ABSTRACT
We describe the design and implementation of a clinical decision support system for assessing risk of cerebral vasospasm in patients who have been treated for aneurysmal subarachnoid hemorrhage. We illustrate the need for such clinical decision support systems in the intensive care environment, and propose a three pronged approach to constructing them, which we believe presents a balanced approach to patient modeling. We illustrate the data collection process, choice and development of models, system architecture, and methodology for user interface design. We close with a description of future work, a proposed evaluation mechanism, and a description of the demo to be presented.
- Clinical Alarms Task Force. Impact of clinical alarms on patient safety. Technical report, American College of Clinical Engineering Healthcare Technology Foundation, 2006.Google Scholar
- Clinical Alarms Task Force. Impact of clinical alarms on patient safety. Journal of Clinical Engineering, 32(1):22--33, 2007.Google Scholar
- J. Edworthy and E. Hellier. Alarms and human behaviour: implications for medical alarms. British Journal of Anaesthesia, 97:12--17, 2006.Google ScholarCross Ref
- J. M. Feldman and M. H. Ebrahim. Robust sensor fusion improves heart rate estimation: Clinical evaluation. Journal of Clinical Monitoring and Computing, 13:379--384, 1997.Google ScholarCross Ref
- M. J. Field, K. N. Lohr, and I. of Medicine U.S.. Committee on Clinical Practice Guidelines. Guidelines for clinical practice : from development to use. National Academy Press, 1992.Google Scholar
- A. X. Garg, N. K. J. Adhikari, H. McDonald, M. P. Rosas-Arellano, P. J. Devereaux, J. Beyene, J. Sam, and R. B. Haynes. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: A systematic review. Journal of the American Medical Association, 293:1223--1238, 2005.Google ScholarCross Ref
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The weka data mining software: An update. SIGKDD Explorations, 11(1), 2009. Google ScholarDigital Library
- D. L. Hunt, R. B. Haynes, S. E. Hanna, and K. Smith. Effects of computerized clinical decision support systems on physician performance and patient outcomes: A systematic review. Journal of the American Medical Association, 280:1339--1346, 1998.Google ScholarCross Ref
- M. Imhoff and R. Fried. The crying wolf: Still crying? Anesthesia and Analgesia, 108(5):1382--1383, 2009.Google ScholarCross Ref
- M. Imhoff and S. Kuhls. Alarm algorithms in critical care monitoring. Anesthesia and Analgesia, 102(5):1525--1536, 2006.Google ScholarCross Ref
- A. King, S. Procter, D. Andresen, J. Hatcliff, S. Warren, W. Spees, R. P. Jetley, P. L. Jones, and S. Weininger. An open test bed for medical device integration and coordination. In ICSE Companion, pages 141--151. IEEE, 2009.Google ScholarCross Ref
- C. Oberli, C. Saez, A. Cipriano, G. Lema, and C. Sacco. An expert system for monitor alarm integration. Journal of Clinical Monitoring and Computing, 15:29--35, 1999.Google ScholarCross Ref
- J. R. Olson and H. H. Rueter. Extracting expertise from experts: Methods for knowledge acquisition. Expert Systems, 4:152--168, 1987.Google ScholarCross Ref
- A. Otero, P. Felix, S. Barro, and F. Palacios. Addressing the flaws of current critical alarms: a fuzzy constraint satisfaction approach. Artificial Intelligence in Medicine, 47(3):219 -- 238, 2009. Google ScholarDigital Library
- R. Schoenberg, D. Z. Sands, and C. Safran. Making icu alarms meaningful: a comparison of traditional vs. trend-based algorithms. Proceedings of the American Medical Informatics Association Symposium, pages 379--383, 1999.Google Scholar
- I. Sim, P. Gorman, R. A. Greenes, R. B. Haynes, B. Kaplan, H. Lehmann, and P. C. Tang. Clinical decision support systems for the practice of evidence-based medicine. Journal of the American Medical Informatics Association : JAMIA, 8(6):527--534, 2001.Google ScholarCross Ref
Index Terms
- Clinical decision support for integrated cyber-physical systems: a mixed methods approach
Recommendations
Developing decision support for dialysis treatment of chronic kidney failure
The complexity, variability, quantitative nature, and data density of treatment for chronic kidney failure make dialysis information systems excellent candidates for computerized decision support. We describe the development of an intelligent system ...
Heuristics in Managing Complex Clinical Decision Tasks in Experts' Decision Making
ICHI '14: Proceedings of the 2014 IEEE International Conference on Healthcare InformaticsBackground: Clinical decision support is a tool to help experts make optimal and efficient decisions. However, little is known about the high level of abstractions in the thinking process for the experts. Objective: The objective of the study is to ...
Clinical decision support for increased-risk organ transplants: participatory design.
OzCHI '21: Proceedings of the 33rd Australian Conference on Human-Computer InteractionCurrently there are over 1,600 Australians awaiting a life-saving organ transplant. Approximately 20% of potential donors have a history of behaviors before their death that increased their risk of acquiring and transmitting HIV, Hepatitis B or ...
Comments