Yearbook of Medical Informatics, Table of Contents Yearb Med Inform 2015; 24(01): 119-124DOI: 10.15265/IY-2015-036 Original Article Georg Thieme Verlag KG StuttgartComputerized Clinical Decision Support: Contributions from 2014 J. Bouaud 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 , V. Koutkias 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 , Section Editors for the IMIA Yearbook Section on Decision Support› Author AffiliationsRecommend Article Abstract Full Text PDF Download Keywords KeywordsMedical informatics - International Medical Informatics Association - Yearbook - Clinical Decision Support Systems References References 1 Sacchi L, Lanzola G, Viani N, Quaglini S. Personalization and patient involvement in decision support systems: current trends. Yearb Med Inform 2015; 10: 106-18. 2 Koutkias V, Thiessard F. Big data - smart health strategies. Findings from the yearbook 2014 special theme. Yearb Med Inform 2014 Aug 15 9 (Suppl. 01) 48-51. 3 Lamy JB, Séroussi B, Griffon N, Kerdelhué G, Jaulent MC, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers. Methods Inf Med 2015; 54 (Suppl. 02) 135-44. 4 Goddard K, Roudsari A, Wyatt JC. Automation bias: empirical results assessing influencing factors. Int J Med Inform 2014; May 83 (Suppl. 05) 368-75. 5 Nwulu U, Brooks H, Richardson S, McFarland L, Coleman JJ. Electronic risk assessment for venous thromboembolism: investigating physicians’ rationale for bypassing clinical decision support recommendations. BMJ Open 2014 Sep 26 4 (Suppl. 09) e005647. 6 McCoy AB, Thomas EJ, Krousel-Wood M, Sittig DF. Clinical decision support alert appropriateness: a review and proposal for improvement. Ochsner J 2014; Summer 14 (Suppl. 02) 195-202. 7 Gupta A, Raja AS, Khorasani R. Examining clinical decision support integrity: is clinician self-reported data entry accurate?. J Am Med Inform Assoc 2014; Jan-Feb 21 (Suppl. 01) 23-6. 8 Bouaud J, Blaszka-Jaulerry B, Zelek L, Spano JP, Lefranc JP, Cojean-Zelek I. et al. Health information technology: use it well, or don’t! Findings from the use of a decision support system for breast cancer management. AMIA Annu Symp Proc 2014 Nov 14 2014: 315-24. 9 Lee J, Han H, Ock M, Lee SI, Lee S, Jo MW. Impact of a clinical decision support system for high-alert medications on the prevention of prescription errors. Int J Med Inform 2014; Dec 83 (Suppl. 12) 929-40. 10 Carnevale TJ, Meng D, Wang JJ, Littlewood M. Impact of an emergency medicine decision support and risk education system on computed tomography and magnetic resonance imaging use. J Emerg Med 2015; Jan 48 (Suppl. 01) 53-7. 11 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. 12 Rodriguez-Maresca M, Sorlozano A, Grau M, Rodriguez-Castaño R, Ruiz-Valverde A, Gutierrez-Fernande J. Implementation of a computerized decision support system to improve the appropriateness of antibiotic therapy using local microbiologic data. Biomed Res Int 2014; 395434. 13 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 (Suppl. 05) e93769. 14 Bell GC, Crews KR, Wilkinson MR, Haidar CE, Hicks JK, Baker DK. et al. Development and use of active clinical decision support for preemptive pharmacogenomics. J Am Med Inform Assoc 2014; Feb 21 e1 e93-9. 15 Nachtigall I, Tafelski S, Deja M, Halle E, Grebe MC, Tamarkin A. et al. 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 (Suppl. 12) e005370. 16 McCullough JM, Zimmerman FJ, Rodriguez HP. Impact of clinical decision support on receipt of antibiotic prescriptions for acute bronchitis and upper respiratory tract infection. J Am Med Inform Assoc 2014; Nov-Dec 21 (Suppl. 06) 1091-7. 17 Bellos CC, Papadopoulos A, Rosso R, Fotiadis DI. Identification of COPD patients’ health status using an intelligent system in the CHRONIOUS wearable platform. IEEE J Biomed Health Inform 2014; May 18 (Suppl. 03) 731-8. 18 Eccher C, Seyfang A, Ferro A. Implementation and evaluation of an Asbru-based decision support system for adjuvant treatment in breast cancer. Comput Methods Programs Biomed 2014; Nov 117 (Suppl. 02) 308-21. 19 Brodin NP, Maraldo MV, Aznar MC, Vogelius IR, Petersen PM, Bentzen SM. et al. Interactive decision-support tool for risk-based radiation therapy plan comparison for Hodgkin lymphoma. Int J Radiat Oncol Biol Phys 2014 Feb 1 88 (Suppl. 02) 433-45. 20 Simpao AF, Ahumada LM, Desai BR, Bonafide CP, Gálvez JA, Rehman MA. et al. Optimization of drug-drug interaction alert rules in a pediatric hospital’s electronic health record system using a visual analytics dashboard. J Am Med Inform Assoc 2015; Mar 22 (Suppl. 02) 361-9. 21 Anani N, Chen R, Prazeres Moreira T, Koch S. Retrospective checking of compliance with practice guidelines for acute stroke care: a novel experiment using openEHR’s Guideline Definition Language. 22 Gultepe E, Green JP, Nguyen H, Adams J, Albert-son T, Tagkopoulos I. From vital signs to clinical outcomes for patients with sepsis: a machine learning basis for a clinical decision support system. J Am Med Inform Assoc 2014; MarApr 21 (Suppl. 02) 315-25. 23 Mani S, Ozdas A, Aliferis C, Varol HA, Chen Q, Carnevale R. et al. Medical decision support using machine learning for early detection of late-onset neonatal sepsis. J Am Med Inform Assoc 2014; Mar-Apr 21 (Suppl. 02) 326-36. 24 Amland RC, Hahn-Cover KE. Clinical decision support for early recognition of sepsis. Am J Med Qual 2014 Nov 10. 25 Jalali A, Buckley EM, Lynch JM, Schwab PJ, Licht DJ, Nataraj C. Prediction of periventricular leukomalacia occurrence in neonates after heart surgery. IEEE J Biomed Health Inform 2014; Jul 18 (Suppl. 04) 1453-60. 26 Bountris P, Haritou M, Pouliakis A, Margari N, Kyrgiou M, Spathis A. et al. An intelligent clinical decision support system for patient-specific predictions to improve cervical intraepithelial neoplasia detection. Biomed Res Int 2014; 2014: 341483. 27 El-Fakdi A, Gamero F, Meléndez J, Auffret V, Haigron P. eXiTCDSS: a framework for a work-flow-based CBR for interventional clinical decision support systems and its application to TAVI. Expert Syst Appl 2014; 41 (Suppl. 02) 284-94.