Informatics Needs and Challenges in Cancer Research Workshop Summary (2012) / Chapter Skim
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2 Overview of the Cancer Informatics Landscape
2 Overview of the Cancer Informatics Landscape
Pages 7-30
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. · Guiding principles for an integrated data warehouse include relevant standards for data entry, deep annotation, a good query interface, and sharing (via entry back into the database)
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In the first session, an overview of the current status of cancer infor matics was provided from the perspectives of cancer centers, cancer cooperative groups, and clinical translational researchers. Panelists also discussed the lessons that could be learned from the ongoing evolution of NCI's caBIG.
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Databases That Foster Learning Shulman stressed that the most successful informatics tools will be those that interconnect research, clinical activities, and data in an organized and efficient manner, with as broad a database as possible. Citing a 2010 IOM workshop on evidence-driven practice in cancer care, he explained that the patient is the center of the system around which there is a cycle of aggregating information (including routinely collected real-time clinical data)
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At Dana-Farber, researchers can query both a clinical data repository and a consented research database. A "transient data mart" houses data involved in a current query, which is then purged when the query is completed.
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Similarly, he said there is need for robust, interoperable research databases containing structured genomic and molecular data, entered according to defined standards, and these research databases should link with relevant clinical databases. S hulman said that we are not even close to these aspirations.
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. NOTE: BWH = Brigham and Women'sFigure 2-2.eps Cancer Center; CA = cancer; CDR = Clinical Data Repository; COE = computer order entry for chemotherapy and all medications; CRIS = Clinical Research Information System; DFCI = Dana-Farber Cancer Institute; IDX = IDX operating system; LMR = longitudinal electronic medical record; Path = pathological; QOL = quality of life; Reg = registry; RPDR = Research Patient Data Registry.
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Dalton outlined four elements of a personalized medicine approach that can lead to overall improved health care: 1.Addresses health care as a public issue and seeks to improve access, affordability, and quality of care by developing an information system to assist in making clinical decisions based on outcomes and com parative effectiveness; 2. Integrates new technologies into the standard of care in an evidence based fashion to identify populations at risk, personalize treatment, and improve individual outcomes; 3.Provides an approach to identify the best treatment for individual patients based on clinical and biological characteristics of patients and their disease; and 4.Creates a network of health care providers, patients, and researchers who contribute and share information from individual patients to ultimately improve the care of all patients by learning from the experience of others (Dalton et al., 2010)
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. The goal was to develop a system FIGURE 2-3 Example of a research information exchange system at the Moffitt Cancer Center, integrating data from multiple sources and providing them to diverse stakeholders.
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, provided an overview of the NCI Clinical Trials Cooperative Group Program. As a result of an ongoing reconfiguration of the cooperative group structure, the program now comprises five major groups: 1.
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Informatics Tools Used by the NCI Cooperative Group Program Comis described the development and implementation of the M edidata Rave Clinical Data Management System software now used by the Cooperative Group Program. In 2005, the cooperative groups recognized that there was a need for a unified data system across the program.
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The ECOG-ACRIN vision, Schnall explained, is to generate an integrated data warehouse incorporating the individual case report form, such as that from the Medidata Rave system, with imaging data, laboratory data, tissue and specimen repository inventory, digitized pathology, and -omics data (e.g., genomics, metabolomics, proteomics) as well as patient-reported outcomes and claims data.
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CLINICAL TRANSLATIONAL RESEARCH INFORMATICS: CONNECTING THE STEPS OF THE RESEARCH PROCESS Bradley Pollock is chair of the Department of Epidemiology and Biostatistics at the University of Texas Health Science Center at San Antonio. He is also chair-elect of the Biostatistics, Epidemiology, and Research Design Committee of a national consortium funded by NIH through a Clinical & Translational Science Award (CTSA)
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As a testament to the importance of study design for obtaining meaningful results, Pollock noted that the New England Journal of Medicine now requests full protocols for all clinical trials. The randomized, controlled trial (RCT)
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Informatics Challenges for Translational Research Studies Using Existing, Non-Research Data Research data sources span the spectrum from EHRs to disease registries to clinical research protocol repositories, with varying degrees of completeness, quality, and research utility. Pollock concurred with Shulman regarding the need for structured data elements.
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Data can be used to assess the feasibility of a study. Big datasets could also lower the cost of conducting clinical translational research, Pollock said, by offering more precollected data, more automation, and more interconnectivity.
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, with the intent that raw and published cancer research data would be available for data mining and integration into reanalyses and meta-analyses. It would be built on shared vocabulary, data elements, and data models that would facilitate information exchange and would have a collection of interoperable applications and tools developed with common standards.
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. In addition, there was very strong community support from cancer centers for the original caBIG vision and goals of interoperability and standards-based exchange of data.
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The review found that the program had been very effective in catalyzing progress in the development of communitydriven standards for data exchange and interoperability; development, maintenance, enhancement, and dissemination of tools developed by academic researchers; and community dialogue on the interoperability of clinical and research software tools. The group found that the main problems with the caBIG approach that limited its uptake and impact included a "cart-before-the-horse grand vision"; a technology-centric approach to data sharing; unfocused expansion; a one-size-fits-all architectural approach; an unsustainable business model for both NCI and users; and a lack of independent scientific oversight.
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· Do not try to solve all clinical and translational research informa tion technology problems in one framework. · Do not worship standards over functionality.
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Will it be used broadly by organizations and institutions outside of NCI cancer centers (e.g., other NIH centers or academic research organizations)
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With regard to increasing the probability of successful adoption of informatics innovation in cancer research specifically, Masys recommended focusing data sharing efforts (both the standards for sharing and the applications to do it) on those data for which there is a preexisting motivation to share.
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NCI is very open and receptive to communications from interested parties, Masys said. George Komatsoulis, interim director for the Center for Biomedical Informatics and Information Technology at NCI, added that caBIG is continuing to move forward, and there are "workspaces" in integrative basic biology, clinical trials, data standards, imaging, biospecimens, and other areas where individuals can bring their ideas to the attention of the scientific advisory group and the caBIG program staff.
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Dalton noted that patient portals are very popular, with more than 70 percent of new cancer patients at Moffitt creating their portal before their first visit. Shulman added that the power of the systems discussed is the multiple sources of complementary data obtained by different methodologies, and this includes the patient portals being implemented by many cancer centers.
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