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This research investigates virtual reality presentation (VR), revealing its impact on users’ mental models and mindful decision-making. We identify two fundamental features of VR: interactive viewing and depth cues. Through the lens of mental model theory, we explore how these two features shape users’ mental models and subsequently influence their mindfulness in decision-making. Two lab experiments were conducted to examine our research model. Study 1 examines interactive viewing and depth cues separately to assess their distinct impacts on the key components of users’ mental models—perceived control and acquired information. Study 2 employs an integrated VR presentation technology (i.e., Meta Quest 2) to explore the overall effect of VR presentations on users’ mental models and mindful decision-making. Results largely support our hypotheses. This research highlights the unique effects of VR presentation and its features, emphasizing enhanced user control and information acquisition in fostering high-quality mental models that increase mindfulness in users’ decisions in the online environment. These findings have significant implications for both research and practice of VR presentations.

Chondrodermatitis nodularis helicis (CNH) is a painful, inflammatory condition that impacts the skin of the ear. It is commonly associated with pressure on the pinna causing a nodule that may have erythema, bleeding and exudate. We present a case of a woman in her 60s with a history of basal cell carcinoma who presented with a new tender spot on the antihelix of her left ear. The tenderness of the new spot forced her to switch from holding her phone to her left ear to using her right ear. A shave biopsy confirmed CNH and ruled out non-melanoma skin cancer. Although most prior cases report association with sleeping on the side of the affected ear, this case was attributed to cell phone use. It is important to remember that non-traditional sources of pressure can also lead to CNH.

Acquired plantar keratoderma refers to the hyperkeratotic plaques on the soles of the feet, which can result from a variety of underlying conditions. Psoriasis, allergic or irritant contact dermatitis and eczema are several causes of acquired plantar psoriasis. This case report describes a female patient in her 50s with plantar keratoderma, initially attributed to psoriasis that was recalcitrant to systemic treatments. Subsequent evaluation suggested the possibility of contact dermatitis triggered by footwear. Allergen avoidance and supportive management led to clinical improvement. This case highlights the critical importance of considering environmental irritants and allergens in the evaluation and management of refractory acquired keratoderma.

Objectives: To study communication in medicine within the context of readmission rates and patient satisfaction, by assessing 1) the perspectives of primary care physicians (PCPs) and home care nurses (HCNs) on why older adults are readmitted to the hospital within 30 days of discharge, and 2) patient perceptions regarding the implementation and value of bedside rounding. Design: Two studies were performed independently. 1) A qualitative study consisting of PCPs and HCNs of patients readmitted to the hospital within 30 days of discharge home. 2) A concurrent mixed methods study consisting of patients admitted to the inpatient medicine service who participated in bedside rounds. Materials and Methods: 1) Semi-structured open-ended qualitative phone interviews, and 2) qualitative in-person interviews followed by surveys including 5-item Likert scales and open-ended written responses. For qualitative analyses, interviews were repeated until thematic saturation was achieved. Results: 1) While PCPs and nurses both mentioned disease progression and multi-morbidity as contributors to readmissions, nurses further described other psychosocial factors like home environment and patient motivation. PCPs often ascribed responsibility for the readmissions to specialists, hospitalists, and emergency physicians. Nurses expressed frustration about the lack of both communication and working relationships between them and PCPs. 2) Patients described positive attributes of bedside rounds: meeting the medical team and understanding more about their illness. Although patients enjoyed undivided attention from physicians, distractions included too many participants in rounds, confusion about roles, and unclear expectations about the goal of rounds. Physicians sought to use patient-centered language, but 53% of patients stated that medical jargon was still used. Male patients reported a statistically significant improvement in their understanding about the plan for the day and borderline significance regarding knowing who was responsible for their care compared to female patients. Conclusion: Communication between HCNs and PCPs, and between patients and hospital teams can be improved. There should be an explicit agreement on roles, responsibilities, and coordination among all providers caring for a patient. Moreover, well-conducted, patient-centered bedside rounds greatly enhance patient-physician rapport and foster patient understanding and satisfaction.

Appropriate evaluation of information systems research papers ensures that our institutions and review processes stay viable. In the short run, we typically assess research value through research awards, while, in the longer term, we typically assess research value based on how the research community sees and draws from particular published research papers. In this study, we examine the consistency between two metrics for assessing research value: research awards and citations. To do so, we focus on a premier journal, MIS Quarterly. We found that rarely are the “papers of the year” the ones cited the most. We offer possible explanations for this discrepancy based on assessing papers’ originality and utility and their citation patterns.

Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized. We present NOHARM (Numerous Options Harm Assessment for Risk in Medicine), a benchmark using 100 real primary care-to-specialist consultation cases to measure frequency and severity of harm from LLM-generated medical recommendations. NOHARM covers 10 specialties, with 12,747 expert annotations for 4,249 clinical management options. Across 31 LLMs, potential for severe harm from LLM recommendations occurs in up to 22.2% (95% CI 21.6-22.8%) of cases, with harm of omission accounting for 76.6% (95% CI 76.4-76.8%) of errors. Safety performance is only moderately correlated (r = 0.61-0.64) with existing AI and medical knowledge benchmarks. The best models outperform generalist physicians on safety (mean difference 9.7%, 95% CI 7.0-12.5%), and a diverse multi-agent approach improves safety compared to solo models (mean difference 8.0%, 95% CI 4.0-12.1%). Therefore, despite strong performance on existing evaluations, widely used AI models can produce severely harmful medical advice at nontrivial rates, underscoring clinical safety as a distinct performance dimension necessitating explicit measurement.

Large language models (LLMs) and medical AI tools are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized. We present NOHARM (Numerous Options Harm Assessment for Risk in Medicine), a 1,100-task benchmark of primary care-to-specialist consultation cases to measure the frequency and severity of potentially harmful errors from LLM-generated medical consultation recommendations. NOHARM covers 10 specialties, with 12,747 expert annotations for 4,249 clinical management options. Across 20 notable LLMs and 4 widely used retrieval-augmented generation (RAG) clinical AI tools, direct application of recommendations carried potential for severe harm in up to 24.6% of cases, with errors of omission accounting for more than 80% of severe errors. Harm potential was not uniform across systems, with clinical AI tools outperforming generalist LLMs, and multi-agent AI teaming further improving performance in generalist models. In a randomized study of 101 U.S.-licensed generalist physicians, AI assistance improved physician performance compared to conventional resources. However, AI-assisted physicians frequently omitted valuable AI-generated recommendations and still scored lower than many AI systems alone. Had those recommendations been incorporated, combined human-AI responses would have outperformed both the human and AI system as used, suggesting complementary strengths and unrealized potential in human-AI teaming. Collectively, these results show that despite strong performance on medical knowledge benchmarks, widely used AI tools can produce medical consultation advice with the potential for severe harm, and highlight the need for explicit measurement of clinical safety. The benchmark and leaderboard are publicly available to support ongoing evaluation and improvement of AI systems used for clinical care.

Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized. We present NOHARM (Numerous Options Harm Assessment for Risk in Medicine), a benchmark using 100 real primary care-to-specialist consultation cases to measure frequency and severity of harm from LLM-generated medical recommendations. NOHARM covers 10 specialties, with 12,747 expert annotations for 4,249 clinical management options. Across 31 LLMs, potential for severe harm from LLM recommendations occurs in up to 22.2% (95% CI 21.6-22.8%) of cases, with harm of omission accounting for 76.6% (95% CI 76.4-76.8%) of errors. Safety performance is only moderately correlated (r = 0.61-0.64) with existing AI and medical knowledge benchmarks. The best models outperform generalist physicians on safety (mean difference 9.7%, 95% CI 7.0-12.5%), and a diverse multi-agent approach improves safety compared to solo models (mean difference 8.0%, 95% CI 4.0-12.1%). Therefore, despite strong performance on existing evaluations, widely used AI models can produce severely harmful medical advice at nontrivial rates, underscoring clinical safety as a distinct performance dimension necessitating explicit measurement.

Microbial production of succinic acid (SA) at an industrially relevant scale has been hindered by high downstream processing costs arising from neutral pH fermentation for over three decades. Here, we metabolically engineer the acid-tolerant yeast Issatchenkia orientalis for SA production, attaining the highest titers in sugar-based media at low pH (pH 3) in fed-batch fermentations, i.e. 109.5 g/L in minimal medium and 104.6 g/L in sugarcane juice medium. We further perform batch fermentation using sugarcane juice medium in a pilot-scale fermenter (300×) and achieve 63.1 g/L of SA, which can be directly crystallized with a yield of 64.0%. Finally, we simulate an end-to-end low-pH SA production pipeline, and techno-economic analysis and life cycle assessment indicate our process is financially viable and can reduce greenhouse gas emissions by 34–90% relative to fossil-based production processes. We expect I. orientalis can serve as a general industrial platform for production of organic acids. Microbial production of succinic acid at an industrially relevant scale has been hindered by high downstream processing costs arising from neutral pH fermentation. Here, the authors report an end-to-end pipeline for succinic acid production at low pH using engineered acid-tolerant Issatchenkia orientalis strain.