Strechen, Inna

Abstract Introduction Large Language Models (LLMs) have proven effective at analyzing unstructured and structured data from electronic health records (EHR). Their reliance on static EHR data means they miss the dynamic, high-resolution physiological signals that are the first indicators of patient decline, creating a fundamental data lag as clinical documentation of an event may occur hours after its onset. This represents a critical gap in the ability to develop truly effective triage tools. Furthermore, while LLMs can synthesize multi-source data for acuity assessment, they exhibit known limitations, including hallucinations that constrain autonomous use in safety-critical settings. In contrast, a Vision-Language Model (VLM) with strong image-grounded clinical reasoning can interpret vital sign trend images, surpassing the limitations of current EHR-based models. This approach provides immediate triage evaluations that lead to earlier detection of patient deterioration. Methods This mixed-methods study deployed a VLM (Gemini 2.5) integrated within the eICU dashboard. We used near-real-time two-hour trends in heart rate, blood pressure, respiratory rate, and oxygen saturation from consecutive patient cases during alerts in hospital floor units monitored by eICU that admit acute medical and surgical patients. To increase the diversity of the sample, we sought routine cases from patients without alerts and emergent cases from patients admitted to the medical, surgical, trauma, and mixed ICUs. A structured context prompt was used to generate a standardized summary including clinical interpretation, data quality and artifact assessment, triage recommendation, and the model’s chain-of-thought reasoning. Results We included patients from Mayo Clinic Rochester, MN, using an 800-patient cohort for prompt development and a 100-patient cohort for validation against clinically adjudicated results. In the validation cohort the VLM achieved 80% accuracy (80/100). An analysis of the 20 incorrect predictions revealed a clear pattern of conservative over-triage: 80% (16/20) of errors were clinically acceptable adjacent-class discrepancies (routine↔urgent or urgent↔emergent). Only 4/20 discrepancies represented a substantive misclassification. Most errors therefore reflected conservative prioritization rather than incorrect assessment. The model demonstrated a perfect safety profile, underestimating patient acuity in 0% of cases, and thus avoiding the most clinically dangerous error. These results confirm the feasibility of VLM-based approach and provide a strong foundation for the proposed work. Conclusion This VLM performs cross-modal reasoning by fusing deep visual perception with broad clinical context. The result is a fully contextualized and explainable triage prioritization, which provides direct decision support and integrates into the clinical workflow, allowing for faster and more confident acute care assessments. This abstract is funded by: none

Abstract Objectives Effective communication is crucial in healthcare, and for patients with a non-English language preference (NELP), professional interpreters are recognized as the gold standard in supporting bidirectional communication. However, interpreters are not always readily available, prompting the exploration of other options for translation and interpretation. The recent developments in artificial intelligence-based neural network translation tools, namely neural machine translation (NMT) may enable robust interpretation and translation. Materials and Methods We conducted a systematic review (SR) to evaluate the literature on NMT for this purpose. We did a comprehensive search of several databases with guidance from a professional librarian. The search was limited to the year 2000 onwards and English language. Title and abstract screening and full-text review were independently conducted by two reviewers with conflicts resolved by a third reviewer Results 2867 studies were identified with 10 studies included in the final analysis. Among these, six evaluated interpretation in real or simulated clinical settings and four examined translation of discharge materials. Google Translate and ChatGPT were assessed in several studies. Accuracy differed by language, with low-resource languages performing worse. Discussion NMT technologies in healthcare have several advantages including broad language accessibility and potential cost savings for institutions. Despite improved accuracy of these novel tools, due to possible critical errors NMT tools are not yet ready for widespread clinical use. Conclusion Future studies should focus on optimizing evaluation methods as well as how best to integrate these technologies into real-time clinical settings.

Abstract Rationale: Ongoing healthcare staffing shortages have prompted new care paradigms that incorporate remote care delivery to support timely detection of patient physiological deterioration through mechanisms other than clinician bedside evaluations. In partnership with institutional Information Technology, our team has developed a unique clinical informatics environment, known as the Advanced Medical Practice (AMP) platform, that incorporates data from multiple sources and platforms and supports remote observation of patients from the electronic intensive care unit(eICU). AMP takes advantage of technologies outside of vendor-supported EHRs, and its advanced user-interface design reduces information overload while incorporating real-time data from novel devices and sensors. We partnered with the hospital practice to co-develop a score within the AMP platform(Clinical Events Detection and Response[CEDAR]) that incorporates heart rate, blood pressure, respiratory rate, and SpO2 to stratify patient risk and generate deterioration alerts for the electronic ICU(eICU) team. This formative evaluation employed an implementation science approach to refine CEDAR and ensure it is acceptable to users and can be feasibly implemented in practice. Methods: The study team conducted this pilot work in the eICU at Mayo Clinic (Rochester, MN). We observed the eICU workflow and documented feedback from eICU staff in fieldnotes. eICU staff were asked to log issues in a tracking file during shifts. Issues were reviewed daily and discussed at multidisciplinary team meetings weekly. Observation and tracking data were coded to constructs in the Consolidated Framework for Implementation Research(CFIR 2.0) framework and mapped to Expert Recommendations for Implementation Change(ERIC) strategies that targeted key determinants. Results: Between 9/23/24 and 10/25/24 we completed 25 days of observations. Most identified issues related to technical/design issues like unclear vital trigger alerts (CFIR: Innovation trialability and design); staff knowledge of CEDAR functionality and attitudes about functionality, complexity, or relative advantage compared to current systems (CFIR: Inner setting access to knowledge and information and relative advantage); and difficulties incorporating CEDAR into workflow(CFIR: Inner setting/compatibility). Implementation strategies mapped to CFIR determinants included promoting adaptability of customized interface features; staging implementation scale-up with iterative improvements; providing local technical assistance; assessing for readiness and identifying facilitators and barriers; and conducting educational outreach visits. Conclusions: Formative evaluation during pilot studies are a critical step in the process of intervention development and testing. User engagement and frameworks from implementation science can identify issues of feasibility and inform strategies to ensure acceptability prior to intervention scale-up in clinical practice and readiness for conducting larger implementation studies.

Abstract Rationale: Effective communication is crucial in healthcare, especially in acute care settings, so that patients/family members can understand treatment options, and share preferences and goals of care. For those with a non-English language preference (NELP), or who have difficulty communicating in English, professional interpreters are recognized as the gold standard in supporting bidirectional communication. However, interpreters are not always readily available for all languages or clinical settings prompting the exploration of other options for translation and interpretation. The recent developments in artificial intelligence (AI), specifically machine translation have enabled more accurate and emotional machine interpretation and translation. Methods: We conducted a systematic review to explore the usability and usefulness of AI-assisted interpretation and translation tools for improving communication between patients and clinicians including verbal and written communication. A comprehensive search of several databases including Ovid MEDLINE(R), Ovid Embase, APA PsycInfo, Ovid Cochrane Central Register of Controlled Trials, Ovid Cochrane Database of Systematic Reviews, and Scopus via Elsevier was performed, resulting in 2629 studies. We included studies evaluating tools for direct patient-clinician communication. For verbal communication, we included articles assessing speech interpretation, and for written communication, we focused on discharge instruction translation. Results: A total of 18 studies were included in the final analysis. Among these, 5 assessed the use of speech interpretation and 13 examined text translation of discharge materials. 14 (78%) studies found these novel AI tools beneficial for patient care, whereas 11 (62%) studies highlighted the risks, indicating that these tools can lead to serious mistakes with potential clinical harm. 5 (28%) studies reported participant satisfaction with the use of these tools in their care. Translation and interpretation accuracy varied notably across languages, with lower accuracy scores observed in languages with limited digital resources, such as Haitian Creole and Vietnamese. Conclusion: AI-assisted interpretation and translation in healthcare has several advantages including broader language accessibility than usual resources and potential cost savings for institutions and demonstrates evidence of patient satisfaction. However, these novel tools operate using multiple phases and each stage is prone to errors which may negatively impact patient care and outcomes.. AI-based interpretation and translation show promising potential, however, it is not yet ready for widespread clinical use, so professional language interpreters should be used whenever possible. Future studies should focus on evaluating accuracy as well as how best to integrate these technologies in real-time clinical settings without compromising patient safety and quality of care.

Poorly designed Electronic Health Records (EHRs) can lead to increased workloads, decreased efficiency, and medical errors. This cross-sectional study surveyed ICU providers’ perceptions of their EHR using the NASA-TLX and CSUQ surveys. We found that providers report increased temporal demand and effort when using the EHR, and that they are dissatisfied with the EHR’s interface quality.

Abstract Background Intensive care unit (ICU) clinicians encounter frequent challenges with managing vast amounts of fragmented data while caring for multiple critically ill patients simultaneously. This may lead to increased provider cognitive load that may jeopardize patient safety. Objectives This systematic review assesses the impact of centralized multipatient dashboards on ICU clinician performance, perceptions regarding the use of these tools, and patient outcomes. Methods A literature search was conducted on February 9, 2023, using the EBSCO CINAHL, Cochrane Central Register of Controlled Trials, Embase, IEEE Xplore, MEDLINE, Scopus, and Web of Science Core Collection databases. Eligible studies that included ICU clinicians as participants and tested the effect of dashboards designed for use by multiple users to manage multiple patients on user performance and/or satisfaction compared with the standard practice. We narratively synthesized eligible studies following the SWiM (Synthesis Without Meta-analysis) guidelines. Studies were grouped based on dashboard type and outcomes assessed. Results The search yielded a total of 2,407 studies. Five studies met inclusion criteria and were included. Among these, three studies evaluated interactive displays in the ICU, one study assessed two dashboards in the pediatric ICU (PICU), and one study examined centralized monitor in the PICU. Most studies reported several positive outcomes, including reductions in data gathering time before rounds, a decrease in misrepresentations during multidisciplinary rounds, improved daily documentation compliance, faster decision-making, and user satisfaction. One study did not report any significant association. Conclusion The multipatient dashboards were associated with improved ICU clinician performance and were positively perceived in most of the included studies. The risk of bias was high, and the certainty of evidence was very low, due to inconsistencies, imprecision, indirectness in the outcome measure, and methodological limitations. Designing and evaluating multipatient tools using robust research methodologies is an important focus for future research.