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Background Patients living with diabetes and chronic conditions may face a significant burden managing their health. Many of these patients use digital medicine tools such as continuous glucose monitoring systems. Although measures exist to assess treatment burden from tasks such as managing medications and attending healthcare visits, there is no patient-reported measure that captures the burden of digital care. Therefore, the purpose of this study is to validate the Treatment Burden Questionnaire Plus Digital (TBQ + D), a patient-reported measure of treatment burden that includes using digital tools for adults with diabetes. Methods Adult patients with type 1 or type 2 diabetes mellitus completed the 25-item TBQ + D (scored 0 [none] to 10 [maximum] per item, total score range 0–250). We evaluated ease of administration, internal consistency, and tested hypotheses about the relationship between TBQ + D scores and treatment complexity, digital tool use intensity, social risk factors, and digital comfort to assess TBQ + D’s validity. Results Of 324 patients approached, 300 (93%) consented and completed the TBQ + D (mean age 57 [ SD 17], 50% female, 50% with type 2). The mean TBQ + D score was 53.7 ( SD 41.6). Internal consistency was excellent (Cronbach’s α  = 0.94). As hypothesized, higher TBQ + D scores were reported by patients with type 1 vs. type 2 diabetes mellitus (61.7 vs. 45.7, p  = .0008), maximal/moderate vs. minimal to no digital tool use (56.5/60.7 vs. 41.3, p  = .001), those on intensive insulin therapy vs. other treatments (61.4 vs. 38.0, p  < .0001), and those with greater social vulnerability ( p  < .0106). TBQ + D scores were not significantly higher in patients with HbA1c ≥ 8% ( p  = .055) or less comfortable with digital technology ( p  = .08). Conclusions TBQ + D is a novel and valid measure of treatment burden in patients living with diabetes, inclusive of digital burden, which can play a role in fostering minimally disruptive care for patients with diabetes.

Patients with diabetes manage complex treatment regimens that include the use of digital medicine tools. Existing instruments do not explicitly capture treatment burden, i.e., workload and its effect on patient's quality of life, from using digital medicine tools. To engage patients and clinical experts in adapting the Treatment Burden Questionnaire (TBQ) to capture digital treatment burden. The adapted instrument underwent cognitive testing and refinements to ensure it captures the burden of using digital medicine tools in diabetes self-management. This two-phase study was conducted with adults with diabetes at the Division of Endocrinology at Mayo Clinic (Rochester, MN). First, we mapped themes from prior concept elicitation interviews to existing TBQ items to identify content gaps related to digital burden. Based on these gaps, the study team and expert panel generated new items and adapted existing ones to better reflect the workload and burdens from using digital medicine tools. The resulting instrument underwent three rounds of cognitive testing with adult patients living with diabetes, using a think-aloud protocol to assess clarity, relevance, and comprehensiveness. Results of cognitive testing informed iterative refinements across three rounds of interviews, leading to improved clarity, reduced redundancy, and improved relevance of items. The final TBQ+D retained the original 15-item TBQ structure, added 8 new items, and modified 8 extant ones to capture burden of digital care (e.g, syncing issues, discomfort from sensors, and device malfunctions). Cognitive testing demonstrated strong content relevance and patient comprehension. The TBQ+D can measure digital treatment burden in patients with diabetes. Limitations include a relatively homogeneous sample drawn from a single center. Next steps include field testing for validation across diverse populations and settings.

The promise of artificial intelligence (AI) in medicine depends on its ability to learn from data that reflect what matters to patients and clinicians in the care process. Most existing models are trained on electronic health records (EHRs), which primarily capture biological measures but rarely the interactions and relationships between patients and clinicians. These relationships, central to how care is understood, negotiated, and delivered, unfold across multiple modalities, including voice, text, and video, yet remain largely absent from current datasets. As a result, AI systems trained solely on EHRs risk perpetuating a narrow biomedical view of medicine and overlooking the lived exchanges that define clinical encounters. This study aims to design, implement, and evaluate the feasibility of a longitudinal, multimodal system for capturing patient-clinician encounters, linking 360° video or audio recordings with postvisit surveys and EHR data, to create a foundational dataset for downstream AI research. This single-site study was conducted in an academic outpatient specialty clinic (Division of Endocrinology, Mayo Clinic, Rochester, Minnesota, United States). Adult patients attending in-person visits with participating clinicians were invited to enroll. Encounters were recorded using a 360° 2D monocular video camera and dual-channel audio. After each visit, patients completed a brief survey assessing relational empathy, satisfaction, visit pace, and treatment burden. Demographic and clinical data were extracted from the EHR. Feasibility was assessed using 5 prespecified end points: clinician consent, patient consent, recording success, survey completion, and data linkage across modalities. Recruitment began in January 2025. By August 2025, 35 of 36 (97%) eligible clinicians and 212 of 281 (75%) approached eligible patients had consented. Of the consented encounters, 162 (76%) resulted in a complete 360° video recording, and the postvisit surveys were completed for 204 of 212 (96%) consented encounters, reflecting 1 survey per encounter. Data collection is ongoing as of December 2025, and further analyses will be reported in subsequent publications. This protocol describes a longitudinal multimodal encounter capture system that links 360° audio or video with postvisit surveys and EHR data. The study specifies operational definitions, workflows, feasibility end points, and governance procedures to support implementation and replication in other clinical settings.

The promise of AI in medicine depends on learning from data that reflect what matters to patients and clinicians. Most existing models are trained on electronic health records (EHRs), which capture biological measures but rarely patient-clinician interactions. These relationships, central to care, unfold across voice, text, and video, yet remain absent from datasets. As a result, AI systems trained solely on EHRs risk perpetuating a narrow biomedical view of medicine and overlooking the lived exchanges that define clinical encounters. Our objective is to design, implement, and evaluate the feasibility of a longitudinal, multimodal system for capturing patient-clinician encounters, linking 360 degree video/audio recordings with surveys and EHR data to create a dataset for AI research. This single site study is in an academic outpatient endocrinology clinic at Mayo Clinic. Adult patients with in-person visits to participating clinicians are invited to enroll. Encounters are recorded with a 360 degree video camera. After each visit, patients complete a survey on empathy, satisfaction, pace, and treatment burden. Demographic and clinical data are extracted from the EHR. Feasibility is assessed using five endpoints: clinician consent, patient consent, recording success, survey completion, and data linkage across modalities. Recruitment began in January 2025. By August 2025, 35 of 36 eligible clinicians (97%) and 212 of 281 approached patients (75%) had consented. Of consented encounters, 162 (76%) had complete recordings and 204 (96%) completed the survey. This study aims to demonstrate the feasibility of a replicable framework for capturing the multimodal dynamics of patient-clinician encounters. By detailing workflows, endpoints, and ethical safeguards, it provides a template for longitudinal datasets and lays the foundation for AI models that incorporate the complexity of care.

Large Language Models (LLMs) are increasingly demonstrating the potential to reach human-level performance in generating clinical summaries from patient-clinician conversations. However, these summaries often focus on patients' biology rather than their preferences, values, wishes, and concerns. To achieve patient-centered care, we propose a new standard for Artificial Intelligence (AI) clinical summarization tasks: Patient-Centered Summaries (PCS). Our objective was to develop a framework to generate PCS that capture patient values and ensure clinical utility and to assess whether current open-source LLMs can achieve human-level performance in this task. We used a mixed-methods process. Two Patient and Public Involvement groups (10 patients and 8 clinicians) in the United Kingdom participated in semi-structured interviews exploring what personal and contextual information should be included in clinical summaries and how it should be structured for clinical use. Findings informed annotation guidelines used by eight clinicians to create gold-standard PCS from 88 atrial fibrillation consultations. Sixteen consultations were used to refine a prompt aligned with the guidelines. Five open-source LLMs (Llama-3.2-3B, Llama-3.1-8B, Mistral-8B, Gemma-3-4B, and Qwen3-8B) generated summaries for 72 consultations using zero-shot and few-shot prompting, evaluated with ROUGE-L, BERTScore, and qualitative metrics. Patients emphasized lifestyle routines, social support, recent stressors, and care values. Clinicians sought concise functional, psychosocial, and emotional context. The best zero-shot performance was achieved by Mistral-8B (ROUGE-L 0.189) and Llama-3.1-8B (BERTScore 0.673); the best few-shot by Llama-3.1-8B (ROUGE-L 0.206, BERTScore 0.683). Completeness and fluency were similar between experts and models, while correctness and patient-centeredness favored human PCS.

Importance Incidental thyroid findings (ITFs) are increasingly detected on imaging performed for non-thyroid indications. Their prevalence, features, and clinical consequences remain undefined. Objective To develop, validate, and deploy a natural language processing (NLP) pipeline to identify ITFs in radiology reports and assess their prevalence, features, and clinical outcomes. Design, Setting, and Participants Retrospective cohort of adults without prior thyroid disease undergoing thyroid-capturing imaging at Mayo Clinic sites from July 1, 2017, to September 30, 2023. A transformer-based NLP pipeline identified ITFs and extracted nodule characteristics from image reports from multiple modalities and body regions. Main Outcomes and Measures Prevalence of ITFs, downstream thyroid ultrasound, biopsy, thyroidectomy, and thyroid cancer diagnosis. Logistic regression identified demographic and imaging-related factors. Results Among 115,683 patients (mean age, 56.8 [SD 17.2] years; 52.9% women), 9,077 (7.8%) had an ITF, of which 92.9% were nodules. ITFs were more likely in women, older adults, those with higher BMI, and when imaging was ordered by oncology or internal medicine. Compared with chest CT, ITFs were more likely via neck CT, PET, and nuclear medicine scans. Nodule characteristics were poorly documented, with size reported in 44% and other features in fewer than 15% (e.g. calcifications). Compared with patients without ITFs, those with ITFs had higher odds of thyroid nodule diagnosis, biopsy, thyroidectomy and thyroid cancer diagnosis. Most cancers were papillary, and larger when detected after ITFs vs no ITF. Conclusions ITFs were common and strongly associated with cascades leading to the detection of small, low-risk cancers. These findings underscore the role of ITFs in thyroid cancer overdiagnosis and the need for standardized reporting and more selective follow-up.