Philosophies for the Design and Development of Clinical Decision-Support Systems

H. A. Heathfield; J. Wyatt

doi:10.1055/s-0038-1634896

Methods of Information in Medicine, Table of Contents

Methods Inf Med 1993; 32(01): 01-08
DOI: 10.1055/s-0038-1634896

Original Article

Schattauer GmbH

Philosophies for the Design and Development of Clinical Decision-Support Systems

H. A. Heathfield

¹Medical Informatics Group, Computer Science, University of Manchester

,

J. Wyatt

²Biomedical Informatics Unit, ICRF Laboratories, London, UK

› Author Affiliations

Abstract

Abstract:

Little significance is attached by medical informatics workers to the many practical issues which affect the development of clinical decision-support systems. We examine the current state of research in clinical decision-support, the characteristics and motivations of developers, and the perceptions of intended end-users. Factors which adversely affect the success of systems are highlighted and pointers to good practice discussed. We then propose a coherent approach to system development, consisting of requirements analysis, software design, implementation, testing, evaluation and maintenance.

Key-words

Software Design - Development Methods - Medical Systems - Decision-Support Systems

Full Text

References

REFERENCES
1 Ledley R, Lusted L. Reasoning foundations of medical diagnosis. Science 1959; 130: 9-21.
2 Cramp D, Goodyear OM. Expert systems in medicine - Report on a European survey. London: Healthcare Informatics Foundation; 1989. (ISBN 1 87277400 8)
3 Goodyear OM, Hobsley M, Jameson DG, Scurr J, Shamsolmaali A. Expert systems in the medical data environment: An example from breast surgery. In: Proceedings of the 6th Annual Meeting on Expert Systems in Medicine. London: British Medical Informatics Society; 1990: 1-10.
4 Möhr JR, Blum BI. Software engineering in health informatics (Editorial). Meth Inform Med 1990; 29: 1-2.
5 Wilson M, Duce D, Simpson D. Life cycles in software and knowledge engineering: a comparative review. The Knowledge Engineering Review 1989; 04: 189-204.
6 Hayward S. How to build knowledge-based systems: techniques, tools and case studies. In: ESPRIT 87. Proceedings of the Esprit conference. Brussels: Commision of the EEC; 1987: 665-87.
7 Keller R. Expert system technology: development and application. Englewood Cliffs NJ: Yourdon Press; 1987
8 Mongomery A. GEMINI: government expert systems methodology initiative. In: Duffin P. (ed). KBS in Government 88. London: Blenheim Online Ltd; 1988: 75-88.
9 Lawson HW. Philosophies for engineering computer-based systems. IEEE Computer 1990; 23: 52-63.
10 The Collins Dictionary and Thesaurus. Glasgow, UK: William Collins Sons and Co Ltd; 1987
11 Breuker J. ed. Model-driven knowledge acquisition interpretation models. ESPRIT Project 1098, Deliverable Task Al. University of Amsterdam; 1987
12 Shortliffe EH, Davis R. Some considerations for the implementation of knowledge-based expert systems. SIGART Newsletter no 55 1975; 9-12.
13 Shortliffe EH, Clancy WJ. Anticipating the second decade. In: Shortliffe EH, Clancy WJ. (eds). Readings in Medical AI: The First Decade. Reading, Massachusetts: Addison Wesley; 1984: 463-72.
14 Schwartz WB. Medicine and the computer: The promise and problems of change. N Engl J Med 1970; 283: 1257-64.
15 Shortliffe EH. MYCIN: Computer-based-Medical Consultations. New York: American Elsevier; 1975
16 Miller RA, Pople HE, Myers JD. INTER-NIST-1, an experimental computer-based diagnostic consultant for general internal medicine. N Engl J Med 1982; 307: 468-76.
17 De Dombal FT, Leaper DJ, Staniland JR. et al. Computer-aided diagnosis of acute abdominal pain. Br Med J 1972; 02: 9-13.
18 Adlassnig K-P, Graber G, Bengtsson S, Hansen R. oeds Proc. Medical Informatics Europe 1991. Heidelberg: Springer Verlag; 1991. (Lecture Notes in Medical Informatics 45).
19 Clayton PD. ed. Proceedings of the 15th Annual Symposium on Computer Applications in Medical Care 1991. Washington DC: McGraw-Hill Inc; 1991
20 O’Neil M, Morris A. Expert systems in the United Kingdom: An evaluation of deveopment methodologies. Expert Systems 1989; 06: 90-9.
21 Blois MS. Clinical judgment and computers. N Engl J Med 1980; 303: 192-7.
22 Kassirer J, Gorry G. Clinical problem solving: a behavioral analysis. Ann Intern Med 1978; 89: 245-55.
23 Patil RS. Artificial intelligence techniques for diagnostic reasoning in medicine. In: Scrobe HE. (ed). Exploring Artificial Intelligence. Palo Alto CA: Morgan-Kaufmann; 1988: 347-79.
24 Heathfield HA, Bose DK, Kirkham N. A knowledge-based decision support tool for the histological diagnosis of breast disease. Clin Pathol 1991; 44: 502-8.
25 Haynes R, McKibbon K, Walker C. et al. Online access to MEDLINE in a clinical setting. Ann Intern Med 1990; 112: 78-84.
26 Heathfield HA, Armstrong J, Kirkham N. Object-oriented design and programming in medical decision-support. Comput Meth Prog Biomed 1991; 36: 239-51.
27 Kay S, Horan B, Goble CA. et al. User-centred design and strategic medical informatics for health care. In: Van Bemmel JH, Zvárová J. (eds). Knowledge, Information and Medical education. Amsterdam: North-Holland Publ Comp; 1991: 434-45.
28 Young D. Can we get doctors to use computers. Health Services J June 1991; 116-8.
29 Wyatt J, Spiegelhalter D. Evaluating medical expert systems: what to test and how. Med Inform 1990; 15: 205-17.
30 Wyatt J. Use and sources of medical knowledge. The Lancet 1991; 338: 1368-73.
31 Schreiner A, Chard T. Expert systems for the prediction of ovulation: comparison of an expert system shell with a program written in a traditional language. Meth Inform Med 1990; 29: 140-5.
32 Newell A, Simon HA. Human Problem-Solving. Englewood Cliffs, New Jersey: pnPrentice Hall: 1972
33 Szolovits P, Ratil RS, Schwartz WB. Artificial intelligence in medical diagnosis. Ann Intern Med 1988; 108: 80-7.
34 Schwarz WB, Patil RS, Szolovits P. Artificial intelligence in medicine. Where do we stand? N Engl J Med 1987; 316: 685-8.
35 Clancy WJ, Letsinger R. Neomycin: reconfiguring a rule-based expert system. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence. Kauf-mann M. Los Altos, California: 1981: 829-36.
36 Martin N, Huws H. Finding method in the madness: methodologies for developing KBS. IEEE Expert 1991; 06: 2-3.
37 Wyatt J. Lessons learned from the field trial of ACORN, an expert system to advise on chest pain. In: Barber B, Cao D, Qin D, Wagner G. (eds). Proceedings of Sixth World Conference on Medical Informatics. Amsterdam: North Holland Publ Comp; 1989: 111-5.
38 Wyatt J. A method for developing medical decision-aids applied to ACORN, a chest pain advisor (DM Thesis). Oxford: Oxford University; 1991
39 Bersoff EH, Davis AM. Impacts of life-cycle models on software configuration. Comm ACM 1991; 34: 104-18.
40 Musen M, Van der Lei J. Knowledge engineering for clinical consultation programs: Modelling the application area. Meth Inf Med 1989; 28: 28-35.
41 Booch G. Software Engineering with Ada. Menlo Park, CA: Benjamin/Cummings Publ; 1986
42 Coad, Yourdon. Object-oriented analysis. New York: Yourdon Press; 1990
43 Preece A. Towards a methodology for evaluating expert systems. Expert Systems 1990; 07: 215-23.
44 Berry DC, Hart AE. Evaluating expert systems. Expert Systems 1990; 07: 199-207.
45 Kapor M. A software design manifesto. Dr Dobb’s Journal 1991; 16: 62-8.