@misc{mckenzie_mayorga_miller_singh_arnold_romero-brufau_2020, title={Notice to comply: A systematic review of clinician compliance with guidelines surrounding acute hospital-based infection management}, volume={48}, ISSN={["1527-3296"]}, DOI={10.1016/j.ajic.2020.02.006}, abstractNote={To identify and characterize studies evaluating clinician compliance with infection-related guidelines, and to explore trends in guideline design and implementation strategies.PubMed database, April 2017. Followed the PRISMA Statement for systematic reviews.Scope was limited to studies reporting compliance with guidelines pertaining to the prevention, detection, and/or treatment of acute hospital-based infections. Initial search (1,499 titles) was reduced to 49 selected articles.Extracted publication and guideline characteristics, outcome measures reported, and any results related to clinician compliance. Primary summary measures were frequencies and distributions of characteristics. Interventions that led to improved compliance results were analyzed to identify trends in guideline design and implementation.Of the 49 selected studies, 18 (37%), 13 (27%), and 10 (20%) focused on sepsis, pneumonia, and general infection, respectively. Six (12%), 17 (35%), and 26 (53%) studies assessed local, national, and international guidelines, respectively. Twenty studies (41%) reported 1-instance compliance results, 28 studies (57%) reported 2-instance compliance results (either before-and-after studies or control group studies), and 1 study (2%) described compliance qualitatively. Average absolute change in compliance for minimal, decision support, and multimodal interventions was 10%, 14%, and 25%, respectively. Twelve studies (24%) reported no patient outcome alongside compliance.Multimodal interventions and quality improvement initiatives seem to produce the greatest improvement in compliance, but trends in other factors were inconsistent. Additional research is required to investigate these relationships and understand the implications behind various approaches to guideline design, communication, and implementation, in addition to effectiveness of protocol impact on relevant patient outcomes.}, number={8}, journal={AMERICAN JOURNAL OF INFECTION CONTROL}, author={McKenzie, Kendall E. and Mayorga, Maria E. and Miller, Kristen E. and Singh, Nishant and Arnold, Ryan C. and Romero-Brufau, Santiago}, year={2020}, month={Aug}, pages={940–947} }
 @inproceedings{agor_mckenzie_mayorga_ozaltin_parikh_huddleston_2017, title={Simulating triage of patients into an internal medicine department to validate the use of an optimization-based workload score}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85044522866&partnerID=MN8TOARS}, DOI={10.1109/wsc.2017.8248011}, abstractNote={This study describes a simulation model that was used to evaluate a proposed workload score. The score was designed to assist in triaging patients into the hospital services of the Division of Hospital Internal Medicine at Mayo Clinic in an effort to more equitably balance workload among the division's provider teams (or services). The first part of this study was the development of a score, using Delphi surveys, conjoint analysis, and optimization methods, that accurately represents provider workload. A simulation model was then built to test the score using historical patient data. Preliminary simulation results reported the proportion of time that each provider team spent working at or above “maximum utilization,” as defined by Mayo Clinic experts. The model yielded a 12.1% decrease (on average) in the proportion of time provider teams spent at or above maximum utilization, while simultaneously displaying a more balanced workload across provider teams.}, booktitle={2017 winter simulation conference (wsc)}, author={Agor, J. and McKenzie, K. and Mayorga, M. E. and Ozaltin, Osman and Parikh, R. S. and Huddleston, J.}, year={2017}, pages={2881–2892} }
 @inproceedings{agor_mckenzie_ozaltin_mayorga_parikh_huddleston_2016, title={Simulation of triaging patients into an internal medicine department to validate the use of an optimization based workload score}, volume={0}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85014275794&partnerID=MN8TOARS}, DOI={10.1109/wsc.2016.7822411}, abstractNote={This extended abstract provides an overview of the development of a simulation model to be used in the assistance of triaging patients into the Hospital Internal Medicine (HIM) Department at The Mayo Clinic in Rochester, MN in an effort to balance workload among the department services. The main contribution of this work is the development of a score that measures provider workload more accurately. Delphi surveys, conjoint analysis, and optimization methods were used in the creation of this score and it is believed to better represent provider workload. Preliminary results were based on the proportion of time of a month that each service was at or above “maximum utilization”, which is how workload is currently viewed at an instance. A simulation model built in SIMIO 8 yielded a 12.1% decrease in the proportion of time that a service was at or above their “max utilization” on average, while also seeing a decrease in the average difference among these proportions by 8.3% (better balance among all services).}, booktitle={2016 winter simulation conference (wsc)}, author={Agor, J. and McKenzie, K. and Ozaltin, Osman and Mayorga, M. and Parikh, R. S. and Huddleston, J.}, year={2016}, pages={3708–3709} }
 @inproceedings{goodarzi_mckenzie_nataraj_ivy_mayorga_mason_tejada_2016, title={A Framework for modeling the complex interaction between breast cancer and diabetes}, DOI={10.1109/wsc.2014.7019981}, abstractNote={In 2010, over 200,000 women in the U.S. were diagnosed with invasive breast cancer, and an estimated 17% of those women died from the disease, according to the Centers for Disease Control and Prevention (CDC). Also in 2010, the CDC reported that 12.6 million women had diabetes, the seventh leading cause of death in the U.S. Recent medical literature provides conflicting evidence regarding a link between insulin resistance and breast cancer risk. Although models have characterized these prevalent diseases individually, little research has been conducted regarding the interaction between breast cancer and diabetes. We build a simulation model framework that explores this complex relationship, with an initial goal of assessing the prognosis for women diagnosed with diabetes considering their breast cancer risk. Using data from national survey and surveillance consortium studies, we estimate morbidity and mortality. This framework could be extended to study other diseases that interact with breast cancer.}, booktitle={2016 10th european conference on antennas and propagation (eucap)}, author={Goodarzi, S. H. and McKenzie, K. and Nataraj, N. and Ivy, J. S. and Mayorga, Maria and Mason, J. and Tejada, J.}, year={2016}, pages={1245–1256} }