Fan, Jungwei W

Background: Incidental thyroid nodules (ITNs) detected on nonthyroid imaging contribute to thyroid cancer overdiagnosis, largely through downstream evaluation initiated by thyroid ultrasound (TUS). However, TUS follow-up rates for ITNs and their drivers in routine practice remain unclear. We quantified TUS utilization after ITN detection and identified patient, imaging, nodule, and reporting factors associated with this first step in the diagnostic cascade. Methods: We conducted a retrospective cohort study of adults without prior thyroid disease who underwent imaging that incidentally captured the thyroid at Mayo Clinic sites (2017–2023). A validated natural language processing pipeline identified ITNs and extracted nodule characteristics from radiology reports across multiple imaging modalities. The main outcome was the rate of TUS within 180 days after ITN detection. Clinical outcomes, including thyroid biopsy, thyroidectomy, and thyroid cancer diagnosis, were compared between patients who underwent TUS and those who did not. Factors associated with TUS utilization were assessed using multivariable logistic regression with penalized variable selection. Results: Among 115,683 patients who underwent thyroid-capturing imaging, 8426 (7.3%) had an ITN. These patients were predominantly female (64.6%) and White (89.7%), with a mean age of 61.7 years (standard deviation 15.2). Most nodules were detected on computed tomography (80.8%), most commonly from chest imaging (52.3%). Overall, 1677 patients with ITNs (19.9%) underwent TUS. In multivariable analysis, TUS use was independently associated with higher body mass index (odds ratio [OR], 1.20 per 10-unit increase; confidence interval [CI] [1.08–1.33]), nodule size ≥1.5 cm (OR, 1.79; CI [1.52–2.10]), and a radiologist recommendation (OR, 2.51; CI [2.12–2.98]), whereas older age was associated with lower odds of TUS (OR, 0.89 per 10-year increase; CI [0.84–0.93]). Patients undergoing TUS had higher rates of thyroid biopsy, partial thyroidectomy, and total thyroidectomy compared with those without it. All detected thyroid cancers (103 cases) occurred in the TUS group, representing 6.1% of that cohort. Conclusions: One in five patients with ITNs underwent TUS, representing a critical gateway to downstream diagnostic procedures and thyroid cancer detection. Factors associated with TUS utilization highlight opportunities to better define appropriateness of ultrasound after incidental nodule detection and to reduce unnecessary testing and treatment.

Abstract Context Incidental thyroid findings (ITFs) are increasingly detected on imaging performed for non-thyroid indications. Their prevalence, features, and 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 Retrospective cohort study. Setting Mayo Clinic sites (Rochester, Arizona, Florida, Mayo Clinic Health System). Participants Adults without prior thyroid disease undergoing thyroid-capturing imaging 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. Outcomes ITF prevalence, downstream thyroid ultrasound, biopsy, thyroidectomy, and cancer diagnosis. Logistic regression identified demographic and imaging-related factors. Results Among 115 683 patients (mean age, 56.8 [SD 17.2]; 52.9% women), 9077 (7.8%) had an ITF (92.9% nodular). ITFs were more likely in women, older adults, higher BMI, and in imaging ordered by specialties different from Emergency 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%. Compared with patients without ITFs, those with ITFs had higher odds of thyroid nodule diagnosis (OR 45, 95%CI 41.1-49.3), biopsy (OR 46.8, 95%CI 39.0-56.2) thyroidectomy (OR 55.8, 95%CI 31.3-99.3), and thyroid cancer diagnosis (OR 61.7, 95%CI 38.6-98.5). Most cancers were papillary (88.5%) and larger when detected after ITFs (2 cm SD 1.4) vs no ITF (1.3 cm SD 0.8). Conclusion ITFs were common and strongly associated with cascades leading to the detection of small, low-risk cancers, highlighting their role in overdiagnosis and the need for standardized reporting and more selective follow-up.

Abstract Background Integrating advanced artificial intelligence (AI) into clinical decision-support often requires the sharing of sensitive patient data with external services, raising privacy concerns. Homomorphic encryption (HE) allows computing directly on encrypted data, without revealing the underlying patient information. Objectives To develop a large language model (LLM)-assisted diagnosis framework while preserving patient privacy in the clinical text analysis, by leveraging HE and using rare disease (RD) diagnosis as a representative application. To demonstrate HE does not hinder the system performance. Materials and Methods Texts from patient histories and a RD knowledge base were embedded by LLMs into vectors, then encrypted using HE to obscure private information while retaining the semantic nuances. Diagnostic recommendations were generated by computing and ranking the similarities between the patient history and RD vectors in the encrypted space. The system was evaluated using 50 synthetic case reports (5 RDs, each with 10 reports). Results Applying HE did protect private information from reverse-embedding attacks. HE imposed little disruption to the diagnostic accuracy, with normalized discounted cumulative gains (nDCG) of 0.6108 ± 0.3412 (encrypted) versus 0.6083 ± 0.3415 (unencrypted). The accuracy and computational performance were tunable and consistent, as demonstrated across five different LLMs. Discussion Our privacy-preserving framework opens tremendous opportunities toward hosting and serving powerful AI solutions across institution boundaries, which would remove the need for local deidentification and incentivize users to access secure external decision-support services. Conclusions Integrating HE with LLM retrieval can promote the dissemination of nonredundant, high-capacity AI services by preserving both privacy and accuracy.

Abstract Purpose Overuse of thyroid ultrasound (TUS) has contributed to rising thyroid cancer diagnoses and is projected to increase US healthcare costs from $1.5 billion to $3.5 billion by 2030. This study evaluated the healthcare cost of inappropriately ordered TUS in a national multicenter academic system. Methods This is a secondary cost analysis of a retrospective cohort study across 4 Mayo Clinic sites (Rochester, MN; Jacksonville, FL; Scottsdale, AZ; and the Midwest Mayo Clinic Health System). Adult patients (≥18 years) undergoing their first TUS between January 1, 2017, and December 31, 2021, with at least 1 year of follow-up were included. TUS indications were classified as appropriate or inappropriate using a guideline-based natural language processing algorithm. The primary outcome was a comparison of adjusted 1-year all-cause healthcare costs. A secondary analysis calculated the direct procedural costs of the inappropriate TUS cascade. Results Among 6984 patients (mean age 56 [SD 16.4]; 76.2% female; 90.9% White), 546 (7.8%) underwent TUS for inappropriate indications. These patients were younger (mean age 53.0 vs 56.3 years, P < .0001) but otherwise demographically similar. Adjusted total healthcare costs over 90 days and 1 year were comparable: $4842 vs $5794 and $13 748 vs $14 257 for inappropriate vs appropriate TUS, respectively. The inappropriate TUS cascade, including an estimated 56 subsequent biopsies and 22 thyroidectomies, resulted in a minimum of $576 134 in direct procedural costs. Conclusion While adjusted total costs were similar, inappropriate TUS represents potentially avoidable spending and remains a viable target for cost-reduction strategies. Reducing low-value imaging remains a critical target for cost-saving interventions.

ImportanceThyroid cancer incidence has tripled in the past 3 decades, largely driven by increased detection of small, indolent papillary thyroid cancers. Overuse of thyroid ultrasonography (TUS) contributes to overdiagnosis, leading to unnecessary biopsies, procedures, and potential patient harm.ObjectiveTo evaluate the frequency of and factors associated with inappropriate TUS (iTUS) orders and assess related clinical outcomes.Design, Setting, and ParticipantsThis retrospective cohort study analyzed adult patients 18 years and older who underwent their first TUS from January 1, 2017, to December 30, 2021, at 4 Mayo Clinic sites. Patients with previous thyroid nodule, thyroid cancer, thyroid surgery, or TUS were excluded. Based on documented clinical indications, a validated natural language processing model classified TUS orders as appropriate or inappropriate. Data were analyzed from April 2024 to May 2025.ExposuresBaseline characteristics of patients, clinicians, and clinical encounters investigated for their association with an iTUS order.Main Outcomes and MeasuresThe primary outcome was the proportion of iTUS orders. Secondary outcomes included factors associated with iTUS (sociodemographic, clinical, order-specific, and clinician-related factors) and clinical outcomes following iTUS, including the detection of thyroid nodules, thyroid procedures, and thyroid cancer diagnoses.ResultsOf 11 442 included patients, 8422 (73.6%) were female, and the mean (SD) age was 57.3 (15.8) years with a mean (SD) body mass index (calculated as weight in kilograms divided by height in meters squared) of 29.7 (7.0). Among 11 442 TUS orders, 866 (7.6%) were classified as inappropriate. In multivariable analysis, younger age (age of 18 to 54 years vs older than 65 years; odds ratio [OR], 1.86; 95% CI, 1.56-2.22), hyperthyroidism (OR, 9.04; 95% CI, 6.75-12.11), ordering by nonendocrinology specialties (eg, oncology/hematology; OR, 3.43; 95% CI, 2.47-4.76), and orders not linked to an in-person appointment (eg, portal messages; OR, 2.42; 95% CI, 2.08-2.82) were strongly associated with increased odds of iTUS. Compared with appropriate TUS, iTUS led to lower rates of thyroid nodule detection (202 of 866 [23.3%] vs 6885 of 10 576 [65.1%]; OR, 0.16; 95% CI, 0.14-0.19), biopsies (89 [10.3%] vs 2647 [25.0%]; OR, 0.34; 95% CI, 0.27-0.43), partial thyroidectomies (14 [1.6%] vs 424 [4.0%]; OR, 0.39; 95% CI, 0.23-0.67), and confirmed thyroid cancer cases (16 [1.8%] vs 425 [4.0%]; OR, 0.45; 95% CI, 0.27-0.74).Conclusions and RelevanceIn this cohort study, approximately 1 in 13 TUS orders were inappropriate. iTUS was more common in younger patients, those with thyroid dysfunction, those not seen in person, and in those referred by nonendocrinology specialties. These findings highlight the need for targeted strategies to optimize TUS use and mitigate overdiagnosis and overtreatment.

Background Electronic health records (EHRs) serve as a critical tool for clinical documentation, yet their complexity has raised concerns about readability and usability for healthcare providers and patients. Readability scores, such as the Flesch-Kincaid (FK) Grade Level, offer a quantitative method to assess documentation clarity. In this study, the authors evaluated the impact of an EHR transition on the readability of clinical notes across multiple medical specialties. Methods All clinical notes recorded between 2016 and 2019 at a large healthcare organization were evaluated across specialties. The organization transitioned EHR systems during the study period, in May 2018. FK Grade Level scores were calculated for each clinical consultation note and stratified by specialty and EHR. Results Across 10 medical specialties, 630,246 clinical notes were evaluated. The median FK grade levels from notes composed in the new EHR were similar to those composed in the previous EHR (8.228 vs 8.240; p=0.58). The variation in FK grade level as measured by standard deviation was lower in the new EHR compared with the old EHR (1.405 vs 1.764; p <0.01). Conclusions Calculation of FK grade level of clinical notes is a feasible method to assess clinical note readability. Study results showed that clinical documentation readability may not be strongly associated with underlying EHR. Migrating to a new EHR alone should not be expected to improve readability of clinical notes.

Abstract Chronic overlapping pain conditions (COPCs) affect a wide population and incur a substantial disease burden including comorbid mental disorders. Although these comorbidities have been extensively documented, the longitudinal trajectories of their onset, particularly for those with three or more conditions, have been rarely studied. We conducted retrospective cohort studies to identify statistically significant COPCs and mental health trajectories in the All of Us Research Program (AoU v8, 338,170 persons) and the Mayo Data Warehouse (3,957,444 individuals). For each trajectory, cases were matched with controls by demographic factors. Logistic regression was then applied to assess the increased likelihood of the final condition given the prior conditions in that sequence. The significant trajectories were visualized as a “pain forest.” More than 88% of trajectories identified from AoU (Male: 78, Female: 361) were reproducible in the Mayo Data Warehouse data (Male: 361, Female: 1286), with female trajectories encompassing more than 96% of the male trajectories. The findings indicate that chronic low back pain generally occurs earlier than other conditions, while fibromyalgia tends to follow other COPCs. Endometriosis seems to be associated with an increased prevalence of COPCs in women. In addition, longer trajectories are associated with a greater risk of developing additional COPC or mental condition. The results offer new insights into the progression of COPCs and associated mental conditions, which may inform healthcare providers and patients in managing and preventing exacerbation of these conditions.

Abstract Objectives The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs—a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care. Materials and Methods We propose a fusion framework to leverage pretrained language models (LMs) with different language advantages via a Convolution Neural Network for precise identification of patient primary concerns via multi-class classification. We examined 3 traditional machine learning models, 9 BERT-based language models, 6 fusion models, and 2 ensemble models. Results The outcomes of our experimentation underscore the superior performance achieved by BERT-based models in comparison to traditional machine learning models. Remarkably, our fusion model emerges as the top-performing solution, delivering a notably improved accuracy score of 77.67 ± 2.74% and an F1 score of 74.37 ± 3.70% in macro-average. Discussion This study highlights the feasibility and effectiveness of multi-class classification for patient primary concern detection and the proposed fusion framework for enhancing primary concern detection. Conclusions The use of multi-class classification enhanced by a fusion of multiple pretrained LMs not only improves the accuracy and efficiency of patient primary concern identification in PPMs but also aids in managing the rising volume of PPMs in healthcare, ensuring critical patient communications are addressed promptly and accurately.

Background: The use of artificial intelligence (AI) in health care has grown exponentially with the promise of facilitating biomedical research and enhancing diagnosis, treatment, monitoring, disease prevention, and health care delivery. We aim to examine the current state, limitations, and future directions of AI in thyroidology. Summary: AI has been explored in thyroidology since the 1990s, and currently, there is an increasing interest in applying AI to improve the care of patients with thyroid nodules (TNODs), thyroid cancer, and functional or autoimmune thyroid disease. These applications aim to automate processes, improve the accuracy and consistency of diagnosis, personalize treatment, decrease the burden for health care professionals, improve access to specialized care in areas lacking expertise, deepen the understanding of subtle pathophysiologic patterns, and accelerate the learning curve of less experienced clinicians. There are promising results for many of these applications. Yet, most are in the validation or early clinical evaluation stages. Only a few are currently adopted for risk stratification of TNODs by ultrasound and determination of the malignant nature of indeterminate TNODs by molecular testing. Challenges of the currently available AI applications include the lack of prospective and multicenter validations and utility studies, small and low diversity of training data sets, differences in data sources, lack of explainability, unclear clinical impact, inadequate stakeholder engagement, and inability to use outside of the research setting, which might limit the value of their future adoption. Conclusions: AI has the potential to improve many aspects of thyroidology; however, addressing the limitations affecting the suitability of AI interventions in thyroidology is a prerequisite to ensure that AI provides added value for patients with thyroid disease.