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BackgroundTypical Infection Prevention to detect pathogen transmission in hospitals has relied on observation of (1) uncommon pathogen phenotypes or (2) greater than expected number of pathogen phenotypes in a given timeframe and/or location. Genome sequencing of targeted organisms in conjunction with routine patient geo-temporal information and antibiotic susceptibility data holds promise in identifying transmissions with greater sensitivity and specificity, saving time and effort in reviewing for transmission events.MethodsIn an on-going genomic sequencing surveillance effort in a tertiary care hospital, drug-resistant clinical isolates from the “ESKAPE” pathogens were routinely sequenced in 2017. In parallel, potential clusters were identified for 2017 through conventional Infection Prevention approaches. Groups identified by their genetic distances along with visualizations on antimicrobial susceptibilities, and patient location histories and dates were displayed in an interactive interface, Philips IntelliSpace Epidemiology (PIE), and reviewed by Infection Prevention.ResultsAmong 656 patients, 1,239 drug-resistant ESKAPE samples were sequenced. Thirty-eight genetically related groups involving 196 patients were identified. Groups ranged in size from two to 44 patients, primarily consisting of VRE and MRSA. Notably, a review of the 38 groups identified 20 groups where the information at hand suggested a concern for transmission. 16 of the 20 were not previously identified by Infection Prevention. Using PIE to review all 38 groups identified from 1 year’s worth of data required 3 hours of time by an Infection Prevention professional, averaging less than 5 minutes per cluster, less than 1 minute per patient, and 11 minutes of review time per actionable opportunity. By conventional means, approximately 23 hours would have been required to review the genomic groups without the aid of the PIE tool.ConclusionThe use of PIE’s genomic-defined groups, along with the integrated clinical data platform, allows for a greater ability, certainty, and speed to detect clusters of organisms representing transmission in the hospital setting. Applied prospectively, PIE can detect transmissions sooner than by conventional means for potential patient safety gains and cost savings.Disclosures D. Chen, Philips: Scientific Advisor, Consulting fee. M. Fortunato-Habib, Philips Healthcare: Collaborator and Employee, Salary. A. Hoss, Philips: Employee, Salary. R. Kolde, Philips: Employee, Salary. A. Dhand, Merck: Speaker’s Bureau, Speaker honorarium. Astellas: Scientific Advisor, Consulting fee. R. Sussner, Philips: Scientific Advisor, Consulting fee. J. Carmona, Philips Healthcare: Employee, Salary. B. Gross, Philips Healthcare: Employee, Investigator, Research Contractor, Scientific Advisor and Shareholder, Salary. J. Fallon, Philips Healthcare: Investigator, Research support.

Background: Infection prevention and control (IPC) workflows are often retrospective and manual. New tools, however, have entered the field to facilitate rapid prospective monitoring of infections in hospitals. Although artificial intelligence (AI)–enabled platforms facilitate timely, on-demand integration of clinical data feeds with pathogen whole-genome sequencing (WGS), a standardized workflow to fully harness the power of such tools is lacking. We report a novel, evidence-based workflow that promotes quicker infection surveillance via AI-assisted clinical and WGS data analysis. The algorithm suggests clusters based on a combination of similar minimum inhibitory concentration (MIC) data, timing of sample collection, and shared location stays between patients. It helps to proactively guide IPC professionals during investigation of infectious outbreaks and surveillance of multidrug-resistant organisms and healthcare-acquired infections. Methods: Our team established a 1-year workgroup comprised of IPC practitioners, clinical experts, and scientists in the field. We held weekly roundtables to study lessons learned in an ongoing surveillance effort at a tertiary care hospital—utilizing Philips IntelliSpace Epidemiology (ISEpi), an AI-powered system—to understand how such a tool can enhance practice. Based on real-time case discussions and evidence from the literature, a workflow guidance tool and checklist were codified. Results: In our workflow, data-informed clusters posed by ISEpi underwent triage and expert follow-up analysis to assess: (1) likelihood of transmission(s); (2) potential vector(s) identity; (3) need to request WGS; and (4) intervention(s) to be pursued, if warranted. In a representative sample (spanning October 17, 2019, to November 7, 2019) of 67 total isolates suggested for inclusion in 19 unique cluster investigations, we determined that 9 investigations merited follow-up. Collectively, these 9 investigations involved 21 patients and required 115 minutes to review in ISEpi and an additional 70 minutes of review outside of ISEpi. After review, 6 investigations were deemed unlikely to represent a transmission; the other 3 had potential to represent transmission for which interventions would be performed. Conclusions: This study offers an important framework for adaptation of existing infection control workflow strategies to leverage the utility of rapidly integrated clinical and WGS data. This workflow can also facilitate time-sensitive decisions regarding sequencing of specific pathogens given the preponderance of available clinical data supporting investigations. In this regard, our work sets a new standard of practice: precision infection prevention (PIP). Ongoing effort is aimed at development of AI-powered capabilities for enterprise-level quality and safety improvement initiatives.Funding: Philips Healthcare provided support for this study.Disclosures: Alan Doty and Juan Jose Carmona report salary from Philips Healthcare.

BackgroundWhole-genome sequencing (WGS) of bacteria is becoming a routine tool within microbiology, yet its utility to help guide infection control (IC) practice longitudinally is underexplored. As with any technology adopted in the hospital, the integration of WGS into IC practice must be carefully managed and considered. We qualitatively report an evidence-based implementation workflow that considers WGS to help proactively guide IC professionals during investigation of infectious outbreaks.MethodsWe built upon lessons learned in an ongoing surveillance effort at a tertiary care hospital—utilizing retrospective WGS data within the Philips IntelliSpace Epidemiology system—to understand facilitators and barriers to the use of bacterial WGS longitudinally to inform IC workflow. Our team established a 9-month workgroup to study the practical aspects of implementing WGS in routine IC practice. From expert opinion collected via the workgroup, in addition to evidence from the literature, a workflow guidance document and checklist were codified. New ideas included incorporating education to promote the establishment of an IC triage process.ResultsFacilitators to implementation included ability to display genomic relatedness alongside relevant patient data to enable clinical actionability, ability to pivot time and resources rapidly when infections are a pseudo outbreak (false positive) or missed outbreak (false negative), opportunities for nuanced staff education, and willingness to be a first-of-kind adopter. Barriers were communication of genomic concepts to IC professionals and relevant institutional stakeholders, maintaining sharable notes of active investigations to promote data-sharing practices, and timing and review of relevant interventions into the facility workflow. Strategies to address these issues are considered.ConclusionThis study provides a novel framework for adaptation of existing IC workflow strategies to leverage the utility of bacterial WGS, and it presents a schema to effectively engage relevant stakeholders, based on an analysis of the unique challenges inherent within IC practice. It also offers an innovative model for the development and implementation of IC workflows to account for, and adapt to, site-specific conditions.Disclosures All authors: No reported disclosures.

BackgroundThe United States is currently experiencing the largest measles outbreak since 1994. The New York outbreak started in October 2018 in several communities with low immunization rates for measles. Our institution is a referral center for the Hudson Valley and New York City. Failure to immediately recognize the disease early in the outbreak resulted in several exposure investigations and significant expenditure of time and resources. With evidence of ongoing transmission in local communities, we initiated a multi-pronged approach to recognize and limit potential measles exposures.MethodsWe developed a clinical pathway to alert Emergency Department (ED) staff and local Emergency Medical Service (EMS) agencies to the signs and symptoms of measles and provided steps for isolation, care, and testing for patients with possible measles. The ED staff and EMS personnel were educated in meetings and by posters, emails, and huddles. Reports of cases were made to infection control in real time, and local Departments of Health (DOH) were subsequently notified of suspected cases and exposures. We describe data pre and post-intervention. Chi-square was used to compare the number of patients requiring contact investigations for staff and patient exposures pre- and post-pathway implementation.ResultsFrom October 2018 through April 2019, 31 patients were evaluated for measles. Measles was diagnosed in 15 patients (1 adult, 14 children). Eight patients were admitted to the hospital, 3 required Pediatric ICU care. Pre-pathway implementation, 2 out of 9 (22%) evaluated patients resulted in exposure investigations; post implementation, 1 out of 22 (4.5%) evaluated patients required an exposure investigation (P = 0.18). The investigations conducted by our infection control department included 153 patients, 141 pre-implementation vs. 12 post-implementation. Nine patients required prophylaxis with immunoglobulin, and 10 patients received MMR vaccine as prophylaxis. No exposures resulted in clinical cases of measles.ConclusionImplementation of a clinical pathway to recognize and isolate suspected measles patients with ED staff and EMS personnel resulted in reduced exposures and improvement in communication with Infection Control and local DOH.Disclosures All authors: No reported disclosures.