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A randomized controlled trial of screening for maternal depression with a clinical decision support system

JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2013年第2期20卷 311-316页

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

Objective To determine if automated screening and just in time delivery of testing and referral materials at the point of care promotes universal screening referral rates for maternal depression. Methods The Child Health Improvement through Computer Automation (CHICA) system is a decision support and electronic medical record system used in our pediatric clinics. All families of patients up to 15 months of age seen between October 2007 and July 2009 were randomized to one of three groups: (1) screening questions printed on prescreener forms (PSF) completed by mothers in the waiting room with physician alerts for positive screens, (2) everything in (1) plus 'just in time' (JIT) printed materials to aid physicians, and (3) a control group where physicians were simply reminded to screen on printed physician worksheets. Results The main outcome of interest was whether physicians suspected a diagnosis of maternal depression and referred a mother for assistance. This occurred significantly more often in both the PSF (2.4%) and JIT groups (2.4%) than in the control group (1.2%) (OR 2.06, 95% CI 1.08 to 3.93). Compared to the control group, more mothers were noted to have depressed mood in the PSF (OR 7.93, 95% CI 4.51 to 13.96) and JIT groups (OR 8.10, 95% CI 4.61 to 14.25). Similarly, compared to the control group, more mothers had signs of anhedonia in the PSF (OR 12.58, 95% CI 5.03 to 31.46) and JIT groups (OR 13.03, 95% CI 5.21 to 32.54). Conclusions Clinical decision support systems like CHICA can improve the screening of maternal depression.

关键词： Maternal depression CHICA screening computer decision support system visualization of data and knowledge data models software engineering: architecture agents distributed systems pediatrics

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Implementation of a deidentified federated data network for population-based cohort discovery

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第E1期19卷 E60-E67页

作者： Anderson, Nicholas Abend, Aaron Mandel, Aaron Geraghty, Estella Gabriel, Davera Wynden, Rob Kamerick, Michael Anderson, Kent Rainwater, Julie Tarczy-Hornoch, Peter Univ Washington Dept Biomed Hlth Informat Seattle WA 98109 USA Recombinant Data Corp Newton MA USA Univ Calif Davis Sacramento CA 95817 USA Univ Calif San Francisco San Francisco CA 94143 USA

Objective The Cross-Institutional Clinical Translational Research project explored a federated query tool and looked at how this tool can facilitate clinical trial cohort discovery by managing access to aggregate patient data located within unaffiliated academic medical centers. Methods The project adapted software from the Informatics for Integrating Biology and the Bedside (i2b2) program to connect three Clinical Translational Research Award sites: University of Washington, Seattle, University of California, Davis, and University of California, San Francisco. The project developed an iterative spiral software development model to support the implementation and coordination of this multisite data resource. Results By standardizing technical infrastructures, policies, and semantics, the project enabled federated querying of deidentified clinical datasets stored in separate institutional environments and identified barriers to engaging users for measuring utility. Discussion The authors discuss the iterative development and evaluation phases of the project and highlight the challenges identified and the lessons learned. Conclusion The common system architecture and translational processes provide high-level (aggregate) deidentified access to a large patient population (>5 million patients), and represent a novel and extensible resource. Enhancing the network for more focused disease areas will require research-driven partnerships represented across all partner sites.

关键词： Information dissemination information management clinical trials as topic cohort discovery federated network ethical study methods knowledge representations information storage and retrieval (text and images) knowledge bases surveys and needs analysis shrine i2b2 transmart data warehousing informatics evaluation machine learning predictive modeling statistical learning privacy technology modeling physiologic and disease processes linking the genotype and phenotype identifying genome and protein structure and function visualization of data and knowledge information dissemination information management clinical trials as topic cohort discovery federated network

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Clinical research informatics: a conceptual perspective

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第E1期19卷 E36-E42页

作者： Kahn, Michael G. Weng, Chunhua Univ Colorado Dept Pediat Aurora CO USA Columbia Univ Dept Biomed Informat New York NY USA

Clinical research informatics is the rapidly evolving sub-discipline within biomedical informatics that focuses on developing new informatics theories, tools, and solutions to accelerate the full translational continuum: basic research to clinical trials (T1), clinical trials to academic health center practice (T2), diffusion and implementation to community practice (T3), and 'real world' outcomes (T4). We present a conceptual model based on an informatics-enabled clinical research workflow, integration across heterogeneous data sources, and core informatics tools and platforms. We use this conceptual model to highlight 18 new articles in the JAMIA special issue on clinical research informatics.

关键词： Clinical research informatics clinical and translational research visualization of data and knowledge knowledge representations methods for integration of information from disparate sources data models data exchange knowledge bases knowledge acquisition knowledge acquisition and knowledge management

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The role of complementary bipartite visual analytical representations in the analysis of SNPs: a case study in ancestral informative markers

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第E1期19卷 E5-E12页

作者： Bhavnani, Suresh K. Bellala, Gowtham Victor, Sundar Bassler, Kevin E. Visweswaran, Shyam Univ Texas Med Branch Inst Translat Sci Galveston TX 77555 USA Univ Michigan Dept Elect Engn & Comp Sci Ann Arbor MI 48109 USA Univ Houston Dept Phys Houston TX USA Univ Houston Texas Ctr Superconduct Houston TX USA Univ Pittsburgh Dept Biomed Informat Pittsburgh PA USA Univ Pittsburgh Intelligent Syst Program Pittsburgh PA USA

Objective Several studies have shown how sets of single-nucleotide polymorphisms (SNPs) can help to classify subjects on the basis of their continental origins, with applications to case-control studies and population genetics. However, most of these studies use dimensionality-reduction methods, such as principal component analysis, or clustering methods that result in unipartite (either subjects or SNPs) representations of the data. Such analyses conceal important bipartite relationships, such as how subject and SNP clusters relate to each other, and the genotypes that determine their cluster memberships. Methods To overcome the limitations of current methods of analyzing SNP data, the authors used three bipartite analytical representations (bipartite network, heat map with dendrograms, and Circos ideogram) that enable the simultaneous visualization and analysis of subjects, SNPs, and subject attributes. Results The results demonstrate (1) novel insights into SNP data that are difficult to derive from purely unipartite views of the data, (2) the strengths and limitations of each method, revealing the role that each play in revealing novel insights, and (3) implications for how the methods can be used for the analysis of SNPs in genomic studies associated with disease. Conclusion The results suggest that bipartite representations can reveal new patterns in SNP data compared with existing unipartite representations. However, the novel insights require multiple representations to discover, verify, and comprehend the complex relationships. The results therefore motivate the need for a complementary visual analytical framework that guides the use of multiple bipartite representations to analyze complex relationships in SNP data.

关键词： visualization of data and knowledge human-computer interaction and human-centered computing networks systems to support and improve diagnostic accuracy linking the genotype and phenotype discovery text and data mining methods uncertain reasoning and decision theory

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Automated extraction of ejection fraction for quality measurement using regular expressions in Unstructured Information Management Architecture (UIMA) for heart failure

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第5期19卷 859-866页

作者： Garvin, Jennifer H. DuVall, Scott L. South, Brett R. Bray, Bruce E. Bolton, Daniel Heavirland, Julia Pickard, Steve Heidenreich, Paul Shen, Shuying Weir, Charlene Samore, Matthew Goldstein, Mary K. SLC VA Healthcare Syst IDEAS Ctr Salt Lake City UT USA Univ Utah Sch Med Div Epidemiol Salt Lake City UT USA Univ Utah Sch Med Dept Biomed Informat Salt Lake City UT USA VA Palo Alto Hlth Care Syst Palo Alto CA USA Stanford Univ Ctr Primary Care & Outcomes Res PCOR Stanford CA 94305 USA SLC VA Healthcare Syst GRECC Salt Lake City UT USA

Objectives Left ventricular ejection fraction (EF) is a key component of heart failure quality measures used within the Department of Veteran Affairs (VA). Our goals were to build a natural language processing system to extract the EF from free-text echocardiogram reports to automate measurement reporting and to validate the accuracy of the system using a comparison reference standard developed through human review. This project was a Translational Use Case Project within the VA Consortium for Healthcare Informatics. Materials and methods We created a set of regular expressions and rules to capture the EF using a random sample of 765 echocardiograms from seven VA medical centers. The documents were randomly assigned to two sets: a set of 275 used for training and a second set of 490 used for testing and validation. To establish the reference standard, two independent reviewers annotated all documents in both sets;a third reviewer adjudicated disagreements. Results System test results for document-level classification of EF of <40% had a sensitivity (recall) of 98.41%, a specificity of 100%, a positive predictive value (precision) of 100%, and an F measure of 99.2%. System test results at the concept level had a sensitivity of 88.9% (95% CI 87.7% to 90.0%), a positive predictive value of 95% (95% CI 94.2% to 95.9%), and an F measure of 91.9% (95% CI 91.2% to 92.7%). Discussion An EF value of <40% can be accurately identified in VA echocardiogram reports. Conclusions An automated information extraction system can be used to accurately extract EF for quality measurement.

关键词： Natural language processing (NLP) heart failure left ventricular ejection fraction (EF) Improving healthcare workflow and process efficiency applied informatics Improving government and community policy relevant to informatics and health quality process modeling and hypothesis generation Informatics Enhancing the conduct of biological/clinical research and trials applications that link biomedical knowledge from diverse primary sources (includes automated indexing) visualization of data and knowledge uncertain reasoning and decision theory languages and computational methods statistical analysis of large datasets advanced algorithms discovery and text and data mining methods other methods of information extraction automated learning human-computer interaction and human-centered computing cognitive study (including experiments emphasizing verbal protocol analysis and usability) knowledge representations knowledge acquisition and knowledge management delivering health information and knowledge to the public processing and display analysis image representation controlled terminologies and vocabularies ontologies knowledge bases ejection fraction machine learning simulation of complex systems (at all levels: molecules to work groups to organizations) developing/using clinical decision support (other than diagnostic) and guideline systems detecting disease outbreaks and biological threats

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Commercial off-the-shelf consumer health informatics interventions: recommendations for their design, evaluation and redesign

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第1期19卷 137-142页

作者： Marquard, Jenna L. Zayas-Caban, Teresa Univ Massachusetts Engn Lab 219 Dept Mech & Ind Engn 160 Governors Dr Amherst MA 01003 USA Agcy Healthcare Res & Qual Rockville MD USA

Objective The goal of this paper is to describe the successful application of a use case-based evaluation approach to guide the effective design, evaluation and redesign of inexpensive, commercial, off-the-shelf consumer health informatics (CHI) interventions. Design Researchers developed four CHI intervention use cases representing two distinct patient populations (patients with diabetes with high blood pressure, post-bariatric surgery patients), two commercial off-the-shelf CHI applications (Microsoft HealthVault, Google Health), and related devices (blood pressure monitor, pedometer, weight scale). Three patient proxies tested each intervention for 10 days. Measurements The patient proxies recorded their challenges while completing use case tasks, rating the severity of each challenge based on how much it hindered their use of the intervention. Two independent evaluators categorized the challenges by human factors domain (physical, cognitive, macroergonomic). Results The use case-based approach resulted in the identification of 122 challenges, with 12% physical, 50% cognitive and 38% macroergonomic. Thirty-nine challenges (32%) were at least moderately severe. Nine of 22 use case tasks (41%) accounted for 72% of the challenges. Limitations The study used two patient proxies and addressed two specific patient populations and low-cost, off-the-shelf CHI interventions, which may not perfectly generalize to a larger number of proxies, actual patient populations, or other CHI interventions. Conclusion CHI designers can employ the use case-based evaluation approach to assess the fit of a CHI intervention with patients' health work, in the context of their daily activities and environment, which would be difficult or impossible to evaluate by laboratory-based studies.

关键词： Classic experimental and quasi-experimental study methods (laboratory and field) cognitive study (including experiments emphasizing verbal protocol analysis and usability) designing usable (responsive) resources and systems human–computer interaction and human-centered computing supporting practice at a distance (telehealth) surveys and needs analysis uncertain reasoning and decision theory visualization of data and knowledge

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Clinical utility of sequence-based genotype compared with that derivable from genotyping arrays

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第E1期19卷 E21-E27页

作者： Morgan, Alexander A. Chen, Rong Butte, Atul Janardhan Stanford Univ Dept Biochem Stanford Genome Technol Ctr Stanford CA 94305 USA Stanford Univ Dept Pediat Div Syst Med Biomed Informat Grad Training Program Stanford CA 94305 USA Lucile Packard Childrens Hosp Palo Alto CA USA

Objective We investigated the common-disease relevant information obtained from sequencing compared with that reported from genotyping arrays. Materials and methods Using 187 publicly available individual human genomes, we constructed genomic disease risk summaries based on 55 common diseases with reported gene-disease associations in the research literature using two different risk models, one based on the product of likelihood ratios and the other on the allelic variant with the maximum associated disease risk. We also constructed risk profiles based on the single nucleotide polymorphisms (SNPs) of these individuals that could be measured or imputed from two common genotyping array platforms. Results We show that the model risk predictions derived from sequencing differ substantially from those obtained from the SNPs measured on commercially available genotyping arrays for several different non-monogenic diseases, although high density genotyping arrays give identical results for many diseases. Conclusions Our approach may be used to compare the ability of different platforms to probe known genetic risks disease by disease.

关键词： genomic medicine personalized medicine genotyping sequencing modeling physiologic and disease processes linking the genotype and phenotype identifying genome and protein structure and function visualization of data and knowledge

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Leveraging medical thesauri and physician feedback for improving medical literature retrieval for case queries

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第5期19卷 851-858页

作者： Sondhi, Parikshit Sun, Jimeng Zhai, ChengXiang Sorrentino, Robert Kohn, Martin S. Univ Illinois Dept Comp Sci Urbana IL 61801 USA IBM Thomas J Watson Res Ctr Yorktown Hts NY USA

Objective This paper presents a study of methods for medical literature retrieval for case queries, in which the goal is to retrieve literature articles similar to a given patient case. In particular, it focuses on analyzing the performance of state-of-the-art general retrieval methods and improving them by the use of medical thesauri and physician feedback. Materials and Methods The Kullback-Leibler divergence retrieval model with Dirichlet smoothing is used as the state-of-the-art general retrieval method. Pseudorelevance feedback and term weighing methods are proposed by leveraging MeSH and UMLS thesauri. Evaluation is performed on a test collection recently created for the ImageCLEF medical case retrieval challenge. Results Experimental results show that a well-tuned state-of-the-art general retrieval model achieves a mean average precision of 0.2754, but the performance can be improved by over 40% to 0.3980, through the proposed methods. Discussion The results over the ImageCLEF test collection, which is currently the best collection available for the task, are encouraging. There are, however, limitations due to small evaluation set size. The analysis shows that further refinement of the methods is necessary before they can be really useful in a clinical setting. Conclusion Medical case-based literature retrieval is a critical search application that presents a number of unique challenges. This analysis shows that the state-of-the-art general retrieval models are reasonably good for the task, but the performance can be significantly improved by developing new task-specific retrieval models that incorporate medical thesauri and physician feedback.

关键词： Case search clinical (L01.700.508.300.190) computer-assisted (L01.700.508.100) decision making decision support systems decision support techniques (L01.700.508.190) high-performance and large-scale computing information management (L01.399) information retrieval information storage and retrieval (L01.700.508.280) language models machine learning medical case-based retrieval medical case retrieval medical informatics (L01.313.500) natural language processing semantic weighing statistical analysis of large datasets uncertain reasoning and decision theory visualization of data and knowledge

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The EpiCanvas infectious disease weather map: an interactive visual exploration of temporal and spatial correlations

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第6期19卷 954-959页

作者： Gesteland, Per Hans Livnat, Yarden Galli, Nathan Samore, Matthew H. Gundlapalli, Adi V. Univ Utah Sch Med Dept Biomed Informat Salt Lake City UT 84113 USA Intermt Healthcare Inst Hlth Care Delivery Res Salt Lake City UT USA Univ Utah Sci & Comp Imaging Inst Salt Lake City UT 84113 USA Univ Utah Sch Med Dept Internal Med Salt Lake City UT USA VA Salt Lake City Hlth Care Syst Salt Lake City UT USA Univ Utah Sch Med Dept Pediat Salt Lake City UT USA

Advances in surveillance science have supported public health agencies in tracking and responding to disease outbreaks. Increasingly, epidemiologists have been tasked with interpreting multiple streams of heterogeneous data arising from varied surveillance systems. As a result public health personnel have experienced an overload of plots and charts as information visualization techniques have not kept pace with the rapid expansion in data availability. This study sought to advance the science of public health surveillance data visualization by conceptualizing a visual paradigm that provides an 'epidemiological canvas' for detection, monitoring, exploration and discovery of regional infectious disease activity and developing a software prototype of an 'infectious disease weather map'. Design objectives were elucidated and the conceptual model was developed using cognitive task analysis with public health epidemiologists. The software prototype was pilot tested using retrospective data from a large, regional pediatric hospital, and gastrointestinal and respiratory disease outbreaks were re-created as a proof of concept.

关键词： Biosurveillance correlation of data data display detecting disease outbreaks and biological threats developing/using clinical decision support (other than diagnostic) and guideline systems discovery human–computer interaction and human-centered computing improving healthcare workflow and process efficiency infectious disease outbreaks measuring/improving outcomes in specific conditions and patient subgroups methods for integration of information from disparate sources modeling physiological and disease processes other methods of information extraction public health surveillance simulation of complex systems (at all levels: molecules to work groups to organizations) statistical data analysis systems to support and improve diagnostic accuracy text and data mining methods visualization of data and knowledge

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A translational engine at the national scale: informatics for integrating biology and the bedside

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2012年第2期19卷 181-185页

作者： Kohane, Isaac S. Churchill, Susanne E. Murphy, Shawn N. Harvard Univ Sch Med Childrens Hosp Informat Program Boston MA 02115 USA Harvard Univ Sch Med Ctr Biomed Informat Boston MA 02115 USA Partners Healthcare Syst Res Comp Informat Technol Charlestown MA USA

Informatics for integrating biology and the bedside (i2b2) seeks to provide the instrumentation for using the informational by-products of health care and the biological materials accumulated through the delivery of health care to conduct discovery research and to study the healthcare system in vivo. This complements existing efforts such as prospective cohort studies or trials outside the delivery of routine health care. i2b2 has been used to generate genome-wide studies at less than one tenth the cost and one tenth the time of conventionally performed studies as well as to identify important risk from commonly used medications. i2b2 has been adopted by over 60 academic health centers internationally.

关键词： Analysis automated learning bioinformatics clinical informatics data models discovery display genomics image representation information storage and retrieval (text and images) knowledge representations linking the genotype and phenotype medical records natural-language processing processing text and data mining methods visualization of data and knowledge

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