Citations

Objective: Multicancer Detection (MCD) tests, such as the GRAIL Galleri, offer a novel approach to cancer screening by detecting cancer-specific methylation patterns in cell-free DNA through a single blood sample. This study evaluated an 18-month implementation of MCD testing in a tertiary ambulatory internal medicine clinic. Patients and Methods: Between June 2022 and November 2023, 2244 asymptomatic (without symptoms attributed to cancer) patients underwent MCD testing. The study focused on operational workflows, patient and physician education, and diagnostic follow-up of positive results. Standardized materials, including electronic health record (EHR) workflows, FAQs, and diagnostic pathways, were developed to facilitate implementation. Challenges included managing false positives, patient anxiety, costs, and ethical considerations. Results: Of the 2244 patients tested, 17 (0.76%) had positive results, and 15 underwent further diagnostic evaluation. Cancer was confirmed in 11 (73.3%) patients, including cases of breast, colon, esophageal, lymphoma, ovarian, and pancreatic cancers. Four patients had no identifiable malignancy despite comprehensive work-up. Conclusions: MCD testing is feasible in routine clinical workflows, with 73% of positive cases yielding cancer diagnoses. While promising, further research is required to assess long-term outcomes, cost-effectiveness, and optimal implementation strategies of cancer interception in broader healthcare settings.

Objective: This retrospective observational study conducted in a general ICU aimed to evaluate the hemodynamic impact of IV paracetamol by monitoring blood pressure, urine output, and core temperature post-administration. Design: The study was designed as a retrospective observational study in a general ICU setting. Setting: The study was conducted in a general intensive care unit (ICU). Patients: Data from 498 patients receiving IV paracetamol in various clinical contexts were analyzed. Interventions: Patients received IV paracetamol for analgesia and fever reduction as part of their clinical care. Measurements and main results: The study analyzed the hemodynamic effects of IV paracetamol by monitoring blood pressure, urine output, and core temperature post-administration. A significant decrease in mean systolic and diastolic blood pressure values was observed across different patient subgroups, notably 45–60 min post-infusion. An increase in noradrenaline dosage and a decrease in urine output indicated a decline in end-organ perfusion following IV paracetamol administration. Multivariate analysis identified associations between clinical factors (such as general anesthesia and cardiac conditions) and changes in blood pressure. Conclusions: While IV paracetamol remains a valuable therapeutic option for pain and fever management, especially in hypertensive patients with specific conditions like traumatic brain injury and cerebral vascular accident, careful monitoring, and individualized dosing strategies are recommended in critically ill patients to maintain hemodynamic stability and optimize clinical outcomes. These findings contribute to enhancing our understanding of IV paracetamol hemodynamic effects and inform evidence-based practices for its use in ICU settings.

Objectives/Goals: Aspiration causes or aggravates lung diseases. While bedside swallow evaluations are not sensitive/specific, gold standard tests for aspiration are invasive, uncomfortable, expose patients to radiation, and are resource intensive. We propose the development and validation of an AI model that analyzes voice to noninvasively predict aspiration. Methods/Study Population: Retrospectively recorded [i] phonations from 163 unique ENT patients were analyzed for acoustic features including jitter, shimmer, harmonic to noise ratio (HNR), etc. Patients were classified into three groups: aspirators (Penetration-Aspiration Scale, PAS 6–8), probable (PAS 3–5), and non-aspirators (PAS 1–2) based on video fluoroscopic swallow (VFSS) findings. Multivariate analysis evaluated patient demographics, history of head and neck surgery, radiation, neurological illness, obstructive sleep apnea, esophageal disease, body mass index, and vocal cord dysfunction. Supervised machine learning using five folds cross-validated neural additive network modelling (NAM) was performed on the phonations of aspirator versus non-aspirators. The model was then validated using an independent, external database. Results/Anticipated Results: Aspirators were found to have quantifiably worse quality of sound with higher jitter and shimmer but lower harmonics noise ratio. NAM modeling classified aspirators and non-aspirators as distinct groups (aspirator NAM risk score 0.528+0.2478 (mean + std) vs. non-aspirator (control) risk score of 0.252+0.241 (mean + std); p Discussion/Significance of Impact: We report the use of voice as a novel, noninvasive biomarker to detect aspiration risk using machine learning techniques. This tool has the potential to be used for the safe and early detection of aspiration in a variety of clinical settings including intensive care units, wards, outpatient clinics, and remote monitoring.

Objectives/Goals: Identifying and indexing rare disease studies is labor intensive, especially in research centers with a large number of trials. To address this gap, we applied natural language processing (NLP) and visualization techniques to develop an efficient pipeline and user-friendly web interface. Our goal is to offer the rare disease study identification (RDSI) tool for adoption by other sites. Methods/Study Population: The RDSI retrieves study information (short and long titles, study abstract) from the IRB system. These descriptive fields are then processed by the MetaMap Lite NLP program for identifying disease terms and standardizing them to UMLS concepts. By terminology identifier mapping, the diseases intersecting with concepts in rare disease databases (Genetic and Rare Disease program and Orphanet) are further scored to pinpoint studies that focus on a rare disease. The web interface displays a scatter bubble chart as an overview of all the rare diseases, with each bubble size proportional to the number of studies for that disease. In addition to the visual navigation, users can search studies by disease name, PI, or IRB number. Search results contain detailed study information as well as the evidence used by algorithms of the pipeline. Results/Anticipated Results: The RDSI identification results and functions were verified manually and spot-checked by several study investigators. The web interface is a self-contained solution available to our staff for various use cases like reporting or environment scan. We have built in a versioning mechanism that logs the date of each major result in the process. Therefore, even as the rare disease data sources evolve over time, we will be able to preserve any historical context or perform updates as needed. The RDSI outputs are replicated to Mayo Clinic’s enterprise data warehouse daily, allowing tech-savvy users to leverage any useful intermediate results at the backend. We anticipate the performance of the rare disease identification to be further enhanced by employing the advancements in AI technology. Discussion/Significance of Impact: The RDSI represents an informatics solution that offers efficiency in identifying and navigating rare disease clinical studies. It features the use of public databases and open-source tools, manifesting return on investment from the broad translational science ecosystem. These considerations are informative and adoptable by other institutions.

Objectives/Goals: This study aims to evaluate the performance of a third-party artificial intelligence (AI) product in predicting diagnosis-related groups (DRGs) in a community healthcare system. We highlight a use case illustrating how clinical practice leverages AI-predicted information in unexpected yet advantageous ways and assess the AI predictions accuracy and practical application. Methods/Study Population: DRGs are crucial for hospital reimbursement under the prospective payment model. The Mayo Clinic Health System (MCHS), a network of clinics and hospitals serving a substantial rural population in Minnesota and Wisconsin, has recently adopted an AI algorithm developed by Xsolis (an AI-focused healthcare solution provider). This algorithm, a 1D convolutional neural network, predicts DRGs based on clinical documentation. To assess the accuracy of AI-generated DRG predictions for inpatient discharges, we analyzed data from 930 patients hospitalized at MCHS Mankato between March 2 and May 13, 2024. The Xsolis platform provided the top three DRG predictions for the first 48 hours of each inpatient stay. The accuracy of these predictions was then compared against the final billed DRG codes from the hospital’s records. Results/Anticipated Results: In our validation set, Xsolis achieved a top-3 DRG prediction accuracy of 71% at 24 hours and 81% at 48 hours, which is lower than the originally reported accuracy of 81.1% and 83.3%, respectively. Interestingly, discussions with clinical practice leaders revealed that the most valuable information derived from the AI predictions was the expected geometric mean length of stay (GMLOS), which Xsolis was perceived to predict accurately. In the Medicare system, each DRG is associated with an expected GMLOS, a critical factor for efficient hospital flow planning. A subsequent analysis comparing predicted GMLOS with the actual length of stay showed variances of -0.10 days on day 1 and 0.14 days on day 2, indicating a high degree of accuracy and aligning with clinical practice perceptions. Discussion/Significance of Impact: Our research underscores that clinical practice can leverage AI predictions in unexpected yet beneficial ways. While initially focused on DRG prediction, the associated GMLOS emerged as more significant. This suggests that AI algorithm validation should be tailored to specific clinical needs rather than relying solely on generalized benchmarks.

Objectives/Goals: As a priority area in translational science, rural health research can benefit from informatics methods for conducting thematic and environment scans. This study demonstrates an efficient approach to gaining insights about the rural health research literature by automated bibliometrics analysis. Methods/Study Population: We developed an automated pipeline to retrieve the 1972–2023 PubMed publications indexed with the MeSH terms “Rural Health” and “United States”. The article metadata in XML format were downloaded and parsed, including title, year, journal, author institutions, and MeSH terms. Each institution address was augmented by Google Maps API to obtain the county and latitude/longitude coordinates. Summary statistics were computed for the publication years, journals, author departments, and locations. A topic network was generated from the frequent co-occurring MeSH terms. The institutions were linked to Rural-Urban Continuum Codes and labeled on a map to visualize their geographic distribution. Results/Anticipated Results: A total of 4564 articles on rural health were analyzed. Two salient peaks of publications were revealed, one around 1978 and the other around 1993. The top author departments include Family Medicine, Nursing, Pediatrics, and Epidemiology. The five leading institutions reside in Chapel Hill, Minneapolis, Iowa City, Seattle, and Atlanta. The geographic distribution shows few institutions that reside in deep rural areas are well published on rural health, although the most scholarly productive institutions do seem adjacent to some moderately rural pockets. The frequently identified topics pertain to age group, study design, and specific concepts such as health services accessibility. Discussion/Significance of Impact: The two publication peaks in history were likely linked to certain policy milestone or seminal publication. Primary care and epidemiology departments have been most active in rural health research. Of concern, the geographic distribution of authors suggests under-investment in rural institutions.

Objectives/Goals: This project will enrich our understanding of basal breast carcinogenesis, highlighting the distinct biological differences in breast cancer risk between non-Hispanic Black (NHB) or non-Hispanic White (NHW) women, but also has the potential to provide real-world solutions thereby contributing to reducing health disparities in breast cancer outcomes Methods/Study Population: In the biological investigation, I am utilizing a unique cohort of normal breast tissues from Mayo Clinic patients who underwent breast reduction surgery that have self-identified as NHB or NHW premenopausal women. From these tissues, we are able to subculture human mammary epithelial cells, which we will use for our consequent experiments. For the qualitative study, I aim to recruit up to 40–50 eligible NHB women identified at increased risk for breast cancer and conduct semi-structured qualitative interviews informed by the National Institute on Minority Health and Health Disparities Research Framework. Results/Anticipated Results: Gene expression profiling on our cohort of 15 NHB and 40 NHW premenopausal women identified a gene expression signature in NHB women indicative of elevated Hedgehog signaling, a key factor in triple-negative breast cancer (TNBC) development and progression. I hypothesize that persistent activation of Hedgehog signaling within NHB women instigates the proliferation and transformation of basal stem cells within the breast, thus fueling TNBC development. Understanding the perceptions and experiences of NHB women that are identified as being at an increased risk for BC will aid in the identification of barriers and facilitators to their risk reduction care and in turn provide implementation strategies that could contribute to alleviating the racial disparity seen in TNBC morbidity, mortality, and preventative care. Discussion/Significance of Impact: The significance of this project lies in its dual approach, uncovering basal breast cancer mechanisms in a high-risk group using normal breast tissue and evaluating the perspectives from NHB women to enhance the practical relevance of our findings.

Objectives/Goals: Rural disparities in chronic disease burden are well documented. However, the impact of individual socioeconomic status on these rural disparities in prevalence and control status of chronic diseases remains less understood. We aim to assess the association of prevalence and control status of chronic diseases among adults with rurality and socioeconomic status (SES). Methods/Study Population: The Expanded Rochester Epidemiology Project medical records linkage system identified prevalence of asthma, diabetes, hypertension, and mood disorder using ICD codes between 2014 and 2019 among adults in 27 Upper Midwest counties. Uncontrolled status was defined by presence of ED visit or hospitalization with corresponding ICD-9/10 codes. SES was measured by HOUSES index, a validated individual-level SES measure, and rural status was defined by primary Rural Urban Commuting Area codes of 4–10. Hierarchical logistic regression models were used to examine the association of rurality with prevalence and control status of the four chronic diseases, adjusting for age, sex, race/ethnicity, and presence of general medical exam extracted by CPT codes (Model 1) and additionally SES measured by HOUSES (Model 2). Results/Anticipated Results: Among 455,802 adults, 42.8% were rural residents and 87.4% were Non-Hispanic White. Model 1 (without HOUSES) showed higher prevalence and lower control rates of chronic diseases in rural residents, except for asthma. In Model 2, accounting for SES, urban residents were more likely to have an uncontrolled mood disorder, hypertension, and diabetes, and there were no differences in prevalence of chronic diseases that existed between rural and urban residents. Lower SES measured by HOUSES was consistently and significantly associated with higher prevalence and uncontrolled status of chronic diseases (p-value Discussion/Significance of Impact: Patient’s SES explains rural disparities in prevalence (null association with rurality once SES is accounted) and changes the directionality of association for uncontrolled status in Upper Midwest, highlighting the importance of considering SES in rural disparities research.

Objectives/Goals: Our study’s objective is to evaluate RadOnc-GPT, a GPT-4o powered LLM, in generating responses to in-basket messages related to prostate cancer treatment in the Radiation Oncology department. By integrating it with electronic health record (EHR) systems, the goal is to assess its impact on clinician workload, response quality, and efficiency in healthcare communication. Methods/Study Population: RadOnc-GPT was integrated with patient EHRs from both hospital-wide and radiation-oncology-specific databases. The study examined 158 pre-recorded in-basket message interactions from 90 non-metastatic prostate cancer patients. Quantitative natural language processing analysis and two randomized single-blinded grading studies, involving four clinicians and four nurses, were conducted to evaluate RadOnc-GPT’s response quality in completeness, correctness, clarity, empathy, and estimated editing time. Response times were measured to estimate the time saved for clinicians and nurses. The study population included patient messages across all phases of care (pre-, during, and post-treatment) for those undergoing radiotherapy. Results/Anticipated Results: In the single-blinded grader study, clinician graders evaluated 316 responses (158 from human care teams and 158 from RadOnc-GPT). Results showed RadOnc-GPT outperformed human responses in empathy and clarity, while humans excelled in completeness and correctness. Sentiment analyses using TextBlob and VADER revealed RadOnc-GPT responses had a positive mean score of 0.25, whereas human responses clustered around neutral. VADER analysis indicated a high median score for RadOnc-GPT, nearing 1.0, reflecting predominantly positive sentiment, while human responses displayed a broader sentiment range, indicating sensitivity to context. Clinicians averaged 3.60 minutes (SD 1.44) to respond, compared to 6.39 minutes (SD 4.05) for nurses, highlighting RadOnc-GPT’s efficiency in generating timely responses. Discussion/Significance of Impact: RadOnc-GPT effectively generated responses to individualized patient in-basket messages, comparable to those from radiation oncologists and nurses. While human oversight is still necessary to avoid errors, RadOnc-GPT can speed up response times and reduce pressure on care teams, shifting their role from drafting to reviewing responses.

Objectives/Goals: Undergraduate Medical Education (UME) may apply Just-in-Time training (JITT) to provide medical students with learning experiences closely aligned with real-time clinical needs. The purpose of this scoping review is to offer an overview of the implementation of JITT training in UME. Methods/Study Population: Following the five-stage framework by Arksey and O’Malley to methodically collect and analyze studies on JITT in UME, five electronic databases were searched, and a supplemental search for grey literature was conducted. Studies exploring the integration of JITT principles into UME clinical training and their time to follow-up after training were included. Bloom’s Taxonomy was used to assess educational goals of JITT interventions. Results/Anticipated Results: The review yielded 21 studies across 4 countries. The majority were cohort studies (13) and randomized control trials (5). Assessment definitions and use of JITT varied widely. Most studies focused on short-term outcomes, defined by being measured immediately after JITT session (15) or at the end of JITT-based rotation or clerkship (3). Three studies evaluated outcomes at a period longer than 2 weeks after completion of session or clerkship. Attitudes (9), followed by skills (8) were the most common educational goals of intervention. The efficacy and utility of JITT in improving educational goal acquisition was demonstrated in 90% (17/19) of the studies with reported outcomes. Discussion/Significance of Impact: The introduction of JITT in UME has been shown to meet the immediate needs of healthcare environments; however, evidence is limited in the evaluation of longer-term outcomes. Further research to determine the impact of JITT on long-term learning retention and education goal acquisition in UME is merited.