{"84a051cd-1f3d-42ba-be23-26f3f9856933": "<p>An innovative automated tool that enhances outbreak detection in hospitals for hundreds of pathogens has successfully served as an early warning system to identify and respond to potential outbreaks. The CLUSTER Trial evaluated this tool in 82 hospitals in a study which spanned several years, including the start of the COVID-19 pandemic. Before the distractions of the COVID-19 pandemic, the tool reduced the size of outbreaks by 64%. </p><p>Contagious bacteria and other pathogens pose a significant threat in hospitals, increasing the risk of harmful infections among patients. While healthcare facilities strive to prevent infections and minimize outbreak opportunities, there is no standardized approach for detecting transmission. Early detection is critical for rapid responses that can mitigate outbreaks effectively.  </p><p> </p><h3><strong>A Practical and Standardized Approach </strong></h3><p>Outbreaks in hospitals are often overlooked or identified too late, resulting in preventable infections. This study introduced a free statistical software program that uses a hospital’s clinical laboratory data and patient locations to detect early transmission and halt potential outbreaks. This outbreak detection tool was able to provide real-time alerts to hospital infection prevention programs along with a standardized protocol for effective response. </p><p>The CLUSTER tool monitors over 100 bacterial and fungal species, automatically reviewing organisms identified in patients’ cultures. A statistical assessment determines whether there is an unusual increase compared to historical data. When such increases are detected, the system triggers automatic notifications, prompting hospital personnel to activate response protocols to prevent additional cases. Read more about the study published in <a href=\"https://evidence.nejm.org/doi/full/10.1056/EVIDoa2300342\" target=\"_blank\" rel=\"noreferrer noopener\"><i>NEJM Evidence</i></a>. </p><p> </p><h3><strong>Scalable Collaboration </strong></h3><p>“This ongoing collaboration continues to leverage the scale of HCA Healthcare – both the number of our hospitals and our advanced data ecosystem – as we work to rapidly answer clinical questions that benefit patients everywhere,” said Kenneth Sands, MD, MPH, chief epidemiologist at HCA Healthcare. “We are using this detection tool in the hospitals where we tested it and are evaluating implementation more widely across our system.” </p><p> </p><h3><strong>Accessible Tools and Protocols </strong></h3><p>The underlying software <a href=\"https://whonet.org/\" target=\"_blank\" rel=\"noreferrer noopener\">WHONET</a> and <a href=\"https://www.satscan.org/\" target=\"_blank\" rel=\"noreferrer noopener\">SaTScan</a> are available for free to all hospital systems but requires integration into their respective electronic health record (EHR) systems or other clinical workflow platforms. These tools have already been successful in detecting outbreaks at facilities beyond the study hospitals, including Brigham and Women’s Hospital and the University of California, Irvine Health system. </p><p>Additionally, an <a href=\"https://corha.org/assets/documents/CLUSTER-Toolkit-for-Outbreak-Response.docx\" target=\"_blank\" rel=\"noreferrer noopener\">outbreak response protocol</a> is available online. This protocol can be adapted by any hospital to provide a standardized framework for responding to clusters of healthcare-associated pathogens.  </p><p>By combining cutting-edge automation with a standardized approach, the CLUSTER trial demonstrates how healthcare systems can transform outbreak detection and prevention, ultimately improving patient safety on a large scale.  </p><p>Explore the valuable resources available on <a href=\"{entry:50967@1:url||https://corha.org/tools/cluster-toolkit}\" target=\"_blank\" rel=\"noreferrer noopener\">CORHA’s CLUSTER Detection resource page</a>.  </p><p> </p><p><i>The study was funded by CDC and conducted through a longstanding scientific consortium, including HCA Healthcare, Harvard Pilgrim Health Care Institute's </i><a href=\"http://www.populationmedicine.org/\" target=\"_blank\" rel=\"noreferrer noopener\"><i>Department of Population Medicine</i></a><i>, the University of California, Irvine and the </i><a href=\"https://www.cdc.gov/hai/epicenters/index.html\" target=\"_blank\" rel=\"noreferrer noopener\"><i>CDC’s Prevention Epicenters Program</i></a><i> . It is part of a growing body of work by the partners to improve patient care and reduce risk of healthcare-associated infections. Support for HCA Healthcare’s participation in the study was provided in kind by HCA. The views expressed in this publication represent those of the authors and do not necessarily represent the official views of HCA Healthcare or any of its affiliated entities.</i>  </p><figure class=\"image\"><img style=\"aspect-ratio:300/375;\" src=\"{asset:51141:transform:medium||/assets/images/transf/headshots/_medium/51141/Meghan-Baker_Elisif_20190530_3246-croppedfullsizecolor.webp}\" alt=\"Meghan Baker Headshot\" width=\"300\" height=\"375\" /><figcaption>Meghan Baker</figcaption></figure><figure class=\"image\"><img style=\"aspect-ratio:300/299;\" src=\"{asset:51140:transform:medium||/assets/images/transf/headshots/_medium/51140/Susan-Huang.webp}\" alt=\"Susan Huang Headshot\" width=\"300\" height=\"299\" /><figcaption>Susan Huang</figcaption></figure><figure class=\"image\"><img style=\"aspect-ratio:300/388;\" src=\"{asset:51142:transform:medium||/assets/images/transf/headshots/_medium/51142/Julia-Moody.webp}\" alt=\"Julia Moody Headshot\" width=\"300\" height=\"388\" /><figcaption>Julia Moody</figcaption></figure>", "acf2caae-0773-4eb2-bb10-57a01b19e200": "Meghan Baker, MD ScD and Susan Huang, MD MPH, Julia Moody, MS SM(ASCP), CIC", "ccabfff6-2908-407e-98d3-a22686d82840": {"score": "good", "social": {"twitter": {"title": "Automated Rapid Outbreak Detection: Analyzing Clinical Laboratory Data to Provide Real-Time Alerts: A Strategy from the CLUSTER Trial - CORHA", "handle": "", "imageId": "", "description": []}, "facebook": {"title": "Automated Rapid Outbreak Detection: Analyzing Clinical Laboratory Data to Provide Real-Time Alerts: A Strategy from the CLUSTER Trial - CORHA", "handle": "", "imageId": "", "description": []}}, "advanced": {"robots": [], "canonical": ""}, "keywords": [{"rating": "poor", "keyword": "cluster trial"}, {"rating": "poor", "keyword": "outbreak response"}, {"rating": "good", "keyword": "outbreak detection"}, {"rating": "good", "keyword": "automated"}], "titleRaw": {"1": "Automated Rapid Outbreak Detection: Analyzing Clinical Laboratory Data to Provide Real-Time Alerts: A Strategy from the CLUSTER Trial"}, "descriptionRaw": ""}, "dd06f1a1-3c4f-4001-adc3-f3c0a94550ac": [1558]}