O’Horo, John C.

Abstract Background The COVID-19 pandemic has underscored challenges with patient adherence to medical directives, particularly vaccine acceptance. While sociodemographic factors have been linked to non-COVID-19 vaccine hesitancy, the relationships between patient experience, the COVID-19 pandemic, and adherence to routine vaccinations are uncertain.Figure 1:Yearly influenza vaccine refusal, influenza vaccine administrations, and overall patient satisfaction since establishing care with a primary care provider from 2017-2023.Panel A (Influenza Refusals): the number of vaccine refusals among individuals who are Intermittently Vaccinated and Never Vaccinated for seasonal influenza since establishing care with a primary care provider over time.Panel B (Influenza Vaccinations): the number of vaccines administered over time since establishing care with a primary care provider among those who are Always Vaccinated and Intermittently Vaccinated for seasonal influenza.Panel C (Overall Satisfaction): the number of patients who indicated that they were maximally satisfied with their overall care following any interaction with the healthcare system over time. Methods We retrospectively analyzed influenza vaccination records of adult primary care patients in Olmsted County, Minnesota. Vaccination history was examined during two phases: Pre-Pandemic (1/1/2017—2/28/2020) and Pandemic-Plus (3/1/2020—12/31/2023). Vaccination status was defined as “always vaccinated” (AV), “never vaccinated” (NV), or “intermittently missed years of vaccinations” (IV) for seasonal influenza. Patient dissatisfaction was determined if patients negatively ranked their overall quality of care on standard patient experience surveys. We explored the relationship between influenza vaccination rates, healthcare engagement, and patient satisfaction.Figure 2:Multinomial logistic regression of patient satisfaction on vaccination status across the phases of the COVID-19 Pandemic, adjusted for engagement in the health care system, participation, and area of deprivation index (ADI) for patients that were (A) intermittently vaccinated compared to always vaccinated and (B) never vaccinated comparted to always vaccinated. The “Pre-pandemic phase” refers to influenza immunization data and patient satisfaction data prior to March 2020. The “Pandemic-plus phase” refers to immunization data and patient satisfaction data from March 2020 to January 2024. Low ADI ranking refers to areas with a low level of deprivation, whereas high ADI ranking refers to areas with a high level of deprivation. Individuals were considered low if they came from an area with a state ADI ranking from 1 to 5 and were considered high if they came from an area with a state ADI ranking from 6 to 10. Results From 1/1/2017 to 12/31/2023, 21% of individuals were AV (n=36,632), 52% were IV (n=90,776), and 27% were NV (n=46,418) for seasonal influenza. Post-pandemic onset, 7% (n=3,556) of the Pre-Pandemic AV and 32% (n=16,710) of the Pre-Pandemic IV never received another influenza vaccine. In the Pandemic-Plus phase, the IV and NV had a higher likelihood of disengagement with the healthcare system [odds ratio (OR) (95% CI): 1.59 (1.55, 1.64); 4.21 (4.09, 4.33)], residing in areas of greater deprivation [1.58 (1.53, 1.62); 1.99 (1.94, 2.05)], and self-identifying their race as Black [2.29 (2.09, 2.51); 3.29 (3.01, 3.60)] compared to the AV. After adjusting for healthcare engagement and area of deprivation, dissatisfaction OR increased from 1.03 (0.99,1.07) Pre-Pandemic to 1.07 (1.03,1.12) in the Pandemic-Plus phase among IV compared to AV. Similarly, the OR of dissatisfaction rose from 1.11 (1.05, 1.18) Pre-Pandemic to 1.16 (1.09, 1.24) in the Pandemic-Plus phase among those who were NV compared to those AV. Conclusion Higher healthcare dissatisfaction was observed among those inconsistently adherent to vaccination recommendations. Targeted efforts to enhance patient satisfaction may boost vaccination rates. Disclosures All Authors: No reported disclosures

Abstract Background The integration of Generative AI (GAI) into healthcare systems is increasingly recognized for its potential to transform patient management. The primary aim of this research was to evaluate and quantify the performance of large language models (LLMs) in generating actionable travel medicine advice. Architectural design of the Travel Clinic LLM project. Four phases of Discovery, Design, Evaluation, and Implementation/Deployment. Methods This study utilized two iterative phases of evaluation. In the initial phase, LLMs were prompted with detailed clinical scenarios including demographic data, medical and immunization histories, and specific travel plans. These prompts were designed to mimic typical inquiries encountered in travel consultations. The LLMs' initial responses were generated using the CDC’s Yellow Book as a foundational knowledge base. In the subsequent phase, the prompts were refined for greater specificity and clarity, and the knowledge base was enhanced by transitioning to Travax’s Travelers’ Health database. Additional structured data inputs included an exhaustive list of vaccines from our pharmacy formulary and a detailed table of vaccine contraindications. The responses were evaluated and scored by ID clinicians from the Mayo Clinic. Results Initial findings after first iteration revealed limited efficacy with recall at 23.9%, an F1 score of 38.6%, accuracy also at 23.9%, and precision maintained at 100%, utilizing the CDC's Yellow Book. With the implementation of Travax and refined prompting techniques, preliminary results suggest a notable improvement in the quality of responses, though detailed scoring is presently underway. Improvements in the LLM’s performance can be attributed to several key adjustments: the adoption of a more comprehensive knowledge base, refined prompt engineering, and the incorporation of structured data to support more accurate and detailed recommendations. The collaborative engagement of Mayo Clinic with Google and Travax facilitated a synergistic approach to optimizing the AI model's utility and integration. Future plans include embedding the LLM into our EMR system. Conclusion The findings from this study highlight the significance of strategic collaborations between large healthcare centers, IT industry, and specialized knowledge database firms in effectively harnessing GAI for clinical use. Disclosures All Authors: No reported disclosures

ImportanceBlood culture (BC) use benchmarks in US hospitals have not been defined.ObjectiveTo characterize BC use in adult intensive care units (ICUs) and wards in US hospitals.Design, Setting, and ParticipantsA retrospective cross-sectional study of BC use in adult medical ICUs, medical-surgical ICUs, medical wards, and medical-surgical wards from acute care hospitals from the 4 US geographic regions was conducted. Critical access hospitals, less than 6 months of BC data, and non-US hospitals were excluded. The study included BC use data from September 1, 2019, to August 31, 2021. Data were analyzed from February 23 to July 14, 2024.Main Outcomes and MeasuresThe primary outcome was BC use per 1000 patient-days. Adjusted means with 95% CIs were calculated using mixed-effects negative binomial regression models adjusted for unit type, hospital bed size, geographic region, seasonality, and state COVID-19 case load, with random intercepts accounting for clustering at unit and hospital levels. Secondary outcomes included blood culture positivity, single BCs, BC contamination, and minimum threshold for BC use where blood culture positivity would be optimized.ResultsA total of 362 327 blood cultures were analyzed from 27 medical ICUs, 35 medical-surgical ICUs, 121 medical wards, and 109 medical-surgical wards from 48 hospitals in 19 states and the District of Columbia. The adjusted mean BC use per 1000 patient-days was 273.1 (95% CI, 270.2-275.9) for medical ICUs, 146.0 (95% CI, 144.5-147.5) for medical-surgical ICUs, 80.3 (95% CI, 79.8-80.7) for medical wards, and 65.1 for medical-surgical wards. Blood culture use was significantly higher across all 4 unit types in hospitals with more than 500 beds compared with 500 or less beds and in the West-Midwest compared with other regions. Single blood culture and positive blood culture rates were below 10% across all 4 unit types. Of the 292 units, 97% had a mean BC contamination rate within 3% of the recommended threshold, and 51% were within 1%. The minimum BC use thresholds (ie, BC use below this number may represent undertesting) were 120 BCs per 1000 patient-days for medical ICUs, 80 BCs per 1000 patient-days for medical-surgical ICUs, and 30 BCs per 1000 patient-days for medical-surgical wards.Conclusions and RelevanceThe findings of this study suggest that blood culture positivity may help determine appropriate BC use for individual unit types.

Abstract Background The Grading Recommendations, Assessment, Development, and Evaluations (GRADE) framework is widely applied in clinical guidelines to facilitate transparent evidence evaluation. While developing Infectious Diseases Society of America (IDSA) guidelines on the management of patients with coronavirus disease 2019 (COVID-19), panel members suggested developing and implementing a visual aid to enable quicker identification of key information by providers at bedside seeking guidance. Methods We conducted a mixed-methods study evaluating the usability of a newly designed infographic/icon using a survey and focus groups. The survey incorporated a simulated COVID-19 IDSA guideline with and without the icon, followed by comprehension questions. Focus group discussions provided qualitative feedback on the GRADE methodology and icon usability. Results The survey was returned by 289 health care providers. There was no statistical difference in the correct response rates between icon-aided and non-icon-aided guideline questions (McNemar's chi-square test, P > .1 for both questions). Interactions with the icon notably increased the time taken and number of clicks required to respond to the first question (Wilcoxon signed-rank test, P < .01). In contrast, response time did not differ between versions for the second question (P = .38). Most subjects (85%) indicated that the icon improved the readability of the guidelines. A focus group follow-up suggested alternative designs for the icon. Conclusions This study highlights the promise of iconography in clinical guidelines, although the specific icons tested did not measurably improve usability metrics. Future research should focus on icon design and testing within a formal usability framework, considering the impact of GRADE language on user experience.

Aim: The association of echocardiographic findings and subsequent risk of left-sided native valve endocarditis (LS-NVE) is undefined. The aim of this study was to determine if transthoracic echocardiography (TTE) measurements are associated with the subsequent development of LS-NVE in patients without cardiac predisposing conditions. Methods: Institutional databases were evaluated for adults diagnosed with LS-NVE from 2008 to 2020. Patients with prosthetic valves, cardiovascular implantable electronic devices, intracardiac devices, injection drug use, and predisposing cardiac conditions were excluded. Only patients who had a TTE performed 6 months to 3 years before the development of LS-NVE were included as cases. Controls were patients within the same Mayo location with a TTE report and were matched in a 1:3 ratio according to age, gender, Charlson comorbidity index, and echocardiography date. Results: There were 148 cases and 431 matched controls. As compared to controls, infective endocarditis (IE) cases had a higher prevalence of diabetes mellitus (46.6% vs. 30.4%) and chronic kidney disease (46.6% vs. 28.1%) (P < 0.001). Left ventricular outflow tract velocity (P = 0.017), left ventricular ejection fraction (P = 0.018), and E:e’ ratio (P = 0.050) were associated with LS-NVE. Conclusions: Echocardiographic measurements were associated with subsequent LS-NVE development in this pilot study. A larger cohort of LS-NVE patients, however, is needed to validate these findings.

Abstract Background We aimed to determine the factors associated with sequential blood culture time to positivity (STTP) and validate the previously defined time to positivity (TTP) ratio threshold of 1.5 in predicting adverse disease outcomes and mortality of Staphylococcus aureus bacteremia (SAB). Methods We conducted an observational study of adult patients with SAB. The TTP ratio was calculated by dividing the TTP of the second blood culture by that of the first. Results Of 186 patients, 69 (37%) were female, with a mean age of 63.6 years. Median TTP was 12 hours (interquartile range [IQR], 10–15 hours) from the initial and 21 hours (17–29) from sequential blood cultures. Methicillin-resistant S aureus (MRSA)–infected patients had significantly shorter STTPs (P &lt; .001) and lower TTP ratios (P &lt; .001) compared to patients with methicillin-susceptible S aureus (MSSA). A significant correlation between initial and STTP was observed in patients with MRSA (r = 0.42, P = .002) but not in those with MSSA. A higher rate of native valve endocarditis (NVE) significantly correlated with a TTP ratio of ≤1.5 (odds ratio, 2.65 [95% confidence interval, 1.3–5.6]; P = .01). The subgroup having an initial TTP &lt;12 hours combined with a TTP ratio ≤1.5 showed the highest prevalence of NVE. Conclusions The STTP varies based on methicillin susceptibility of S aureus isolate. This study suggests a potential clinical utility of the STTP to identify patients at a higher risk of NVE. However, prospective studies are required to validate these findings.

Introduction: The association of echocardiographic measurements and subsequent risk of left-sided native valve endocarditis (LS-NVE) is undefined. Aim: To determine if transthoracic echocardiographic (TTE) measurements are associated with the subsequent development of LS-NVE in patients without recognized cardiac predisposing conditions. Methods: Institutional databases were evaluated for adults diagnosed with LS-NVE from 2008 to 2020. Patients with prosthetic valves, cardiovascular implantable electronic devices, intracardiac devices, injection drug use, and recognized predisposing cardiac conditions were excluded. Patients with a TTE performed 6 months to 3 years before developing LS-NVE were included as cases. Controls were patients within the same Mayo Clinic location with a TTE report and were matched in a 1:3 ratio according to age, gender, Charlson Comorbidity Index (CCI), and echocardiography date. Multivariate logistic-regression analyses adjusting significant variables between cohort's were performed. A p-value <0.05 was considered significant. Results: There were 148 cases and 431 matched controls. The median age was 63.8 (IQR: 53.6 - 74.6) years, and 329 (56.8%) patients were males. LS-NVE cases had a higher prevalence of diabetes mellitus (46.6% vs. 30.4%) and chronic kidney disease (46.6% vs. 28.1%) when compared with controls (p<0.001 both). The median CCI score was 3 (IQR: 0-6) for cases and 2 (IQR: 0-6) for controls. After performing adjusted analyses, left ventricular outflow tract velocity (p=0.017), left ventricular ejection fraction (p=0.018), and E: e’ ratio (p=0.050) were associated with the development of LS-NVE ( Figure 1) . Conclusions: Echocardiographic measurements were associated with subsequent LS-NVE development in this preliminary investigation. A larger cohort of LS-NVE patients is needed to validate these findings.