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Business Intelligence Platform for Nosocomial Infection Incidence


  • University of Minho, ALGORITMI Research Center, Portugal


Background/Objectives: Nosocomial infection prevention is essential for patients’ safety and well-being. It can be efficiently performed through the analysis of the information available. With this analysis, it is possible to build knowledge that helps to identify the risk factors and the activities related to the nosocomial infection occurrence and it also allows characterizing the infection. Methods/Statistical Analysis: This paper presents a Business Intelligence (BI) system built to allow the study of nosocomial infection incidence in the Medicine Units of Centro Hospitalar do Porto (CHP), a hospital center in the north of Portugal. This BI platform is responsible for presenting nosocomial infection indicators. Findings: This platform enables to query important information and to analyze it, supporting healthcare professionals in their decisions. The knowledge obtained by this analysis allows preventing, monitoring and reducing nosocomial infections. So, the system acts as a Clinical Decision Support System (CDSS) capable of increasing patient safety and well-being. The platform developed shows that, for example, in 2013 the rate of nosocomial infection in CHP Medicine Units varied between 9.43% and 12.95% and the respiratory and the urinary tract infections were the most frequent nosocomial infections. Application/Improvements: This work and the platform developed demonstrate that BI technology can be applied to healthcare with utility.


Business Intelligence, Clinical Decision Support System, Data Warehousing, Nosocomial Infection, On-Line Analytical Processing.

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  • Inweregbu K, Dave J, Pittard A. Nosocomial infections. Continuing Education in Anaesthesia, Critical Care and Pain. 2005; 5(1):14–17. Crossref
  • Rigor H, Machado J, Abelha A, Neves J, Alberto C. A webbased system to reduce the nosocomial infection impact in healthcare units. Proceedings of the WEBIST- International Conference on Web Information Systems, Funchal, Madeira, Portugal; 2008.
  • Clean care is safer care team. Report on the burden of endemic health care-associated infection worldwide: Clean care is safer care, World Health Organization; 2014.
  • Hospital do Futuro. Infeção hospitalar: Um problema do mundo. um problema de todos, Blog of Hospital do Futuro (in Portuguese); 2014.
  • Silva E, Cardoso L, Portela F, Abelha A, Santos MF, Machado J. Predicting nosocomial infection by using data mining technologies. New Contributions in Information Systems and Technologies, Advances in Intelligent Systems and Computing. 2015; 354:189–98. Crossref
  • Faria R, Vicente H, Abelha A, Santos M, Machado J, Neves J. A case-based approach to nosocomial infection detection. Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science. 2016; 9693:159–68. Crossref
  • Ghazanfari M, Jafari M, Rouhani S. A tool to evaluate the business intelligence of enterprise systems. Scientia Iranica. 2011; 18(6):1579–90. Crossref
  • Power DJ. Understanding data-driven decision support systems. Information Systems Management. 2008, 25(8):149–54. Crossref
  • Bonney W. Applicability of business intelligence in electronic health record. Procedia - Social and Behavioral Sciences. 2013; 73:257–62. Crossref
  • Glaser J, Stone J. Effective use of business intelligence. Healthcare Financial Management: Journal of the Healthcare Financial Management Association. 2008; 62(2):68–72.
  • Prevedello LM, Andriole KP, Hanson R, Kelly P, Khorasani R. Business intelligence tools for radiology: Creating a prototype model using open-source tools. Journal of Digital Imaging. 2010; 23(2):133–41. Crossref
  • Portela F, Santos M, Machado J, Abelha A, Silva A. Pervasive and intelligent decision support in critical health care using ensemble. Lecture Notes in Computer Science Information Technology in Bio- and Medical Informatics. 2013; 8060:1–16. Crossref
  • Popovič A, Hackney R, Coelho PS, Jaklič J. Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems. 2012; 54(1):729–39. Crossref
  • Mettler T, Vimarlund V. Understanding business intelligence in the context of health care. Health Informatics Journal. 2009; 15(3):254–64. Crossref
  • Shahraki A, Dezhkam A, Dejkam R. Developed model of management of successful customer relationship in the context of business intelligence. Indian Journal of Science and Technology. 2015 Dec; 8(35):1–8. Crossref
  • Hasan HM, Lotfollah F, Negar M. Comprehensive model of business intelligence: a case study of nano’s companies. Indian Journal of Science and Technology. 2012 Jun; 5(6):1–9.
  • Dehkordi MN. A novel association rule hiding approach in OLAP data cubes. Indian Journal of Science and Technology. 2013 Feb; 6(2):1–13.
  • Inmon WH. Building the data warehouse (3rd ed.). John Wiley & Sons Inc, USA; 2002.
  • El-Sappagh SHA, Hendawi AMA, Bastawissy AHE. A proposed model for data warehouse ETL processes. Journal of King Saud University - Computer and Information Sciences. 2013; 23(2):91–104. Crossref
  • Thangaraju G, Rani XAK. Multi user profile orient access control based integrity management for security management in data warehouse. Indian Journal of Science and Technology. 2016 Jun; 9(22):1–7. Crossref
  • Portela F, Veloso R, Oliveira S, Santos MF, Abelha A, Machado J, Silva A, Rua F. Predict hourly patient discharge probability in intensive care units using data mining. Indian Journal of Science and Technology. 2015; 8(32):1–11.
  • Loshin D. Business intelligence: The savvy manager’s guide, (2nd ed.). Morgan Kaufman Publishers Inc, USA; 2012.
  • Kimball R. The data warehouse toolkit: practical techniques for building dimensional data warehouses. John Wiley & Sons; 1996 Feb.
  • Thalhammer T, Schre M, Mohania M. Active data warehouses: Complementing OLAP with analysis rules. Data and Knowledge Engineering. 2001; 39 (3):241–69. Crossref
  • Kimball R, Ross M. The data warehouse toolkit: The definitive guide to dimensional modeling, (3rd ed.). John Wiley & Sons Inc, USA; 2013.
  • Chaudhuri S, Dayal U. An overview of data warehousing and OLAP technology. SIGMOD Rec. 1997; 26(1):65–74. Crossref
  • Chaudhuri S, Dayal U, Narasayya V. An overview of business intelligence technology. Communications of the ACM. 2011; 54(8):88. Crossref
  • Machado J, Alves V, Abelha A, Neves J. Ambient Intelligence via Multiagent Systems in Medical arena. International Journal of Engineering Intelligent Systems, Special issue on Decision Support Systems. 2007; 15(3):167–73.
  • Cardoso L, Marins F, Portela F, Santos M, Abelha A, Machado J. The next generation of interoperability agents in healthcare. International Journal of Environmental Research and Public Health. 2014; 11(5):5349–71. Crossref
  • Spruit M, Vroon R, Batenburg R. Towards healthcare business intelligence in long-term care. Computers in Human Behavior. 2014; 30:698–707. Crossref
  • Foshay N, Kuziemsky C. Towards an implementation framework for business intelligence in healthcare. International Journal of Information Management. 2014; 34(1):20–7. Crossref


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