National Mesothelioma Virtual Bank: A standard based biospecimen and clinical data resource to enhance translational research

Background: Advances in translational research have led to the need for well characterized biospecimens for research. The National Mesothelioma Virtual Bank is an initiative which collects annotated datasets relevant to human mesothelioma to develop an enterprising biospecimen resource to fulfill researchers' need. Methods: The National Mesothelioma Virtual Bank architecture is based on three major components: (a) common data elements (based on College of American Pathologists protocol and National North American Association of Central Cancer Registries standards), (b) clinical and epidemiologic data annotation, and (c) data query tools. These tools work interoperably to standardize the entire process of annotation. The National Mesothelioma Virtual Bank tool is based upon the caTISSUE Clinical Annotation Engine, developed by the University of Pittsburgh in cooperation with the Cancer Biomedical Informatics Grid™ (caBIG™, see http://cabig.nci.nih.gov). This application provides a web-based system for annotating, importing and searching mesothelioma cases. The underlying information model is constructed utilizing Unified Modeling Language class diagrams, hierarchical relationships and Enterprise Architect software. Result: The database provides researchers real-time access to richly annotated specimens and integral information related to mesothelioma. The data disclosed is tightly regulated depending upon users' authorization and depending on the participating institute that is amenable to the local Institutional Review Board and regulation committee reviews. Conclusion: The National Mesothelioma Virtual Bank currently has over 600 annotated cases available for researchers that include paraffin embedded tissues, tissue microarrays, serum and genomic DNA. The National Mesothelioma Virtual Bank is a virtual biospecimen registry with robust translational biomedical informatics support to facilitate basic science, clinical, and translational research. Furthermore, it protects patient privacy by disclosing only de-identified datasets to assure that biospecimens can be made accessible to researchers. © 2008 Amin et al; licensee BioMed Central Ltd

Integration of decision support systems to improve decision support performance

Decision support system (DSS) is a well-established research and development area. Traditional isolated, stand-alone DSS has been recently facing new challenges. In order to improve the performance of DSS to meet the challenges, research has been actively carried out to develop integrated decision support systems (IDSS). This paper reviews the current research efforts with regard to the development of IDSS. The focus of the paper is on the integration aspect for IDSS through multiple perspectives, and the technologies that support this integration. More than 100 papers and software systems are discussed. Current research efforts and the development status of IDSS are explained, compared and classified. In addition, future trends and challenges in integration are outlined. The paper concludes that by addressing integration, better support will be provided to decision makers, with the expectation of both better decisions and improved decision making processes

An Integrated Oncology Data Warehouse for Clinical Decision Support and Complex Patient Cohort Identification in a Hybrid Cancer Center

BACKGROUND: A data warehouse is a repository that centralizes and integrates data from disparate systems to provide the ability to easily access historical, consistent data. Integration of disparate source systems into one centralized location can enable rapid identification of more robust research cohorts and enable data-driven decision making. The objective of the Miami Cancer Institute (MCI) Oncology Data Warehouse (ODW) is to collect and organize data from clinical records, research, and administrative systems to support information retrieval, business intelligence, and analytics for high-level decision making for oncology patients. The design, architecture, and implementation aligns with industry best practices which includes Data Governance, Enterprise Data Modeling, and Metadata Management. METHODS: We integrated structured and unstructured data from disparate sources into one centralized data model optimized for querying known as the ODW. The ODW is modeled as a star schema, with fact tables and conformed dimension tables, and expands to a galaxy schema with constellation facts and dimensions that can snowflake to other data models as needed. Each fact table represents a subject area (i.e. pathology), that is directly related to the conformed dimension tables using surrogate and foreign keys. Conformed dimensions represent the attributes associated to the subject area (i.e. date of encounter). The source data is extracted, transformed and loaded (ETL) automatically from different databases into a set of tables. The ETL code performs incremental loads at regular prescribed intervals into two parallel storage areas, a relational database management system (RDMS) as well as a Big Data file storage system. RESULTS: An interdisciplinary team of physicians, engineers, scientists, and subject matter experts at the Miami Cancer Institute of Baptist Health South Florida, has designed, developed, and implemented the ODW with information originating from different data sources which include: Electronic Medical Record (EMR) systems, Financial Systems, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Pathology synoptic reports and archives, and Next Generation Sequencing services. Structurally it is a subject-oriented, integrated collection of data leveraging conformed dimensions. The ODW is capable of connecting most business intelligence (i.e. Tableau) or statistical (i.e. SAS) tools for automated or static report development. CONCLUSION: The growing ODW enables physicians, clinical management teams, and medical analysts to systematically mine and review the molecular, genomic, and associated clinical or administrative information of patients, and identify patterns that may influence treatment decisions and potential outcomes. By implementing an innovative combination of technology tools and methods, we were able to organize enterprise information about oncology patients which can be utilized for clinical decision support and precision medicine use cases

Methodology for Assessing the Logistics Potential of the Foreign Economic Activity of a Pharmaceutical Company

The aim of the article is to determine the essence of the logistics potential of a foreign trade activity of a pharmaceutical enterprise and justify the methods for determining it.The materials used in the study include statistical data of the investigated pharmaceutical enterprises, namely JSC FF “Darnitsa”, PJSC NPC “Borschagovsky Chemical and Pharmaceutical Plant”, PJSC “Pharmak”, LLC “FC Zdorovia” and JSC “Lekhim-Kharkiv”. The study used methods of analysis and synthesis, generalization, content analysis, questionnaires and methods for assessing potential. The questionnaire was used to select indicators that should be part of the logistics potential of a foreign trade activity of a pharmaceutical enterprise.The experts were 100 leading specialists of pharmaceutical companies. They are all involved in foreign economic activity. Experts are gender–divided into women (73 %), men (27 %); by age: up to 25 years – 8 %, 25–35 years – 14 %, 35–45 years – 27 %, 45–55 years – 36 %, over 55 years – 15 %; by experience: up to 5 years – 11 %, 5–10 years – 15 %, 10–20 years – 32 %, 20–30 years – 36 %, over 30 years – 6 %. The experts\u27 conclusions are valid, the coefficient of concordance is 0.86, and the Pearson test exceeds the table value.The essence of the definition of "potential of logistics of pharmaceutical enterprise\u27s foreign trade activity" is investigated. The types of logistics potential of foreign economic activity and indicators that are appropriate to use for determining the level of development of the logistic potential in foreign economic activity are offered.The potential of logistics of foreign trade activities of pharmaceutical enterprises consists of the potentials of logistics in the field of export and import. The system of indicators for measuring the logistics potential of a foreign trade activity of a pharmaceutical enterprise contains indicators selected through content analysis and questionnaires.The method of estimation of logistics potential in foreign economic activity of pharmaceutical enterprise is offered

The Global academic research organization network: Data sharing to cure diseases and enable learning health systems.

Introduction:Global data sharing is essential. This is the premise of the Academic Research Organization (ARO) Council, which was initiated in Japan in 2013 and has since been expanding throughout Asia and into Europe and the United States. The volume of data is growing exponentially, providing not only challenges but also the clear opportunity to understand and treat diseases in ways not previously considered. Harnessing the knowledge within the data in a successful way can provide researchers and clinicians with new ideas for therapies while avoiding repeats of failed experiments. This knowledge transfer from research into clinical care is at the heart of a learning health system. Methods:The ARO Council wishes to form a worldwide complementary system for the benefit of all patients and investigators, catalyzing more efficient and innovative medical research processes. Thus, they have organized Global ARO Network Workshops to bring interested parties together, focusing on the aspects necessary to make such a global effort successful. One such workshop was held in Austin, Texas, in November 2017. Representatives from Japan, Taiwan, Singapore, Europe, and the United States reported on their efforts to encourage data sharing and to use research to inform care through learning health systems. Results:This experience report summarizes presentations and discussions at the Global ARO Network Workshop held in November 2017 in Austin, TX, with representatives from Japan, Korea, Singapore, Taiwan, Europe, and the United States. Themes and recommendations to progress their efforts are explored. Standardization and harmonization are at the heart of these discussions to enable data sharing. In addition, the transformation of clinical research processes through disruptive innovation, while ensuring integrity and ethics, will be key to achieving the ARO Council goal to overcome diseases such that people not only live longer but also are healthier and happier as they age. Conclusions:The achievement of global learning health systems will require further exploration, consensus-building, funding aligned with incentives for data sharing, standardization, harmonization, and actions that support global interests for the benefit of patients

Collaborative Tagging of Phenotypic Data for Clinical and Translational Sciences

To fully understand results derived from genetic research, a patient’s genotype data must be integrated with other information about the individual (vital signs, height/weight, lab values, disease history – the phenotype of the patient) that can be obtained through clinical records. Within the clinical and translational sciences awards (CTSA), significant effort has been supported to expand translational research through the creation and mining of a phenotypic data warehouse (i2b2) that can be further linked to genotype data. However, this is just a first step towards meaningful use of the available information. Much of the information in electronic clinical records is trapped in unstructured free text, and inaccessible. Transforming this information into usable data has great potential to improve personalized healthcare and enhance the scientific enterprise. We are using “collaborative tagging,” a newer web 2.0 phenomenon used to structure information for accessibility online in which groups of individuals can add any word or phrase as a tag to identify an object (a weblog entry, a picture, etc.). Because taggers create whatever they deem as the most important tags, and are not required to select from a complex tree of predetermined tags, folksonomies can be a more palatable form of data entry than selected from a complex, predetermined list of tags. We will present our results on tagging of clinical notes by UMMS and community-based providers. This presentation was part of the retreat mini-symposium entitled: Data-Driven Approaches for Health Informatics

ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data.

Objectives:Electronic health record (EHR) data are increasingly used for biomedical discoveries. The nature of the data, however, requires expertise in both data science and EHR structure. The Observational Medical Out-comes Partnership (OMOP) common data model (CDM) standardizes the language and structure of EHR data to promote interoperability of EHR data for research. While the OMOP CDM is valuable and more attuned to research purposes, it still requires extensive domain knowledge to utilize effectively, potentially limiting more widespread adoption of EHR data for research and quality improvement. Materials and methods:We have created ROMOP: an R package for direct interfacing with EHR data in the OMOP CDM format. Results:ROMOP streamlines typical EHR-related data processes. Its functions include exploration of data types, extraction and summarization of patient clinical and demographic data, and patient searches using any CDM vocabulary concept. Conclusion:ROMOP is freely available under the Massachusetts Institute of Technology (MIT) license and can be obtained from GitHub (http://github.com/BenGlicksberg/ROMOP). We detail instructions for setup and use in the Supplementary Materials. Additionally, we provide a public sandbox server containing synthesized clinical data for users to explore OMOP data and ROMOP (http://romop.ucsf.edu)