ICT for Patient Safety: Towards a European Research Roadmap

  • Veli N. Stroetmann
  • Daniel Spichtinger
  • Karl A. Stroetmann
  • Jean Pierre Thierry
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4345)


This paper analyses key issues towards a research roadmap for eHealth-supported patient safety. The raison d’etre for research in this area is the high number of adverse patient events and deaths that could be avoided if better safety and risk management mechanisms were in place. The benefits that ICT applications can bring for increased patient safety are briefly reviewed, complemented by an analysis of key ICT tools in this domain. The paper outlines the impact of decision support tools, CPOE, as well as incident reporting systems. Some key research trends and foci like data mining, ontologies, modelling and simulation, virtual clinical trials, preparedness for large-scale events are touched upon. Finally, the synthesis points to the fact that only a multilevel analysis of ICT in patient safety will be able to address this complex issue adequately. The eHealth for Safety study will give insights into the structure of such an analysis in its lifetime and arrive at a vision and roadmap for more detailed research on increasing patient safety through ICT.


Patient Safety Clinical Decision Support System Computerize Physician Order Entry Computerize Physician Order Entry System Incident Reporting System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    IOM Report: To err is human: Building a safer health system. Institute of Medicine, p. 287 (2000), Available at:
  2. 2.
    IOM Report : Crossing the Quality Chasm: A New Health System for the 21st Century, Institute of Medicine, p. 364 (2001),
  3. 3.
    Commission on Systemic Interoperability: Ending the Document Game: Connecting and Transforming Your Healthcare Through Information Technology, U.S. Government Printing Office (GPO), Washington, p. 249 (2005),
  4. 4.
    Young, S.: The Role of Health IT in Reducing Medical Errors and Improving Healthcare Quality & Patient Safety. Agency for Healthcare Research and Quality (August 2005),
  5. 5.
    JRC/IPTS: eHealth in the Context of a European Ageing Society. A Prospective Study. P.17 (2004)Google Scholar
  6. 6.
    European Commission: The Social Situation in the European Union 2003, p. 69. Office for Official Publications, Luxembourg (2003)Google Scholar
  7. 7.
    Proctor, P.R., et al. (eds.): Building a Better Delivery System: A New Engineering/Health Care Partnership. Committee on Engineering and the Health Care System, p. 276. National Academies Press, Washington (2005),
  8. 8.
    Wachter, R.: The End Of The Beginning: Patient Safety Five Years After To Err Is Human. Health Affairs Web Exclusive. W4- 539 (2004)Google Scholar
  9. 9.
    Milstein, A., et al.: Improving the Safety of Health Care: The Leapfrog Initiative. Effective Clinical Practice 3(6), 313–316 (2000); Versel, N.: Performance Driving Investment Up. Modern Physician 15, 23 (November 2003) Google Scholar
  10. 10.
    NAO (National Audit Office): A Safer Place for Patients: Learning to improve patient safety, Department of Health, p. 86 (2005),
  11. 11.
    Lessens, V., Lloyd-Williams, D.: Workshop on Risk Management for Health Professionals. In: Use of ICT, Workshop report, p. 14 (2004)Google Scholar
  12. 12.
    Ash, J.S., Berg, M., Coiera, E.: Some Unintended Consequences of Information Technology in Health Care: The Nature of Patient Care Information System–related Errors. Journal of the American Informatics Association 11(2), 104–112 (2004)CrossRefGoogle Scholar
  13. 13.
    Coiera, E., Westbrook, J.I., Wyatt, J.C.: The Safety and Quality of Decision Support Systems. In: Haux, R., Kulikowski, C. (eds.) IMIA Yearbook of Medical Informatics 2006. Methods Inf. Med. 2006, vol. 45 (Suppl. 1) (2006)Google Scholar
  14. 14.
    Hunt, et al.: Effects of computer-based clinical decision support system on physicians performance and patient outcomes: a systematic review. JAMA 280, 1339–1346 (1998)CrossRefGoogle Scholar
  15. 15.
    Sintchenko, et al.: Comparative impact of guidelines, clinical data and decision support on prescribing decision: an interactive web experiment with simulated cases. J. Am. Med. Inform. Assoc. 11(1), 71–77 (2004)CrossRefGoogle Scholar
  16. 16.
    Tierney, et al.: Effects of computerized guidelines for managing heart disease in primary care. J. Gen. Intern. Med. 18(12), 967–976 (2003)CrossRefGoogle Scholar
  17. 17.
    Rousseau, et al.: Practice based, longitudinal, qualitative interview study of computerised evidence based guidelines in primary care. BMJ 326(7384), 314 (2003)CrossRefGoogle Scholar
  18. 18.
    Garg, A.X., Adhikari, N.K., McDonald, H., Rosas-Arellano, M.P., Devereaux, P.J., Beyene, S., Sam, J., Haynes, R.B.: Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 293, 1223–1238 (2005)CrossRefGoogle Scholar
  19. 19.
    Eccles, M., et al.: Effect of computerised evidence based guidelines on management of asthma and angina in adults in primary care: cluster randomised controlled trial. BMJ 325, 941–944 (2002)CrossRefGoogle Scholar
  20. 20.
    Ash, et al.: Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. Journal of the American Medical Informatics Ass. 11, 104–112 (2004)CrossRefGoogle Scholar
  21. 21.
    Kawamoto, et al.: Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ 330(7494), 765 (2005)CrossRefGoogle Scholar
  22. 22.
    FCG: Computerized Physician Order Entry: Costs, Benefits and Challenges. A Case Study Approach (2003)Google Scholar
  23. 23.
    Bonnabry, P.: Information Technologies for the Prevention of Medication Errors. Business Briefing: European Pharmacotherapy, 1–5 (2003)Google Scholar
  24. 24.
    Bates, D.W., et al.: Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. Journal of the American Medical Association 280(15), 1311–1316 (1998); Leapfrog Group. 2000. Leapfrog Patient Safety Standards: The Potential Benefit of Universal CrossRefGoogle Scholar
  25. 25.
    Rainu, K., Bates, D. W. : Computerized Physician Order Entry (CPOE) with Clinical Decision Support Systems (CDSSs). In: Making Health Care Safer: A Critical Analysis of Patient Safety Practices. Evidence Report/Technology Assessment: Number 43. AHRQ Publication No. 01-E058, July 2001. p.59–70 (2003),
  26. 26.
    Sittig, Stead.:Computer based physician Order Entry: the state of the art. Journal of the American Medical Informatics Association, 108–123 (1994)Google Scholar
  27. 27.
    Han, Y., et al.: Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system in: Pediatrics.  116(6), 1506–1512 (2005)Google Scholar
  28. 28.
    Bates, D.W., et al.: Detecting Adverse Events Using Information Technology. JAMIA 10, 119 (2003)Google Scholar
  29. 29.
    Kuperman, et al.: Improving Response to Critical Laboratory Results with Automation: Results of a Randomized Controlled Trial. JAMIA 6, 512–522 (1999)Google Scholar
  30. 30.
    Gandhi, T.K., Bates, D.W.: Computer Adverse Drug Event (ADE) Detection and Alerts. Making Health Care Safer: A Critical Analysis of Patient Safety Practices, p.81Google Scholar
  31. 31.
    Runciman, W.B.: Lessons from the Australian Patient Safety Foundation: setting up a national patient safety surveillance system – is this the right model? Quality and Safety in Health Care, 250 (November 2002)Google Scholar
  32. 32.
    Information Society Technologies Advisory Group (ISTAG). In: Wahlster, W. (ed.) Grand Challenges in the Evolution of the Information Society, pp. 26–29 (2004)Google Scholar
  33. 33.
    IUPS Physiome Project Roadmap (2005),
  34. 34.
    Towards Virtual Physiological Human.: Multilevel modelling and simulation of the human anatomy and physiology, White Paper, p. 3 (2005),
  35. 35.
    Safer Medicines, Academy of Medical Sciences, A report from the Academy’s FORUM with industry p. 22 (2005),
  36. 36.
    Agur, Z.: Biomathematics in the development of personalized medicine in oncology. Future Oncology 2(1), 39–42 (2006)CrossRefGoogle Scholar
  37. 37.
    Models that take drugs. Biosimulation: Designing drugs in computers is still some way off. But software is starting to change the way drugs are tested, The Economist (June 9, 2005)Google Scholar
  38. 38.
    Gorman, P.J., Meier, A.H., Krummel, T.M.: Computer-assisted training and learning in surgery. Comput Aided Surg. 5, 120–130 (2000)CrossRefGoogle Scholar
  39. 39.
    Fried, M.P., et al.: Identifying and reducing errors with surgical simulation, Qual Saf Health Care, vol. 13(Suppl 1), pp. i19–i26 (2004), doi: 10.1136/qshc.2004.009969,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Veli N. Stroetmann
    • 1
  • Daniel Spichtinger
    • 1
  • Karl A. Stroetmann
    • 1
  • Jean Pierre Thierry
    • 2
  1. 1.empirica Communication and Technology ResearchBonnGermany
  2. 2.Symbion, Maisons-LaffitteFrance

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