Citations

Effective healthcare referral triage poses a significant challenge, especially extracting referral notes on time for scheduling. To offset this issue, this study aims to develop an optimized Random Forest classifier to recreate the referral notes content and identify patients requiring Spinal Cord Stimulation procedures. A performance index was introduced to improve accuracy, while a consistency index was designed to prevent overfitting. The optimized classifier, with the optimal selection at the number of estimates being 5 trees and tree depth being 4, achieved an accuracy of 0.74 with a lower standard deviation, demonstrating its reliability and effectiveness.

Purpose of review The rising prevalence of obesity, now affecting over 40% of U.S. adults, poses critical implications for colorectal cancer screening, as obesity increases the risk of both colorectal adenomas and cancer. Despite these elevated risks, patients with obesity have lower colonoscopy screening participation and face unique barriers that compromise procedural quality. This review aims to highlight the challenges encountered during colonoscopy in obese patients and examine emerging solutions that may enhance screening effectiveness and patient outcomes. Recent findings Obesity is associated with suboptimal bowel preparation, prolonged cecal intubation times, lower adenoma detection rates, and increased sedation-related complications. These challenges stem from altered gastrointestinal physiology, body habitus, and comorbid conditions. Recent innovations in bowel preparation strategies, sedation protocols, and endoscopic technologies—such as robotic-assisted colonoscopy and artificial intelligence-enhanced visualization—have demonstrated promise in addressing these limitations. Institutional initiatives, including tailored protocols and endoscopy team training, are also contributing to improved outcomes. Summary As obesity rates climb, adapting colonoscopy practices to meet the needs of this population is essential. Incorporating evidence-based strategies and emerging technologies can help overcome procedural barriers, improve detection rates, and reduce disparities in colorectal cancer screening. Continued research and guideline refinement are needed to optimize care delivery for patients with obesity.

Abstract Background Patients with short bowel syndrome–associated intestinal failure (SBS‐IF) require complex, personalized, and multidisciplinary care; however, there are disparities in access. This study measured the impact of unmet needs and treatment burden among patients and their caregivers. Methods This cross‐sectional, mixed‐method study included US adults self‐reporting SBS‐IF and receiving home parenteral nutrition for ≥6 months and their caregivers. One‐hour interviews informed the development of a 30‐min survey administered to participants from an online health community. Results Among 68 patients, the mean age was 42 years, 79% were female, and 88% were White. Most of the 16 caregivers were female (69%), and younger than 45 years (69%). Of 32 patients (47%) receiving care from an SBS specialist, only 19 (59%) were referred after diagnosis; in 58% of these, referral occurred >6 months after diagnosis. Depression was reported in 44 patients (65%), with 54% not receiving professional mental health care. Financial concerns were common, with 44 patients (65%) unable to work and 40 patients (59%) reporting annual household income of <$50,000. Of 32 patients reporting difficulty paying medical bills, 22 (32%) could not fill prescriptions and 19 (28%) canceled/delayed healthcare provider visits. Social determinant of health challenges were reported by 44 patients (65%). Caregivers' responsibilities impacted their ability to work (50%) and future outlook (63%). Conclusion Patients with SBS‐IF face difficulties accessing specialized healthcare, and are at risk of adverse healthcare outcomes, financial hardships, and poor quality of life. Mental health and work/financial issues were common among both patients and caregivers.

Background/Objectives: Mild Cognitive Impairment (MCI), a condition between normal aging and dementia, is characterized by cognitive changes that do not significantly affect instrumental activities of daily living. The Memory Support System (MSS), an evidence-based behavioral intervention developed by the Mayo Clinic, has been shown to aid those living with MCI and their support partners in coping with cognitive challenges. However, the MSS has not been offered clinically within the Canadian context. Therefore, we conducted a study assessing the feasibility of the MSS from the perspectives of individuals living with MCI and their support partners. Methods: Participants from an institutional registry of research participants, patients, and support partners at a memory clinic, as well as members of a local Dementia Society, were approached to complete an online or paper version of a survey assessing feasibility dimensions. Responses were compared between and within groups for differences in mean scores and associations between linked binary choice response questions. Results: A total of 77 responses were received; 39 surveys were completed by participants with MCI, and 38 by support partners. Respondents found the MSS to be acceptable and practical. On average, participants thought it would be more difficult to train in using the MSS than support partners. Both groups expressed interest in the intervention. On average, participants with MCI and support partners preferred virtual MSS training to in-person and indicated more interest in participating in training over six weeks as compared to two weeks. Conclusions: Flexibility in duration and format when offering the MSS are important considerations when offering the intervention as part of a clinical program. Future research should evaluate cost-effectiveness (e.g., financial, staff resources, etc.) of the MSS approach if it were to be institutionalized in the Ontario healthcare system.

Cardiovascular–Kidney–Metabolic (CKM) syndrome symbolizes a single pathophysiologic entity including obesity, type 2 diabetes, chronic kidney disease, and cardiovascular disease. These conditions altogether accelerate adverse outcomes when they coexist. Recent evidence has shown that the function of glucagon-like peptide-1 receptor agonists (GLP-1RA) and sodium–glucose cotransporter-2 inhibitors (SGLT2i) alleviate stress on multiple organs. SGLT2i has been demonstrated to benefit heart failure, hemodynamic regulation, and renal protection while GLP-1RA on the other hand has been shown to demonstrate a strong impact on glycemic management, weight loss, and atherosclerotic cardiovascular disease. This review will aim to understand and evaluate the mechanistic rationalization, clinical evidence, and the potential therapeutic treatment of SGLT2 inhibitors and GLP-1 receptor agonists to treat individuals who have CKM syndrome. This analysis also assesses whether combination therapy can be a synergistic approach that may benefit patients but is still underutilized because of the lack of clear guidelines, the associated costs, and disparities in accessibility. Therefore, in this review, we will be discussing the combination therapy’s additive and synergistic effects, current recommendations and clinical evidence, and mechanistic insights of these GLT2 inhibitors and GLP-1 receptor agonists in CKM syndrome patients. Overall, early and combination usage of GLP-1RA and SGLT2i may be essential to demonstrating a significant shift in modern cardiometabolic therapy toward patient-centered care.

Background: Lung cancer ranks as the leading cause of cancer-related mortality worldwide. The complexity of tumor delineation, crucial for radiation therapy, requires expertise often unavailable in resource-limited settings. Artificial Intelligence (AI), particularly with advancements in deep learning (DL) and natural language processing (NLP), offers potential solutions yet is challenged by high false positive rates. Purpose: The Oncology Contouring Copilot (OCC) system is developed to leverage oncologist expertise for precise tumor contouring using textual descriptions, aiming to increase the efficiency of oncological workflows by combining the strengths of AI with human oversight. Methods: Our OCC system initially identifies nodule candidates from CT scans. Employing Language Vision Models (LVMs) like GPT-4V, OCC then effectively reduces false positives with clinical descriptive texts, merging textual and visual data to automate tumor delineation, designed to elevate the quality of oncology care by incorporating knowledge from experienced domain experts. Results: The deployment of the OCC system resulted in a 35.0% reduction in the false discovery rate, a 72.4% decrease in false positives per scan, and an F1-score of 0.652 across our dataset for unbiased evaluation. Conclusions: OCC represents a significant advance in oncology care, particularly through the use of the latest LVMs, improving contouring results by (1) streamlining oncology treatment workflows by optimizing tumor delineation and reducing manual processes; (2) offering a scalable and intuitive framework to reduce false positives in radiotherapy planning using LVMs; (3) introducing novel medical language vision prompt techniques to minimize LVM hallucinations with ablation study; and (4) conducting a comparative analysis of LVMs, highlighting their potential in addressing medical language vision challenges.

Importance Diagnostic errors represent a major patient safety concern, with the potential to significantly impact patient outcomes. To address this, various trigger-based strategies have been developed to identify diagnostic errors, aiming to enhance clinical decision-making and improve patient safety. Objective To evaluate the performance of three pre-established triggers (T) in the emergency department (ED) setting and assess their effectiveness in detecting diagnostic errors. Design Consecutive cohort, retrospective observational design. Setting Academic ED with 80 000 annual visits. Participants Adults and children presenting to a single ED in the USA between 1 May 2018 and 1 January 2020. Intervention/outcomes Electronic health records (EHRs) were retrieved and categorised into trigger-positive and trigger-negative cases using the following criteria: T1—unscheduled returnvisits to the ED with admission within 7–10 days of theinitial visit; T2—care escalation from the inpatient unitto the intensive care unit (ICU) within 6, 12 or 24 hoursof ED admission; and T3—all deaths in the ED or within24 hours of ED admission, excluding palliative care. A random sample of trigger-positive cases was reviewed using the SaferDx tool to determine the presence or absence of a diagnostic error. Results A total of 5791 trigger-positive and 118262 trigger-negative cases were identified. Among trigger-positive cases, 4159 (72%) were associated with T1, 1415 (24%) with T2, and 217 (4%) with T3. A preliminary chart review of 462 trigger-positive and 251 trigger-negative cases showed most were error-negative (279 and 217, respectively). Detailed reviews found 32 diagnostic errors among 183 trigger-positive cases, yielding PPVs of 5.4% (T1), 8.9% (T2), and 6.9% (T3). No errors were found in 34 reviewed trigger-negative cases, resulting in a 100% NPV. Sepsis was the most common diagnosis among error-positive cases (n=11, 34.4%). Those with non-specific chief complaints like altered mental status or shortness of breath had higher diagnostic error risk. Conclusion and relevance While previously proposed EHR-based triggers can identify some diagnostic errors, they are insufficient for detecting all cases. To improve error detection performance, we recommend exploring data-driven strategies, such as machine learning techniques, to more effectively identify underlying contributing factors to diagnostic errors and enhance detection accuracy in the ED.

Abstract Background/ Objectives: Agentic AI represents a promising evolution of AI technology applied to healthcare, with systems increasingly capable of operating autonomously to achieve defined clinical goals. However, the literature lacks conceptual clarity between “AI agents” and “agentic AI”, and few studies have rigorously explored their clinical applications. Therefore, this study aims to conduct a novel systematic review addressing this gap by examining agentic AI systems in healthcare settings, characterizing their applications, features, outcomes, and limitations, and clarifying the conceptual distinctions between AI agents and agentic AI systems using predefined and objective criteria. Methods A comprehensive search was conducted across PubMed, Embase, Cochrane, Scopus, and Google Scholar on April 6th, 2025. Studies were included if they involved AI systems in healthcare settings that demonstrated the following agentic features: autonomous operation, goal-directed behavior, and initiating action. Data on the clinical tasks achieved by the agents, key findings, features, and limitations were collected from the included studies. Screening and extraction followed PRISMA guidelines, with Risk of Bias assessed using ROBINS-I and Cochrane's Risk of Bias tools. Results Of 984 retrieved records, seven studies met the inclusion criteria, spanning domains such as emergency medicine, oncology, radiology, and rehabilitation. Multi-agent architecture was frequently used to decompose and coordinate complex workflows. Among the included studies, the AIs showed high accuracy in diagnosing cancer patients, conducting treatment plans, sending alerts, coaching messages, analysing image data, and adapting to challenging experimental scenarios. While demonstrating potential for improved efficiency, task accuracy, and patient engagement, significant limitations were noted: narrow task scope, lack of physical agency, limited clinical validation, and barriers to integration into real-world healthcare systems. Only one system had been deployed in a patient-facing trial setting. Conclusion The current literature suggests an emerging role and application of Agentic AI, holding promise with the potential to revolutionize diagnostics, triage, treatment planning, and patient management. However, real-world implementation and evaluations in the literature are limited. Future research must address critical validation, regulation, ethics, and clinical integration challenges to realize their full potential. Clear operational definitions and frameworks for evaluating agency are essential to support safe and effective deployment of these systems.