Rasmusson, Jenna

Background: We had a large-scale measles exposure event affecting 626 patients. Management of exposed patients was urgent, involving quarantine, post-exposure prophylaxis (PEP), and symptom monitoring on a case-by-case basis. Cases were initially tracked and managed using a combination of multiple spreadsheets amongst stakeholders as well as lists within the electronic health record (EHR). Using shared spreadsheets and lists created significant challenges with maintaining and editing multiple copies and updating workbooks simultaneously. Methods: Infection Prevention and Control (IPAC) worked with primary care personnel and EHR developers to create a shared registry that would prioritize communication and follow-up for exposed patients based on age and immunity. Patients are initially added to the shared registry within the EHR by reviewing non-registry reports and tagging patients, identified using exposure timeframes that overlapped with the index measles cases, in a manner that transferred them to the shared registry. From this registry, measles, mumps, and rubella vaccine status can be confirmed, immune status can be identified, and criteria for PEP assigned. High-risk patients are then contacted with recommendations from IPAC and primary care. Communication and follow-up are tracked directly within the shared registry to ensure appropriate patient management. The exposure management registry is further leveraged throughout the measles outbreak timeframe by allowing real-time monitoring of patients that require additional precautions or guidance related to their exposure. Result: The shared registry enhanced documentation accuracy and transparency between stakeholders. It led to availability of real-time status of exposed patients for the duration of the measles outbreak timeframe. Overall, the registry enabled time-sensitive patient communication to all exposed patients and rapid identification of those who needed PEP. Communication could happen rapidly and seamlessly between care teams within the organization, and the necessary information could be shared quickly with public health authorities as required. Conclusion: Large scale exposures are a challenge to manage and ensure appropriate action and communication occurs. Errors can be prevented by having a single shared exposure management registry. A registry enables care teams to quickly identify and prioritize patients for measles follow-up, communication, PEP, and ongoing management across outbreak timeframes. This process reduces both time and operational burdens of exposure follow-up tracking outside of the EHR.

Background: Measles is a highly transmissible virus. A single case can result in significant exposures within a healthcare facility and prompt action is needed to prevent secondary cases. In October 2025, Infection Prevention and Control (IPAC) was notified of a patient being admitted to our facility with recent international travel and symptoms consistent with measles. The patient was unvaccinated and had five healthcare facility visits prior to measles being suspected and subsequently confirmed. Objective: To describe our facility’s effective identification, prioritization, and prophylaxis of individuals exposed to measles. Methods: IPAC leveraged the electronic medical record to identify potentially exposed patients. Patients were quickly assessed for age and immune status, then prioritized and assigned to primary care teams. These teams promptly contacted patients and coordinated PEP administration. Results: Across five encounters, the index patient exposed 539 other patients. Due to delayed notification, our teams had less than four days to administer PEP for those exposed and still within PEP window. PEP administration began within hours of the index case’s positive test and patients received PEP within three days (Table 1). No secondary measles cases occurred within our facility. Conclusion: Timely and coordinated multidisciplinary effort enabled us to rapidly respond to our first measles case and control secondary transmission. Although the index case resulted in a large exposure, patients that were exposed and eligible for PEP were quickly prioritized to prevent secondary transmission.

Background: Advanced planning and adaptable response strategies are essential for preventing measles transmission in healthcare facilities. Objective: To describe a measles management plan and subsequent modifications in response to a cluster of measles cases. Methods: Drawing on previously published work (doi:10.1017/ice.2025.49), the Infection Prevention and Control (IPAC) team initiated a multi-disciplinary team and developed a measles preparedness plan with the following domains: Guidance and education; early detection and testing; isolation and patient movement; contact tracing and exposure management (Table 1). In October 2025, a patient was admitted during their fifth healthcare encounter and was diagnosed with measles. Two family members were later hospitalized with measles. Delayed recognition of the first case prompted enhancements to our preparedness framework (Table 1). Results: The first case resulted in 539 exposed patients. The second resulted in 87 exposures, and the third resulted in no exposure. Despite the extensive exposure, zero secondary measles were reported. Conclusion: Proactive planning enabled rapid response to our first case and controlled secondary transmission. Despite extensive planning, delayed identification of the first case caused significant exposures. As the cluster unfolded, we refined our plan, resulting in zero exposures from the last patient and zero secondary cases, despite over 850 exposures. These efforts may guide other healthcare facilities in improving their measles preparedness and response.

Background: In 2025, 50 measles outbreaks drove 87% of confirmed cases reported in the United States. As a highly infectious virus with an estimated R ranging from 12-18, we sought rapidly scalable and implementable measles preparedness measures for our facilities. Methods: The electronic health record (EHR) has flags that can be placed on patient records to denote the presence of, or concern for, active, transmissible infections. These flags are highly visible in the EHR and represent a good usability heuristic as front line providers are already trained to look for this information. Infection Prevention and Control (IPAC) worked with nursing informatics to develop measles-specific flags for suspected, confirmed and exposed patients in the EHR, expanding flagging functionality in preparation for potential measles cases and exposures. The flagging was tied to registry functionality to allow for tracking of exposed and confirmed cases longitudinally for supporting IPAC investigations in addition to clinical operations. Results: Three different flags were developed in the EHR to address measles. Specific criteria were created for each flag, as shown in Table 1. Table 1: Attached as Image Conclusion: As a highly contagious virus, measles presents unique challenges to healthcare facilities managing both cases and exposures. Measles flags within the EHR can be leveraged to assist facilities with case tracking, exposure management, and infection prevention and control.

Background: Candida auris is a globally emerging, multidrug-resistant fungal pathogen that causes serious, difficult-to-treat infections in hospitalized patients. C. auris cases in the United States have been linked to receipt of healthcare overseas. Outbreaks have also occurred in New York City, New Jersey, Illinois, Nevada, and California. We provide care to patients from all 50 states and 138 countries and are therefore, at risk for encountering C.auris within our facility. Methods: A case-finding tool was created by Infection Prevention and Control (IPAC) within the electronic medical record (EMR) to identify patients for screening. Inclusion criteria was centric to patients who were admitted in the previous 24 hours with a primary address in a foreign country or in areas of the United States with known C. auris transmission. Patients who are positive for Carbapenamase-producing organisms (CPO) are also included in screening criteria. In 2020, Clinical Microbiology validated a polymerase chain reaction (PCR) test in partnership with the State Health Department and IPAC. In January 2022, IPAC created a protocol that allows IPAC staff to order C. auris screening directly in the EMR utilizing the validated PCR test. The order is paired with communication from IPAC alerting the patient care team that C.auris screening is indicated. Instructions on test collection and information on C. auris are also included in messaging. The patient’s primary nurse then obtains verbal consent from the patient to collect a composite axilla–groin skin swab. Swabs are sent to Clinical Microbiology for testing, and results are reported directly into the EMR. Results: Since the introduction of the nursing protocol in January 2022, 2,660 patients have been identified for C. auris screening through November 2025. Of the 2,660 patients identified for testing, 1,111 patients had screening completed. Two patients tested positive for C. auris on screening. Both patients had prolonged travel to regions with known C. auris transmission. Conclusion: The EMR can be leveraged for early identification and screening of patients at risk of C.auris colonization. Case finding tools, combined with nursing protocols and communication strategies within the EMR can be effectively replicated and modified to respond to emerging infections.

Background: Carbapenemase-producing organisms (CPO) are a serious public health threat. The Centers for Disease Control and Prevention (CDC) guidelines for combating CPO includes a recommendation to screen selected high risk patients. We describe a program to identify and screen patients at risk for CPO. Setting: An academic, tertiary care center with 2,059 licensed beds and 74,359 admissions a year. Methods: A report was created in the electronic medical record (EMR) to identify adult patients admitted in the previous 24 hours who were from countries and states with known CPO transmission based on address and zip code. A nursing protocol was subsequently developed in the EMR to facilitate CPO screening for eligible patients at admission. After the CPO order is placed, an electronic communication is sent via the EMR alerting the patient care team of the order, the rationale for screening, details on swab collection, and links to a toolkit with resources to help answer patient questions. A single perirectal swab is obtained by the patient’s primary nurse and is tested for Klebsiella pneumoniae Carbapenemase (KPC), New Delhi metallo-β-lactamase (NDM), oxacillinase-48 (OXA-48), and Verona integron-encoded metallo-β-lactamase (VIM) by polymerase chain reaction (PCR). Patients may refuse testing. Results: From May 2018 through November 2025, 6,039 patients were identified for CPO screening using case-finding report. Of these patients, 2,424 had CPO swabs completed. Eighteen patients with CPO were identified with an overall yield of 0.75% on admission screening. There was one patient who tested positive for KPC, 10 patients who tested positive for NDM, and 5 patients who tested positive for OXA-48. Two patients were identified to have both NDM and OXA-48. Conclusions: The EMR can be leveraged for early identification and screening of patients with epidemiologically significant pathogens. Use of the nursing protocol has enabled IPAC to complete timely CPO surveillance and prevent transmission to other patients. Protocols within the EMR can be effectively replicated for a timely response to emerging infections.

Abstract Healthcare personnel (HCP) are at risk for occupational exposure to tuberculosis. Current guidelines for managing exposed HCP are broad and resource intensive. Based on review of our internal data, we propose a risk-based stratification approach to streamline exposure follow-up testing and optimize resource utilization.

Objective Analyzing pressure injury (PI) risk factors is complex because of multiplicity of associated factors and the multidimensional nature of this injury. The main objective of this study was to identify patients at risk of developing PI. Method Prediction performances of multiple popular supervised learning were tested. Together with the typical steps of a machine learning project, steps to prevent bias were carefully conducted, in which analysis of correlation covariance, outlier removal, confounding analysis, and cross-validation were used. Result The most accurate model reached an area under receiver operating characteristic curve of 99.7%. Ten-fold cross-validation was used to ensure that the results were generalizable. Random forest and decision tree had the highest prediction accuracy rates of 98%. Similar accuracy rate was obtained on the validation cohort. Conclusions We developed a prediction model using advanced analytics to predict PI in at-risk hospitalized patients. This will help address appropriate interventions before the patients develop a PI.

Candida auris is an emerging fungal pathogen with cases reported in countries around the world and in 19 states within the United States as of August 2020. The CDC has recommended that hospitals perform active surveillance upon admission for patients with the appropriate risk factors. Currently, active surveillance requires that local hospitals send surveillance swabs to a public health laboratory for analysis. In this work, a real-time PCR assay was developed for the specific detection of C. auris from surveillance swabs, blood, and urine to enable rapid detection of this pathogen. The assay uses commercially available primers and reporter probes and it was verified on the LightCycler 480 PCR platform. Contrived specimens and prospectively collected composite groin/axilla surveillance swabs were used to validate the assay. The performance of the PCR assay on surveillance swabs was also compared to a second PCR assay targeting C. auris that was performed at the Minnesota Department of Health–Public Health Laboratory (MDH-PHL). Our PCR assay is able to detect and differentiate C. auris from closely related Candida species such as C. duobushaemulonii, C. haemulonii, and C. pseudohaemulonii on the basis of melting curve temperature differences.