Archive

2015: IMIA Yearbook 2015

Journal: IMIA Yearbook
ISSN: 0943-4747
Topic:

Patient-Centered Care Coordination


59 articles
21.

Section 2: Human Factors and Organizational Issues

Flewwelling CJ, Easty AC, Vicente KJ, Cafazzo JA. The use of fault reporting of medical equipment to identify latent design flaws. J Biomed Inform 2014 Oct;51:80-5 http://dx.doi.org/10.1016/j.jbi.2014.04.009

Friedman A, Crosson JC, Howard J, Clark EC, Pellerano M, Karsh BT, Crabtree B, Jaén CR, Cohen DJ. A typology of electronic health record workarounds in small-to-medium size primary care practices. J Am Med Inform Assoc 2014 Feb;21(e1):e78-83 http://dx.doi.org/10.1136/amiajnl-2013-001686

Russ AL, Zillich AJ, Melton BL, Russell SA, Chen S, Spina JR, Weiner M, Johnson EG, Daggy JK, McManus MS, Hawsey JM, Puleo AG, Doebbeling BN, Saleem JJ. Applying human factors principles to alert design increases efficiency and reduces prescribing errors in a scenario-based simulation. J Am Med Inform Assoc 2014 Oct;21(e2):e287-96 http://dx.doi.org/10.1136/amiajnl-2013-002045

Yearb Med Inform 2015 : 77-78

[Summary]
22.

Section 3: Clinical Information Systems

Survey

A. Hoerbst, M. Schweitzer

Research Division eHealth and Telemedicine, UMIT - University for Health Sciences, Medical
Informatics, and Technology, Hall in Tirol, Austria

Yearb Med Inform 2015 : 79-89

https://doi.org/10.15265/IY-2015-018

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23.

Section 3: Clinical Information Systems

Synopsis

T. Ganslandt (1), W. O. Hackl (2)

(1) Medical Center for Information and Communication, Erlangen University Hospital, DE91054 Erlangen, Germany; (2) Institute of Biomedical Informatics, UMIT-University for Health Sciences, Medical Informatics and Technology, Hall in Tirol, Austria

Yearb Med Inform 2015 : 90-94

https://doi.org/10.15265/IY-2015-037

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24.

Section 3: Clinical Information Systems

D'Amore JD, Mandel JC, Kreda DA, Swain A, Koromia GA, Sundareswaran S, Alschuler L, Dolin RH, Mandl KD, Kohane IS, Ramoni RB. Are Meaningful Use Stage 2 certified EHRs ready for interoperability? Findings from the SMART C-CDA Collaborative. J Am Med Inform Assoc 2014;21(6):1060-8 http://dx.doi.org/10.1136/amiajnl-2014-002883

Li Q, Melton K, Lingren T, Kirkendall ES, Hall E, Zhai H, Ni Y, Kaiser M, Stoutenborough L, Solti I. Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care. J Am Med Inform Assoc 2014;21(5):776-84 http://dx.doi.org/10.1136/amiajnl-2013-001914

Meeks DW, Smith MW, Taylor L, Sittig DF, Scott JM, Singh H. An analysis of electronic health recordrelated patient safety concerns. J Am Med Inform Assoc 2014;21(6):1053-9 http://dx.doi.org/10.1136/amiajnl-2013-002578

Plischke M, Wagner M, Haarbrandt B, Rochon M, Schwartze J, Tute E, Bartkiewicz T, Kleinschmidt T, Seidel C, Schüttig H, Haux R. The lower saxony bank of health. Rationale, principles, services, organization and architectural framework. Methods Inf Med 2014;53(2):73-81 http://dx.doi.org/10.3414/ME13-02-0003

Yearb Med Inform 2015 : 93-94

[Summary]
25.

Section 4: Sensor, Signal and Imaging Informatics

Survey

J. De jonckheere, V. Bonhomme, M. Jeanne, E. Boselli, M. Gruenewald, R. Logier, P. Richebé

(1) INSERM CIC-IT 1403, Maison Régionale de la Recherche Clinique, CHRU de Lille, France; (2) University Department of Anesthesia and ICM, CHR Citadelle, Liege, Belgium; (3) Department of Anaesthesiology and Intensive Care, Hopitale Roger Salengro, CHRU de Lille, France; (4) Department of Anaesthesiology and Intensive Care, Édouard Herriot Hospital, Hospices Civils de Lyon, Lyon, France; (5) Department of Anaesthesiology and Intensive Care, University Hospital Schleswig Holstein Campus Kiel, Kiel, Germany; (6) UDSL EA2694, Univeristé Lille Nord de France, Lille, France; (7) Department of Anaesthesiology, University of Montreal, Quebec, Canada

Yearb Med Inform 2015 : 95-101

https://doi.org/10.15265/IY-2015-004

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26.

Section 4: Sensor, Signal and Imaging Informatics

Synopsis

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

(1) Univ. Grenoble Alpes, TIMC-IMAG; CNRS, INSERM, TIMC-IMAG, F-38000, Grenoble, France; (2) Inserm CIC 1406; CHU Grenoble, Pôle Santé Publique, CIC-IT, F-38000, Grenoble, France

Yearb Med Inform 2015 : 102-105

https://doi.org/10.15265/IY-2015-025

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27.

Section 4: Sensor, Signal and Imaging Informatics  

Amir-Khalili A, Peyrat J-M, Abinahed J, Al-Alao O, Al-Ansari A, Hamarneh G, Abugharbieh R. Auto localization and segmentation of occluded vessels in robot-assisted partial nephrectomy. Med Image Comput Comput Assist Interv 2014;17(1):407–14 Link

Mi H, Petitjean C, Dubray B, Vera P, Ruan S. Prediction of lung tumor evolution during radiotherapy in individual patients with PET. IEEE Trans Med Imaging 2014 Apr;33(4):995-1003 http://dx.doi.org/10.1109/TMI.2014.2301892

Pheiffer TS, Thompson RC, Rucker DC, Simpson AL, Miga MI. Model-based correction of tissue compression for tracked ultrasound in soft tissue image-guided surgery. Ultrasound Med Biol 2014 Apr;40(4):788-803 http://dx.doi.org/10.1016/j.ultrasmedbio.2013.11.003

Quellec G, Charrière K, Lamard M, Droueche Z, Roux C, Cochener B, Cazuguel G. Real-time recognition of surgical tasks in eye surgery videos. Med Image Anal 2014 Apr;18(3):579-90  http://dx.doi.org/10.1016/j.media.2014.02.007

Tanter M, Fink M. Ultrafast imaging in biomedical ultrasound. IEEE Trans Ultrason Ferroelectr Freq Control 2014 Jan;61(1):102-19  http://dx.doi.org/10.1109/TUFFC.2014.2882

Yearb Med Inform 2015 : 104-105

[Summary]
28.

Section 5: Decision Support

Survey

S. Quaglini, L. Sacchi, G. Lanzola, N. Viani

Yearb Med Inform 2015 : 106-118

https://doi.org/10.15265/IY-2015-015

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29.

Section 5: Decision Support

Synopsis

J. Bouaud (1), V. Koutkias (2)

(1) AP-HP, Dept. of Clinical Research and Development, Paris, 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), Bobigny, France

Yearb Med Inform 2015 : 119-124

https://doi.org/10.15265/IY-2015-036

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30.

Section 5: Decision Support

Goddard K, Roudsari A, Wyatt JC. Automation bias: empirical results assessing influencing factors. Int J Med Inform 2014 May;83(5):368-75 http://dx.doi.org/10.1016/j.ijmedinf.2014.01.001

Klann JG, Szolovits P, Downs SM, Schadow G. Decision support from local data: creating adaptive order menus from past clinician behavior. J Biomed Inform 2014 Apr;48:84-93 http://dx.doi.org/10.1016/j.jbi.2013.12.005

Miñarro-Giménez JA, Blagec K, Boyce RD, Adlassnig KP, Samwald M. An ontology-based, mobile-optimized system for pharmacogenomic decision support at the point-of-care. PLoS One. 2014 May 2;9(5):e93769 http://dx.doi.org/10.1371/journal.pone.0093769

Nachtigall I, Tafelski S, Deja M, Halle E, Grebe MC, Tamarkin A, Rothbart A, Uhrig A, Meyer E, Musial- Bright L, Wernecke KD, Spies C. Long-term effect of computer-assisted decision support for antibiotic treatment in critically ill patients: a prospective 'before/after' cohort study. BMJ Open 2014 Dec 22;4(12):e005370 http://dx.doi.org/10.1136/bmjopen-2014-005370

Yearb Med Inform 2015 : 123-124

[Summary]
31.

Section 6: Knowledge Representation and Managment

Survey

M. Da Silveira (1), J. C. Dos Reis (2), C. Pruski (1)

(1) Luxembourg Institute of Science and Technology (LIST), Esch/Alzette, Luxembourg; (2) Institute of Computing, University of Campinas, Cidade Universitária Zeferino Vaz, Campinas-SP, Brazil

Yearb Med Inform 2015 : 125-133

https://doi.org/10.15265/IY-2015-002

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32.
Knowledge Representation and Management. From Ontology to Annotation
Findings from the Yearbook 2015 Section on Knowledge Representation and Management

Section 6: Knowledge Representation and Managment

Synopsis

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

(1) AP-HP, Dept. of Clinical Research and Development, Paris, 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) Department of Biomedical Informatics, Rouen University Hospital, Normandy & TIBS, LITIS EA 4108, Institute for Research and Innovation in Biomedicine, Rouen, France

Yearb Med Inform 2015 3: 134-136

https://doi.org/10.15265/IY-2015-038

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33.

Section 6: Knowledge Representation and Managment

Choi S, Choi J, Yoo S, Kim H, Lee Y. Semantic concept-enriched dependence model for medical information retrieval. J Biomed Inform 2014 Feb;47:18-27 http://dx.doi.org/10.1016/j.jbi.2013.08.013

Clark K, Sharma D, Qin R, Chute CG, Tao C. A use case study on late stent thrombosis for ontologybased temporal reasoning and analysis. J Biomed Semantics 2014 Dec 11;5(1):49 http://dx.doi.org/10.1186/2041-1480-5-49

Dramé K, Diallo G, Delva F, Dartigues JF, Mouillet E, Salamon R, Mougin F. Reuse of terminoontological resources and text corpora for building a multilingual domain ontology: an application to Alzheimer's disease. J Biomed Inform.2014 Apr;48:171-82 http://dx.doi.org/10.1016/j.jbi.2013.12.013

Funk C, Baumgartner W Jr, Garcia B, Roeder C, Bada M, Cohen KB, Hunter LE, Verspoor K. Large-scale biomedical concept recognition: an evaluation of current automatic annotators and their parameters. BMC Bioinformatics 2014;15:59 http://dx.doi.org/10.1186/1471-2105-15-59

Yearb Med Inform 2015 : 136-136

[Summary]
34.

Section 7: Education and Consumer Health Informatics

Survey

K. Denecke (1), P. Bamidis (2), C. Bond (3), E. Gabarron (4), M. Househ (5), A. Y. S. Lau (6), M. A. Mayer (7), M. Merolli (8), M. Hansen (9)

(1) University of Leipzig, Innovation Center Computer Assisted Surgery, Leipzig, Germany; (2) Aristotle University of Thessaloniki, Faculty of Health Sciences, Thessaloniki, Greece; (3) Bournemouth University, School of Health and Social Care, Bournemouth, United Kingdom; (4) The Arctic University of Norway, Faculty of Health Sciences, Tromsø, Norway; (5) College of Public Health and Health Informatics, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard - Health Affairs, Riyadh, Kingdom of Saudi Arabia; (6) Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia; (7) Department of Experimental and Health Sciences, Universitat Pompeu Fabra - IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain; (8) University of Melbourne, Health and Biomedical Informatics Centre, Melbourne, Australia; (9) University of San Francisco, School of Nursing and Health Professions, San Francisco, USA

Yearb Med Inform 2015 : 137-147

https://doi.org/10.15265/IY-2015-001

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35.

Section 7: Education and Consumer Health Informatics

Working Group Contributions

M. Rigby, A. Georgiou, H. Hyppönen (1), E. Ammenwerth, N. de Keizer (2), F. Magrabi, P. Scott

(1) National Institute for Health and Welfare, Information Department, Helsinki, Finland; (2) Academic Medical Center, Department of Medical Informatics, Amsterdam, The Netherlands

Yearb Med Inform 2015 : 148-159

https://doi.org/10.15265/IY-2015-007

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36.
Health Social Media and Patient-Centered Care: Buzz or Evidence?
Findings from the Section “Education and Consumer Health Informatics” of the 2015 Edition of the IMIA Yearbook

Section 7: Education and Consumer Health Informatics

Synopsis

P. Staccini (1), L. Fernandez-Luque (2)

(1) INSERM UMR 912 SESSTIM, IRIS Dept, UFR Médecine, Université Nice-Sophia Antipolis, France; (2) Northern Research Institute, Tromsø, Norway

Yearb Med Inform 2015 : 160-163

https://doi.org/10.15265/IY-2015-032

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37.

Section 7: Education and Consumer Health Information

 Brown J, Michie S, Geraghty AW, Yardley L, Gardner B, Shahab L, Stapleton JA, West R. Internetbased intervention for smoking cessation (StopAdvisor) in people with low and high socioeconomic status: a randomised controlled trial. Lancet Respir Med 2014 Dec;2(12):997-1006 http://dx.doi.org/10.1016/S2213-2600(14)70195-X

Obadina ET, Dubenske LL, McDowell HE, Atwood AK, Mayer DK, Woods RW, Gustafson DH, Burnside ES. Online support: Impact on anxiety in women who experience an abnormal screening mammogram. Breast 2014 Dec;23(6):743-8 http://dx.doi.org/10.1016/j.breast.2014.08.002

Vaughan Sarrazin MS, Cram P, Mazur A, Ward M, Reisinger HS. Patient perspectives of dabigatran: analysis of online discussion forums. Patient 2014;7(1):47-54 http://dx.doi.org/10.1007/s40271-013-0027-y

Wicks P, Sulham KA, Gnanasakthy A. Quality of life in organ transplant recipients participating in an online transplant community. Patient 2014;7(1):73-84 http://dx.doi.org/10.1007/s40271-013-0033-0

Yearb Med Inform 2015 : 162-163

[Summary]
38.

Section 8: Bioinformatics and Translational Informatics

Survey

K. Regan, P. R.O. Payne

(1) The Ohio State University, Department of Biomedical Informatics, Columbus, OH, USA

Yearb Med Inform 2015 : 164-169

https://doi.org/10.15265/IY-2015-005

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39.
Bioinformatics Methods and Tools to Advance Clinical Care
Findings from the Yearbook 2015 Section on Bioinformatics and Translational Informatics

Section 8: Bioinformatics and Translational Informatics

Synopsis

L. F. Soualmia (1), T. Lecroq (1)

(1) Normandie Univ., University of Rouen, NormaSTIC FR CNRS 3638, IRIB and LITIS EA 4108, Information Processing in Biology & Health, Saint Étienne du Rouvray, France

Yearb Med Inform 2015 : 170-173

https://doi.org/10.15265/IY-2015-026

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40.

Section 8: Bioinformatics and Translational Informatics

Antonov AV, Krestyaninova M, Knight RA, Rodchenkov I, Melino G, Barlev NA. PPIsurv: a novel bioinformatics tool for uncovering the hidden role of specific genes in cancer survival outcome. Oncogene 2014 Mar 27; 33(13):1621-8 http://dx.doi.org/10.1038/onc.2013.119

Bendl J, Stourac J, Salanda O, Pavelka A, Wieben ED, Zendulka J, Brezovsky J, Damborsky J. PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations. PLoS Comput Biol. 2014 Jan;10(1):e1003440 http://dx.doi.org/10.1371/journal.pcbi.1003440

Kim MS, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R, Madugundu AK, Kelkar DS, Isserlin R, Jain S, Thomas JK, Muthusamy B, Leal-Rojas P, Kumar P, Sahasrabuddhe NA, Balakrishnan L, Advani J, George B, Renuse S, Selvan LD, Patil AH, Nanjappa V, Radhakrishnan A, Prasad S, Subbannayya T, Raju R, Kumar M, Sreenivasamurthy SK, Marimuthu A, Sathe GJ, Chavan S, Datta KK, Subbannayya Y, Sahu A, Yelamanchi SD, Jayaram S, Rajagopalan P, Sharma J, Murthy KR, Syed N, Goel R, Khan AA, Ahmad S, Dey G, Mudgal K, Chatterjee A, Huang TC, Zhong J, Wu X, Shaw PG, Freed D, Zahari MS, Mukherjee KK, Shankar S, Mahadevan A, Lam H, Mitchell CJ, Shankar SK, Satishchandra P, Schroeder JT, Sirdeshmukh R, Maitra A, Leach SD, Drake CG, Halushka MK, Prasad TS, Hruban RH, Kerr CL, Bader GD, Iacobuzio-Donahue CA, Gowda H, Pandey A. A draft map of the human proteome. Nature 2014 May 29; 509(7502):575-81 http://dx.doi.org/10.1038/nature13302

Taati B, Snoek J, Aleman D, Ghavamzadeh A. Data mining in bone marrow transplant records to identify patients with high odds of survival. IEEE J Biomed Health Inform 2014 Jan; 18(1):21-7 http://dx.doi.org/10.1109/JBHI.2013.2274733

Yearb Med Inform 2015 : 173-173

[Summary]