Asian Pacific Journal of Cancer Prevention

  • 제16권15호
  • /
  • Pages.6605-6608
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  • 2015
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  • 1513-7368(pISSN)
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  • 2476-762X(eISSN)
아시아태평양암예방학회 (Asian Pacific Journal of Cancer Prevention)
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Information Engineering and Workflow Design in a Clinical Decision Support System for Colorectal Cancer Screening in Iran

  • Maserat, Elham (School of Allied Medical Sciences, Tehran University of Medical Sciences) ;
  • Farajollah, Seiede Sedigheh Seied (School of Allied Medical Sciences, Tehran University of Medical Sciences) ;
  • Safdari, Reza (School of Allied Medical Sciences, Tehran University of Medical Sciences) ;
  • Ghazisaeedi, Marjan (School of Allied Medical Sciences, Tehran University of Medical Sciences) ;
  • Aghdaei, Hamid Asadzadeh (Research Center of Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Science) ;
  • Zali, Mohammad Reza (Research Center of Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Science)
  • 발행 : 2015.10.06
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초록

Background: Colorectal cancer is a major cause of morbidity and mortality throughout the world. Colorectal cancer screening is an optimal way for reducing of morbidity and mortality and a clinical decision support system (CDSS) plays an important role in predicting success of screening processes. DSS is a computer-based information system that improves the delivery of preventive care services. The aim of this article was to detail engineering of information requirements and work flow design of CDSS for a colorectal cancer screening program. Materials and Methods: In the first stage a screening minimum data set was determined. Developed and developing countries were analyzed for identifying this data set. Then information deficiencies and gaps were determined by check list. The second stage was a qualitative survey with a semi-structured interview as the study tool. A total of 15 users and stakeholders' perspectives about workflow of CDSS were studied. Finally workflow of DSS of control program was designed by standard clinical practice guidelines and perspectives. Results: Screening minimum data set of national colorectal cancer screening program was defined in five sections, including colonoscopy data set, surgery, pathology, genetics and pedigree data set. Deficiencies and information gaps were analyzed. Then we designed a work process standard of screening. Finally workflow of DSS and entry stage were determined. Conclusions: A CDSS facilitates complex decision making for screening and has key roles in designing optimal interactions between colonoscopy, pathology and laboratory departments. Also workflow analysis is useful to identify data reconciliation strategies to address documentation gaps. Following recommendations of CDSS should improve quality of colorectal cancer screening.

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참고문헌

  1. American Cancer Society (2015). Cancer facts & figures 2014: special section. Available from: http://www.cancer.org/research/cancerfactsstatistics/cancerfactsfigures2014/.
  2. Carney TJ, Morgan GP, Jones J, et al (2014). Using computational modeling to assess the impact of clinical decision support on cancer screening improvement strategies within the community health centers. J Biomed Inform, 51, 200-9. https://doi.org/10.1016/j.jbi.2014.05.012
  3. Cicchetti DV(1992). Neural networks and diagnosis in the clinical laboratory: state of the art. Clin Chem, 38, 9-10.
  4. Gooran Ourimi Navid, Shima Amin Sharifi, Elham Maserat, et al (2010). The role of information systems in cancer prevention. Gastroentrol Hepatol Bed Bench, 3.
  5. Guilan Kong, Dong-Ling Xu, Jian-Bo Yang (2008). Clinical decision support systems: A review of knowledge representation and inference under uncertainties. Int J Computational Intelligence Systems, 2, 159-67.
  6. Joseph A Cruz, David S Wishart (2006). Applications of machine learning in cancer prediction and prognosis. Cancer inform, 2, 59-77.
  7. Kawamoto K, Houlihan CA, Balas EA, et al (2005). Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success, BMJ, 2, 765.
  8. Kilsdonk E1, Peute LW, Riezebos RJ, et al (2013). From an expert-driven paper guideline to a user-centred decisionsupport system: A usability comparison study. Artif Intell Med, 59, 5-13. https://doi.org/10.1016/j.artmed.2013.04.004
  9. Maserat Elham , Fatemi R, Zali MR (2009). New perspective for integrated information management in national colorectal cancer screening in Iran. Asian Pac J Cancer Prev, 10, 701-6.
  10. Maclin PS, Dempsey J, Brooks J (1991). Using neural networks to diagnose cancer. J Med Syst, 15, 11-9. https://doi.org/10.1007/BF00993877
  11. Patel V. L, Kushniruk , Yang, S (2000). Impact of a computerbased patient record system on data collection, knowledge organization, and reasoning. J Am Med Inform Assoc, 7, 569-85. https://doi.org/10.1136/jamia.2000.0070569
  12. Simes RJ(1985). Treatment selection for cancer patients: application of statistical decision theory to the treatment of advanced ovarian cancer. J Chronic Dis, 38, 171-86. https://doi.org/10.1016/0021-9681(85)90090-6
  13. Wagholikar K, Sohn S, Wu S, Kaggal V, Buehler S, et al (2013). Workflow-based data reconciliation for clinical decision support: case of colorectal cancer screening and surveillance. AMIA Jt Summits Transl Sci Proc, 18, 269-73.