICTS resulted in a new level of regional partnership with other academic, health care, and industrial and community partners. In its second year of existence, the ICTS is increasingly able to integrate the different strengths of its partners to reach its goals of reinventing and reinvigorating CTS research and training. In designing the ICTS, we used a conceptual model that identified important phases in the progress of a clinical or translational research project (Figure 1). For each phase, we identifed needed resources and potential barriers to completion, and have structured the ICTS in a way that attempts to provide needed resources and expertise for each phase of research from development and refinement of research ideas through translation of results to clinical practice and community implementation. Education, training, and career development are at the heart of our ICTS, and are linked to all research phases and programmatic activities. Our ICTS has 15 key Program Functions, each linked to 1 or more research phases, and each with specific goals, milestones, and evaluation criteria for various services, cores, and programs. We will highlight 2 new and important program functions below: our community-based research efforts and our programs in biomedical informatics. Phases in the evolution of clinical research projects. The 3 CCBR arms serve as a model for community participation to improve research relevant to public health. A central transformative element offour ICTS was the establishment of the Center for Community-Based Research (CCBR), which has initiated community engagement ef orts in 3 important groups: community organizations, community-based health care providers, and individuals from medically underserved populations. Each of the 3 arms is designed to solicit community input to increase the relevance of research to community needs, and to encourage partnerships between academic researchers and community groups. Our Community Advisory Board, established by the 3 arms, meets quarterly, to ensure relevance of the CCBR programs and the ICTS programs as a whole. The Community Organizations program has focused on cultivating partnerships with community organizations and conducting community needs assessments. This new model has spearheaded a series of conferences with our Regional Health Commission to address how our academic medical center can better serve the needs of our local community and improve the public health. In the fall of 2009, we will begin a series of shared conferences with community organizations highlighting successful community-based participatory research projects. These conferences will include joint presentations by academics and community partners and will strive to facilitate the formation of new partnerships. To further stimulate new community-based participatory research projects, we will soon initiate a research grants program, supported by a regional coalition of universities and health care institutions, to fund research projects that include a high degree of cooperation between community organizations and academic researchers. The program has also initiated a traveling poster presentation forum that translates data in a community relevant fashion. The Community Health Care Practitioners program seeks to expand community networks of practitioners to increase opportunities for clinical and translational (T2 and T3) research. One such effort is the Washington University Pediatric and Adolescent Ambulatory Research Consortium (WU PAARC). Currently comprised of approximately 60 community pediatricians and 5 pediatric nurse practitioners, the network provides a formal structure to develop research ef orts that identify best practices and translate research findings into practice. Community pediatricians participate in the consortium in different ways, from contributing ideas and receiving study updates to helping develop study protocols and recruit patients from their practices. Some studies are initiated by WU PAARC physicians, such as a recent study on a Telephone Asthma Program that resulted from physicians’ frustration with patients not using their asthma maintenance medications. For this randomized controlled trial, 362 families with asthmatic children were recruited from 95 community pediatric offices over 13 months. The 12-month telephone asthma coaching intervention helped parents better manage their child's asthma care and improved children's asthma control and parents’ quality-of-life scores. An upcoming study will compare 2 corticosteroid treatments for croup, a disease commonly seen in community practice but never before studied in this setting. In addition to WU PAARC-sponsored studies, community pediatricians have opportunities to participate in other studies developed by ICTS researchers including participating in study development and implementation. An example is a prevalence study for community-acquired methicillin-resistant Staph aureu s for which 1,300 children were recruited from 11 WU PAARC practices over an 8-month period. WU PAARC serves as a model for cooperative research between academic and community physicians, and these examples show how those involved have a real opportunity to improve the health of children and adolescents. As our ICTS matures, we plan to expand our practice-based research network activities to include new patient and provider populations. The CCBR has also started HealthStreet, a project linking populations that have been underrepresented in research to health information, referrals to social, medical, and psychiatric services, as well as opportunities to participate in research studies. HealthStreet utilizes Community Health Workers and a comprehensive outreach approach to establish trust between the lay and medical communities. The hub of CCBR activity is a new storefront facility in an underserved neighborhood near our medical center where community members can receive free health screenings, health referrals, and information about WU clinical studies. Community Health Workers also reach people in sites such as beauty shops, laundromats, bus stops, and community agencies to spread the word about services offered, and opportunities for research. The 3 CCBR arms serve as a model for community participation to improve research relevant to public health. CCBR investigators are actively training and engaging other ICTS investigators to collaborate on community-based projects with the goal of addressing “outcomes that matter.” Major institutional goals for improving CTS research are the creation of efficient communication and coordination across key function activities; the creation of an informatics infrastructure capable of integrating ICTS program components for data sharing; and the development of electronic resources of existing clinical and biospecimen data that may be used to develop new clinical studies, identify potential research participants, and execute novel molecular correlative studies. The ICTS Center for Biomedical Informatics (CBMI) is addressing these complex core issues through industry-standard hardware infrastructure and software tools to store, integrate, query, analyze, and visualize complex clinical and molecular data sets. Among the several activities of CBMI are the integration and implementation of the research management tool ClinPortal, development and management of biospecimen tracking tools, and creation of the Clinical Investigation Data Exploration Repository (CIDER), a research repository of medical records. ClinPortal is a research management tool that allows investigators and study coordinators to integrate the data collection and management needs of a clinical study with critical regulatory compliance requirements. Investigators create case report forms using data elements (or variables) that are defined using controlled vocabularies, and ClinPortal then automatically generates the backend database schema as well as the forms to enter the data and conduct queries. Use of ClinPortal offers a number of advantages to the institution and to the investigator. By default, data stored in ClinPortal is available for sharing while following all HIPAA regulations, although the investigator that owns the data may turn of sharing if so desired. As a result, once an investigator has established his or her study within the ClinPortal system, there is potential for sharing relevant data with and from other studies. Data access is controlled both by investigator permissions and by regulatory requirements. Future phases include automated access to clinical data from our hospital and outpatient systems. Assuming appropriate release by the patient and access rights for the researcher, investigators using ClinPortal will eventually have access to clinical data from hospital and clinic sources for their study participants. Tissue Suite, a Web-based biospecimen inventory, tracking, and annotation tool, may be used for study-based biospecimen collection efforts and by other non-study investigators interested in identifying existing biospecimens throughout the medical campus that may be available for translational research. The ICTS Translational Pathology and Molecular Phenotyping (TPMP) Core supports the physical storage of biospecimens, while Tissue Suite is the application supported by both the CBMI and the TPMP to provide the informatics to manage data about those biospecimens. Investigators who are managing their own biospecimen collection efforts outside of the TPMP Core may still make use of the Tissue Suite application to manage their inventory in compliance with caBIG standards. Web accessibility, the ability to share biospecimens and biospecimen-related data, advanced search functionality, and capture of clinical, pathological, and quality assurance data for collected biospecimens are key features of Tissue Suite. Together, ClinPortal and Tissue Suite facilitate collaboration between investigators and data integration across studies. As clinical data becomes available through the ClinPortal tool, biospecimens referenced in the complementary Tissue Suite tool will gain increasing clinical annotation, making them even more valuable for translational research studies. Another major focus of the CBMI has been the development of a comprehensive research patient data warehouse. CIDER contains a wealth of data from the electronic medical records of most inpatients and outpatients seen at the WU medical center and affiliated hospitals in the BJC HealthCare system. This system will become available to ICTS investigators in 2009 and will initially contain data on patient encounters, laboratory testing, and medications. We sought community input in designing the patient notification and opt-out procedures and in strictly designating access and use of this information by qualified investigators. We expect that CIDER will become a very useful tool for many clinical and translational researchers to perform a review preparatory to research, identify potential participants to recruit to active clinical studies both by querying CIDER and as they are encountered in the health care system, and implement quality assurance and improvement initiatives. In addition to these highlighted functions, the CBMI is working on other ways to simplify communication and tracking within the ICTS. One new project is the creation of an electronic Research Concierge function to serve as the focal point to help clinical and translational researchers navigate through the study planning, design, review, and execution processes. This Research Concierge function will be added to WU's existing site that currently provides single sign-on access to the resources necessary to propose, manage, and close research projects and satisfy compliance requirements for research. In a nod to St. Louis history, these resources can be found at the WU Research Gateway Web site (http://research.wustl.edu). More information about the WU ICTS can be found at our main site http://icts.wustl.edu.