Assessing clinical researchers' information needs to create responsive portals and tools: my Research Assistant (MyRA) at the University of Utah: a case study.

QUESTION: How can health sciences librarians and biomedical informaticians offer relevant support to Clinical and Translational Science Award (CTSA) personnel? SETTING: The Spencer S. Eccles Health Sciences Library and the associate vice president for information technology for the health sciences office at the University of Utah conducted a needs assessment. METHODS: Faculty and staff from these two units, with the services of a consultant and other CTSA partners, employed a survey, focus groups, interviews, and committee discussions. An information portal was created to meet identified needs. RESULTS: A directive white paper was created. The process employed to plan a virtual and physical collaborative, collegial space for clinical researchers at the university and its three inter-institutional CTSA partners is described. CONCLUSION: The university's model can assist other librarians and informaticians with how to become part of a CTSA-focused infrastructure for clinical and translational research and serve researchers in general.

Comparison of compliance for colorectal cancer screening and surveillance by colonoscopy based on risk.

PURPOSE: To compare colonoscopy screening/surveillance rates by level of risk for colorectal cancer based on age, personal history of adenomatous polyps or colorectal cancer, or family history of colorectal cancer. METHODS: Participants were aged 30-90 years, were seen within 5 years at Intermountain Healthcare, and had family history in the Utah Population Database. Colonoscopy rates were measured for those with/without risk factors. RESULTS: Among those aged 60-69 years, 48.4% had colonoscopy in the last 10 years, with rates declining after age 70 years. Percentages of those having had a colonoscopy in the last 10 years generally increased by risk level from 38.5% in those with a familial relative risk <1.0 to 47.6% in those with a familial relative risk >3.0. Compared with those with no family history, the odds ratio for being screened according to guidelines was higher for those with one first-degree relative diagnosed with colorectal cancer ≥ 60 years or two affected second-degree relatives (1.54, 95% confidence interval: 1.46-1.61) than those with one affected first-degree relative diagnosed <60 years or ≥2 affected first-degree relatives (1.25, 95% confidence interval: 1.14-1.37). CONCLUSIONS: Compliance with colonoscopy guidelines was higher for those with familial risk but did not correspond with the degree of risk.

How well does family history predict who will get colorectal cancer? Implications for cancer screening and counseling.

PURPOSE: Using a large, retrospective cohort from the Utah Population Database, we assess how well family history predicts who will acquire colorectal cancer during a 20-year period. METHODS: Individuals were selected between ages 35 and 80 with no prior record of colorectal cancer diagnosis, as of the year 1985. Numbers of colorectal cancer-affected relatives and diagnosis ages were collected. Familial relative risk and absolute risk estimates were calculated. Colorectal cancer diagnoses in the cohort were counted between years 1986 and 2005. Cox regression and Harrell's C were used to measure the discriminatory power of resulting models. RESULTS: A total of 431,153 individuals were included with 5,334 colorectal cancer diagnoses. Familial relative risk ranged from 0.83 to 12.39 and 20-year absolute risk from 0.002 to 0.21. With familial relative risk as the only predictor, Harrell's C = 0.53 and with age only, Harrell's C = 0.66. Familial relative risk combined with age produced a Harrell's C = 0.67. CONCLUSION: Family history by itself is not a strong predictor of exactly who will acquire colorectal cancer within 20 years. However, stratification of risk using absolute risk probabilities may be more helpful in focusing screening on individuals who are more likely to develop the disease.

Integrating historical clinical and financial data for pharmacological research.

BACKGROUND: Retrospective research requires longitudinal data, and repositories derived from electronic health records (EHR) can be sources of such data. With Health Information Technology for Economic and Clinical Health (HITECH) Act meaningful use provisions, many institutions are expected to adopt EHRs, but may be left with large amounts of financial and historical clinical data, which can differ significantly from data obtained from newer systems, due to lack or inconsistent use of controlled medical terminologies (CMT) in older systems. We examined different approaches for semantic enrichment of financial data with CMT, and integration of clinical data from disparate historical and current sources for research. METHODS: Snapshots of financial data from 1999, 2004 and 2009 were mapped automatically to the current inpatient pharmacy catalog, and enriched with RxNorm. Administrative metadata from financial and dispensing systems, RxNorm and two commercial pharmacy vocabularies were used to integrate data from current and historical inpatient pharmacy modules, and the outpatient EHR. Data integration approaches were compared using percentages of automated matches, and effects on cohort size of a retrospective study. RESULTS: During 1999-2009, 71.52%-90.08% of items in use from the financial catalog were enriched using RxNorm; 64.95%-70.37% of items in use from the historical inpatient system were integrated using RxNorm, 85.96%-91.67% using a commercial vocabulary, 87.19%-94.23% using financial metadata, and 77.20%-94.68% using dispensing metadata. During 1999-2009, 48.01%-30.72% of items in use from the outpatient catalog were integrated using RxNorm, and 79.27%-48.60% using a commercial vocabulary. In a cohort of 16304 inpatients obtained from clinical systems, 4172 (25.58%) were found exclusively through integration of historical clinical data, while 15978 (98%) could be identified using semantically enriched financial data. CONCLUSIONS: Data integration using metadata from financial/dispensing systems and pharmacy vocabularies were comparable. Given the current state of EHR adoption, semantic enrichment of financial data and integration of historical clinical data would allow the repurposing of these data for research. With the push for HITECH meaningful use, institutions that are transitioning to newer EHRs will be able to use their older financial and clinical data for research using these methods.

Using information prescriptions to refer patients with metabolic conditions to the Genetics Home Reference website.

OBJECTIVES: The objectives of this study were to assess the reactions of adult patients and parents of children with metabolic conditions to receipt of an "information prescription" (IP) to visit Genetics Home Reference (GHR), a National Institutes of Health/National Library of Medicine online resource, and evaluate the perceived utility of information found on the site. METHODS: Patients seen at the University of Utah Metabolic Service Clinic were invited to participate in the study and asked to complete an initial survey to gather demographic data and an online survey six weeks later to obtain information about user experience. RESULTS: Fifty-three of 82 individuals completed both surveys, for an overall response rate of 64.6%. Most respondents (88.7%) agreed that receiving the IP was a "good idea," and nearly all used the IP to visit GHR. More than three-quarters (79.6%) agreed that information on GHR supplemented a physician's advice; 60.4% reported an improved understanding of a health condition; and 41.5% either looked for or would consider looking for additional information. Eighty-six percent of respondents were satisfied with the information found on GHR, and 80% would recommend the site. CONCLUSIONS: Use of an IP to direct patients to GHR was well received, and retrieved information was perceived as useful in key areas. The high level of satisfaction with GHR argues for expanded use of the IP approach in this patient population.

Evaluating the informatics for integrating biology and the bedside system for clinical research.

BACKGROUND: Selecting patient cohorts is a critical, iterative, and often time-consuming aspect of studies involving human subjects; informatics tools for helping streamline the process have been identified as important infrastructure components for enabling clinical and translational research. We describe the evaluation of a free and open source cohort selection tool from the Informatics for Integrating Biology and the Bedside (i2b2) group: the i2b2 hive. METHODS: Our evaluation included the usability and functionality of the i2b2 hive using several real world examples of research data requests received electronically at the University of Utah Health Sciences Center between 2006 - 2008. The hive server component and the visual query tool application were evaluated for their suitability as a cohort selection tool on the basis of the types of data elements requested, as well as the effort required to fulfill each research data request using the i2b2 hive alone. RESULTS: We found the i2b2 hive to be suitable for obtaining estimates of cohort sizes and generating research cohorts based on simple inclusion/exclusion criteria, which consisted of about 44% of the clinical research data requests sampled at our institution. Data requests that relied on post-coordinated clinical concepts, aggregate values of clinical findings, or temporal conditions in their inclusion/exclusion criteria could not be fulfilled using the i2b2 hive alone, and required one or more intermediate data steps in the form of pre- or post-processing, modifications to the hive metadata, etc. CONCLUSION: The i2b2 hive was found to be a useful cohort-selection tool for fulfilling common types of requests for research data, and especially in the estimation of initial cohort sizes. For another institution that might want to use the i2b2 hive for clinical research, we recommend that the institution would need to have structured, coded clinical data and metadata available that can be transformed to fit the logical data models of the i2b2 hive, strategies for extracting relevant clinical data from source systems, and the ability to perform substantial pre- and post-processing of these data.

Efficiency of CYP2C9 genetic test representation for automated pharmacogenetic decision support.

OBJECTIVES: We investigated the suitability of representing discrete genetic test results in the electronic health record (EHR) as individual single nucleotide polymorphisms (SNPs) and as alleles, using the CYP2C9 gene and its polymorphic states, as part of a pilot study. The purpose of our investigation was to determine the appropriate level of data abstraction when reporting genetic test results in the EHR that would allow meaningful interpretation and clinical decision support based on current knowledge, while retaining sufficient information in order to enable reinterpretation of the results in the context of future discoveries. METHODS: Based on the SNP & allele models, we designed two separate lab panels within the laboratory information system, one containing SNPs and the other containing alleles, built separate rules in the clinical decision support system based on each model, and evaluated the performance of these rules in an EHR simulation environment using real-world scenarios. RESULTS: Although decision-support rules based on allele model required significantly less computational time than rules based on SNP model, no difference was observed on the total time taken to chart medication orders between rules based on these two models. CONCLUSIONS: Both, SNP- and allele-based models, can be used effectively for representing genetic test results in the EHR without impacting clinical decision support systems. While storing and reporting genetic test results as alleles allow for the construction of simpler decision-support rules, and make it easier to present these results to clinicians, SNP-based model can retain a greater amount of information that could be useful for future reinterpretation.

Nanoinformatics: new challenges for biomedical informatics at the nano level.

Over the last decades Nanotechnology has promised to advance science and technology in many areas. Within medicine, Nanomedicine promises to deliver new methods for diagnosis, prognosis and therapy. As the amount of available information is rapidly growing, new Biomedical Informatics approaches have to be developed to satisfy the increasing demand on data and knowledge management. In 2007, a new sub-discipline, already named "Nanoinformatics", was created with support from the US National Science Foundation. In Europe, a project named ACTION-Grid was launched in 2008 with support from the European Commission to analyze the challenges and agenda for developing Nanoinformatics as a discipline related to Nanotechnology, Biomedicine and Informatics. For MIE 2009, members of this consortium proposed a workshop to discuss the scientific and strategic issues associated with this topic. Nanoinformatics aims to create a bridge between Nanomedicine and Information Technology applying computational methods to manage the information created in the nanomedical domain.

Effectiveness of topic-specific infobuttons: a randomized controlled trial.

OBJECTIVE: Infobuttons are decision support tools that provide links within electronic medical record systems to relevant content in online information resources. The aim of infobuttons is to help clinicians promptly meet their information needs. The objective of this study was to determine whether infobutton links that direct to specific content topics ("topic links") are more effective than links that point to general overview content ("nonspecific links"). DESIGN: Randomized controlled trial with a control and an intervention group. Clinicians in the control group had access to nonspecific links, while those in the intervention group had access to topic links. MEASUREMENTS: Infobutton session duration, number of infobutton sessions, session success rate, and the self-reported impact that the infobutton session produced on decision making. RESULTS: The analysis was performed on 90 subjects and 3,729 infobutton sessions. Subjects in the intervention group spent 17.4% less time seeking for information (35.5 seconds vs. 43 seconds, p = 0.008) than those in the control group. Subjects in the intervention group used infobuttons 20.5% (22 sessions vs. 17.5 sessions, p = 0.21) more often than in the control group, but the difference was not significant. The information seeking success rate was equally high in both groups (89.4% control vs. 87.2% intervention, p = 0.99). Subjects reported a high positive clinical impact (i.e., decision enhancement or knowledge update) in 62% of the sessions. Limitations The exclusion of users with a low frequency of infobutton use and the focus on medication-related information needs may limit the generalization of the results. The session outcomes measurement was based on clinicians' self-assessment and therefore prone to bias. CONCLUSION: The results support the hypothesis that topic links are more efficient than nonspecific links regarding the time seeking for information. It is unclear whether the statistical difference demonstrated will result in a clinically significant impact. However, the overall results confirm previous evidence that infobuttons are effective at helping clinicians to answer questions at the point of care and demonstrate a modest incremental change in the efficiency of information delivery for routine users of this tool.

Status of clinical gene sequencing data reporting and associated risks for information loss.

Clinical gene sequencing is growing in importance and cost-effectiveness. In the past two years, the number of genes associated with disease has grown by roughly 25%. Knowledge of genetic variations will soon guide drug selection and dosages, predict risks from toxin exposures, and inform nutritional needs. Despite the significance of sequencing, methods for reporting results are problematic. Frequent use of paper and infrequent use of naming standards impede data exchange and make incorporation into the electronic medical record difficult. Reports often describe only variations found, rather than all data (all patient bases sequenced). Also, reports frequently do not describe reference data used to define variations. These practices create risks for loss of both data and information. Standardized electronic reporting of all data (all bases sequenced and all reference data) and electronic record systems capable of storing these results would both prevent data loss and simplify the preservation of information those data provide.

Fuzzy measures on the Gene Ontology for gene product similarity.

One of the most important objects in bioinformatics is a gene product (protein or RNA). For many gene products, functional information is summarized in a set of Gene Ontology (GO) annotations. For these genes, it is reasonable to include similarity measures based on the terms found in the GO or other taxonomy. In this paper, we introduce several novel measures for computing the similarity of two gene products annotated with GO terms. The fuzzy measure similarity (FMS) has the advantage that it takes into consideration the context of both complete sets of annotation terms when computing the similarity between two gene products. When the two gene products are not annotated by common taxonomy terms, we propose a method that avoids a zero similarity result. To account for the variations in the annotation reliability, we propose a similarity measure based on the Choquet integral. These similarity measures provide extra tools for the biologist in search of functional information for gene products. The initial testing on a group of 194 sequences representing three proteins families shows a higher correlation of the FMS and Choquet similarities to the BLAST sequence similarities than the traditional similarity measures such as pairwise average or pairwise maximum.

Challenges and strategies of the Genetics Home Reference.

OBJECTIVE: This paper focuses on the first two years of operation of Genetics Home Reference (GHR), a Web-based resource for the general public that helps to explain the health implications of findings from the Human Genome Project. METHODS AND FINDINGS: Key challenges of Web-based consumer health communication encountered in the growth and maintenance of GHR are discussed: prioritizing topics for GHR, streamlining the development process while keeping genetic information accurate, and designing a system that helps consumers navigate complex genetic relationships. Various strategies are used to address these challenges. Tying content development to topics of national priority and addressing topics requested by users makes the site increasingly important for both consumers and health professionals. Informatics methods are essential for quality control, particularly for genetic information that changes frequently. Indexing and hierarchical browsing features help to facilitate navigation. CONCLUSIONS: GHR is a credible, dynamic Website that uses lay language to explain the effects of genetic variation on human health. Informatics strategies are key to effective management of a large and expanding body of genetics information. Feedback from formal and informal sources indicates increasing usage and favorable acceptance of GHR.

A model of interprofessional informatics education.

An emphasis on patient safety and an administrative mandate to have information systems in place in most health care agencies in the USA by 2014 has put pressure on nursing informatics programs to increase the number of graduates. At the same time a need for change in health professions education was emphasized at an educational summit sponsored by the Institute of Medicine. Interprofessional education (IPE) will help to provide needed educational reform in informatics and is defined as planned occasions when two or more professions learn from each other and about each other in a structured manner. This paper discusses an evolving interprofessional (IPE) model of informatics education that has been developed at the University of Utah. Because of interprofessional collaboration, faculty, students, and support staff from both the medical and nursing informatics programs moved into a suite on the fifth floor of a state-of-art technology-rich health sciences education building. The co-located space has enabled the informatics programs to increase activities that promote interprofessional education.

Using literature-based discovery to identify disease candidate genes.

We present BITOLA, an interactive literature-based biomedical discovery support system. The goal of this system is to discover new, potentially meaningful relations between a given starting concept of interest and other concepts, by mining the bibliographic database MEDLINE. To make the system more suitable for disease candidate gene discovery and to decrease the number of candidate relations, we integrate background knowledge about the chromosomal location of the starting disease as well as the chromosomal location of the candidate genes from resources such as LocusLink and Human Genome Organization (HUGO). BITOLA can also be used as an alternative way of searching the MEDLINE database. The system is available at http://www.mf.uni-lj.si/bitola/.

Design of Genetics Home Reference: a new NLM consumer health resource.

The authors have developed the Genetics Home Reference, a consumer resource that addresses the health implications of the Human Genome Project. The research results made possible by the Human Genome Project are being made available increasingly in scientific databases on the Internet, but, because of the often highly technical nature of these databases, they are not readily accessible to the lay public. The authors' goal is to provide a bridge between the clinical questions of the public and the richness of the data emanating from the Human Genome Project. The Genetics Home Reference currently focuses on single gene or polygenic conditions that are also topics on MEDLINEplus, the National Library of Medicine's primary consumer health site. As knowledge of genetics expands, the interrelationships between genes and diseases will continue to unfold, and the site will reflect these developments.

The impact of genomics on E-health.

The Human Genome Project (HGP) and e-Health are two fundamental changes that will alter the way we approach human health and life. These two scientific and societal forces will inevitably impact each other. This paper not only explores the ways that the HGP will change health care but also investigates the ways that e-Health systems will be influenced by the genomic data. The Electronic Medical Record (EMR) is discussed at length, including the probable impact on the laboratory, pharmacy, computerized provider order entry (CPOE), and other components. Thirteen points of a possible genomic future involving the EMR are presented. The genomic impact on other e-Health systems includes those at all levels of data: population, disease, patient, tissue and organ banks, cellular and for specific genes. The genomic impact on consumer health systems is explored, including Internet consumer information resources and the movement for direct-to-consumer genetic testing. The paper concludes that technology and trends of e-Health will enable the upcoming revolution caused by the health implications of the research emanating from the Human Genome Project.

Evaluation of WordNet as a source of lay knowledge for molecular biology and genetic diseases: a feasibility study.

OBJECTIVES: While several sources of biomedical knowledge are available, these resources are often highly specialized and usually not suitable for a lay audience. This paper evaluates whether concepts needed for molecular biology and genetic diseases are present in WordNet, the electronic lexical database. METHODS: Terms for four broad categories of concepts (phenotype, molecular function, biological process, and cellular component) were extracted from LocusLink and mapped to WordNet. All terms from the Gene Ontology database (gene products and ontology concepts) were also mapped to WordNet in order to evaluate its global coverage of the domain. Additionally, we tested two methods for improving the mapping of genetic disease names to WordNet. RESULTS: The coverage of concepts ranged from 0% (gene product symbols) to 2.8% (cellular components). Removing specialization markers from the terms and using synonyms significantly increased the rate of mapping of genetic disease names to WordNet. CONCLUSIONS: Many of the most common single gene disorders are present in WordNet, as well as many high-level concepts in Gene Ontology. Therefore, WordNet is likely to be a useful source of lay knowledge in the framework of a consumer health information system on genetic diseases.

Improving literature based discovery support by genetic knowledge integration.

We present an interactive literature based biomedical discovery support system (BITOLA). The goal of the system is to discover new, potentially meaningful relations between a given starting concept of interest and other concepts, by mining the bibliographic database Medline. To make the system more suitable for disease candidate gene discovery and to decrease the number of candidate relations, we integrate background knowledge about the chromosomal location of the starting disease as well as the chromosomal location of the candidate genes from resources such as LocusLink, HUGO and OMIM. The BITOLA system can be also used as an alternative way of searching the Medline database. The system is available at http://www.mf.uni-lj.si/bitola/.

The BioMediator system as a data integration tool to answer diverse biologic queries.

We present the BioMediator (www.biomediator.org) system and the process of executing queries on it. The system was designed as a tool for posing queries across semantically and syntactically heterogeneous data particularly in the biological arena. We use examples from researchers at the University of Washington, and the University of Missouri-Columbia, to discuss the BioMediator system architecture, query execution, modifications to the system to support the queries, and summarize our findings and our future directions. Finally, we discuss the system's flexibility and generalized approach and give examples of how the system can be extended for a variety of objectives.