Methods Inf Med 2002; 41(01): 20-24
DOI: 10.1055/s-0038-1634308
Original Article
Schattauer GmbH

The Micro-Macro Spectrum of Medical Informatics Challenges: From Molecular Medicine to Transforming Health Care in a Globalizing Society

C. A. Kulikowski
1   Department of Computer Science, Rutgers University, New Jersey
› Author Affiliations
Further Information

Publication History

Publication Date:
07 February 2018 (online)


Background: Medical informatics has always encompassed a very broad spectrum of techniques for clinical and biomedical research, education and practice. There has been a concomitant variety of depth of specialization, ranging from the routine application of information processing methods to cutting-edge research on fundamental problems of computer-based systems and their relations to cognition and perception in biomedicine.

Objectives: Challenges for the field can be placed in perspective by considering the scale of each – from the highly detailed scientific problems in bioinformatics and emerging molecular medicine to the broad and complex social problems of introducing medical informatics into web-related global settings. Methods: The scale of an informatics problem is not only determined by the inherent physical space in which it exists, but also by the conceptual complexity that it involves, reinforcing the need to investigate the semantic web within which medical informatics is defined.

Results and Conclusion: Bioinformatics, biomedical imaging and language understanding provide examples that anchor research and practice in biomedical informatics at the detailed, scientific end of the spectrum. Traditional concerns of medical informatics in the clinical arena make up the broad mid-range of the spectrum, while novel social interaction models of competition and cooperation will be needed to understand the implications of distributed health information technology for individual and societal change in an increasingly interconnected world.

  • References

  • 1 Koenderink JJ. The structure of images. Biol Cybern 1984; 50: 363-70.
  • 2 Altman R. The interactions between clinical informatics and bioinformatics. JAMIA 2000; 7: 439-43.
  • 3 Simon H. A mechanism for social selection and successful altruism. Science 1990; 250: 1665-9.
  • 4 Parrish D, Liebman M, Miller P, Altman R. Bioinformatics and molecular medicine applicability and future impact on healthcare informatics. AMIA 2000 Annual Symposium Los Angeles, CA.:
  • 5 Talmon JL, Hasman A. Medical informatics at the beginning of the 21st century. Methods Inf Med 2002; 41: 4-7.
  • 6 Ackerman MJ, Spitzer VM, Scherzinger AL, Whitlock DG. The Visible Human Data Set:An image resource for anatomical visualization. Medinfo 8 1995; 1195-8.
  • 7 Brinkley JF, Bradley SW, Sundsten JW, Rosse C. The Digital Anatomist information system and its use in the generation and delivery of web-based anatomy atlases. Comp Biomed Res 1997; 472-503.
  • 8 Rosse C, Mejino JL, Modayur BR. et al. Motivation and organizational principles for the Digital Anatomist Symbolic Knowledge-base: an approach towards standards in anatomical knowledge representation. JAMIA 1998; 5: 17-40.
  • 9 Kulikowski CA, Gong L, Mezrich RS. Knowledge-based medical image analysis and representation for integrating content definition with the radiological report. Methods Inf Med 1995; 34: 96-103.
  • 10 Imielinska C, Metaxas D, Udupa J. Hybrid Segmentation of the Visible Human data, Proc. Third Visible Human Project Conference, National Library of Medicine, NIH Bethesda, MD: 2000: 41-4.
  • 11 Schiemann T, Tiede U, Hohne KH. Segmentation of the Visible Human for high quality volume-based visualization. Med Image Anal 1996; 1: 263-70.
  • 12 Udupa JK, Herman GT. eds. 3D Imaging in Medicine. (2nd Ed) CRC Press; Boca Raton, FL: 2001
  • 13 Toga AW. Three Dimensional Neuroimaging. New York: Raven Press; 1990
  • 14 Karp PD. Pathway Databases: A Case Study in Computational Symbolic Theories. Science 2001; 293: 2040-4.
  • 15 Chen RO, Altman RB. Automated diagnosis of data-model conflicts using metadata. JAMIA 1999; 6: 374-92.
  • 16 Karp PD. Design methods for scientific hypothesis formation and their application to molecular biology. Machine Learning 1993; 12: 89-116.
  • 17 Ridley M. The Origins of Virtue: Human instincts and the evolution of coorperation. London: Penguin Books; 1996
  • 18 Kaplan B, Brennan PF, Dowling AF. et al. Towards an informatics research agenda: Key people and organizational issues. JAMIA 2001; 8: 235-41.
  • 19 Takahashi T. Current status of the world health card system. Yearbook of Medical Informatics 98 1998; 103-7.
  • 20 Institute of Medicine, Committee on Improving the Patient Record.. The Computer-based patient record: An essential technology for health care. Washington, DC: National Academy Press; 1997
  • 21 Lindberg DAB, Humphreys BL, McCray AT. The Unified Medical Language System. Methods Inf Med 1993; 32: 281-91.
  • 22 McCray AT. Conceptual complexity in biomedical terminologies:The UMLS approach, in Classification and Knowledge Organization. Berlin: Springer Verlag; 1997: 475-89.
  • 23 Wang C, Ohe K. A CORBA-based object framework with patient identification, translation and dynamic linking. Methods for exchanging patient data. Methods Inf Med 1999; 38: 56-65.
  • 24 Wyatt JC. Commentary: measuring quality and impact of the World Wide Web. BMJ 1997; 314: 1879-81.
  • 25 Jadad AR, Gagliardi A. Rating health information on the Internet: navigating to knowledge or to Babel?. JAMA 1998; 279: 611-4.
  • 26 Morris TA, Guard JR, Marine SA. Approaching equity in consumer health information delivery. JAMIA 1997; 4: 6-13.
  • 27 Musen MA. Domain ontologies in software engineering: Use of Protégé with the EON architecture. Methods Inf Med 1998; 37: 540-50.
  • 28 Joubert M, Fieschi M, Robert J-J. et al. UMLS-based conceptual queries to biomedical information databases. JAMIA 1998; 5: 52-61.
  • 29 Li Y-C. Towards a medical information collective: trends in the development of digital libraries in medicine. IMIA Yearbook; 2001: 77-82.
  • 30 Brown JS, Duguid P. The Social Life of Information. Boston, MA: Harvard Business School Press; 2000
  • 31 Ohno-Machado L, Gennari JH, Murphy SN. et al. The GuideLine interchange format:A model for representing guidelines. JAMIA 1998; 5: 357-72.
  • 32 Miller PL. Domain-constrained generation of clinical condition sets to help test computer-based clinical guidelines. JAMIA 2001; 8: 131-45.
  • 33 Sonnenberg FA. Decision analysis in disease management. Disease Management and Clinical Outcomes 1997; 1: 20-34.
  • 34 Hagerty CG, Pickens D, Kulikowski CA, Sonnenberg FA. HGML:A hypertext guideline markup language, Proc. AMIA Fall Symposium 2000
  • 35 Shahar Y, Musen MA. Knowledge-based temporal abstraction in clinical domains. Artif Intelligence in Medicine 1996; 8: 267-98.
  • 36 Maedche A, Staab S. Ontology learning for the semantic web. IEEE Intelligent Syst. 2001 March/April 72-9.
  • 37 Golland P, Kokinis R, Halle M. et al. Anatomy Browser:A novel approach to visualization and integration of medical information. Comput Aided Surg 1999; 4: 129-43.
  • 38 Bashshur R. (ed.). Telemedicine: Theory and Practice; CC Thomas Publications; 1997
  • 39 Halamka JD, Szolovits P, Rind D, Safran C. A WWW implementation of national recommendations for protecting electronic health information. JAMIA 1997; 4: 458-64.
  • 40 van Bemmel JH. Medical informatics, Art or science?. Methods Inf Med 1996; 35: 157-72.