Croghan, Ivana T

Objectives Long-term symptoms following infection with acute respiratory syndrome coronavirus-2 (SARS-CoV-2), termed post-acute sequelae of SARS-CoV-2 infection (PASC) or Long COVID (LC), persist beyond 90 days and may impact at least 10% of patients with acute infection. The exact cause of LC remains unknown, but several theories include persistent viral particles, immune dysfunction, and autonomic nervous system (ANS) imbalance, particularly involving the vagus nerve. Given the role of the vagus nerve in inflammation regulation, vagal nerve stimulation (VNS) is being explored as a treatment option. The present study aimed to evaluate the feasibility, safety, and exploratory effects of a handheld external transcutaneous cervical non-invasive VNS (tcVNS) on the primary endpoint of Post-COVID Functional Status (PCFS) grade, alongside secondary outcomes such as LC-related fatigue and other symptoms. Methods Participants from our Post COVID Care Clinic were randomized into treatment (tcVNS treatments two minutes, three times a day) and control (no VNS) groups for a study period of 12 weeks. Participants completed validated questionnaires, PET-CT scans, and inflammatory cytokines at baseline and 12 weeks. Results All randomized patients (8 tcVNS, 10 control) completed the 12-week study. Participants randomized to tcVNS were observed to have improvements in PHQ-9, PROMIS-F, and COMPASS-31 when compared to controls, but these differences were not statistically significant. Cortisol and DHEA were also observed to increase less in the tcVNS group when compared to the control group, but these changes were also not statistically significant. Conclusions tcVNS was well tolerated and may be a promising therapeutic approach for LC, a chronic disease with no approved treatments and very low rates of complete recovery.

Primary care is a language-intensive environment where clinicians frequently synthesize, document, and communicate large amounts of information. Given the amount of information in primary care, large language models (LLMs) have emerged as tools to support primary care clinician workflows, aiming to reduce administrative burden and augment patient care. LLMs are artificial intelligence tools that can generate text and assist with chart summarization, message drafting, and clinical decision support. However, because LLMs are probabilistic, identical inputs may yield inconsistent responses. The LLMs output can be significantly improved through prompt engineering. Prompt engineering is the intentional design of text entered into the LLM to help produce better LLM results. Prompting is best approached as an iterative process where a clinician defines the task, the LLM generates output, and the clinician critically reviews and refines the results. Low-quality prompts are often vague and produce generic or misleading responses. High-quality prompts are specific, context-rich, and well-structured. Using structured prompting frameworks can improve the LLMs response. Including core elements, such as the LLMs role, the clinical context, the requested task, and the desired output formatting, can help improve the LLMs response by providing situational background and framing response formatting. Additional frameworks, such as Ask-Context-Expectation and PICO+O, can be utilized for specific patient circumstances and cases. However, despite their promise, LLMs have important limitations. They may generate inaccurate or hallucinated information and produce inconsistent results. Given these limitations, LLMs should not be viewed as knowledge authorities but rather as cognitive assistants. Thoughtful prompt design can help reduce these limitations and improve response accuracy, always keeping in mind that LLMs should not replace human judgment or clinical reasoning. As LLMs continue to be integrated into primary care, prompt engineering will become an increasingly important clinical skill. Careful prompt design and critical appraisal of LLM responses will be key to maintaining excellent patient care in the AI era.

Objective To evaluate patient satisfaction with the experience of using an autonomous wheelchair to transport patients in a large outpatient clinical environment. Methods The autonomous wheelchair pilot was approved as a feasibility pilot by the institutional committees and deemed a quality improvement project by the Institutional Review Board (IRB). A total of 409 adult patients using an autonomous wheelchair at a large academic medical center who volunteered to complete a paper survey were included. The survey was administered immediately after autonomous wheelchair use, using a cross-sectional, anonymous survey, between 15 Oct 2025 and 14 Jan 2026. Of 409 completed surveys, six were excluded because participants did not identify their endpoint for stratification purposes. Descriptive analysis included frequencies and percentages of responses. Results No collisions or adverse events were observed during the pilot, and the system operated reliably within the predefined routes. Most survey respondents were first-time users (335/402 [83.3%]). A majority reported they would use the autonomous wheelchair again (341/395 [86.3%]) and would recommend it to others (364/397 [91.7%]). Overall, the experience was rated better than expected by 293 of 393 participants (74.6%). When given a choice, 271 of 379 respondents (71.5%) preferred the autonomous wheelchair over a staff-operated wheelchair. Conclusion These findings suggest that autonomous wheelchairs are feasible and acceptable to patients in a controlled outpatient setting and support continued piloting and prospective evaluation.

Background: Artificial intelligence (AI) is increasingly being integrated into healthcare, with applications ranging from predictive analytics to clinical decision support. In clinical nutrition, AI tools offer opportunities to improve workflow efficiency, enhance dietary assessment, and personalize nutrition care. Despite growing interest, little is known about registered dietitian nutritionists’ (RDNs) perceptions of AI in clinical practice. The aim of the present study was to assess RDNs’ attitudes toward AI use within a large healthcare system, along with their perceived barriers in this regard. Methods: A cross-sectional survey was developed through expert review and distributed electronically via REDCap to RDNs across Mayo Clinic’s academic campuses and affiliated health system sites. The 23-item survey included Likert-scale items addressing AI’s potential utilization within clinical care, perceived benefits and risks, and readiness for adoption. Responses were summarized using descriptive statistics. Factor analysis identified underlying constructs related to AI attitudes. Differences stratified by age and years of experience were evaluated using ANOVA. Results: Of the 185 RDNs invited, 64 (35%) responded. Two factors emerged: optimism regarding AI usage (Cronbach’s α = 0.94) and skepticism about implementation (α = 0.76). The overall mean ± SD score for optimism was 0.1 ± 0.6 (neutral), while skepticism averaged 1.0 ± 0.6 (moderate). Skepticism differed by years of experience (p = 0.012), with the lowest levels observed among RDNs with ≥21 years of practice. No significant differences were observed across age groups. Discussion: RDNs demonstrated neutral attitudes toward AI use but expressed concerns about accuracy, training, and implementation challenges. Addressing these concerns through education and structured implementation strategies may facilitate successful adoption of AI in dietetic practice.

Background: Artificial intelligence (AI) platforms can potentially enhance clinical decision-making (CDM) in primary care settings. OpenEvidence (OE), an AI tool, draws from trusted sources to generate evidence-based medicine (EBM) recommendations to address clinical questions. However, its effectiveness in real-world primary care cases remains unknown. Objective: To evaluate the performance of OE in providing EBM recommendations for five common chronic conditions in primary care: hypertension, hyperlipidemia, diabetes mellitus type 2, depression, and obesity. Methods: Five patient cases were retrospectively analyzed. Physicians posed specific clinical questions, and OE responses were evaluated on clarity, relevance, evidence support, impact on CDM, and overall satisfaction. Four independent physicians provided ratings using a 0 to 4 scale. Results: OE provided accurate, evidence-based recommendations in all cases, aligning with physician plans. OE was scored on a scale of zero to four, where zero was very unclear, and four was very clear. Mean scores across cases were clarity (3.55 ± 0.60), relevance (3.75 ± 0.44), support (3.35 ± 0.49), and satisfaction (3.60 ± 0.60). However, the impact on CDM was limited (1.95 ± 1.05), as OE primarily reinforced rather than modified plans. Conclusion: OE was rated high in clarity, relevance, and evidence-based support, reinforcing physician decisions in common chronic conditions. While the impact on CDM was minimal due to the study’s retrospective nature, OE shows promise in augmenting the primary care physician. Prospective trials are needed to evaluate its utility in complex cases and multidisciplinary settings.

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 study evaluated the feasibility and satisfaction of using a wearable brain activity sensing device for stress reduction among patients experiencing Long COVID (LC). Patients and Methods: Patients with LC (N = 45) were invited to participate in an open-label pilot study. Participants were asked to use a brain-sensing electroencephalogram (S-EEG) wearable device (Muse-S™) daily for 90 days and followed for an additional 90 days (180 days total participation). Study enrollment began June 28, 2022, and ended July 28, 2023. Results: Patients were predominantly female (82%), white (96%), and with an average age of 47.6 (±12.5) years. At the end of treatment (90 days), cognition, sleep, self-efficacy, and quality-of-life quality of life improved ( P < .001 to P = .04). In addition, stress and anxiety were significantly reduced ( P < .001). These significant changes persisted by end-of-study (180 days). For stress ( P = .023) and anxiety ( P = .033), a change from the baseline during follow-up was significantly associated with the amount of S-EEG usage. The end-of-study satisfaction survey indicated that patients were very satisfied with the S-EEG device, and 97% planned to continue using it. Conclusion: Results suggest that the S-EEG device improved cognition in LC, which is one of the main severe symptoms we see in our LC clinic. In addition, it helped reduce stress and anxiety. These findings have important clinical implications for LC patients using S-EEG as an adjunct to concurrent LC clinical care which includes pharmacotherapies.

Background More than 1 in 5 US adults live with mental illness. Novel therapies as complements to standard therapies are needed to improve patient well-being. Reiki is a biofield therapy that may improve well-being in mental health. Objective The primary aim of this study was to evaluate the feasibility of providing Reiki at a behavioral health clinic serving a low-income population. The secondary aim was to evaluate outcomes in terms of patients’ symptoms, emotions, and feelings before and after Reiki. Methods This was a mixed-methods, feasibility pilot study with a pre-post experimental design. Reiki was offered to adult outpatients at a community behavioral health center in Rochester, Minnesota. Patients with a stable mental health diagnosis seen between July 22, 2021, and May 18, 2023, completed surveys before and after the Reiki intervention and provided qualitative feedback. Patients were asked to report their ratings of pain, anxiety, fatigue, and feelings (eg, happy, calm) on 0- to 10-point numeric rating scales. Data were analyzed with Wilcoxon signed rank tests. Results Among 91 patients who completed a Reiki session during the study period, 74 (81%) were women. Major depressive disorder (71%), posttraumatic stress disorder (47%), and generalized anxiety disorder (43%) were the most common diagnoses. The study was feasible in terms of recruitment, retention, data quality, acceptability, and fidelity of the intervention. Patient ratings of pain, fatigue, anxiety, stress, sadness, and agitation were significantly lower, and ratings of happiness, energy levels, relaxation, and calmness were significantly higher after a single Reiki session. Conclusion The results of this study suggest that Reiki is feasible and could be fit into the flow of clinical care in an outpatient behavioral health clinic. It improved positive emotions and feelings and decreased negative measures. Implementing Reiki in clinical practice should be further explored to improve mental health and well-being.

Objectives: To identify patient characteristics, health concerns, and barriers to care based on overweight or obesity. Methods: A 50-question anonymous survey was administered to patients. Data were compared by body mass index (BMI). Results: Among 3976 distributed surveys, 899 were returned and 682 were analyzed. Most respondents were women (60%), White (94%), and married/partnered (65%) and had some college education (80%). Younger and unmarried persons had higher BMI ( P < .001). Concern for quality of life ( P < .001) and importance of lifestyle changes ( P = .006) increased with BMI, but confidence in making changes decreased as BMI increased ( P < .001). Perceived good health decreased with increasing BMI ( P < .001), whereas depression and other comorbid conditions increased. Self-esteem decreased and stigma increased with higher BMI (both P < .001). Weight discussions with clinicians ( P < .001) and belief that clinicians should be involved in weight management ( P = .002) increased with BMI, yet self-perception of being judged by clinicians also increased ( P < .001). As BMI increased, delays in seeking health care increased ( P < .001). Conclusions: This survey study highlights perceived health concerns and barriers to care among persons with overweight and obesity. With higher BMI, self-esteem decreased, and stigma, self-perception of poor health, perception of being judged by clinicians, and delay in seeking medical care increased.

Background: Angiosarcomas are rare tumors that can be difficult to diagnose due to subtle changes in the vascular endothelium. When there is evidence to suggest malignancy, such as a pathologic fracture, further investigation is needed, and a high suspicion for angiosarcoma needs to be present so that appropriate immunohistochemical stains are utilized on biopsied tissue. In situations where such suspicion is high and prior biopsies have been negative, performance of splenectomy, can be both diagnostic and therapeutic when splenomegaly is present. Case Report: This is a case of a 52-year-old woman with splenomegaly, initially attributed to infection, in the setting of upper respiratory symptoms and thrombocytopenia. Three months later, however, she presented with back pain. Imaging showed lytic bone lesions with pathologic vertebral fracture and numerous liver lesions that were too small to characterize further. Initial biopsies of the liver and bone did not reveal a pathologic process. Several months later, still without a unifying diagnosis, she presented to our institution. MRI of the brain was done for neurologic concerns and showed pathologic enhancement in the calvarium. A PET scan showed diffuse avidity of the skeleton and spleen. After discussing the case with a hematologist, splenectomy was performed for both diagnostic and therapeutic purposes. Angiosarcoma was identified in the spleen and in a PET-directed bone biopsy. With a definitive diagnosis, she returned home and subsequently elected to pursue hospice care. Conclusion: When there is a high clinical suspicion for malignant angiosarcoma, a multidisciplinary approach is necessary to direct both tissue acquisition and necessary histochemical staining.