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CC BY-NC-ND 4.0 · Yearb Med Inform 2018; 27(01): 122-128
DOI: 10.1055/s-0038-1641222
Section 5: Decision Support
Synopsis
Georg Thieme Verlag KG Stuttgart

Contributions from the 2017 Literature on Clinical Decision Support

V. Koutkias
1   Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece
,
J. Bouaud
2   Assistance Publique–Hôpitaux de Paris, Delegation for Clinical Research and Innovation, Paris, France
3   Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, LIMICS, Paris, France
,
Section Editors for the IMIA Yearbook Section on Decision Support › Author Affiliations
Further Information

Publication History

Publication Date:
29 August 2018 (online)

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Summary

Objectives: To summarize recent research and select the best papers published in 2017 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook.

Methods: A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation.

Results: Among the 1,194 retrieved papers, the entire review process resulted in the selection of four best papers. The first paper studies the impact of recency and of longitudinal extent of electronic health record (EHR) datasets used to train a data-driven predictive model of inpatient admission orders. The second paper presents a decision support tool for surgical team selection, relying on the history of surgical team members and the specific characteristics of the patient. The third paper compares three commercial drug-drug interaction knowledge bases, particularly against a reference list of highly-significant known interactions. The fourth paper focuses on supporting the diagnosis of postoperative delirium using an adaptation of the “anchor and learn” framework, which was applied in unstructured texts contained in EHRs.

Conclusions: The conducted review illustrated also this year that research in the field of CDSS is very active. Of note is the increase in publications concerning data-driven CDSSs, as revealed by the review process and also reflected by the four papers that have been selected. This trend is in line with the current attention that “Big Data” and data-driven artificial intelligence have gained in the domain of health and CDSSs in particular.

Keywords

Medical informatics - International Medical Informatics Association - Yearbook - Decision Support Systems
 

  • References

  • 1 Cho I, Bates DW. Behavioral economics interventions in clinical decision support systems. Yearb Med Inform 2018; 114-21
    Thieme ConnectPubMedSearch in Google Scholar
  • 2 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 (02) 135-44
    Thieme ConnectPubMedSearch in Google Scholar
  • 3 Koutkias V, Bouaud J. Contributions from the 2016 Literature on Clinical Decision Support. Yearb Med Inform 2017; Aug; 26 (01) 133-8
    Thieme ConnectPubMedSearch in Google Scholar
  • 4 Chen JH, Alagappan M, Goldstein MK, Asch SM, Altman RB. Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets. Int J Med Inform 2017; Jun; 102: 71-9
    CrossrefPubMedSearch in Google Scholar
  • 5 Ebadi A, Tighe PJ, Zhang L, Rashidi P. DisTeam: A decision support tool for surgical team selection. Artif Intell Med 2017; Feb; 76: 16-26
    CrossrefPubMedSearch in Google Scholar
  • 6 Fung KW, Kapusnik-Uner J, Cunningham J, Higby-Baker S, Bodenreider O. Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support. J Am Med Inform Assoc 2017; Jul 1; 24 (04) 806-12
    CrossrefPubMedSearch in Google Scholar
  • 7 Mikalsen KØ, Soguero-Ruiz C, Jensen K, Hindberg K, Gran M, Revhaug A, Lindsetmo RO, Skrøvseth SO, Godtliebsen F, Jenssen R. Using anchors from free text in electronic health records to diagnose postoperative delirium. Comput Methods Programs Biomed 2017; Dec; 152: 105-14
    CrossrefPubMedSearch in Google Scholar
  • 8 McEvoy DS, Sittig DF, Hickman TT, Aaron S, Ai A, Amato M. , et al. Variation in high-priority drug-drug interaction alerts across institutions and electronic health records. J Am Med Inform Assoc 2017; Mar 1; 24 (02) 331-8
    PubMedSearch in Google Scholar
  • 9 Halpern Y, Horng S, Choi Y, Sontag D. Electronic medical record phenotyping using the anchor and learn framework. J Am Med Inform Assoc 2016; Jul; 23 (04) 731-40
    CrossrefPubMedSearch in Google Scholar
  • 10 Merone M, Pedone C, Capasso G, Incalzi RA, Soda P. A Decision Support System for Tele-Monitoring COPD-Related Worrisome Events. IEEE J Biomed Health Inform 2017; Mar; 21 (02) 296-302
    CrossrefPubMedSearch in Google Scholar
  • 11 Yet B, Perkins ZB, Tai NRM, Marsh WR. Clinical evidence framework for Bayesian networks. Knowl Inf Syst 2017; 50 (01) 117-43
    CrossrefPubMedSearch in Google Scholar
  • 12 Oliveira T, Silva A, Neves J, Novais P. Decision support provided by a temporally oriented health care assistant: an implementation of computer- interpretable guidelines. J Med Syst 2017; Jan; 41 (01) 13
    CrossrefPubMedSearch in Google Scholar
  • 13 Mohammadhassanzadeh H, Van Woensel W, Abidi SR, Abidi SSR. Semantics-based plausible reasoning to extend the knowledge coverage of medical knowledge bases for improved clinical decision support. BioData Min 2017; Feb 10; 10: 7
    CrossrefPubMedSearch in Google Scholar
  • 14 Gräßer F, Beckert S, Küster D, Schmitt J, Abraham S, Malberg H, Zaunseder S. Therapy decision support based on recommender system methods. J Healthc Eng 2017; 2017: 8659460
    PubMedSearch in Google Scholar
  • 15 Danahey K, Borden BA, Furner B, Yukman P, Hussain S, Saner D, Volchenboum SL, Ratain MJ, O’Donnell PH. Simplifying the use of pharmacogenomics in clinical practice: Building the genomic prescribing system. J Biomed Inform 2017; Nov; 75: 110-21
    CrossrefPubMedSearch in Google Scholar
  • 16 Peleg M, Shahar Y, Quaglini S, Broens T, Budasu R, Fung N. , et al. Assessment of a personalized and distributed patient guidance system. Int J Med Inform 2017; May; 101: 108-30
    CrossrefPubMedSearch in Google Scholar
  • 17 Kannampallil TG, Abraham J, Solotskaya A, Philip SG, Lambert BL, Schiff GD. , et al. Learning from errors: analysis of medication order voiding in CPOE systems. J Am Med Inform Assoc 2017; Jul 1; 24 (04) 762-8
    CrossrefPubMedSearch in Google Scholar
  • 18 Baysari MT, Hardie RA, Lake R, Richardson L, McCullagh C, Gardo A. , et al. Longitudinal study of user experiences of a CPOE system in a pediatric hospital. Int J Med Inform 2018; Jan; 109: 5-14
    CrossrefPubMedSearch in Google Scholar
  • 19 Ip IK, Lacson R, Hentel K, Malhotra S, Darer J, Langlotz C. , et al. JOURNAL CLUB: Predictors of provider response to clinical decision support: Lessons learned from the Medicare Imaging Demonstration. AJR Am J Roentgenol 2017; Feb; 208 (02) 351-7
    CrossrefPubMedSearch in Google Scholar
  • 20 Liberati EG, Ruggiero F, Galuppo L, Gorli M, González-Lorenzo M, Maraldi M. , et al. What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implement Sci 2017; Sep 15; 12 (01) 113
    CrossrefPubMedSearch in Google Scholar