Share This Article:

Healthcare intelligence risk detection systems

Full-Text HTML Download Download as PDF (Size:448KB) PP. 461-469
DOI: 10.4236/ojpm.2013.38062    2,757 Downloads   4,213 Views   Citations

ABSTRACT

Background: Today, in healthcare field that is changing rapidly, decision-makers encounter with ever-increasing inquiries on clinical and administrative information to realize customers’ legal and clinical requirements. Therefore, making decisions on healthcare has changed into a vital, complex and unstructured issue. The present paper mainly focuses on describing decision-making advantages, possible risk to improve efficiency of decision-making on healthcare, and especially medical procedures. Methods: The present research is a review study, which has been carried out by searching through the authentic scientific sources, including Pubmed, Google scholar, Iranmedex, and other information sources. While defining care intelligence, here, we introduce Knowledge Discovery Database, the Clinical Support Systems, and Intelligence Risk Detection Model and provide the conceptual model. Other issues studied in this paper include the Risk Possibility Assessment Technique, Risk Possibility Detection using knowledge management techniques, and expert systems. Results & Conclusion: Modeling the Intelligence Support System is necessary for designing Real-Time Risk Detection Information Systems in clinical measures. As taking medical procedures involves complex decision-makings and possibility of high risk, operational application of the techniques derived from knowledge and data mining models under study will play a crucial role in increasing possibility of success of the measure and promoting safety of patients.

Cite this paper

Safdari, R. , Farzi, J. , Ghazisaeidi, M. , Mirzaee, M. and Goodini, A. (2013) Healthcare intelligence risk detection systems. Open Journal of Preventive Medicine, 3, 461-469. doi: 10.4236/ojpm.2013.38062.

References

[1] Wickramasinghe, N. and Schaffer, J. (2006) Creating knowledge-driven healthcare process with the intelligence continuum. International Journal of Electronic Healthcare, 2, 164-174.
[2] Moghimi, H., Zadeh, H., Schaffer, J. and Wickramasinghe, N. (2012) Incorporating intelligent risk detection to enable superior decision support: The example of orthopaedic surgeries. Health and Technology, 2, 33-41.
http://dx.doi.org/10.1007/s12553-011-0014-z
[3] Mettler, T. and Vimarlund, V. (2008) Understanding business intelligence in the context of health care. Proceedings of the 13th International Symposium for Health Information Management Research (ISHIMR), Massey University, 61-69.
[4] Safdari, R., Torabi, M., Farzi, J., Mirzaee, M. and Goodini, A. (2012) Intelligence risk detection models: Tools to promote patients safety level. Proceedings of the 3th Symposium for E-hospital & Telemedicine, Tehran University of Medical Sciences, Tehran, 56.
[5] Mahler, E.M., Schmidt, R.M. and Kvitash, V.I. (1993) An artificial intelligence system to predict progression of immune dysfunction in healthy older patients. Journal of Medical Systems, 17, 173-181.
http://dx.doi.org/10.1007/BF00996942
[6] Turban, E., Mclean, E. and Wetherbe. J. (2002) Information technology for management. 3rd Edition, John Wiely & Sons INS., Hoboken, 520-545.
[7] Wu, J., Roy, J. and Stewart, W.F. (2010) Prediction modeling using EHR data: Challenges, strategies, and a comparison of machine learning approaches. Medical Care, 48, S106-S113.
http://dx.doi.org/10.1097/MLR.0b013e3181de9e17
[8] Wickramasinghe, N., Bali, R., Gibbons, C., Choi, C. and Schaffer, J. (2009) Optimization of health care operations with knowledge management. JHIMS, 3, 44-50.
[9] Aral, K.D., Güvenir, H.A., Sabuncuoglu, I. and Akar, A.R. (2012) A prescription fraud detection model. Computer Methods and Programs in Biomedicine, 106, 37-46.
http://dx.doi.org/10.1016/j.cmpb.2011.09.003
[10] Miller, R.A. (1994) Medical diagnostic decision support systems—Past, present, and future: A threaded bibliography and brief commentary. Journal of the American Medical Informatics Association, 1, 8-27.
http://dx.doi.org/10.1136/jamia.1994.95236141
[11] Hunt, D.L., Haynes, R.B., Hanna, S.E. and Smith, K. (1998) Effects of computer-based clinical decision support outcomes: A systematic review systems on physician performance and patient. JAMA, 280, 1339-1346.
http://dx.doi.org/10.1001/jama.280.15.1339
[12] Fieschi, M., Dufour, J.C., Staccini, P., Gouvernet, J. and Bouhaddou, O. (2003) Medical decision support systems: Old dilemmas and new paradigms? Tracks for successful integration and adoption. Methods of Information in Medicine, 42, 191-198.
[13] Moghimi, F., Seif Zadeh, H., Cheung M. and Wickramasinghe, N. (2011) An intelligent risk detection framework using business intelligence tools to improve decision efficiency in healthcare contexts. Proceedings of the 17th Americas Conference on Information Systems (AMCIS), United States, 4-7 August 2011, 1-8.
http://aisel.aisnet.org/amcis2011_submissions/173/
[14] Larrazabal, L.A., Del Nido, P.J., Jenkins, K.J. and Gauvreau, K. (2007) Measurement of technical performance in congenital heart surgery: A pilot study. The Annals of Thoracic Surgery, 83, 179-184.
http://dx.doi.org/10.1016/j.athoracsur.2006.07.031
[15] Keenan, P., Buntin Beeuwkes, M., McGuire, T. and Newhouse, J.P. (2001) The prevalence of formal risk adjustment in health plan purchasing. Inquiry, 38, 245-259.
http://dx.doi.org/10.5034/inquiryjrnl_38.3.245
[16] Hornbrook, M.C. and Goodman, M.J. (1996) Chronic disease, functional health status, and demographics: A multi-dimensional approach to risk adjustment. Health Services Research, 31, 283-307.
[17] Kang, N., Cole, T., Tsang, V., Elliott, M. and de Leval, M. (2004) Risk stratification in paediatric open-heart surgery. European Journal Cardio-Thoracic Surgery, 26, 3-11.
http://dx.doi.org/10.1016/j.ejcts.2004.03.038
[18] Kumar, A. and Gosain, A. (2009) Analysis of health care data using different data mining techniques. International Conference on Intelligent Agent & Multi-Agent Systems (IAMA), Chennai.
[19] Safdari, R., Farzi, J., Mirzaee, M. and Goodini, A. (2013) Intelligence Risk Detection systems for medical procedures. Proceedings of the 1st conference for Telemedicine, Amirkabir University, Tehran, 46.
[20] Wilson, I.B. and Cleary, P.D. (1995) Linking clinical variables with health-related quality of life: A conceptual model of patient outcomes. JAMA, 273, 59-65.
http://dx.doi.org/10.1001/jama.1995.03520250075037
[21] Farzi, J., Salem Safi, P., Zohoor, A.R. and Ebadi Fardazar, F. (2008) The study of National Diabetes Registry System: Model suggestion for Iran. Journal of Ardabil University of Medical Sciences & Health Services, 3, 288-293.
[22] Garg, A.X., Adhikari, N.K., McDonald, H., Rosas-Are-llano, M.P., Devereaux, P.J., Beyene, J., Sam, J. and Haynes, R.B. (2005) Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: A systematic review. JAMA, 293, 1223-1238.

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.