Yearb Med Inform 2008; 17(01): 41-43
DOI: 10.1055/s-0038-1638581
Original Article
Georg Thieme Verlag KG Stuttgart

A Bio-Medical Informatics Perspective on Human Factors

Findings from the Yearbook 2008 Section on Human Factors
P. Ruch
University of Geneva, Department of Imaging and Medical Information Processing, Geneva, Switzerland
,
Managing Editor for the IMIA Yearbook Section on Human Factors › Author Affiliations
Further Information

Correspondence to:

Prof. Dr. Patrick Ruch
University and University Hospitals of Geneva
Department of Medical Informatics
Geneva, Switzerland
Phone: +41 22 372 61 64   

Publication History

Publication Date:
07 March 2018 (online)

 

Summary

Objectives To summarize current excellent research in the field of human factors.

MethodsWe provide a synopsis of the articles selected for the IMIA Yearbook 2008, from which we attempt to derive a synthetic overview of the activity and new trends in the field.

Results while the state of the research in the field of human factors is illustrated by a set of fairly heterogeneous studies, it is possible to identify trends. Thus, clearly, the importance of issues related to medical order entry, which also founded human factors studies in medical informatics, still occupies a central role in the field. In parallel, we observe an emerging interest for human factors from the field of bioinformatics, where the mass of data generated by high/ throughput experiments and large-scale genome analysis projects, raises specific processing challenges. Such challenges will have to be addressed to achieve post-genomics era medicine.

Conclusions The best paper selection of articles on human factors shows examples of excellent research on methods concerning original development as well as quality assurance of previously reported studies. The crucial role of preserving interpersonal communication among healthcare staff in computerized working environments is complemented by more original scientific investigations, which demonstrate the needs for computerized applications to transform the biomedical data overflow into more operational clinical knowledge. Altogether these papers support the idea that more elaborated computer tools, likely to combine contextual contents, are needed.


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  • References

  • 1 Gobeill J, Tbahriti I, Ehrler F, Mottaz A, Veuthey AL, Ruch P. Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction. BMC Bioinformatics 2008; 09 (Suppl. 03) S9.
  • 2 Goh CS, Gianoulis TA, Liu Y, Li J, Paccanaro A, Lussier YA. et al. Integration of curated databases to identify genotype-phenotype associations. BMC Genomics 2006; 07: 257.
  • 3 Han YY, Carcillo JA, Venkataraman ST, Clark RS, Watson RS, Nguyen TC, Bayir H, Orr RA. Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics 2005; Dec; 116 (06) 1506-12 Erratum in: Pediatrics. 2006 Feb;117(2):59.
  • 4 Pakhomov SV, Buntrock JD, Chute CG. Automating the assignment of diagnosis codes to patient encounters using example-based and machine learning techniques. J Am Med Inform Assoc 2006; Sep-Oct; 13 (05) 516-25. Epub 2006 Jun 23.5.
  • 5 Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A. UniProtKB/Swiss-Prot: The Manually Annotated Section of the UniProt KnowledgeBase. Methods Mol Biol 2007; 406: 89-112.
  • 6 Spahni S, Lovis C, Ackermann M, Mach N, Bonnabry P, Geissbuhler A. Securing chemotherapies: fabrication, prescription, administration and complete traceability. Medinfo 2007; 12 (Pt 2): 953-7.
  • 7 Kohn LT, Corrigan JM, Donaldson MS. eds. To Err Is Human: Building a Safer Health System. Institute of Medicine (U.S.) Committee on Quality of Health Care in America. Washington, DC: National Academy Press; 1999

Correspondence to:

Prof. Dr. Patrick Ruch
University and University Hospitals of Geneva
Department of Medical Informatics
Geneva, Switzerland
Phone: +41 22 372 61 64   

  • References

  • 1 Gobeill J, Tbahriti I, Ehrler F, Mottaz A, Veuthey AL, Ruch P. Gene Ontology density estimation and discourse analysis for automatic GeneRiF extraction. BMC Bioinformatics 2008; 09 (Suppl. 03) S9.
  • 2 Goh CS, Gianoulis TA, Liu Y, Li J, Paccanaro A, Lussier YA. et al. Integration of curated databases to identify genotype-phenotype associations. BMC Genomics 2006; 07: 257.
  • 3 Han YY, Carcillo JA, Venkataraman ST, Clark RS, Watson RS, Nguyen TC, Bayir H, Orr RA. Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics 2005; Dec; 116 (06) 1506-12 Erratum in: Pediatrics. 2006 Feb;117(2):59.
  • 4 Pakhomov SV, Buntrock JD, Chute CG. Automating the assignment of diagnosis codes to patient encounters using example-based and machine learning techniques. J Am Med Inform Assoc 2006; Sep-Oct; 13 (05) 516-25. Epub 2006 Jun 23.5.
  • 5 Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A. UniProtKB/Swiss-Prot: The Manually Annotated Section of the UniProt KnowledgeBase. Methods Mol Biol 2007; 406: 89-112.
  • 6 Spahni S, Lovis C, Ackermann M, Mach N, Bonnabry P, Geissbuhler A. Securing chemotherapies: fabrication, prescription, administration and complete traceability. Medinfo 2007; 12 (Pt 2): 953-7.
  • 7 Kohn LT, Corrigan JM, Donaldson MS. eds. To Err Is Human: Building a Safer Health System. Institute of Medicine (U.S.) Committee on Quality of Health Care in America. Washington, DC: National Academy Press; 1999