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Targeted screening for pediatric conditions with the CHICA system

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第4期18卷 485-490页

作者： Carroll, Aaron E. Biondich, Paul G. Anand, Vibha Dugan, Tamara M. Sheley, Meena E. Xu, Shawn Z. Downs, Stephen M. Indiana Univ Sch Med Childrens Hlth Serv Res Indianapolis IN 46202 USA Indiana Univ Sch Med Regenstrief Inst Hlth Care Indianapolis IN 46202 USA

Objective The Child Health Improvement through Computer Automation (CHICA) system is a decision-support and electronic-medical-record system for pediatric health maintenance and disease management. The purpose of this study was to explore CHICA's ability to screen patients for disorders that have validated screening criteria specifically tuberculosis (TB) and iron-deficiency anemia. Design Children between 0 and 11 years were randomized by the CHICA system. In the intervention group, parents were asked about TB and iron-deficiency risk, and physicians received a tailored prompt. In the control group, no screens were performed, and the physician received a generic prompt about these disorders. Results 1123 participants were randomized to the control group and 1116 participants to the intervention group. Significantly more people reported positive risk factors for iron-deficiency anemia in the intervention group (17.5% vs 3.1%, OR 6.6, 95% CI 4.5 to 9.5). In general, far fewer parents reported risk factors for TB than for iron-deficiency anemia. Again, there were significantly higher detection rates of positive risk factors in the intervention group (1.8% vs 0.8%, OR 2.3, 95% CI 1.0 to 5.0). Limitations It is possible that there may be more positive screens without improving outcomes. However, the guidelines are based on studies that have evaluated the questions the authors used as sensitive and specific, and there is no reason to believe that parents misunderstood them. Conclusions Many screening tests are risk-based, not universal, leaving physicians to determine who should have a further workup. This can be a time-consuming process. The authors demonstrated that the CHICA system performs well in assessing risk automatically for TB and iron-deficiency anemia.

关键词： Tuberculosis iron-deficiency anemia CHICA screening computer decision-support system visualization of data and knowledge knowledge representations communications networking methods mobility uncertain reasoning and decision theory languages computational methods advanced algorithms methods for integration of information from disparate sources distributed systems agents software engineering: architecture data models automated learning developing/using clinical decision support (other than diagnostic) and guideline systems discovery text and data mining methods

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Health information exchange usage in emergency departments and clinics: the who, what, and why

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第5期18卷 690-697页

作者： Johnson, Kevin B. Unertl, Kim M. Chen, Qingxia Lorenzi, Nancy M. Nian, Hui Bailey, James Frisse, Mark Vanderbilt Univ Sch Med Dept Biomed Informat Nashville TN 37232 USA Vanderbilt Univ Sch Med Dept Pediat Nashville TN 37232 USA Vanderbilt Univ Sch Med Dept Biostat Nashville TN 37232 USA Univ Tennessee Hlth Sci Ctr Div Gen Internal Med Nashville TN USA

Objective Health information exchange (HIE) systems are being developed across the nation. Understanding approaches taken by existing successful exchanges can help new exchange efforts determine goals and plan implementations. The goal of this study was to explore characteristics of use and users of a successful regional HIE. Design We used a mixed-method analysis, consisting of cross-sectional audit log data, semi-structured interviews, and direct observation in a sample of emergency departments and ambulatory safety net clinics actively using HIE. For each site, we measured overall usage trends, user logon statistics, and data types accessed by users. We also assessed reasons for use and outcomes of use. Results Overall, users accessed HIE for 6.8% of all encounters, with higher rates of access for repeat visits, for patients with comorbidities, for patients known to have data in the exchange, and at sites providing HIE access to both nurses and physicians. Discharge summaries and test reports were the most frequently accessed data in the exchange. Providers consistently noted retrieving additional history, preventing repeat tests, comparing new results to retrieved results, and avoiding hospitalizations as a consequence of HIE access. Conclusion HIE use in emergency departments and ambulatory clinics was focused on patients where missing information was believed to be present in the exchange and was related to factors including the roles of people with access, the setting, and other site-specific issues that impacted the overall breadth of routine system use. These data should form an important foundation as other sites embark upon HIE implementation.

关键词： Health information exchange usage clinical informatics biomedical informatics pediatrics e-prescribing human factors qualitative/ethnographic field study designing usable (responsive) resources and systems improving healthcare workflow and process efficiency system implementation and management issues surveys and needs analysis social/organizational study health information exchange collaborative technologies methods for integration of information from disparate sources demonstrating return on IT investment distributed systems agents software engineering: architecture supporting practice at a distance (telehealth) data exchange communication and integration across care settings (inter- and intra-enterprise) visualization of data and knowledge policy legal historical ethical study methods

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Computationally translating molecular discoveries into tools for medicine: translational bioinformatics articles now featured in JAMIA

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第4期18卷 352-353页

作者： Butte, Atul J. Shah, Nigam H. Stanford Univ Sch Med Dept Pediat Div Syst Med Stanford CA 94305 USA Lucile Packard Childrens Hosp Palo Alto CA USA Stanford Univ Sch Med Stanford Ctr Biomed Informat Res Stanford CA 94305 USA

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2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第5期18卷 552-556页

作者： Uzuner, Oezlem South, Brett R. Shen, Shuying DuVall, Scott L. SUNY Albany Coll Comp & Informat Dept Informat Studies Albany NY 12222 USA VA Salt Lake City Hlth Care Syst Salt Lake City UT USA Univ Utah Dept Internal Med Salt Lake City UT 84112 USA Univ Utah Dept Biomed Informat Salt Lake City UT USA

The 2010 i2b2/VA Workshop on Natural Language Processing Challenges for Clinical Records presented three tasks: a concept extraction task focused on the extraction of medical concepts from patient reports;an assertion classification task focused on assigning assertion types for medical problem concepts;and a relation classification task focused on assigning relation types that hold between medical problems, tests, and treatments. i2b2 and the VA provided an annotated reference standard corpus for the three tasks. Using this reference standard, 22 systems were developed for concept extraction, 21 for assertion classification, and 16 for relation classification. These systems showed that machine learning approaches could be augmented with rule-based systems to determine concepts, assertions, and relations. Depending on the task, the rule-based systems can either provide input for machine learning or post-process the output of machine learning. Ensembles of classifiers, information from unlabeled data, and external knowledge sources can help when the training data are inadequate.

关键词： Information storage and retrieval (text and images) discovery and text and data mining methods Other methods of information extraction Natural-language processing Automated learning visualization of data and knowledge uncertain reasoning and decision theory languages and computational methods statistical analysis of large datasets advanced algorithms discovery other methods of information extraction automated learning human-computer interaction and human-centered computing NLP machine learning Informatics

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Translational bioinformatics: linking knowledge across biological and clinical realms

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第4期18卷 354-357页

作者： Sarkar, Indra Neil Butte, Atul J. Lussier, Yves A. Tarczy-Hornoch, Peter Ohno-Machado, Lucila Univ Vermont Ctr Clin & Translat Sci Burlington VT 05405 USA Univ Vermont Coll Med Dept Microbiol & Mol Genet Burlington VT 05405 USA Univ Vermont Coll Engn & Math Sci Dept Comp Sci Burlington VT 05405 USA Stanford Univ Sch Med Dept Pediat Div Syst Med Stanford CA 94305 USA Univ Chicago Dept Med Med Genet Sect Chicago IL 60637 USA Univ Chicago Ludwig Ctr Metastasis Res UC Comprehens Canc Ctr Chicago IL 60637 USA Univ Chicago Inst Translat Med Computat Inst Inst Genom & Syst Biol Chicago IL 60637 USA Univ Washington Div Biomed & Hlth Informat Seattle WA 98195 USA Univ Washington Inst Translat Hlth Sci Seattle WA 98195 USA Univ Washington Inst Genom Med Seattle WA 98195 USA Univ Washington Dept Comp Sci Seattle WA 98195 USA Univ Calif San Diego Div Biomed Informat La Jolla CA 92093 USA

Nearly a decade since the completion of the first draft of the human genome, the biomedical community is positioned to usher in a new era of scientific inquiry that links fundamental biological insights with clinical knowledge. Accordingly, holistic approaches are needed to develop and assess hypotheses that incorporate genotypic, phenotypic, and environmental knowledge. This perspective presents translational bioinformatics as a discipline that builds on the successes of bioinformatics and health informatics for the study of complex diseases. The early successes of translational bioinformatics are indicative of the potential to achieve the promise of the Human Genome Project for gaining deeper insights to the genetic underpinnings of disease and progress toward the development of a new generation of therapies.

关键词： Translational bioinformatics systems medicine systems biology bioinformatics biomedical informatics knowledge representation information retrieval phylogenetics modeling physiologic and disease processes linking the genotype and phenotype identifying genome and protein structure and function visualization of data and knowledge simulation of complex systems (at all levels: molecules to work groups to organizations) knowledge representations uncertain reasoning and decision theory languages computational methods statistical analysis of large datasets advanced algorithms discovery text and data mining methods natural-language processing automated learning ontologies

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Enabling distributed electronic research data collection for a rural Appalachian tobacco cessation study

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第SUPPLEMENT_1期18卷 I140-I143页

作者： Borlawsky, Tara B. Lele, Omkar Jensen, Daniel Hood, Nancy E. Wewers, Mary Ellen Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA Ohio State Univ Coll Publ Hlth Columbus OH 43210 USA

Tobacco use is increasingly prevalent among vulnerable populations, such as people living in rural Appalachian communities. Owing to limited access to a reliable internet service in such settings, there is no widespread adoption of electronic data capture tools for conducting community-based research. By integrating the REDCap data collection application with a custom synchronization tool, the authors have enabled a workflow in which field research staff located throughout the Ohio Appalachian region can electronically collect and share research data. In addition to allowing the study data to be exchanged in near-real-time among the geographically distributed study staff and centralized study coordinator, the system architecture also ensures that the data are stored securely on encrypted laptops in the field and centrally behind the Ohio State University Medical Center enterprise firewall. The authors believe that this approach can be easily applied to other analogous study designs and settings.

关键词： data collection community-based participatory research medical informatics software data modeling and integration machine learning predictive modeling statistical learning privacy technology visualization of data and knowledge knowledge representations methods for integration of information from disparate sources data models data exchange

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Development and preliminary evidence for the validity of an instrument assessing implementation of human-factors principles in medication-related decision-support systems-I-MeDeSA

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第SUPPLEMENT_1期18卷 I62-I72页

作者： Zachariah, Marianne Phansalkar, Shobha Seidling, Hanna M. Neri, Pamela M. Cresswell, Kathrin M. Duke, Jon Bloomrosen, Meryl Volk, Lynn A. Bates, David W. Partners Healthcare Syst CIRD Wellesley MA 02481 USA Brigham & Womens Hosp Div Gen Internal Med Boston MA 02115 USA Harvard Univ Sch Med Boston MA USA Univ Heidelberg Dept Clin Pharmacol & Pharmacoepidemiol Heidelberg Germany Univ Hosp Geneva Div Med Informat Sci Geneva Switzerland Univ Edinburgh Ctr Populat Hlth Sci eHlth Res Grp Edinburgh Midlothian Scotland Regenstrief Inst Hlth Care Indianapolis IN USA Amer Med Informat Assoc Bethesda MD USA

Background Medication-related decision support can reduce the frequency of preventable adverse drug events. However, the design of current medication alerts often results in alert fatigue and high over-ride rates, thus reducing any potential benefits. Methods The authors previously reviewed human-factors principles for relevance to medication-related decision support alerts. In this study, instrument items were developed for assessing the appropriate implementation of these human-factors principles in drug-drug interaction (DDI) alerts. User feedback regarding nine electronic medical records was considered during the development process. Content validity, construct validity through correlation analysis, and inter-rater reliability were assessed. Results The final version of the instrument included 26 items associated with nine human-factors principles. Content validation on three systems resulted in the addition of one principle (Corrective Actions) to the instrument and the elimination of eight items. Additionally, the wording of eight items was altered. Correlation analysis suggests a direct relationship between system age and performance of DDI alerts (p=0.0016). Inter-rater reliability indicated substantial agreement between raters (kappa=0.764). Conclusion The authors developed and gathered preliminary evidence for the validity of an instrument that measures the appropriate use of human-factors principles in the design and display of DDI alerts. Designers of DDI alerts may use the instrument to improve usability and increase user acceptance of medication alerts, and organizations selecting an electronic medical record may find the instrument helpful in meeting their clinicians' usability needs.

关键词： Human factors medication-related decision support clinical decision support medication alerts name visualization of data and knowledge designing usable (responsive) resources and systems developing/using clinical decision support (other than diagnostic) and guideline systems human–computer interaction and human-centered computing policy health IT innovation patient safety decision support data exchange

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The TOKEn project: knowledge synthesis for in silico science

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第SUPPLEMENT_1期18卷 I125-I131页

作者： Payne, Philip R. O. Borlawsky, Tara B. Lele, Omkar James, Stephen Greaves, Andrew W. Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA Moores UCSD Canc Ctr CLL Res Consortium La Jolla CA USA

Objective The conduct of investigational studies that involve large-scale data sets presents significant challenges related to the discovery and testing of novel hypotheses capable of supporting in silico discovery science. The use of what are known as Conceptual knowledge Discovery in databases (CKDD) methods provides a potential means of scaling hypothesis discovery and testing approaches for large data sets. Such methods enable the high-throughput generation and evaluation of knowledge-anchored relationships between complexes of variables found in targeted data sets. Methods The authors have conducted a multipart model formulation and validation process, focusing on the development of a methodological and technical approach to using CKDD to support hypothesis discovery for in silico science. The model the authors have developed is known as the Translational Ontology-anchored knowledge Discovery Engine (TOKEn). This model utilizes a specific CKDD approach known as Constructive Induction to identify and prioritize potential hypotheses related to the meaningful semantic relationships between variables found in large-scale and heterogeneous biomedical data sets. Results The authors have verified and validated TOKEn in the context of a translational research data repository maintained by the NCI-funded Chronic Lymphocytic Leukemia Research Consortium. Such studies have shown that TOKEn is: (1) computationally tractable;and (2) able to generate valid and potentially useful hypotheses concerning relationships between phenotypic and biomolecular variables in that data collection. Conclusions The TOKEn model represents a potentially useful and systematic approach to knowledge synthesis for in silico discovery science in the context of large-scale and multidimensional research data sets.

关键词： Computing methodologies knowledge bases information theory research design phenotype biological markers visualization of data and knowledge translational research—application of biological knowledge to clinical care linking the genotype and phenotype methods for integration of information from disparate sources knowledge acquisition and knowledge management data modeling and integration knowledge representations data models methods for integration of information from disparate sources informatics

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EliXR: an approach to eligibility criteria extraction and representation

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第SUPPLEMENT_1期18卷 I116-I124页

作者： Weng, Chunhua Wu, Xiaoying Luo, Zhihui Boland, Mary Regina Theodoratos, Dimitri Johnson, Stephen B. Columbia Univ Dept Biomed Informat New York NY 10032 USA New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA

Objective To develop a semantic representation for clinical research eligibility criteria to automate semistructured information extraction from eligibility criteria text. Materials and Methods An analysis pipeline called eligibility criteria extraction and representation (EliXR) was developed that integrates syntactic parsing and tree pattern mining to discover common semantic patterns in 1000 eligibility criteria randomly selected from http://***. The semantic patterns were aggregated and enriched with unified medical language systems semantic knowledge to form a semantic representation for clinical research eligibility criteria. Results The authors arrived at 175 semantic patterns, which form 12 semantic role labels connected by their frequent semantic relations in a semantic network. Evaluation Three raters independently annotated all the sentence segments (N=396) for 79 test eligibility criteria using the 12 top-level semantic role labels. Eight-six per cent (339) of the sentence segments were unanimously labelled correctly and 13.8% (55) were correctly labelled by two raters. The Fleiss' kappa was 0.88, indicating a nearly perfect interrater agreement. Conclusion This study present a semi-automated data-driven approach to developing a semantic network that aligns well with the top-level information structure in clinical research eligibility criteria text and demonstrates the feasibility of using the resulting semantic role labels to generate semistructured eligibility criteria with nearly perfect interrater reliability.

关键词： AMTB analytical processing clinical research eligibility criteria clinical research informatics collaborative technologies databases data exchange data integration data mining data models data warehousing knowledge acquisition knowledge acquisition and knowledge management knowledge representations linking the genotype and phenotype machine learning methods for integration of information from disparate sources natural-language processing predictive modelling privacy technology semantic network semantics social/organizational study statistical learning text-based ontology learning visualization of data and knowledge

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A multi-layered framework for disseminating knowledge for computer-based decision support

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2011年第SUPPLEMENT_1期18卷 I132-I139页

作者： Boxwala, Aziz A. Rocha, Beatriz H. Maviglia, Saverio Kashyap, Vipul Meltzer, Seth Kim, Jihoon Tsurikova, Ruslana Wright, Adam Paterno, Marilyn D. Fairbanks, Amanda Middleton, Blackford Univ Calif San Diego Sch Med Div Biomed Informat Dept Med La Jolla CA 92093 USA Brigham & Womens Hosp Boston MA 02115 USA Harvard Univ Sch Med Dept Med Boston MA USA Cigna Healthcare Hartford CT USA

Background There are several challenges in encoding guideline knowledge in a form that is portable to different clinical sites, including the heterogeneity of clinical decision support (CDS) tools, of patient data representations, and of workflows. Methods We have developed a multi-layered knowledge representation framework for structuring guideline recommendations for implementation in a variety of CDS contexts. In this framework, guideline recommendations are increasingly structured through four layers, successively transforming a narrative text recommendation into input for a CDS system. We have used this framework to implement rules for a CDS service based on three guidelines. We also conducted a preliminary evaluation, where we asked CDS experts at four institutions to rate the implementability of six recommendations from the three guidelines. Conclusion The experience in using the framework and the preliminary evaluation indicate that this approach has promise in creating structured knowledge, to implement in CDS systems, that is usable across organizations.

关键词： knowledge bases Translational research - application of biological knowledge to clinical care developing/using clinical decision support (other than diagnostic) and guideline systems knowledge acquisition and knowledge management clinical informatics clinical decision support semantic web Manning Maddux bioinformatics datamining predictive modeling developing/using computerized provider order entry knowledge representations classical experimental and quasi-experimental study methods (lab and field) designing usable (responsive) resources and systems statistical analysis of large datasets visualization of data and knowledge knowledge representations knowledge acquisition and knowledge management distributed systems agents software engineering: architecture developing and refining ehr data standards (including image standards) data models data exchange controlled terminologies and vocabularies communication and integration across care settings (inter- and intra-enterprise) knowledge bases knowledge representations uncertain reasoning and decision theory designing usable (responsive) resources and systems personal health records and self-care systems knowledge acquisition and knowledge management demonstrating return on it investment other specific ehr applications (results review medication administration disease progression

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