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A Review of the Empirical Evidence of the Value of Structuring and Coding of Clinical Information Within Electronic Health Records for Direct Patient Care
Informatics in Primary Care 2012;20:171–80 # 2012 PHCSG, British Computer Society Refereed paper A review of the empirical evidence of the value of structuring and coding of clinical information within electronic health records for direct patient care Dipak Kalra PhD FRCGP FBCS Clinical Professor of Health Informatics, Centre for Health Informatics and Multiprofessional Education, University College London, UK Bernard Fernando MBBS MRCS LRCP MSc MBCS Clinical Research Fellow Zoe Morrison MSc MCIM FHEA Research Associate Aziz Sheikh MD MSc FRCGP FRCP Professor of Primary Care Research & Development eHealth Research Group, Centre for Population Health Sciences, The University of Edinburgh, UK ABSTRACT Background The case has historically been pre- improved proxy and actual clinical outcomes if a sented that structured and/or coded electronic structured and/or coded EHR was combined with health records (EHRs) benefit direct patient care, alerting or advisory systems in a focused clinical but the evidence base for this is not well docu- domain. Three studies demonstrated improvement mented. in safety outcomes. No studies were found report- Methods We searched for evidence of direct ing value in relation to patient-centredness, timeli- patient care value from the use of structured and/ ness, efficiency or equitability. or coded information within EHRs. We interro- Conclusions We conclude that, to date, there has gated nine international databases from 1990 to been patchy effort to investigate empirically the 2011. Value was defined using the Institute of value from structuring and coding EHRs for direct Medicine’s six areas for improvement for healthcare patient care. Future investments in structuring and systems: effectiveness, safety, patient-centredness, coding of EHRs should be informed by robust timeliness, efficiency and equitability. -
Visualization of Biomedical Data
Visualization of Biomedical Data Corresponding author: Seán I. O’Donoghue; email: [email protected] • Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Eveleigh NSW 2015, Australia • Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney NSW 2010, Australia • School of Biotechnology and Biomolecular Sciences, UNSW, Kensington NSW 2033, Australia Benedetta Frida Baldi; email: [email protected] • Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney NSW 2010, Australia Susan J Clark; email: [email protected] • Genomics and Epigenetics Division, Garvan Institute of Medical Research, Sydney NSW 2010, Australia Aaron E. Darling; email: [email protected] • The ithree institute, University of Technology Sydney, Ultimo NSW 2007, Australia James M. Hogan; email: [email protected] • School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD, 4000, Australia Sandeep Kaur; email: [email protected] • School of Computer Science and Engineering, UNSW, Kensington NSW 2033, Australia Lena Maier-Hein; email: [email protected] • Div. Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany Davis J. McCarthy; email: [email protected] • European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, CB10 1SD, Hinxton, Cambridge, UK • St. Vincent’s Institute of Medical Research, Fitzroy VIC 3065, Australia William -
Data Visualization by Nils Gehlenborg
Data Visualization Nils Gehlenborg ([email protected]) Center for Biomedical Informatics / Harvard Medical School Cancer Program / Broad Institute of MIT and Harvard ISMB/ECCB 2011 http://www.biovis.net Flyers at ISCB booth! Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg A good sketch is better than a long speech. Napoleon Bonaparte Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg Minard 1869 Napoleon’s March on Moscow Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 4 I believe when I see it. Unknown Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg Anscombe 1973, The American Statistician Anscombe’s Quartet mean(X) = 9, var(X) = 11, mean(Y) = 7.5, var(Y) = 4.12, cor(X,Y) = 0.816, linear regression line Y = 3 + 0.5*X Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 6 Anscombe 1973, The American Statistician Anscombe’s Quartet Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 7 Exploration: Hypothesis Generation trends gaps outliers clusters - A large data set is given and the goal is to learn something about it. - Visualization is employed to perform pattern detection using the human visual system. - The goal is to generate hypotheses that can be tested with statistical methods or follow-up experiments. Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 8 Visualization Use Cases Presentation Confirmation Exploration Data Visualization / ISMB/ECCB 2011 / Nils Gehlenborg 9 Definition The use of computer-supported, interactive, visual representations of data to amplify cognition. Stu Card, Jock Mackinlay & Ben Shneiderman Computer-based visualization systems provide visual representations of datasets intended to help people carry out some task more effectively.effectively. -
Visualization and Exploration of Transcriptomics Data Nils Gehlenborg
Visualization and Exploration of Transcriptomics Data 05 The identifier 800 year identifier Nils Gehlenborg Sidney Sussex College To celebrate our 800 year history an adaptation of the core identifier has been commissioned. This should be used on communications in the time period up to and including 2009. The 800 year identifier consists of three elements: the shield, the University of Cambridge logotype and the 800 years wording. It should not be redrawn, digitally manipulated or altered. The elements should not be A dissertation submitted to the University of Cambridge used independently and their relationship should for the degree of Doctor of Philosophy remain consistent. The 800 year identifier must always be reproduced from a digital master reference. This is available in eps, jpeg and gif format. Please ensure the appropriate artwork format is used. File formats European Molecular Biology Laboratory, eps: all professionally printed applications European Bioinformatics Institute, jpeg: Microsoft programmes Wellcome Trust Genome Campus, gif: online usage Hinxton, Cambridge, CB10 1SD, Colour United Kingdom. The 800 year identifier only appears in the five colour variants shown on this page. Email: [email protected] Black, Red Pantone 032, Yellow Pantone 109 and white October 12, 2010 shield with black (or white name). Single colour black or white. Please try to avoid any other colour combinations. Pantone 032 R237 G41 B57 Pantone 109 R254 G209 B0 To Maureen. This dissertation is my own work and contains nothing which is the outcome of work done in collaboration with others, except as specified in the text and acknowledgements. This dissertation is not substantially the same as any I have submit- ted for a degree, diploma or other qualification at any other university, and no part has already been, or is currently being submitted for any degree, diploma or other qualification. -
Quality Metrics for Detailed Clinical Models
i n t e r n a t i o n a l j o u r n a l o f m e d i c a l i n f o r m a t i c s 8 2 ( 2 0 1 3 ) 408–417 journa l homepage: www.ijmijournal.com Quality metrics for detailed clinical models a,b c,d a,e,∗ f,g SunJu Ahn , Stanley M. Huff , Yoon Kim , Dipak Kalra a Department of Health Policy and Management, College of Medicine in Seoul National University, 28 Yeongeon-dong, Jongno-gu, Seoul, Republic of Korea b Graduate School, Interdisciplinary Program in Biomedical Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, Republic of Korea c Intermountain Healthcare, Salt Lake City, UT, USA d Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA e Institute of Health Policy and Management, Seoul National University Medical Research Center (SNUMRC), 28 Yeongeon-dong, Jongno-gu, Seoul, Republic of Korea f Department of Health Informatics, University College London, London, United Kingdom g Centre for Health Informatics and Multiprofessional Education, University College London, United Kingdom a r t i c l e i n f o a b s t r a c t Article history: Objective: To develop quality metrics for detailed clinical models (DCMs) and test their valid- Received 30 June 2012 ity. Received in revised form Methods: Based on existing quality criteria which did not include formal metrics, we devel- 18 September 2012 oped quality metrics by applying the ISO/IEC 9126 software quality evaluation model. -
Digital Scholarship Commons Presentation 09.24.14
A short introduction: Information visualizations in teaching & research Isabel Meirelles | [email protected] Associate Professor, Graphic Design, CAMD Digital Scholarship Commons NU Sept.24 Information design Infographics! Information visualization Data visualization Information design Infographics Graphical representations that aim at communicating information with the purpose to reveal patterns and relationships not known or not easily deduced without the aid of the visual presentation of information. ! Information visualization Data visualization “the use of computer-supported, interactive, visual representations of abstract data to amplify cognition” Card et al. : Readings in Information Visualization: Using Vision to Think Information From Latin informare to give form or shape to, from in into + formare to form, from forma a form or shape + -ation indicating a process or condition The Oxford American Thesaurus of Current English Information Definition* Definition information = well-formed and meaningful data *Weak definition. The strong definition includes the further condition of truthfulness. Luciano Floridi (2010): Information, A Very Short Introduction Information taxonomy by Floridi M. Chen & L. Floridi (2012): An Analysis of Information in Visualization in Synthese, Springer (historical) intermezzo Visual/diagrammatic representations over history 9th-ct., France: M. Capella, De nuptiis,. c. 1310, England: Table of the Ten Commandments Planetary diagrams from the De Lisle Psalter To increase working memory 1582: Giordano -
Steps to an Ecology of Networked Knowledge and Innovation Enabling New Forms of Collaboration Among Sciences, Engineering, Arts, and Design
Steps to an Ecology of Networked Knowledge and Innovation Enabling New Forms of Collaboration among Sciences, Engineering, Arts, and Design Roger F. Malina, Carol Strohecker, and Carol LaFayette on behalf of SEAD network contributors The MIT Press, Cambridge, Massachusetts and London, England Steps to an Ecology of Networked Knowledge and Innovation Enabling New Forms of Collaboration among Sciences, Engineering, Arts, and Design Roger F. Malina, Carol Strohecker, and Carol LaFayette on behalf of SEAD network contributors Cover image: “Map of Science Derived from Clickstream Data” (2009). Maps of science resulting from large-scale clickstream data provide a detailed, contemporary view of scientific activity and correct the under-representation of the social sciences and humanities that is commonly found in citation data. © Johan Bollen. Used with permission. Originally published in Bollen, J., H. Van de Sompel, A. Hagberg, L. Bettencourt, R. Chute, et al. (2009), “Clickstream Data Yields High-Resolution Maps of Science.” PLoS ONE 4 (3): e4803. doi: 10.1371/journal.pone.0004803. 3 This material is based on work supported by the National Science Foundation under Grant No. 1142510, IIS, Human-Centered Computing, “Collaborative Research: EAGER: Network for Science, Engineering, Arts and Design (NSEAD).” Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. © 2015 ISAST Published under a Creative Commons Attribution-NonCommercial 4.0 International license (CC BY-NC 4.0) eISBN: 978-0-262-75863-5 CONTENTS Acknowledgments ......................................................................................i SEAD White Papers Committees ...............................................................ii Introduction ...............................................................................................1 1. SEAD White Papers Methodology ......................................................13 2. -
School of Architecture 2016–2017 School of Architecture School Of
BULLETIN OF YALE UNIVERSITY BULLETIN OF YALE BULLETIN OF YALE UNIVERSITY Periodicals postage paid New Haven ct 06520-8227 New Haven, Connecticut School of Architecture 2016–2017 School of Architecture 2016 –2017 BULLETIN OF YALE UNIVERSITY Series 112 Number 4 June 30, 2016 BULLETIN OF YALE UNIVERSITY Series 112 Number 4 June 30, 2016 (USPS 078-500) The University is committed to basing judgments concerning the admission, education, is published seventeen times a year (one time in May and October; three times in June and employment of individuals upon their qualifications and abilities and a∞rmatively and September; four times in July; five times in August) by Yale University, 2 Whitney seeks to attract to its faculty, sta≠, and student body qualified persons of diverse back- Avenue, New Haven CT 0651o. Periodicals postage paid at New Haven, Connecticut. grounds. In accordance with this policy and as delineated by federal and Connecticut law, Yale does not discriminate in admissions, educational programs, or employment against Postmaster: Send address changes to Bulletin of Yale University, any individual on account of that individual’s sex, race, color, religion, age, disability, PO Box 208227, New Haven CT 06520-8227 status as a protected veteran, or national or ethnic origin; nor does Yale discriminate on the basis of sexual orientation or gender identity or expression. Managing Editor: Kimberly M. Go≠-Crews University policy is committed to a∞rmative action under law in employment of Editor: Lesley K. Baier women, minority group members, individuals with disabilities, and protected veterans. PO Box 208230, New Haven CT 06520-8230 Inquiries concerning these policies may be referred to Valarie Stanley, Director of the O∞ce for Equal Opportunity Programs, 221 Whitney Avenue, 3rd Floor, 203.432.0849. -
Visualizing Biological Data
THIS MONTH y y x x 22 1 22 1 21 21 POINTS OF VIEW 20 20 2 2 19 19 18 18 3 3 17 17 Visualizing biological 16 16 4 4 15 15 data 14 5 14 5 Data visualization is increasingly important, 13 6 13 6 12 7 12 7 8 8 9 but it requires clear objectives and improved 11 11 9 10 10 implementation. Figure 1 | Visualization of whole-genome rearrangement. Representative Researchers today have access to an unprecedented amount of Circos plots1 of whole-genome sequence data from two different tumors data. The challenge is to benefit from this abundance without showing gene duplications and chromosome rearrangements. The outer ring depicts chromosomes arranged end to end. The inner ring displays being overwhelmed. Data visualization for efficient exploration copy-number data in green and interchromosomal translocations in purple. and effective communication is integral to scientific progress. Reprinted from ref. 2 with permission from Elsevier. For visualization to continue to be an important tool for discov- ery, its practitioners need to be present as members of research teams. rapidly turn sketches into software prototypes at a pace relevant One of the goals of data visualization is to enable people to to the research: as data types and research questions evolve, there explore and explain data through interactive software that takes is a constant need to refine and adapt visualizations. One funding advantage of human beings’ ability to recognize patterns. Its suc- mechanism that has enabled an integrated focus on visualiza- cess depends on the development of methods and techniques to tion in the context of biological research is supplemental sup- transform information into a visual form for comprehension. -
EHR Information Model
Release 1.0.2 The openEHR Reference Model EHR Information Model Editors: {T Beale, S Heard}a, {D Kalra, D Lloyd}b Revision: 5.1.1 Pages: 85 Date of issue: 16 Aug 2008 Status: STABLE a. Ocean Informatics b. Centre for Health Informatics and Multi-professional Education, University College London Keywords: EHR, reference model, openehr EHR Extract EHR Demographic Integration Template OM Composition openEHR Archetype Profile Security Common Archetype OM ADL Data Structures Data Types Support © 2003-2008 The openEHR Foundation. The openEHR Foundation is an independent, non-profit community, facilitating the sharing of health records by consumers and clinicians via open-source, standards-based implementations. Founding David Ingram, Professor of Health Informatics, Chairman CHIME, University College London Founding Dr P Schloeffel, Dr S Heard, Dr D Kalra, D Lloyd, T Beale Members email: [email protected] web: http://www.openEHR.org EHR Information Model Rev 5.1.1 Copyright Notice © Copyright openEHR Foundation 2001 - 2008 All Rights Reserved 1. This document is protected by copyright and/or database right throughout the world and is owned by the openEHR Foundation. 2. You may read and print the document for private, non-commercial use. 3. You may use this document (in whole or in part) for the purposes of making presentations and education, so long as such purposes are non-commercial and are designed to comment on, further the goals of, or inform third parties about, openEHR. 4. You must not alter, modify, add to or delete anything from the document you use (except as is permitted in paragraphs 2 and 3 above). -
Trustworthy Decision Support: Is the Electronic Health Record Dependable?
Trustworthy decision support: is the Electronic Health Record dependable? Dr Dipak Kalra Centre for Health Informatics and Multiprofessional Education (CHIME) University College London [email protected] Abstract There are many clinical and health service drivers for integrating electronic health record (EHR) information, most of which require this information to be processed and understood by computers in order to deliver real benefits. Some key example benefits are the delivery of evidence based care and the prevention of errors: both of these rely upon different kinds of decision support system being able to query or search within EHRs to identify facts that are relevant to a care decision or to identify if an alert or reminder should be raised. This challenge is often more complex than simply searching for the presence of a given code or numeric value: some individual entries can only be properly understood in the context of the care event that gave rise to them, or alongside complementary information like the state of the patient or the concomitant administration of a drug. Much of the R&D work on the development of electronic health records has been to formalise the contextual information that is normally needed for the safe interpretation and sharing of EHR entries. The strength of the approach taken internationally on the representation of electronic health record information has been the development of a rigorous and comprehensive generic representation suitable for all kinds of entries. Provided that the communications architecture is common to both a sending and a receiving information system, any health record extract will contain all of the structure and names required for it to be interpreted faithfully on receipt even if its organisation and the nature of the clinical content have not been “agreed” in advance. -
The Scientist As Illustrator
TREIMM 1262 No. of Pages 4 Special Issue: and technology. Leonardo's careful study of visual communicators as it ever was Communicating Science of human anatomy and interest in propor- in the past. tions was demonstrated in the Vitruvian Scientific Life man, one of his most famous drawings. Drawing to Understand In molecular and cellular biology, our The Scientist as With the rise of the printing press, scien- understanding of processes is typically Illustrator tists could reach a larger audience with based on experimental data that are indi- whom they could share their findings. In rect, abstract, and collected by different [1_TD$IF] 1, Sidereus Nuncius, Galileo Galilei was the laboratories using an assortment of tech- Janet H. Iwasa * first to publish observations made using a niques over the course of decades. To telescope. A polymath who excelled in understand processes that are taking fi Pro ciency in art and illustration astronomy, mathematics, and physics, place at scales smaller than the wave- was once considered an essential Galileo had also studied medicine and length of light, biologists must synthesize skill for biologists, because text had once considered a career in painting diverse data to generate a working model alone often could not suffice to [1]. His manuscript included over 70 or hypothesis. In contrast with scientists of describe observations of biological detailed illustrations, including the first the past, we must rely on visualizations not systems. With modern imaging realistic depictions of the craggy and pit- to record and share our observations, but technology, it is no longer neces- ted surface of the moon.