Complex Sciences. Second International Conference, COMPLEX 2012, Santa Fe, NM, USA, December 5-7, 2012, Revised Selected Papers

Research Article

Hospitals as Complex Social Systems: Agent-Based Simulations of Hospital-Acquired Infections

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  • @INPROCEEDINGS{10.1007/978-3-319-03473-7_15,
        author={Jos\^{e} Jim\^{e}nez and Bryan Lewis and Stephen Eubank},
        title={Hospitals as Complex Social Systems: Agent-Based Simulations of Hospital-Acquired Infections},
        proceedings={Complex Sciences. Second International Conference, COMPLEX 2012, Santa Fe, NM, USA, December 5-7, 2012, Revised Selected Papers},
        proceedings_a={COMPLEX},
        year={2013},
        month={11},
        keywords={Complex systems healthcare systems healthcare-acquired infections 
                    
                   agent-based simulation},
        doi={10.1007/978-3-319-03473-7_15}
    }
    
  • José Jiménez
    Bryan Lewis
    Stephen Eubank
    Year: 2013
    Hospitals as Complex Social Systems: Agent-Based Simulations of Hospital-Acquired Infections
    COMPLEX
    Springer
    DOI: 10.1007/978-3-319-03473-7_15
José Jiménez1,*, Bryan Lewis1, Stephen Eubank1
  • 1: Virginia Bioinformatics Institute
*Contact email: jjimenez@vbi.vt.edu

Abstract

The objective of this study was to develop a highly-detailed, agent-based simulation to compare medical treatments against healthcare-acquired infections (HAIs). A complex hospital model was built using patient information and healthcare worker data from two regional hospitals in Southwest Virginia. A specific HAI, , was chosen among other HAIs as the pathogen for the study due to its increased prevalence in the United States. The complex hospital simulation was created using the first principles of agent-based simulation. The simulation was then tested using a disease model with two different scenarios: a baseline with no medical treatment antimicrobials, and the use of an antimicrobial (fidaxomicin). The model successfully simulated over 164,000 personal contacts between patients and healthcare workers. Each medical treatment was evaluated one hundred times using one month of real hospital data. The mean case count was 2.66 for scenario 1 and 2.33 for scenario 2. The highest case count for scenario 1 was 21 cases whereas scenario 2 had a maximum of 11 cases. Understanding complex interactions between patients and hospital personnel could help hospitals understand transmission of infections while simultaneously reducing healthcare costs.