{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T10:10:17Z","timestamp":1742379017342,"version":"3.40.1"},"reference-count":31,"publisher":"Cambridge University Press (CUP)","issue":"3","license":[{"start":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T00:00:00Z","timestamp":1736380800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Infect. Control Hosp. Epidemiol."],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec id=\"S0899823X24002241_as1\"><jats:title>Background:<\/jats:title><jats:p>Clinical trials for assessing the effects of infection prevention and control (IPC) interventions are expensive and have shown mixed results. Mathematical models can be relatively inexpensive tools for evaluating the potential of interventions. However, capturing nuances between institutions and in patient populations have adversely affected the power of computational models of nosocomial transmission.<\/jats:p><\/jats:sec><jats:sec id=\"S0899823X24002241_as2\"><jats:title>Methods:<\/jats:title><jats:p>In this study, we present an agent-based model of ICUs in a tertiary care hospital, which directly uses data from the electronic medical records (EMR) to simulate pathogen transmission between patients, HCWs, and the environment. We demonstrate the application of our model to estimate the effects of IPC interventions at the local hospital level. Furthermore, we identify the most important sources of uncertainty, suggesting areas for prioritization in data collection.<\/jats:p><\/jats:sec><jats:sec id=\"S0899823X24002241_as3\"><jats:title>Results:<\/jats:title><jats:p>Our model suggests that the stochasticity in ICU infections was mainly due to the uncertainties in admission prevalence, hand hygiene compliance\/efficacy, and environmental disinfection efficacy. Analysis of interventions found that improving mean HCW compliance to hand hygiene protocols to 95% from 70%, mean terminal room disinfection efficacy to 95% from 50%, and reducing post-handwashing residual contamination down to 1% from 50%, could reduce infections by an average of 36%, 31%, and 26%, respectively.<\/jats:p><\/jats:sec><jats:sec id=\"S0899823X24002241_as4\"><jats:title>Conclusions:<\/jats:title><jats:p>In-silico models of transmission coupled to EMR data can improve the assessment of IPC interventions. However, reducing the uncertainty of the estimated effectiveness requires collecting data on unknown or lesser known epidemiological and operational parameters of transmission, particularly admission prevalence, hand hygiene compliance\/efficacy, and environmental disinfection efficacy.<\/jats:p><\/jats:sec>","DOI":"10.1017\/ice.2024.224","type":"journal-article","created":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T11:37:07Z","timestamp":1736422627000},"page":"298-304","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":0,"title":["Using electronic medical records in hospital simulation for infection control intervention assessment"],"prefix":"10.1017","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2158-8486","authenticated-orcid":false,"given":"Fardad","family":"Haghpanah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1304-5289","authenticated-orcid":false,"given":"Eili Y","family":"Klein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"for the CDC MInD-Healthcare Program","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2025,1,9]]},"reference":[{"key":"S0899823X24002241_ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhin.2009.04.019"},{"key":"S0899823X24002241_ref22","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-0691.2010.03202.x"},{"key":"S0899823X24002241_ref6","doi-asserted-by":"publisher","DOI":"10.1542\/peds.2010-2562"},{"key":"S0899823X24002241_ref11","doi-asserted-by":"publisher","DOI":"10.1097\/CCM.0b013e31823bc7c8"},{"key":"S0899823X24002241_ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajic.2018.10.017"},{"key":"S0899823X24002241_ref13","doi-asserted-by":"publisher","DOI":"10.1017\/ice.2018.342"},{"key":"S0899823X24002241_ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajic.2008.05.007"},{"key":"S0899823X24002241_ref28","doi-asserted-by":"publisher","DOI":"10.1086\/510811"},{"key":"S0899823X24002241_ref29","first-page":"912","article-title":"Evaluating a prediction-driven targeting strategy for reducing the transmission of multidrug-resistant organisms","volume":"32","author":"Barnes","year":"2020","journal-title":"INFORMS J Comput"},{"key":"S0899823X24002241_ref25","doi-asserted-by":"publisher","DOI":"10.1001\/archinte.160.7.1017"},{"key":"S0899823X24002241_ref31","doi-asserted-by":"publisher","DOI":"10.1128\/JCM.38.2.724-726.2000"},{"key":"S0899823X24002241_ref16","doi-asserted-by":"publisher","DOI":"10.1017\/ice.2019.77"},{"key":"S0899823X24002241_ref17","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.325.7360.362"},{"key":"S0899823X24002241_ref20","unstructured":"20. 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