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Developed as part of the  <a href=\\\"https://evidence.nejm.org/doi/full/10.1056/EVIDoa2300342\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><strong>CLUSTER Trial </strong>(<strong>C</strong>luster <strong>L</strong>inkage <strong>U</strong>sing <strong>S</strong>tatistics to <strong>T</strong>rigger and <strong>E</strong>valuate <strong>R</strong>esponse)</a>, this protocol offers step-by-step guidance on initial actions and escalating tiers of intervention based on ongoing assessments and outbreak progression.</p><p>Because this protocol is detailed and data-driven, users are encouraged to begin with the <strong>Quick Guides for Tier 1 and Tier 2</strong> interventions, followed by the <strong>Outbreak Response Activation Checklist</strong>. For those unfamiliar with the surveillance tools referenced in the protocol, such as <a href=\\\"https://whonet.org/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><strong>WHONET</strong></a> and <a href=\\\"https://www.satscan.org/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><strong>SaTScan</strong></a>, we recommend visiting their official websites for setup and usage guidance. These tools support early outbreak detection and enhance the impact of the response.</p><p><strong>Getting started tip:</strong> Start with the “Initial Outbreak Alert” checklist and complete the associated practice assessments. Then proceed to select an intervention based on your results. 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However, data sharing between state, local and private health entities is not without challenges. </p><p>Data sharing and reporting outbreaks of healthcare-associated infections (HAIs) is one of the most pressing issues. The success of this type of outbreak response hinges on access to accurate, timely data. While technology limitations represent some challenges, like data system access for public health professionals and system interoperability, other challenges are administrative like dedicating resources for up-to-date data systems and informatics and having access to staff with informatics expertise. Any solutions must also carefully balance public awareness and patient confidentiality. </p><p>Even if challenges in accessing and using available electronic health information are overcome, gaps in collecting or reporting data between health departments and local healthcare organizations may still exist. For instance, demographic data that could identify populations disproportionately impacted by HAI outbreaks and individual cases of antimicrobial resistance (AR) are not collected consistently, posing a challenge to understanding health inequities. </p><p>Manually sharing reports between healthcare organizations and public health can further strain thinly stretched healthcare staff. The best time to work through technical issues and reporting inconsistencies is prior to an outbreak incident. It is integral to effective outbreak response efforts. Some ways to work through challenges include establishing data dictionaries, checking data system interoperability, mandating reporting perimeters, and standardizing data. </p><p><a href=\\\"https://www.corha.org/collaborative-workgroups\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">CORHA’s Policy Workgroup</a> aims to advocate for and establish more transparency related to data collection and reporting. We recognize that accomplishing this goal requires additional guidance, resources, and buy-in from leadership across public health and healthcare. Given the variations from state to state in public health data sharing rules, national-level mandates and public health law advocacy may also be necessary to address this challenge. </p><p>The CORHA Policy Workgroup continues to develop resources on public data sharing and reporting of HAI/AR outbreaks with the goal of balancing public data sharing and the protection of patient confidentiality. The following CORHA resources can be used to inform policies and legal frameworks for outbreak reporting, investigation, notification, and disclosure: </p><ul style=\\\"list-style-type:disc;\\\"><li><a href=\\\"https://corha.org/assets/documents/Framework-for-HAI-Outbreak-Notification.pdf\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">Framework for HAI Outbreak Notification</a> </li><li><a href=\\\"https://corha.org/assets/documents/HAI-Patient-Notification-Framework-one-pager_Oct-2023.pdf\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">Framework for HAI Outbreak Notification One-Pager </a></li><li><a href=\\\"{entry:9266@1:url||https://corha.org/chapters/detection-reporting}\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">Principles &amp; Practices Chapter 4: Outbreak Detection and Reporting</a> </li><li><a href=\\\"{entry:9513@1:url||https://corha.org/chapters/notification-communication}\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">Principles &amp; Practices Chapter 8: Notification and Communication</a> </li></ul>\", \"acf2caae-0773-4eb2-bb10-57a01b19e200\": \"Moon Kim, MD, MPH, Dawn Terashita, MD, MPH, & Jaclyn Abramson, MPH\", \"ccabfff6-2908-407e-98d3-a22686d82840\": {\"score\": \"poor\", \"social\": {\"twitter\": {\"title\": \"Importance of Data Sharing and Reporting: A Conversation with CORHA’s Policy Workgroup Co-Chairs - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": \"A conversation with CORHA\'s policy workgroup co-chairs highlights the importance of data sharing and reporting in public health.\"}, \"facebook\": {\"title\": \"Importance of Data Sharing and Reporting: A Conversation with CORHA’s Policy Workgroup Co-Chairs - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": \"A conversation with CORHA\'s policy workgroup co-chairs highlights the importance of data sharing and reporting in public health.\"}}, \"advanced\": {\"robots\": [], \"canonical\": \"\"}, \"keywords\": [{\"rating\": \"good\", \"keyword\": \"data sharing\"}, {\"rating\": \"poor\", \"keyword\": \"policy workgroup\"}, {\"rating\": \"poor\", \"keyword\": \"healthcare reporting\"}, {\"rating\": \"poor\", \"keyword\": \"regulation\"}, {\"rating\": \"poor\", \"keyword\": \"reportable disease\"}, {\"rating\": \"poor\", \"keyword\": \"surveillance data\"}, {\"rating\": \"poor\", \"keyword\": \"CORHA\"}], \"titleRaw\": {\"1\": \"Importance of Data Sharing and Reporting: A Conversation with CORHA’s Policy Workgroup Co-Chairs\"}, \"descriptionRaw\": \"A conversation with CORHA\'s policy workgroup co-chairs highlights the importance of data sharing and reporting in public health.\"}, \"dd06f1a1-3c4f-4001-adc3-f3c0a94550ac\": [1558], \"f88904a1-7870-468f-8885-f14ce93db364\": []}',1,'2025-03-24 19:06:26','2025-03-24 19:06:27','17e672a6-87b8-44ea-a962-30b320470bd3'),(51438,51030,1,'State: How the Texas State Healthcare-Associated Infections (HAI) Program Increased Capacity in Public Health Regions to Support Healthcare Facilities During the COVID-19 Response','state-how-the-texas-state-healthcare-associated-infections-hai-program-increased-capacity-in-public-health-regions-to-support-healthcare-facilities-during-the-covid-19-response','news/state-how-the-texas-state-healthcare-associated-infections-hai-program-increased-capacity-in-public-health-regions-to-support-healthcare-facilities-during-the-covid-19-response','{\"84a051cd-1f3d-42ba-be23-26f3f9856933\": \"<p>The COVID-19 pandemic presented challenges in how our Healthcare-Associated Infections and Antimicrobial-Resistant Pathogens (HAI/AR) Program provides support to long-term care facilities (LTCF) experiencing outbreaks. As the pandemic unfolded, we quickly realized the need to increase our infection prevention capacity. We sought out support from leadership and partners to modify our staffing structures in order to provide continued quality and focused support to our healthcare facilities.</p><p>The Texas Department of State Health Services (DSHS) is divided into 8 public health regions, with 64 local health departments that operate at the county or city level. Texas has approximately 4,000 licensed nursing facilities, assisted living facilities, and group homes including 1,200 Centers for Medicare &amp; Medicaid Services (CMS)-certified nursing homes, across 254 counties. Traditionally, one HAI Epidemiologist is assigned to each public health region to lead and support HAI outbreak response activities, including providing direct assistance and on-site infection control assessments to healthcare facilities. The Texas Department of State Health Services (DSHS) is divided into 8 public health regions, with 64 local health departments that operate at the county or city level. Texas has approximately 4,000 licensed nursing facilities, assisted living facilities, and group homes including 1,200 Centers for Medicare &amp; Medicaid Services (CMS)-certified nursing homes, across 254 counties. Traditionally, one HAI Epidemiologist is assigned to each public health region to lead and support HAI outbreak response activities, including providing direct assistance and on-site infection control assessments to healthcare facilities.</p><p>This structure worked for our team until the onset of the COVID-19 pandemic when it became evident that one HAI Epidemiologist per public health region was insufficient to support the surge in COVID-19 cases. So, we explored the options of increasing infection prevention staff at our public health regions to build capacity for our COVID-19 response.</p><p>We received approval and support to build staffing capacity from DSHS leadership, and they provided us with the resources to contract with 3 Infection Preventionists per public health region, for a total of 24 additional Infection Preventionists throughout the state. The real challenge, however, was finding certified Infection Preventionists to recruit. At that time, healthcare facilities and local health departments were also in the process of recruiting Infection Preventionists, so we were competing for contractors in a limited pool. We decided to partner with the Association for Professionals in Infection Control and Epidemiology (APIC) who aided with the recruitment process. Out of the 24 Infection Preventionists we sought to recruit, we were able to contract with 17. These additional Infection Preventionists were instrumental in building our capacity during the response. They performed on-site infection control assessments and provided dedicated support to meet the needs of facilities.</p><p>From the first quarter of 2021, we started seeing a decrease in the number of requests for assistance from our regulatory partners and healthcare facilities, and as a result, we reduced the number of APIC contractors. It was important for us to still maintain a reasonable number of staff to assist with the continued pandemic, so we currently have at least one contracted Infection Preventionist in each public health region to assist the assigned HAI Epidemiologist with outbreak response activities.</p><p>Coordinating with the Texas Health and Human Services Commission (HHSC)—our regulatory branch—and utilizing established communication pathways facilitated the coordination of our response activities. We held daily meetings with HHSC, DSHS, and the Division of Emergency Management’s State Operations Center (SOC), where all communication plans and guidance were developed for dissemination to facilities by HHSC.</p><p>Thanks to the support of DSHS leadership and partners, as well as the dedication of our team, we worked together in a coordinated fashion to build capacity and respond to COVID-19 outbreaks in healthcare facilities.</p><figure class=\\\"image\\\"><img src=\\\"{asset:43965:transform:medium||/assets/images/transf/_medium/43965/enyinnaya-merengwa.webp}\\\" alt=\\\"Enyinnaya Merengwa Headshot\\\" width=\\\"300\\\" height=\\\"275\\\" /></figure><p><i>Enyinnaya Merengwa, Healthcare Safety Investigations Group Manager and State HAI Coordinator at the Texas Department of State Health Services</i></p><figure class=\\\"image\\\"><img src=\\\"{asset:43964:transform:medium||/assets/images/transf/_medium/43964/thi-dang.webp}\\\" alt=\\\"Thi Dang Headshot\\\" width=\\\"300\\\" height=\\\"301\\\" /></figure><p><i>Thi Dang, HAI Epidemiologist at the Texas Department of State Health Services</i></p>\", \"acf2caae-0773-4eb2-bb10-57a01b19e200\": \"Enyinnaya Merengwa, MD, DrPH, MHA, MPH & Thi Dang\", \"ccabfff6-2908-407e-98d3-a22686d82840\": {\"score\": \"poor\", \"social\": {\"twitter\": {\"title\": \"State: How the Texas State Healthcare-Associated Infections (HAI) Program Increased Capacity in Public Health Regions to Support Healthcare Facilities During the COVID-19 Response - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": \"Texas\'s HAI program enhances capacity in public health regions to support healthcare facilities during the COVID-19 response efforts.\"}, \"facebook\": {\"title\": \"State: How the Texas State Healthcare-Associated Infections (HAI) Program Increased Capacity in Public Health Regions to Support Healthcare Facilities During the COVID-19 Response - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": \"Texas\'s HAI program enhances capacity in public health regions to support healthcare facilities during the COVID-19 response efforts.\"}}, \"advanced\": {\"robots\": [], \"canonical\": \"\"}, \"keywords\": [{\"rating\": \"poor\", \"keyword\": \"best practices\"}, {\"rating\": \"poor\", \"keyword\": \"health communication\"}, {\"rating\": \"poor\", \"keyword\": \"infectious disease\"}, {\"rating\": \"poor\", \"keyword\": \"health challenges\"}, {\"rating\": \"good\", \"keyword\": \"HAI\"}, {\"rating\": \"good\", \"keyword\": \"Texas\"}, {\"rating\": \"poor\", \"keyword\": \"state health department\"}], \"titleRaw\": {\"1\": \"State: How the Texas State Healthcare-Associated Infections (HAI) Program Increased Capacity in Public Health Regions to Support Healthcare Facilities During the COVID-19 Response\"}, \"descriptionRaw\": \"Texas\'s HAI program enhances capacity in public health regions to support healthcare facilities during the COVID-19 response efforts.\"}, \"dd06f1a1-3c4f-4001-adc3-f3c0a94550ac\": [1558], \"f88904a1-7870-468f-8885-f14ce93db364\": []}',1,'2025-03-24 19:07:08','2025-03-24 19:07:08','6aa251cb-538d-48ca-9c07-c9e645ed610e'),(51439,51031,1,NULL,'__temp_xvexlirlbrwikocwjvbesmzqtuudcktanfdg',NULL,'{\"b691f1c7-ad7d-4a8b-a9fa-b4feec58e073\": [49844]}',1,'2025-03-24 19:07:08','2025-03-24 19:07:08','21ae9587-9f0a-4a74-9aef-2914edf2c564'),(51442,51034,1,'State: How the Texas State Healthcare-Associated Infections (HAI) Program Increased Capacity in Public Health Regions to Support Healthcare Facilities During the COVID-19 Response','state-how-the-texas-state-healthcare-associated-infections-hai-program-increased-capacity-in-public-health-regions-to-support-healthcare-facilities-during-the-covid-19-response','news/state-how-the-texas-state-healthcare-associated-infections-hai-program-increased-capacity-in-public-health-regions-to-support-healthcare-facilities-during-the-covid-19-response','{\"84a051cd-1f3d-42ba-be23-26f3f9856933\": \"<p>The COVID-19 pandemic presented challenges in how our Healthcare-Associated Infections and Antimicrobial-Resistant Pathogens (HAI/AR) Program provides support to long-term care facilities (LTCF) experiencing outbreaks. As the pandemic unfolded, we quickly realized the need to increase our infection prevention capacity. We sought out support from leadership and partners to modify our staffing structures in order to provide continued quality and focused support to our healthcare facilities.</p><p>The Texas Department of State Health Services (DSHS) is divided into 8 public health regions, with 64 local health departments that operate at the county or city level. Texas has approximately 4,000 licensed nursing facilities, assisted living facilities, and group homes including 1,200 Centers for Medicare &amp; Medicaid Services (CMS)-certified nursing homes, across 254 counties. Traditionally, one HAI Epidemiologist is assigned to each public health region to lead and support HAI outbreak response activities, including providing direct assistance and on-site infection control assessments to healthcare facilities. The Texas Department of State Health Services (DSHS) is divided into 8 public health regions, with 64 local health departments that operate at the county or city level. Texas has approximately 4,000 licensed nursing facilities, assisted living facilities, and group homes including 1,200 Centers for Medicare &amp; Medicaid Services (CMS)-certified nursing homes, across 254 counties. Traditionally, one HAI Epidemiologist is assigned to each public health region to lead and support HAI outbreak response activities, including providing direct assistance and on-site infection control assessments to healthcare facilities.</p><p>This structure worked for our team until the onset of the COVID-19 pandemic when it became evident that one HAI Epidemiologist per public health region was insufficient to support the surge in COVID-19 cases. So, we explored the options of increasing infection prevention staff at our public health regions to build capacity for our COVID-19 response.</p><p>We received approval and support to build staffing capacity from DSHS leadership, and they provided us with the resources to contract with 3 Infection Preventionists per public health region, for a total of 24 additional Infection Preventionists throughout the state. The real challenge, however, was finding certified Infection Preventionists to recruit. At that time, healthcare facilities and local health departments were also in the process of recruiting Infection Preventionists, so we were competing for contractors in a limited pool. We decided to partner with the Association for Professionals in Infection Control and Epidemiology (APIC) who aided with the recruitment process. Out of the 24 Infection Preventionists we sought to recruit, we were able to contract with 17. These additional Infection Preventionists were instrumental in building our capacity during the response. They performed on-site infection control assessments and provided dedicated support to meet the needs of facilities.</p><p>From the first quarter of 2021, we started seeing a decrease in the number of requests for assistance from our regulatory partners and healthcare facilities, and as a result, we reduced the number of APIC contractors. It was important for us to still maintain a reasonable number of staff to assist with the continued pandemic, so we currently have at least one contracted Infection Preventionist in each public health region to assist the assigned HAI Epidemiologist with outbreak response activities.</p><p>Coordinating with the Texas Health and Human Services Commission (HHSC)—our regulatory branch—and utilizing established communication pathways facilitated the coordination of our response activities. We held daily meetings with HHSC, DSHS, and the Division of Emergency Management’s State Operations Center (SOC), where all communication plans and guidance were developed for dissemination to facilities by HHSC.</p><p>Thanks to the support of DSHS leadership and partners, as well as the dedication of our team, we worked together in a coordinated fashion to build capacity and respond to COVID-19 outbreaks in healthcare facilities.</p><figure class=\\\"image\\\"><img src=\\\"{asset:43965:transform:medium||/assets/images/transf/_medium/43965/enyinnaya-merengwa.webp}\\\" alt=\\\"Enyinnaya Merengwa Headshot\\\" width=\\\"300\\\" height=\\\"275\\\" /></figure><p><i>Enyinnaya Merengwa, Healthcare Safety Investigations Group Manager and State HAI Coordinator at the Texas Department of State Health Services</i></p><figure class=\\\"image\\\"><img src=\\\"{asset:43964:transform:medium||/assets/images/transf/_medium/43964/thi-dang.webp}\\\" alt=\\\"Thi Dang Headshot\\\" width=\\\"300\\\" height=\\\"301\\\" /></figure><p><i>Thi Dang, HAI Epidemiologist at the Texas Department of State Health Services</i></p>\", \"acf2caae-0773-4eb2-bb10-57a01b19e200\": \"Enyinnaya Merengwa, MD, DrPH, MHA, MPH & Thi Dang\", \"ccabfff6-2908-407e-98d3-a22686d82840\": {\"score\": \"poor\", \"social\": {\"twitter\": {\"title\": \"State: How the Texas State Healthcare-Associated Infections (HAI) Program Increased Capacity in Public Health Regions to Support Healthcare Facilities During the COVID-19 Response - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": \"Texas\'s HAI program enhances capacity in public health regions to support healthcare facilities during the COVID-19 response efforts.\"}, \"facebook\": {\"title\": \"State: How the Texas State Healthcare-Associated Infections (HAI) Program Increased Capacity in Public Health Regions to Support Healthcare Facilities During the COVID-19 Response - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": \"Texas\'s HAI program enhances capacity in public health regions to support healthcare facilities during the COVID-19 response efforts.\"}}, \"advanced\": {\"robots\": [], \"canonical\": \"\"}, \"keywords\": [{\"rating\": \"poor\", \"keyword\": \"best practices\"}, {\"rating\": \"poor\", \"keyword\": \"health communication\"}, {\"rating\": \"poor\", \"keyword\": \"infectious disease\"}, {\"rating\": \"poor\", \"keyword\": \"health challenges\"}, {\"rating\": \"good\", \"keyword\": \"HAI\"}, {\"rating\": \"good\", \"keyword\": \"Texas\"}, {\"rating\": \"poor\", \"keyword\": \"state health department\"}], \"titleRaw\": {\"1\": \"State: How the Texas State Healthcare-Associated Infections (HAI) Program Increased Capacity in Public Health Regions to Support Healthcare Facilities During the COVID-19 Response\"}, \"descriptionRaw\": \"Texas\'s HAI program enhances capacity in public health regions to support healthcare facilities during the COVID-19 response efforts.\"}, \"dd06f1a1-3c4f-4001-adc3-f3c0a94550ac\": [1558], \"f88904a1-7870-468f-8885-f14ce93db364\": []}',1,'2025-03-24 19:07:53','2025-03-24 19:07:53','abc62d86-480a-4283-affd-b42a38fe6080'),(51443,51035,1,NULL,'__temp_xvexlirlbrwikocwjvbesmzqtuudcktanfdg',NULL,'{\"b691f1c7-ad7d-4a8b-a9fa-b4feec58e073\": [49827]}',1,'2025-03-24 19:07:53','2025-03-24 19:07:53','d01de6bb-f52e-4fc5-b976-f1d8854a462c'),(51444,51036,1,'Nontuberculous Mycobacteria','ntm','diseases-pathogens/ntm','{\"8a0179a9-5d67-4563-85ee-348d0dc6b55d\": false, \"b0e373e6-78db-4059-8c91-7bf51b328e7b\": true, \"f9bb51ff-9dbb-464f-9bc4-ed50685e6e0b\": {\"score\": \"poor\", \"social\": {\"twitter\": {\"title\": \"Nontuberculous Mycobacteria - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": \"Discover nontuberculous mycobacteria (NTM), their sources, health risks, and treatment options for those affected. 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Developed as part of the  <a href=\\\"https://evidence.nejm.org/doi/full/10.1056/EVIDoa2300342\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">CLUSTER Trial (<strong>C</strong>luster <strong>L</strong>inkage <strong>U</strong>sing <strong>S</strong>tatistics to <strong>T</strong>rigger and <strong>E</strong>valuate <strong>R</strong>esponse)</a>, this protocol offers step-by-step guidance on initial actions and escalating tiers of intervention based on ongoing assessments and outbreak progression.</p><p>Because this protocol is detailed and data-driven, users are encouraged to begin with the <strong>Quick Guides for Tier 1 and Tier 2</strong> interventions, followed by the <strong>Outbreak Response Activation Checklist</strong>. For those unfamiliar with the surveillance tools referenced in the protocol, such as <a href=\\\"https://whonet.org/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><strong>WHONET</strong></a> and <a href=\\\"https://www.satscan.org/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><strong>SaTScan</strong></a>, we recommend visiting their official websites for setup and usage guidance. These tools support early outbreak detection and enhance the impact of the response.</p><p><strong>Getting started tip:</strong> Start with the “Initial Outbreak Alert” checklist and complete the associated practice assessments. Then proceed to select an intervention based on your results. Use the toolkit’s forms and image guides to support accurate monitoring and response documentation.</p>\", \"f394434c-cdd6-422c-857c-73fdb47ec55d\": \"Outbreak Response Protocol\"}',1,'2025-06-02 15:12:30','2025-06-02 15:12:30','38bea049-4601-4eb4-ab5c-47718ceaca5b'),(51536,51128,1,'CLUSTER Detection','cluster-toolkit','tools/cluster-toolkit','{\"083cf2b9-0557-4c10-aac6-0ba9c8747dce\": {\"score\": \"\", \"social\": {\"twitter\": {\"title\": \"CLUSTER Toolkit for Outbreak Response - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": []}, \"facebook\": {\"title\": \"CLUSTER Toolkit for Outbreak Response - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": []}}, \"advanced\": {\"robots\": [], \"canonical\": \"\"}, \"keywords\": [], \"titleRaw\": {\"1\": \"CLUSTER Toolkit for Outbreak Response\"}, \"descriptionRaw\": \"\"}, \"0d23b6e6-763d-4507-bdf1-c48d5aa10873\": false, \"2bbee18c-8340-4ef8-955a-91e4bec4bd09\": false, \"50fd5cf3-c097-475d-9aa5-534e9467bd8b\": false, \"b8cfe543-1fb8-4cb8-b247-8b46432bdbc7\": [50969]}',1,'2025-06-02 15:29:45','2025-06-02 15:29:45','0b66b36b-62ab-4a8f-a079-ef77cecbade8'),(51537,51129,1,NULL,'__temp_dogbjmlroqxkbjxrdbwrfdxxfjnftmnoydar',NULL,'{\"0f72865d-fe3e-4163-918f-27e7c84781c3\": \"<p>The Outbreak Response Protocol standardizes hospital outbreak management with tiered interventions based on infection prevention assessments.</p>\", \"b691f1c7-ad7d-4a8b-a9fa-b4feec58e073\": [50973]}',1,'2025-06-02 15:29:45','2025-06-02 15:29:45','ee6a4f2a-2121-4fbe-bf72-cceaaf46c3b7'),(51538,51130,1,NULL,'__temp_jhcsswvahhyonpzfctoxhumlswxugzawrfpw',NULL,'{\"19da6ed0-2367-47b1-bc05-09e55409b7f1\": [50966], \"7dfa0f4c-f5c2-41e9-95e9-603e7c0ee68d\": \"docIcon\"}',1,'2025-06-02 15:29:45','2025-06-02 15:29:45','f2f60d42-891c-4a60-aada-3606d4b0928b'),(51539,51131,1,NULL,NULL,NULL,NULL,1,'2025-06-02 15:29:45','2025-06-02 15:29:45','cc19d740-b287-43e4-a239-5a35315e77fe'),(51540,51132,1,NULL,'__temp_lwgjrxozeikhoozttfmtwjhvuxtdscnefgrt',NULL,'{\"c9374286-f051-4a4a-a20b-da25d3c319ff\": \"<p>The <strong>Outbreak Response Protocol</strong> provides a structured approach for hospitals and healthcare facilities to respond to outbreaks of infectious pathogens, primarily bacterial, mycobacterial, and fungal, though it may also be applied to viral outbreaks. Developed as part of the  <a href=\\\"https://evidence.nejm.org/doi/full/10.1056/EVIDoa2300342\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><strong>CLUSTER Trial </strong>(<strong>C</strong>luster <strong>L</strong>inkage <strong>U</strong>sing <strong>S</strong>tatistics to <strong>T</strong>rigger and <strong>E</strong>valuate <strong>R</strong>esponse)</a>, this protocol offers step-by-step guidance on initial actions and escalating tiers of intervention based on ongoing assessments and outbreak progression.</p><p>Because this protocol is detailed and data-driven, users are encouraged to begin with the <strong>Quick Guides for Tier 1 and Tier 2</strong> interventions, followed by the <strong>Outbreak Response Activation Checklist</strong>. For those unfamiliar with the surveillance tools referenced in the protocol, such as <a href=\\\"https://whonet.org/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><strong>WHONET</strong></a> and <a href=\\\"https://www.satscan.org/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><strong>SaTScan</strong></a>, we recommend visiting their official websites for setup and usage guidance. These tools support early outbreak detection and enhance the impact of the response.</p><p><strong>Getting started tip:</strong> Start with the “Initial Outbreak Alert” checklist and complete the associated practice assessments. Then proceed to select an intervention based on your results. Use the toolkit’s forms and image guides to support accurate monitoring and response documentation.</p>\", \"f394434c-cdd6-422c-857c-73fdb47ec55d\": \"About the Outbreak Response Protocol\"}',1,'2025-06-02 15:29:45','2025-06-02 15:29:45','696611cc-f848-4896-8fc5-1aec90268689'),(51541,51133,1,'CLUSTER Detection','cluster-toolkit','tools/cluster-toolkit','{\"083cf2b9-0557-4c10-aac6-0ba9c8747dce\": {\"score\": \"\", \"social\": {\"twitter\": {\"title\": \"CLUSTER Toolkit for Outbreak Response - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": []}, \"facebook\": {\"title\": \"CLUSTER Toolkit for Outbreak Response - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": []}}, \"advanced\": {\"robots\": [], \"canonical\": \"\"}, \"keywords\": [], \"titleRaw\": {\"1\": \"CLUSTER Toolkit for Outbreak Response\"}, \"descriptionRaw\": \"\"}, \"0d23b6e6-763d-4507-bdf1-c48d5aa10873\": false, \"2bbee18c-8340-4ef8-955a-91e4bec4bd09\": false, \"50fd5cf3-c097-475d-9aa5-534e9467bd8b\": false, \"b8cfe543-1fb8-4cb8-b247-8b46432bdbc7\": [50969]}',1,'2025-06-02 15:29:57','2025-06-02 15:29:57','2b1ab1ce-2aed-4c5b-901d-c575818cb516'),(51542,51134,1,NULL,'__temp_dogbjmlroqxkbjxrdbwrfdxxfjnftmnoydar',NULL,'{\"0f72865d-fe3e-4163-918f-27e7c84781c3\": \"<p>The Outbreak Response Protocol standardizes hospital outbreak management with tiered interventions based on infection prevention assessments.</p>\", \"b691f1c7-ad7d-4a8b-a9fa-b4feec58e073\": [50973]}',1,'2025-06-02 15:29:57','2025-06-02 15:29:57','aee3fc47-8a3f-42b6-a6c8-661ee5325a2f'),(51543,51135,1,NULL,'__temp_jhcsswvahhyonpzfctoxhumlswxugzawrfpw',NULL,'{\"19da6ed0-2367-47b1-bc05-09e55409b7f1\": [50966], \"7dfa0f4c-f5c2-41e9-95e9-603e7c0ee68d\": \"docIcon\"}',1,'2025-06-02 15:29:57','2025-06-02 15:29:57','2ca6e2b5-0182-4424-914b-f1e0eb341ff6'),(51544,51136,1,NULL,NULL,NULL,NULL,1,'2025-06-02 15:29:57','2025-06-02 15:29:57','6dc13adb-64aa-4c42-b5d1-c3c68b5033ec'),(51545,51137,1,NULL,'__temp_lwgjrxozeikhoozttfmtwjhvuxtdscnefgrt',NULL,'{\"c9374286-f051-4a4a-a20b-da25d3c319ff\": \"<p>The <strong>Outbreak Response Protocol</strong> provides a structured approach for hospitals and healthcare facilities to respond to outbreaks of infectious pathogens, primarily bacterial, mycobacterial, and fungal, though it may also be applied to viral outbreaks. Developed as part of the  <a href=\\\"https://evidence.nejm.org/doi/full/10.1056/EVIDoa2300342\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><strong>CLUSTER Trial </strong>(<strong>C</strong>luster <strong>L</strong>inkage <strong>U</strong>sing <strong>S</strong>tatistics to <strong>T</strong>rigger and <strong>E</strong>valuate <strong>R</strong>esponse)</a>, this protocol offers step-by-step guidance on initial actions and escalating tiers of intervention based on ongoing assessments and outbreak progression.</p><p>Because this protocol is detailed and data-driven, users are encouraged to begin with the <strong>Quick Guides for Tier 1 and Tier 2</strong> interventions, followed by the <strong>Outbreak Response Activation Checklist</strong>. For those unfamiliar with the surveillance tools referenced in the protocol, such as <a href=\\\"https://whonet.org/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><strong>WHONET</strong></a> and <a href=\\\"https://www.satscan.org/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><strong>SaTScan</strong></a>, we recommend visiting their official websites for setup and usage guidance. These tools support early outbreak detection and enhance the impact of the response.</p><p><strong>Getting started tip:</strong> Start with the “Initial Outbreak Alert” checklist and complete the associated practice assessments. Then proceed to select an intervention based on your results. Use the toolkit’s forms and image guides to support accurate monitoring and response documentation.</p>\", \"f394434c-cdd6-422c-857c-73fdb47ec55d\": \"About the Outbreak Response Protocol\"}',1,'2025-06-02 15:29:57','2025-06-02 15:29:57','481dde17-a1eb-4269-817b-d6cceffff896'),(51546,51138,1,'Automated Rapid Outbreak Detection: Analyzing Clinical Laboratory Data to Provide Real-Time Alerts - A Strategy from the CLUSTER Trial','automated-rapid-outbreak-detection-analyzing-clinical-laboratory-data-to-provide-real-time-alerts-a-strategy-from-the-cluster-trial','news/automated-rapid-outbreak-detection-analyzing-clinical-laboratory-data-to-provide-real-time-alerts-a-strategy-from-the-cluster-trial','{\"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], \"f88904a1-7870-468f-8885-f14ce93db364\": []}',1,'2025-06-02 15:30:13','2025-06-02 16:41:09','7047266e-fae0-49c0-bc18-c4cc57c7b510'),(51547,51139,1,NULL,'__temp_imtixevykaqurzlkpfuhfyjymnhtaiahhwqf',NULL,'{\"b691f1c7-ad7d-4a8b-a9fa-b4feec58e073\": []}',1,'2025-06-02 15:30:18','2025-06-02 15:30:19','597f8a17-3ea4-4e4f-984e-b7a73e5cd844'),(51548,51140,1,'Susan Huang',NULL,NULL,NULL,1,'2025-06-02 15:43:34','2025-06-02 15:43:34','822b9c08-30be-4985-b1b7-1d48150d62cd'),(51549,51140,2,'Susan Huang',NULL,NULL,NULL,1,'2025-06-02 15:43:34','2025-06-02 15:43:34','27076cc3-b44f-460d-9867-540eb51299c3'),(51550,51141,1,'Meghan Baker Elisif 20190530 3246 croppedfullsizecolor',NULL,NULL,NULL,1,'2025-06-02 15:43:37','2025-06-02 15:43:37','e7e7fc11-dcf3-4d86-bf4c-2945673567c8'),(51551,51141,2,'Meghan Baker Elisif 20190530 3246 croppedfullsizecolor',NULL,NULL,NULL,1,'2025-06-02 15:43:38','2025-06-02 15:43:38','1505bd8c-451a-4039-a4c6-b53473f21f72'),(51552,51142,1,'Julia Moody',NULL,NULL,NULL,1,'2025-06-02 15:43:39','2025-06-02 15:43:39','2ab5254e-abb0-4f44-a6b3-3db41e813051'),(51553,51142,2,'Julia Moody',NULL,NULL,NULL,1,'2025-06-02 15:43:40','2025-06-02 15:43:40','bce4c0d8-1f3c-434e-8aa6-2693b152dcff'),(51555,51144,1,'Local Health Department’s Use of Data Sharing Modeled by Snohomish County, WA','local-health-departments-use-of-data-sharing-modeled-by-snohomish-county-wa','news/local-health-departments-use-of-data-sharing-modeled-by-snohomish-county-wa','{\"84a051cd-1f3d-42ba-be23-26f3f9856933\": \"<p> </p><p>In the previous spotlight, we learned about the importance of data sharing to support outbreak response and some of the challenges public health and healthcare professionals face. Here, I will highlight Snohomish County Health Department’s (SCHD) lessons learned and strategies for successfully gaining access to electronic health records (EHRs) of healthcare system partners within our jurisdiction and how it streamlines our case reporting, outbreak detection and response, and case investigations. </p><p>SCHD, based in Washington State, developed close relationships with healthcare organizations (HCOs) during the pandemic which were elemental in gaining access to our jurisdiction’s HCO’s EHRs. The pandemic also emphasized the benefits of public health having direct EHR access. Direct access helps streamline a process that can be confusing and burdensome for HCO staff. Previously, this type of information exchange could involve multiple rounds of faxes or phone calls.  </p><p>When we reach out to pursue a new EHR access agreement, my colleagues and I have conversations with the HCO’s executive/c-suite leaders to identify their concerns. We underscore our willingness to support the data sharing agreement process and its flexibility regarding potential HCO requirements, such as limiting public health account access to requesting specific types of patient information or records. This establishes a basis to build trust from which growth can occur, with the long-term goal of gaining full read-only access to all HCO partner EHRs as is allowable under HIPAA and public health law. With these foundational relationships, LHDs can expand data sharing opportunities to improve community health. </p><p>Another strategy SCHD has leveraged was to first target the HCOs with the lowest barriers to EHR access for both public health and healthcare providers from different systems. Once we had multiple HCOs that granted access, it was easier to persuade other organizations that were more hesitant. We also worked internally to identify the HCOs for which EHR access would be most beneficial to our disease and outbreak investigation work.  </p><p>Snohomish County also recommends a proactive approach in which EHR access is obtained prior to outbreaks and relevant accounts are ready to go. We’re hopeful that our experience implementing these strategies serves as a learning opportunity for other jurisdictions to establish data sharing agreements with their HCOs. </p><p> </p><figure class=\\\"image\\\"><img style=\\\"aspect-ratio:300/349;\\\" src=\\\"{asset:45397:transform:medium||/assets/images/transf/_medium/45397/James-Lewis-photo.webp}\\\" alt=\\\"James Lewis Headshot\\\" width=\\\"300\\\" height=\\\"349\\\" /><figcaption>James Lewis</figcaption></figure>\", \"acf2caae-0773-4eb2-bb10-57a01b19e200\": \"James Lewis, MD, MPH\", \"ccabfff6-2908-407e-98d3-a22686d82840\": {\"score\": \"poor\", \"social\": {\"twitter\": {\"title\": \"Local Health Department’s Use of Data Sharing Modeled by Snohomish County, WA - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": \"The local health department in Snohomish County (WA) demonstrates effective use of data sharing to enhance public health initiatives.\"}, \"facebook\": {\"title\": \"Local Health Department’s Use of Data Sharing Modeled by Snohomish County, WA - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": \"The local health department in Snohomish County (WA) demonstrates effective use of data sharing to enhance public health initiatives.\"}}, \"advanced\": {\"robots\": [], \"canonical\": \"\"}, \"keywords\": [{\"rating\": \"poor\", \"keyword\": \"data sharing\"}, {\"rating\": \"poor\", \"keyword\": \"regulation\"}, {\"rating\": \"poor\", \"keyword\": \"health information exchange\"}, {\"rating\": \"poor\", \"keyword\": \"surveillance data\"}, {\"rating\": \"poor\", \"keyword\": \"WA\"}, {\"rating\": \"poor\", \"keyword\": \"Snohomish\"}, {\"rating\": \"poor\", \"keyword\": \"jurisdiction\"}], \"titleRaw\": {\"1\": \"Local Health Department’s Use of Data Sharing Modeled by Snohomish County, WA\"}, \"descriptionRaw\": \"The local health department in Snohomish County (WA) demonstrates effective use of data sharing to enhance public health initiatives.\"}, \"dd06f1a1-3c4f-4001-adc3-f3c0a94550ac\": [1558]}',1,'2025-06-02 15:47:31','2025-06-02 15:47:31','8e25303d-5a01-4cd7-9f79-96bcf54d2970'),(51556,51145,1,'Automated Rapid Outbreak Detection: Analyzing Clinical Laboratory Data to Provide Real-Time Alerts: A Strategy from the CLUSTER Trial','automated-rapid-outbreak-detection-analyzing-clinical-laboratory-data-to-provide-real-time-alerts-a-strategy-from-the-cluster-trial','news/automated-rapid-outbreak-detection-analyzing-clinical-laboratory-data-to-provide-real-time-alerts-a-strategy-from-the-cluster-trial','{\"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 CORHA’s CLUSTER Detection resource page.  </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}\\\" width=\\\"300\\\" height=\\\"299\\\" alt=\\\"Susan Huang Headshot\\\" /><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}\\\" width=\\\"300\\\" height=\\\"388\\\" alt=\\\"Julia Moody Headshot\\\" /><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\": \"\", \"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\": [], \"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]}',1,'2025-06-02 15:48:25','2025-06-02 15:48:25','ee3c5331-c2ca-4e9d-ad8c-892acb1bad8b'),(51557,51146,1,NULL,'__temp_imtixevykaqurzlkpfuhfyjymnhtaiahhwqf',NULL,NULL,1,'2025-06-02 15:48:25','2025-06-02 15:48:25','fb1f7ccd-aba8-41e5-8465-bc6d13f542a8'),(51559,51148,1,'Automated Rapid Outbreak Detection: Analyzing Clinical Laboratory Data to Provide Real-Time Alerts: A Strategy from the CLUSTER Trial','automated-rapid-outbreak-detection-analyzing-clinical-laboratory-data-to-provide-real-time-alerts-a-strategy-from-the-cluster-trial','news/automated-rapid-outbreak-detection-analyzing-clinical-laboratory-data-to-provide-real-time-alerts-a-strategy-from-the-cluster-trial','{\"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=\\\"{entry:50967@1:url||https://corha.org/tools/cluster-toolkit}\\\" 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 CORHA’s CLUSTER Detection resource page.  </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]}',1,'2025-06-02 15:52:52','2025-06-02 15:52:52','6bfb87f8-753f-4794-b7b8-fcef65b05edc'),(51560,51149,1,'CLUSTER Detection: Outbreak Response Protocol','cluster-toolkit','tools/cluster-toolkit','{\"083cf2b9-0557-4c10-aac6-0ba9c8747dce\": {\"score\": \"\", \"social\": {\"twitter\": {\"title\": \"CLUSTER Toolkit for Outbreak Response - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": []}, \"facebook\": {\"title\": \"CLUSTER Toolkit for Outbreak Response - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": []}}, \"advanced\": {\"robots\": [], \"canonical\": \"\"}, \"keywords\": [], \"titleRaw\": {\"1\": \"CLUSTER Toolkit for Outbreak Response\"}, \"descriptionRaw\": \"\"}, \"0d23b6e6-763d-4507-bdf1-c48d5aa10873\": false, \"2bbee18c-8340-4ef8-955a-91e4bec4bd09\": false, \"50fd5cf3-c097-475d-9aa5-534e9467bd8b\": false, \"b8cfe543-1fb8-4cb8-b247-8b46432bdbc7\": [50969]}',1,'2025-06-02 15:54:32','2025-06-02 15:54:32','5aedee51-5e23-4d45-9fec-04ab1f06399e'),(51561,51150,1,NULL,'__temp_dogbjmlroqxkbjxrdbwrfdxxfjnftmnoydar',NULL,'{\"0f72865d-fe3e-4163-918f-27e7c84781c3\": \"<p>The Outbreak Response Protocol standardizes hospital outbreak management with tiered interventions based on infection prevention assessments.</p>\", \"b691f1c7-ad7d-4a8b-a9fa-b4feec58e073\": [50973]}',1,'2025-06-02 15:54:32','2025-06-02 15:54:32','d6e519e6-4279-4ca1-9e45-3e427455f1ae'),(51562,51151,1,NULL,'__temp_jhcsswvahhyonpzfctoxhumlswxugzawrfpw',NULL,'{\"19da6ed0-2367-47b1-bc05-09e55409b7f1\": [50966], \"7dfa0f4c-f5c2-41e9-95e9-603e7c0ee68d\": \"docIcon\"}',1,'2025-06-02 15:54:32','2025-06-02 15:54:32','7aec3c3d-77d5-4ecf-9593-7a3a3ffd13e4'),(51563,51152,1,NULL,NULL,NULL,NULL,1,'2025-06-02 15:54:32','2025-06-02 15:54:32','60f7beda-cdbe-44c8-8cf7-8c246f1b992d'),(51564,51153,1,NULL,'__temp_lwgjrxozeikhoozttfmtwjhvuxtdscnefgrt',NULL,'{\"c9374286-f051-4a4a-a20b-da25d3c319ff\": \"<p>The <strong>Outbreak Response Protocol</strong> provides a structured approach for hospitals and healthcare facilities to respond to outbreaks of infectious pathogens, primarily bacterial, mycobacterial, and fungal, though it may also be applied to viral outbreaks. Developed as part of the  <a href=\\\"https://evidence.nejm.org/doi/full/10.1056/EVIDoa2300342\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><strong>CLUSTER Trial </strong>(<strong>C</strong>luster <strong>L</strong>inkage <strong>U</strong>sing <strong>S</strong>tatistics to <strong>T</strong>rigger and <strong>E</strong>valuate <strong>R</strong>esponse)</a>, this protocol offers step-by-step guidance on initial actions and escalating tiers of intervention based on ongoing assessments and outbreak progression.</p><p>Because this protocol is detailed and data-driven, users are encouraged to begin with the <strong>Quick Guides for Tier 1 and Tier 2</strong> interventions, followed by the <strong>Outbreak Response Activation Checklist</strong>. For those unfamiliar with the surveillance tools referenced in the protocol, such as <a href=\\\"https://whonet.org/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><strong>WHONET</strong></a> and <a href=\\\"https://www.satscan.org/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><strong>SaTScan</strong></a>, we recommend visiting their official websites for setup and usage guidance. These tools support early outbreak detection and enhance the impact of the response.</p><p><strong>Getting started tip:</strong> Start with the “Initial Outbreak Alert” checklist and complete the associated practice assessments. Then proceed to select an intervention based on your results. Use the toolkit’s forms and image guides to support accurate monitoring and response documentation.</p>\", \"f394434c-cdd6-422c-857c-73fdb47ec55d\": \"About the Outbreak Response Protocol\"}',1,'2025-06-02 15:54:32','2025-06-02 15:54:32','80b6cc8e-b49b-40b5-9d7b-638f540240be');
INSERT INTO `elements_sites` VALUES (51565,51154,1,'Local Health Department’s Use of Data Sharing Modeled by Snohomish County, WA','local-health-departments-use-of-data-sharing-modeled-by-snohomish-county-wa','news/local-health-departments-use-of-data-sharing-modeled-by-snohomish-county-wa','{\"84a051cd-1f3d-42ba-be23-26f3f9856933\": \"<p> </p><p>In the previous spotlight, we learned about the importance of data sharing to support outbreak response and some of the challenges public health and healthcare professionals face. Here, I will highlight Snohomish County Health Department’s (SCHD) lessons learned and strategies for successfully gaining access to electronic health records (EHRs) of healthcare system partners within our jurisdiction and how it streamlines our case reporting, outbreak detection and response, and case investigations. </p><p>SCHD, based in Washington State, developed close relationships with healthcare organizations (HCOs) during the pandemic which were elemental in gaining access to our jurisdiction’s HCO’s EHRs. The pandemic also emphasized the benefits of public health having direct EHR access. Direct access helps streamline a process that can be confusing and burdensome for HCO staff. Previously, this type of information exchange could involve multiple rounds of faxes or phone calls.  </p><p>When we reach out to pursue a new EHR access agreement, my colleagues and I have conversations with the HCO’s executive/c-suite leaders to identify their concerns. We underscore our willingness to support the data sharing agreement process and its flexibility regarding potential HCO requirements, such as limiting public health account access to requesting specific types of patient information or records. This establishes a basis to build trust from which growth can occur, with the long-term goal of gaining full read-only access to all HCO partner EHRs as is allowable under HIPAA and public health law. With these foundational relationships, LHDs can expand data sharing opportunities to improve community health. </p><p>Another strategy SCHD has leveraged was to first target the HCOs with the lowest barriers to EHR access for both public health and healthcare providers from different systems. Once we had multiple HCOs that granted access, it was easier to persuade other organizations that were more hesitant. We also worked internally to identify the HCOs for which EHR access would be most beneficial to our disease and outbreak investigation work.  </p><p>Snohomish County also recommends a proactive approach in which EHR access is obtained prior to outbreaks and relevant accounts are ready to go. We’re hopeful that our experience implementing these strategies serves as a learning opportunity for other jurisdictions to establish data sharing agreements with their HCOs. </p><p> </p><figure class=\\\"image\\\"><img style=\\\"aspect-ratio:300/349;\\\" src=\\\"{asset:45397:transform:medium||/assets/images/transf/_medium/45397/James-Lewis-photo.webp}\\\" alt=\\\"James Lewis Headshot\\\" width=\\\"300\\\" height=\\\"349\\\" /><figcaption>James Lewis</figcaption></figure>\", \"acf2caae-0773-4eb2-bb10-57a01b19e200\": \"James Lewis, MD, MPH\", \"ccabfff6-2908-407e-98d3-a22686d82840\": {\"score\": \"poor\", \"social\": {\"twitter\": {\"title\": \"Local Health Department’s Use of Data Sharing Modeled by Snohomish County, WA - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": \"The local health department in Snohomish County (WA) demonstrates effective use of data sharing to enhance public health initiatives.\"}, \"facebook\": {\"title\": \"Local Health Department’s Use of Data Sharing Modeled by Snohomish County, WA - CORHA\", \"handle\": \"\", \"imageId\": \"\", \"description\": \"The local health department in Snohomish County (WA) demonstrates effective use of data sharing to enhance public health initiatives.\"}}, \"advanced\": {\"robots\": [], \"canonical\": \"\"}, \"keywords\": [{\"rating\": \"poor\", \"keyword\": \"data sharing\"}, {\"rating\": \"poor\", \"keyword\": \"regulation\"}, {\"rating\": \"poor\", \"keyword\": \"health information exchange\"}, {\"rating\": \"poor\", \"keyword\": \"surveillance data\"}, {\"rating\": \"poor\", \"keyword\": \"WA\"}, {\"rating\": \"poor\", \"keyword\": \"Snohomish\"}, {\"rating\": \"poor\", \"keyword\": \"jurisdiction\"}], \"titleRaw\": {\"1\": \"Local Health Department’s Use of Data Sharing Modeled by Snohomish County, WA\"}, \"descriptionRaw\": \"The local health department in Snohomish County (WA) demonstrates effective use of data sharing to enhance public health initiatives.\"}, \"dd06f1a1-3c4f-4001-adc3-f3c0a94550ac\": [1558]}',1,'2025-06-02 15:55:30','2025-06-02 15:55:30','79f5b9de-e49b-4015-8c7e-93c22059183d'),(51567,51156,1,'Automated Rapid Outbreak Detection: Analyzing Clinical Laboratory Data to Provide Real-Time Alerts: A Strategy from the CLUSTER Trial','automated-rapid-outbreak-detection-analyzing-clinical-laboratory-data-to-provide-real-time-alerts-a-strategy-from-the-cluster-trial','news/automated-rapid-outbreak-detection-analyzing-clinical-laboratory-data-to-provide-real-time-alerts-a-strategy-from-the-cluster-trial','{\"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}\\\" 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Trial','automated-rapid-outbreak-detection-analyzing-clinical-laboratory-data-to-provide-real-time-alerts-a-strategy-from-the-cluster-trial','news/automated-rapid-outbreak-detection-analyzing-clinical-laboratory-data-to-provide-real-time-alerts-a-strategy-from-the-cluster-trial','{\"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}\\\" 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--
