Archive

2014: IMIA Yearbook 2014

Journal: IMIA Yearbook
ISSN: 0943-4747
Topic:

Big Data - Smart Health Strategies


60 articles
21.

Section 2: Human Factors and Organizational Issues

Lanham HJ, Sittig DF, Leykum LK, Parchman ML, Pugh JA, McDaniel RR Understanding differences in electronic health record (EHR) use: linking individual physicians' perceptions of uncertainty and EHR use patterns in ambulatory care J Am Med Inform Assoc. 2014 Jan 1;21(1):73-81  http://dx.doi.org/10.1136/amiajnl-2012-001377

Smith SW, Koppel R Healthcare information technology's relativity problems: a typology of how patients' physical reality, clinicians' mental models, and healthcare information technology differ J Am Med Inform Assoc. 2014 Jan 1;21(1):117-31 http://dx.doi.org/10.1136/amiajnl-2012-001419

Hilligoss B, Zheng K Chart biopsy: an emerging medical practice enabled by electronic health records and its impacts on emergency department-inpatient admission handoffs J Am Med Inform Assoc 2013 Mar-Apr;20(2):260-7 http://dx.doi.org/10.1136/amiajnl-2012-001065

Flanagan ME, Saleem JJ, Millitello LG, Russ AL, Doebbeling BN Paper- and computer-based workarounds to electronic health record use at three benchmark institutions J Am Med Inform Assoc. 2013 Jun;20(e1):e59-66  http://dx.doi.org/10.1136/amiajnl-2012-000982

Babbott S, Manwell LB, Brown R, Montague E, Williams E, Schwartz M, Hess E, Linzer M Electronic medical records and physician stress in primary care: results from the MEMO Study J Am Med Inform Assoc 2014 Feb;21(e1):e100-6 http://dx.doi.org/10.1136/amiajnl-2013-001875 

Yearb Med Inform 2014 : 93-95

[Summary]
22.

Section 3: Health Information Systems

Survey

M. K. Ross (1), W. Wei (1), L. Ohno-Machado (1)

(1) Division of Biomedical Informatics, University of California, San Diego, USA

Yearb Med Inform 2014 : 97-104

https://doi.org/10.15265/IY-2014-0003

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23.
Future Direction of IMIA Standardization
Report from the IMIA Standardization Working Group

Section 3: Health Information Systems

Working Group Contributions

J. Nakaya (1), M. Kimura (2), S. Ogishima (3), A. Shabo (4), I. K. Kim (5), C. Parisot (6), B. de Faria Leao (7)

(1) Department of Medical informatics, School of Medicine, Tohoku University, Sendai, Japan; (2) Department of Medical Informatics, School of Medicine, Hamamatsu University, Hamamatsu, Japan; (3) Department of Medical ICT, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Japan; (4) Head of Healthcare and Life Sciences Standards Program, Haifa Research Lab, IBM, Haifa, Israel; (5) School of Computer Science & Engineering, Kyungpook National University, Daegu, Korea; (6) GE Healthcare Milwaukee, Wisconsin, USA and Buc, France; (7) Health Informatics Consultant at Bleao Informática em Saúde, Brazil

Yearb Med Inform 2014 : 105-109

https://doi.org/10.15265/IY-2014-0010

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24.

Section 3: Health Information Systems

Working Group Contributions

Online Supplementary Material

A. K. Lyseen (1), C. Nøhr (1), E. M. Sørensen (1), O. Gudes (2), E. M. Geraghty (3), N. T. Shaw (4), C. Bivona-Tellez (5), on behalf of the IMIA Health GIS Working Group

(1) Department of Development and Planning, Aalborg University, Aalborg, Denmark; (2) Department of Spatial Sciences, Curtin University, Australia; (3) University of California Davis, Division of General Medicine, California, USA; (4) Algoma University, Sault Ste. Marie, Ontario, Canada; (5) Azusa Pacific University, California, USA

Yearb Med Inform 2014 : 110-124

https://doi.org/10.15265/IY-2014-0008

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25.

Section 3: Health Information Systems

Synopsis

L. Toubiana (1), M. Cuggia (2)

(1) INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France; Université Paris 13, Sorbonne Paris Cité, LIMICS, (UMR_S 1142), F-93430, Villetaneuse, France.; (2) INSERM U936, Faculté de Médecine, DIM - CHU Pontchaillou, Rennes, F-35000, France

Yearb Med Inform 2014 : 125-127

https://doi.org/10.15265/IY-2014-0034

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26.

Section 3: Health Information Systems

Avillach P, Coloma PM, Gini R, Schuemie M, Mougin F, Dufour JC, Mazzaglia G, Giaquinto C, Fornari C, Herings R, Molokhia M, Pedersen L, Fourrier-Réglat A, Fieschi M, Sturkenboom M, van der Lei J, Pariente A, Trifirò G; EU-ADR consortium Harmonization process for the identification of medical events in eight European healthcare databases: the experience from the EU-ADR project J Am Med Inform Assoc 2013; 20(1):184-92 http://dx.doi.org/10.1136/amiajnl-2012-000933

Natter MD, Quan J, Ortiz DM, Bousvaros A, Ilowite NT, Inman CJ, Marsolo K, McMurry AJ, Sandborg CI, Schanberg LE, Wallace CA, Warren RW, Weber GM, Mandl KD  An i2b2-based, generalizable, open source, self-scaling chronic disease registry J Am Med Inform Assoc 2013; 20(1): 172-9 http://dx.doi.org/10.1136/amiajnl-2012-001042

Sáez C, Bresó A, Vicente J, Robles M, García-Gómez JM An HL7-CDA wrapper for facilitating semantic interoperability to rule-based Clinical Decision Support Systems Comput Methods Programs Biomed 2013;109(3): 239-49 http://dx.doi.org/10.1016/j.cmpb.2012.10.003

Hurdle JF, Haroldsen SC, Hammer A, Spigle C, Fraser AM, Mineau GP, Courdy SJ Identifying clinical/translational research cohorts: ascertainment via querying an integrated multi-source database J Am Med Inform Assoc 2013; 20(1):164-71 http://dx.doi.org/10.1136/amiajnl-2012-001050

Yearb Med Inform 2014 : 126-127

[Summary]
27.

Section 4: Sensor, Signal and Imaging Informatics

Survey

G. Carrault (1, 2, 3), P. Mabo (1, 2, 3, 4)

(1) INSERM, U1099, Rennes, F-35000, France; (2) INSERM, CIC-IT 1414, Rennes, F-35000, France; (3) Université de Rennes 1, LTSI, Rennes, F-35000, France; (4) CHU Rennes, Service de Cardiologie et Maladies Vasculaires, Rennes, F-35000, France

Yearb Med Inform 2014 : 128-134

https://doi.org/10.15265/IY-2014-0021

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28.
What Does Big Data Mean for Wearable Sensor Systems?
Contribution of the IMIA Wearable Sensors in Healthcare WG

Section 4: Sensor, Signal and Imaging Informatics

Working Group Contributions

S. J. Redmond (1), N. H. Lovell (1), G. Z. Yang (2), A. Horsch (3, 4, 5), P. Lukowicz (6), L. Murrugarra (7), M. Marschollek (8)

(1) Graduate School of Biomedical Engineering, UNSW Australia, Sydney, Australia; (2) Imperial College London, London, United Kingdom; (3) Department of Medical Statistics and Epidemiology, Technische Universität München, Munich, Germany; (4) Department of Clinical Medicine, Telemedicine Working Group, University of Tromsø, Tromsø, Norway; (5) Department of Computer Science, MI&T Group, University of Tromsø, Tromsø, Norway; (6) German Research Center for Artificial Intelligence, Kaiserslautern, Germany; (7) Alexander von Humboldt Institute for Tropical Medicine, Universidad Peruana Cayetano Heredia, Lima, Peru; (8) Peter L. Reichertz Institute for Medical Informatics, University of Braunschweig-Institute of Technology and Hanover Medical School, Hanover, Germany

Yearb Med Inform 2014 : 135-142

https://doi.org/10.15265/IY-2014-0019

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29.

Section 4: Sensor, Signal and Imaging Informatics

Working Group Contributions

V. Vimarlund (1, 2), S. Wass (1)

(1) Jönköping International Business School, Jönköping, Sweden; (2) Department of Computer Science, Linköping University, Linköping, Sweden

Yearb Med Inform 2014 : 143-149

https://doi.org/10.15265/IY-2014-0011

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30.

Section 4: Sensor, Signal and Imaging Informatics

Synopsis

S. Voros (1), A. Moreau-Gaudry (1, 2)

(1) UJF-Grenoble 1 / CNRS / INSERM, TIMC-IMAG UMR 5525, Grenoble, F-38041, France; (2) UJF-Grenoble 1 / CHU / INSERM CIT803, Grenoble, F-38041, France

Yearb Med Inform 2014 : 150-153

https://doi.org/10.15265/IY-2014-0035

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31.

Section 4: Sensor, Signal and Imaging Informatics

De Lorenzo D, Koseki Y, De Momi E, Chinzei K, Okamura AM Coaxial needle insertion assistant with enhanced force feedback IEEE Trans Biomed Eng 2013 Feb;60(2):379-89 http://dx.doi.org/10.1109/TBME.2012.2227316

Asman AJ, Chambless LB, Thompson RC, Landman BA Out-of-atlas likelihood estimation using multi-atlas segmentation Med Phys 2013 Apr; 40(4):043702 http://dx.doi.org/10.1118/1.4794478

Xie Y, Ho J, Vemuri BC Multiple Atlas construction from a heterogeneous brain MR image collection IEEE Trans Med Imaging 2013 Mar;32(3):628-35 http://dx.doi.org/10.1109/TMI.2013.2239654

Clifton DA, Wong D, Clifton L Wilson S, Way R Pullinger R, Tarassenko L A Large-Scale Clinical Validation of an Integrated Monitoring System in the Emergency Department IEEE J Biomed Health Inform 2013;17(4):835-42 http://dx.doi.org/10.1109/JBHI.2012.2234130

Yearb Med Inform 2014 : 151-152

[Summary]
32.

Section 5: Decision Support

Survey

M. S. Kohn (1), J. Sun (2), S. Knoop (3), A. Shabo (4), B. Carmeli (5), D. Sow (6), T. Syed-Mahmood (3), W. Rapp (7)

(1) Jointly Health (formerly IBM Research); (2) College of Computing, Georgia Institute of Technology, Atlanta, Georgia (formerly IBM Research); (3) IBM Almaden Research Center, San Jose, CA, USA; (4) Records of Health (formerly IBM Research), Haifa, Israel; (5) IBM Haifa Research Lab, Haifa, Israel; (6) IBM Watson Research Center, Yorktown Heights NY, USA; (7) IBM Watson Solutions Development, Rochester MN, USA

Yearb Med Inform 2014 : 154-162

https://doi.org/10.15265/IY-2014-0002

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33.

Section 5: Decision Support

Synopsis

J. Bouaud (1, 2), J.-B. Lamy (2)

(1) AP-HP, Dept. of Clinical Research and Development, Paris, France; (2) Université Paris 13, Sorbonne Paris Cité, LIMICS, (UMR_S 1142), Bobigny, France; INSERM, U1142, LIMICS, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1142, LIMICS, Paris, France

Yearb Med Inform 2014 : 163-166

https://doi.org/10.15265/IY-2014-0036

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34.

Section 5: Decision Support

Gay P, López B, Plà A, Saperas J, Pous C Enabling the use of hereditary information from pedigree tools in medical knowledge-based systems J Biomed Inform 2013 Aug;46(4):710-20 http://dx.doi.org/10.1016/j.jbi.2013.06.003

Hackl WO, Ammenwerth E, Marcilly R, Chazard E, Luyckx M, Leurs P, Beuscart R Clinical evaluation of the ADE scorecards as a decision support tool for adverse drug event analysis and medication safety management Br J Clin Pharmacol 2013 Sep;76 Suppl 1:78-90 http://dx.doi.org/10.1111/bcp.12185

Hayward J, Thomson F, Milne H, Buckingham S, Sheikh A, Fernando B, Cresswell K, Williams R, Pinnock H Too much, too late': mixed methods multi-channel video recording study of computerized decision support systems and GP prescribing J Am Med Inform Assoc 2013 Jun;20(e1):e76-84 http://dx.doi.org/10.1136/amiajnl-2012-001484

Yearb Med Inform 2014 : 166-166

[Summary]
35.

Section 6: Knowledge Representation and Management

Synopsis

N. Griffon (1, 2), J. Charlet (2, 3), S. J. Darmoni (1, 2)

(1) CISMeF, Rouen University Hospital, Normandy & TIBS, LITIS EA 4108, Institute for Research and Innovation in Biomedicine, Rouen, France; (2) INSERM, U1142, LIMICS, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1142, LIMICS, Paris, France; Université Paris 13, Sorbonne Paris Cité, LIMICS, (UMR_S 1142), Villetaneuse, France; (3) AP-HP, Dept. of Clinical Research and Development, Paris, France

Yearb Med Inform 2014 : 167-169

https://doi.org/10.15265/IY-2014-0037

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36.

Section 6: Knowledge Representation and Management

Chowdhury MF, Zweigenbaum P A controlled greedy supervised approach for co-reference resolution on clinical text J Biomed Inform 2013;46(3):506–15 http://dx.doi.org/10.1016/j.jbi.2013.03.007

Deleger L, Molnar K, Savova G, Xia F, Lingren T, Li Q, Marsolo K, Jegga A, Kaiser M, Stoutenborough L, Solti I Large-scale evaluation of automated clinical note de-identification and its impact on information extraction J Am Med Inform Assoc 2013 Jan 1;20(1):84-94 http://dx.doi.org/10.1136/amiajnl-2012-001012

Wang HQ, Li JS, Zhang YF, Suzuki M, Araki K Creating personalised clinical pathways by semantic interoperability with electronic health records Artif Intell Med 2013;58(2):81–9 http://dx.doi.org/10.1016/j.artmed.2013.02.005

MacLean DL, Heer J Identifying medical terms in patient-authored text: a crowdsourcing-based approach J Am Med Inform Assoc 2013;20(6):1120–7 http://dx.doi.org/10.1136/amiajnl-2012-001110

Yearb Med Inform 2014 : 168-169

[Summary]
37.

Section 7: Education and Consumer Health Informatics

Survey

D. Z. Sands (1, 2, 3), J. S. Wald (2, 4, 3)

(1) Beth Israel Deaconess Medical Center, Boston, MA, USA; (2) Society for Participatory Medicine, Boston, MA, USA; (3) Harvard Medical School, Boston, MA, USA; (4) RTI International, Waltham, MA, USA

Yearb Med Inform 2014 : 170-176

https://doi.org/10.15265/IY-2014-0017

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38.
Big Data: Are Biomedical and Health Informatics Training Programs Ready?
Contribution of the IMIA Working Group for Health and Medical Informatics Education

Section 7: Education and Consumer Health Informatics

Working Group Contributions

P. Otero (1), W. Hersh (2), A. U. Jai Ganesh (3)

(1) IMIA WGEd Chair, Department of Health Informatics, Hospital, Italiano de Buenos Aires, Buenos Aires, Argentina; (2) IMIA WGEd Past-Chair, Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; (3) IMIA WGEd Co-Chair, Sri Sathya Sai Central Trust, Prasanthi Nilayam, Puttaparthi, Andhra Pradesh, India

Yearb Med Inform 2014 : 177-181

https://doi.org/10.15265/IY-2014-0007

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39.

Section 7: Education and Consumer Health Informatics

Working Group Contributions

A. Hartzler (1), T. Wetter (2, 3)

(1) The Information School, University of Washington, Seattle, WA, USA; (2) Institute of Medical Biometry and Informatics, Heidelberg University, Heidelberg, Germany; (3) Dept. of Biomedical Informatics and Medical Information, University of Washington, Seattle, WA, USA

Yearb Med Inform 2014 : 182-194

https://doi.org/10.15265/IY-2014-0022

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40.
Social Media and Patient Health Outcomes
Findings from the Yearbook 2014 Section on Consumer Health Informatics

Section 7: Education and Consumer Health Informatics

Synopsis

P. Staccini (1), N. Douali (2)

(1) INSERM UMR 912 SESSTIM, IRIS, UFR Médecine, Université Nice-Sophia Antipolis, Nice, France; (2) INSERM UMRS 1142 LIMICS, Université Pierre et Marie Curie, Paris, France

Yearb Med Inform 2014 : 195-198

https://doi.org/10.15265/IY-2014-0038

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