Methicillin-resistant Staphylococcus aureus (MRSA) is the cause of many hospital-acquired infections (HAIs). In addition to the clinical burden of infection, the growing number of MRSA-colonized patients has profound implications for patient outcomes and resource utilization, which have yet to be adequately studied and addressed. MRSA-colonized patients, who are placed on contact precautions when admitted, may experience longer waiting times for hospital bed assignment and decreased interactions with providers, more preventable adverse events, as well as increased risks of inappropriate antibiotic use and dissatisfaction with care. Clinicians and policymakers may consider several strategies to address this problem, each of which has clinical and resource trade-offs. The value of applying mathematical modeling techniques, and specifically that of discrete event simulation (DES), to infection control research, has yet to be fully realized. This K01 application proposes three hypothesis-driven aims to advance the fields of infection control, hospital epidemiology and antimicrobial resistance: 1) to estimate the relationship between colonization history and antimicrobial prescribing, time-to-bed-assignment and within-hospital patient transfers, using a novel patient data warehouse; 2) to design and validate a DES model of patient flow in a tertiary care hospital; and 3) to apply the validated DES model to estimate the clinical and resource utilization outcomes of alternative infection control strategies. This innovative and multi-disciplinary study will provide clinicians and policymakers with valuable information through the quantification of the trade-offs of competing screening approaches. I am a physician at the Massachusetts General Hospital and an Instructor in Medicine at Harvard Medical School trained in infectious diseases with a doctorate in health policy/economics. I have designed and implemented two clinical research studies in the field of MRSA surveillance and discontinuation of contact precautions as well as two national surveys on the impact of contact precautions. I will use the K award to expand my current skill set to include analysis and management of large databases, mathematical modeling (specifically, discrete event simulation) and optimization methods. I will be mentored by Dr. David Hooper, an infectious disease clinician and expert in the fields of infection control and antimicrobial resistance, and Dr. Rochelle Walensky, an infectious disease clinician and mathematical modeler. I will collaborate with experts in the field of biostatistics, operations research and information systems. The targeted educational curriculum, mentoring plan and research strategy will ensure my successful transition over the period of the Award to an Independent Investigator with expertise in the use of modeling techniques to evaluate infection control approaches and implement optimal strategies in the clinical setting. I will use the valuable information gleaned from modeling to design rigorous research studies to advance the fields of infection control, hospital epidemiology and antimicrobial resistance.
The growing number of patients colonized with methicillin-resistant Staphylococcus aureus (MRSA) threatens patient care and resource utilization for all patients, not just those with personal histories of MRSA. Through the analysis of a novel, large historical database of over 100,000 patient admissions to a large tertiary care hospital and the development of mathematical models to test different hospital-based infection control strategies, this research will inform clinical care and assist policy makers to develop optimal MRSA surveillance approaches. This research will apply a particular type of mathematical modeling, discrete event simulation, to investigate screening strategies and project their impact on patient care and resource utilization, thus informing the evidence base for delivery of safe, high-quality patient care.
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