Journal of Medical Internet Research最新文献

{"title":"Usefulness of Mixed Reality in Surgical Treatment: Delphi Study.","authors":"Renato Magalhães, Ana Carolina Lima, António Marques, Javier Pereira, Lúcio Lara Santos","doi":"10.2196/69964","DOIUrl":"https://doi.org/10.2196/69964","url":null,"abstract":"<p><strong>Background: </strong>Mixed reality (MR) combines real and virtual elements and has shown promise in diverse fields, including surgical procedures. MR headsets may support surgical navigation, planning, and training. It is crucial to determine whether medical professionals consider this technology indispensable. This study uses the Delphi method, facilitated by the Welphi web-based platform, to assess the utility of MR in surgical settings and analyzes the results of the first round using a systematic approach modeled on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework.</p><p><strong>Objective: </strong>This study aims to examine the feasibility and advantages of MR technology in surgical contexts. The findings are intended to inform and direct health care professionals, researchers, and developers in advancing MR integration into surgical environments to optimize treatment quality and safety.</p><p><strong>Methods: </strong>A 3-round Delphi approach was implemented to ascertain consensus on the utility of MR in surgical treatment. Participants (n=22) were purposefully selected from among experts with professional experience in technologies such as virtual reality, augmented reality, 3D laparoscopy, and robotics. In the first round, participants provided insights into the potential applications of MR in surgical procedures through open-ended questions structured across 5 distinct sections. Responses were analyzed to develop the second-round questionnaire, which was hierarchically organized into main topics and subtopics. In the third round, the questions were identical to those in the second round, including the percentage results, allowing participants to reconsider their responses. A consensus round was subsequently conducted. The majority consensus level was defined as agreement by ≥70% of the participants in a given round.</p><p><strong>Results: </strong>The study was conducted from January to May 2024. All 22 invited experts provided responses in both the first and second rounds (100% response rate). In the third and consensus rounds, 20 (91%) of the 22 experts participated. The consensus round, conducted to present the results, yielded a majority consensus (19/20, 95%) on the usefulness of MR in surgical treatment. The primary benefits of MR in surgery were identified as surgical navigation (15/20, 75%), planning (15/20, 75%), and teaching and training (14/20, 70%). In addition, 75% (15/20) of the experts identified cost and investments as primary constraints. We used the Kendall tau-b coefficient for correlation analysis, and significant correlations were identified between distinct aspects.</p><p><strong>Conclusions: </strong>MR technology is most beneficial in surgical navigation, planning, and training. However, the costs and investments required for implementation may present a potential limitation for the integration of this technology into surgical procedures. Moreover, it is of cruc","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e69964"},"PeriodicalIF":5.8,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144591463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"External Validation of an Upgraded AI Model for Screening Ileocolic Intussusception Using Pediatric Abdominal Radiographs: Multicenter Retrospective Study.","authors":"Jeong Hoon Lee, Pyeong Hwa Kim, Nak-Hoon Son, Kyunghwa Han, Yeseul Kang, Sejin Jeong, Eun-Kyung Kim, Haesung Yoon, Sergios Gatidis, Shreyas Vasanawala, Hee Mang Yoon, Hyun Joo Shin","doi":"10.2196/72097","DOIUrl":"10.2196/72097","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) is increasingly used in radiology, but its development in pediatric imaging remains limited, particularly for emergent conditions. Ileocolic intussusception is an important cause of acute abdominal pain in infants and toddlers and requires timely diagnosis to prevent complications such as bowel ischemia or perforation. While ultrasonography is the diagnostic standard due to its high sensitivity and specificity, its accessibility may be limited, especially outside tertiary centers. Abdominal radiographs (AXRs), despite their limited sensitivity, are often the first-line imaging modality in clinical practice. In this context, AI could support early screening and triage by analyzing AXRs and identifying patients who require further ultrasonography evaluation.</p><p><strong>Objective: </strong>This study aimed to upgrade and externally validate an AI model for screening ileocolic intussusception using pediatric AXRs with multicenter data and to assess the diagnostic performance of the model in comparison with radiologists of varying experience levels with and without AI assistance.</p><p><strong>Methods: </strong>This retrospective study included pediatric patients (≤5 years) who underwent both AXRs and ultrasonography for suspected intussusception. Based on the preliminary study from hospital A, the AI model was retrained using data from hospital B and validated with external datasets from hospitals C and D. Diagnostic performance of the upgraded AI model was evaluated using sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). A reader study was conducted with 3 radiologists, including 2 trainees and 1 pediatric radiologist, to evaluate diagnostic performance with and without AI assistance.</p><p><strong>Results: </strong>Based on the previously developed AI model trained on 746 patients from hospital A, an additional 431 patients from hospital B (including 143 intussusception cases) were used for further training to develop an upgraded AI model. External validation was conducted using data from hospital C (n=68; 19 intussusception cases) and hospital D (n=90; 30 intussusception cases). The upgraded AI model achieved a sensitivity of 81.7% (95% CI 68.6%-90%) and a specificity of 81.7% (95% CI 73.3%-87.8%), with an AUC of 86.2% (95% CI 79.2%-92.1%) in the external validation set. Without AI assistance, radiologists showed lower performance (overall AUC 64%; sensitivity 49.7%; specificity 77.1%). With AI assistance, radiologists' specificity improved to 93% (difference +15.9%; P<.001), and AUC increased to 79.2% (difference +15.2%; P=.05). The least experienced reader showed the largest improvement in specificity (+37.6%; P<.001) and AUC (+14.7%; P=.08).</p><p><strong>Conclusions: </strong>The upgraded AI model improved diagnostic performance for screening ileocolic intussusception on pediatric AXRs. It effectively enhanced the specificity and overa","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e72097"},"PeriodicalIF":5.8,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144626519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Psychometric Evaluation of Large Language Model Embeddings for Personality Trait Prediction.","authors":"Julina Maharjan, Ruoming Jin, Jianfeng Zhu, Deric Kenne","doi":"10.2196/75347","DOIUrl":"10.2196/75347","url":null,"abstract":"<p><strong>Background: </strong>Recent advancements in large language models (LLMs) have generated significant interest in their potential for assessing psychological constructs, particularly personality traits. While prior research has explored LLMs' capabilities in zero-shot or few-shot personality inference, few studies have systematically evaluated LLM embeddings within a psychometric validity framework or examined their correlations with linguistic and emotional markers. Additionally, the comparative efficacy of LLM embeddings against traditional feature engineering methods remains underexplored, leaving gaps in understanding their scalability and interpretability for computational personality assessment.</p><p><strong>Objective: </strong>This study evaluates LLM embeddings for personality trait prediction through four key analyses: (1) performance comparison with zero-shot methods on PANDORA Reddit data, (2) psychometric validation and correlation with LIWC (Linguistic Inquiry and Word Count) and emotion features, (3) benchmarking against traditional feature engineering approaches, and (4) assessment of model size effects (OpenAI vs BERT vs RoBERTa). We aim to establish LLM embeddings as a psychometrically valid and efficient alternative for personality assessment.</p><p><strong>Methods: </strong>We conducted a multistage analysis using 1 million Reddit posts from the PANDORA Big Five personality dataset. First, we generated text embeddings using 3 LLM architectures (RoBERTa, BERT, and OpenAI) and trained a custom bidirectional long short-term memory model for personality prediction. We compared this approach against zero-shot inference using prompt-based methods. Second, we extracted psycholinguistic features (LIWC categories and National Research Council emotions) and performed feature engineering to evaluate potential performance enhancements. Third, we assessed the psychometric validity of LLM embeddings: reliability validity using Cronbach α and convergent validity analysis by examining correlations between embeddings and established linguistic markers. Finally, we performed traditional feature engineering on static psycholinguistic features to assess performance under different settings.</p><p><strong>Results: </strong>LLM embeddings trained using simple deep learning techniques significantly outperform zero-shot approaches on average by 45% across all personality traits. Although psychometric validation tests indicate moderate reliability, with an average Cronbach α of 0.63, correlation analyses spark a strong association with key linguistic or emotional markers; openness correlates highly with social (r=0.53), conscientiousness with linguistic (r=0.46), extraversion with social (r=0.41), agreeableness with pronoun usage (r=0.40), and neuroticism with politics-related text (r=0.63). Despite adding advanced feature engineering on linguistic features, the performance did not improve, suggesting that LLM embeddings inherently capture ke","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e75347"},"PeriodicalIF":5.8,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12262148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144591461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical and Cost-Effectiveness of Telehealth-Supported Home Oxygen Therapy on Adherence, Hospital Readmission, and Health-Related Quality of Life in Patients With Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis of Randomized Controlled Trials.","authors":"Cuirong Hu, Xinqi Liao, Yi Fang, Shu Zhu, Xia Lan, Guilan Cheng","doi":"10.2196/73010","DOIUrl":"10.2196/73010","url":null,"abstract":"<p><strong>Background: </strong>Chronic obstructive pulmonary disease (COPD) is a common respiratory disorder frequently requiring oxygen therapy to relieve symptoms and improve survival. In recent years, telehealth-supported interventions have emerged as promising strategies to optimize home oxygen therapy by promoting adherence, reducing hospitalizations, and enhancing health-related quality of life. However, evidence regarding their effectiveness remains inconsistent and equivocal, underscoring the need for further rigorous evaluation.</p><p><strong>Objective: </strong>This study aimed to evaluate the clinical and cost-effectiveness of telehealth-supported home oxygen therapy on adherence, hospital readmission, and health-related quality of life in patients with COPD.</p><p><strong>Methods: </strong>A comprehensive search was conducted across 6 databases (PubMed, Cochrane Central, Embase, Web of Science, PsycINFO, and CINAHL) up to October 1, 2024, and updated on April 28, 2025. Randomized controlled trials involving patients with COPD comparing telehealth-supported home oxygen therapy with usual care, and reporting outcomes on adherence, hospital readmissions, or health-related quality of life, were included. In addition, 2 reviewers independently screened the studies, extracted data, assessed the risk of bias using the Cochrane Risk of Bias 2 tool, and evaluated the certainty of evidence with the Grading of Recommendations Assessment, Development, and Evaluation approach. Meta-analyses and heterogeneity assessments were conducted using R software (R Core Team). Standardized mean differences with 95% CIs were calculated to evaluate the intervention effects under a random-effects model.</p><p><strong>Results: </strong>In total, 8 studies comprising 1275 patients were included in the review. Telehealth-supported home oxygen therapy significantly reduced hospital readmissions (standardized mean difference [SMD]=-0.40, 95% CI -0.60 to -0.21) and improved health-related quality of life (SMD=0.49, 95% CI 0.25-0.73). No significant effect was observed on therapy adherence (SMD=0.19, 95% CI -0.76 to 1.14). Furthermore, 3 economic evaluations suggested that although telehealth interventions may incur higher initial costs, they are likely to result in long-term savings by reducing hospital admissions. Sensitivity analyses confirmed the robustness of the findings for hospital readmissions and health-related quality of life, for which the quality of evidence was rated as high, whereas the evidence for therapy adherence was rated as low.</p><p><strong>Conclusions: </strong>Telehealth-supported home oxygen therapy significantly reduces hospital admissions and improves health-related quality of life in patients with COPD, but does not significantly improve therapy adherence. Tailored interventions that consider patient demographics, combined with supportive policies, may further enhance clinical outcomes. Future research should incorporate economic evaluati","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e73010"},"PeriodicalIF":5.8,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12262104/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144591459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Scalable Precision Psychiatry With an Objective Measure of Psychological Stress: Prospective Real-World Study.","authors":"Helena Wang, Norman Farb, Bechara Saab","doi":"10.2196/56086","DOIUrl":"10.2196/56086","url":null,"abstract":"<p><strong>Background: </strong>Before meaningful progress toward precision psychiatry is possible, objective (unbiased) assessment of patient mental well-being must be validated and adopted broadly.</p><p><strong>Objective: </strong>This study aims to compare the fidelity of a precision psychiatry therapy recommendation algorithm when trained with an objective quantification of psychological stress versus subjective ecological momentary assessments (EMAs) of stress and mood.</p><p><strong>Methods: </strong>From 2786 unique individuals engaging between March 2015 and December 2022 in English language psychotherapy sessions and providing pre- and postsession self-report and facial biometric data via a mobile health platform (Mobio Interactive Pte Ltd, Singapore), analysis was conducted on 67 \"super users\" that completed a minimum of 28 sessions with all pre- and postsession measures. The platform used has previously demonstrated reduced psychiatric symptom severity and improved overall mental well-being. Psychotherapy recordings (\"sessions\") within the platform, available asynchronously and on demand, span mindfulness, meditation, cognitive behavioral therapy, client-centered therapy, music therapy, and self-hypnosis. The platform also has the unusual ability to rapidly assess mental well-being without bias via an easy-to-use objective measure of psychological stress derived from artificial intelligence-based analysis of facial biomarkers (objective stress level [OSL]). In tandem with the objective measure, EMAs obtain self-reported values of stress (SRS) and mood (SRM). ∆OSL, ∆SRS, and ∆SRM (with delta referring to the presession subtracted from the postsession measurement) were used to independently train a therapy recommendation algorithm designed to predict what future sessions would prove most efficacious for each individual. Algorithm predictions were compared against the efficacy of the individual's self-selected sessions.</p><p><strong>Results: </strong>The objective measure of psychological stress provided a differentiated delta for the measurement of therapeutic efficacy compared to the 2 EMA deltas, as shown by clear divergence in ∆OSL vs ∆SRS or ∆SRM (r<0.03), while the EMA deltas showed significant convergence (r=0.53, P<.01). The recommendation algorithm selected increasingly efficacious therapy sessions as a function of training data when trained with either ∆OSL (F<sub>1,16</sub>=5.37, P=.03) or ∆SRM data (F<sub>1,16</sub>=3.69, P<.05). However, the sequential improvement in prediction efficacy only surpassed the efficacy of self-selected therapy when the algorithm was trained using objective data (P<.01). Training the algorithm with EMA data showed potential trends that did not reach significance (∆SRS: P=.09; ∆SRM: P=.12). As a final insight, self-selected therapy sessions were overrepresented among the algorithmically recommended sessions, an effect most pronounced when the algorithm was trained with ∆OSL data (F<sub>1,14</sub>=","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e56086"},"PeriodicalIF":5.8,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144575663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic Evaluation Methodologies of Remote Patient Monitoring for Chronic Conditions: Scoping Review.","authors":"Siri Bjorvig, Elin Breivik, Jordi Piera-Jiménez, Carme Carrion","doi":"10.2196/71565","DOIUrl":"10.2196/71565","url":null,"abstract":"<p><strong>Background: </strong>Remote patient monitoring (RPM) offers a potential solution to manage the increasing prevalence of chronic condition challenges in health care systems worldwide, but its economic evaluation remains challenging.</p><p><strong>Objective: </strong>This scoping review aimed to explore the methodologies used in economic evaluations of RPM interventions for chronic conditions, with a particular focus on cost identification, measurement and valuation, and reporting quality.</p><p><strong>Methods: </strong>A scoping review was conducted following the Joanna Briggs Institute methodology for scoping reviews. Systematic searches were carried out in Embase, MEDLINE, CINAHL, and Web of Science in week 40 of 2023, with no restrictions on the start date. No geographical restrictions were applied beyond requiring English-language publications. Studies were included if they reported a full or partial economic evaluation of an RPM intervention targeting patients with one or more chronic conditions. Screening and selection were performed independently by 2 reviewers. A total of 5473 records were identified, of which, 41 records met inclusion criteria after screening. Data were synthesized into key themes: study characteristics (design, population, setting), economic evaluation methods (types of analysis, comparator, perspectives, and outcome measures), cost estimation (identification, measurement, valuation), and adherence to the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) 2022. Discrepancies were resolved through discussion. The review protocol was registered in the Open Science Framework.</p><p><strong>Results: </strong>A total of 41 papers, representing 40 studies, were included in the final review. Studies used diverse evaluation methods, such as cost-effectiveness analysis (20 studies), within which, 13 studies specifically conducted cost-utility analysis. Other approaches included cost-consequence analysis (7 studies), cost-minimization analysis (3 studies), cost-benefit analysis (2 studies), cost analysis (8 studies), and budget impact analysis (1 study). Cost estimation approaches varied across studies, with differences in cost identification, measurement, and valuation. Cost estimation methodologies varied, both in terms of which cost components were included and how costs were identified, measured, and valued. Commonly reported costs related to health care resource use and technology, but the data sources used, and the level of transparency provided, varied. Studies reported a range of outcome measures, including quality-adjusted life years, mortality, and financial indicators. Some studies reported multiple outcomes. Reporting inconsistencies were observed, and adherence to updated CHEERS 2022 standards was limited, particularly in sensitivity analyses and cost data transparency.</p><p><strong>Conclusions: </strong>This review highlights the diversity and methodological variability in economic eval","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e71565"},"PeriodicalIF":5.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12248258/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144564862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building an Ethical Framework for European Pharmacists in e-Pharmacies.","authors":"Alexandra Toma, Ofelia Crişan","doi":"10.2196/64750","DOIUrl":"10.2196/64750","url":null,"abstract":"<p><p>Patients using e-pharmacies benefit substantially from their privacy and accessibility; however, they must also be aware of the health and safety risks. These risks can be mitigated through the implementation of robust legal and ethical frameworks. European pharmacists working in e-pharmacies are obligated to respect the legal frameworks governing the remote sale of medicines, as well as the rules of professional ethics, while considering the unique benefits and challenges of establishing online therapeutic relationships with patients. This Viewpoint paper aims to propose a comprehensive ethical framework for European pharmacists in e-pharmacies that promotes consistency and ensures patient rights while fostering professional integrity and responsibility. To this end, the codes of ethics of pharmacists in the European Union (EU) member states were explored to determine the extent to which they address e-pharmacies and to begin from their provisions the building of a shared ethical framework, supported by previously published findings. The ethical guidance provided by professional associations and other competent authorities of European pharmacists varies significantly across member states. In most EU member states, pharmacists' codes of ethics do not contain rules addressing e-pharmacies. Only 8 EU member states have adopted rules guiding pharmacists' conduct in e-pharmacies, providing detailed regulations aimed at safeguarding patients' rights during the remote supply of medicines, health care products, and services. Considering these findings, the development of a common ethical framework for European pharmacists operating in e-pharmacies would be helpful for the development of ethics in their conduct on the internet. On the basis of the fundamental principles of biomedical ethics, we engaged in a reiterative process of reflection and discussion to develop an ethical framework tailored to the e-pharmacy context as our main contribution to knowledge. The proposed framework-representing a novel contribution to e-pharmacy ethics research-highlights ethical issues that should be incorporated into pharmacists' codes of ethics or dedicated guidelines. These issues range from ensuring patient autonomy in selecting e-pharmacies to showing solidarity in the global e-pharmacy environment. Its novelty could be a major input for advancing e-pharmacy ethics. The following key messages are intended for pharmacists and their professional associations or other competent authorities interested in this field. Pharmacists' codes of ethics should keep pace with the development of e-pharmacies to ensure they remain relevant and effective in guiding ethical conduct and decision-making in such an environment. Establishing a comprehensive, shared ethical framework for European pharmacists in e-pharmacies can foster a deeper understanding and appreciation of the opportunities afforded by emerging technologies. By facilitating the development of ethical conduct of","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e64750"},"PeriodicalIF":5.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12274778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144560398","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perspectives of Health Care Professionals on the Use of AI to Support Clinical Decision-Making in the Management of Multiple Long-Term Conditions: Interview Study.","authors":"Jennifer Cooper, Shamil Haroon, Francesca Crowe, Krishnarajah Nirantharakumar, Thomas Jackson, Leah Fitzsimmons, Eleanor Hathaway, Sarah Flanagan, Tom Marshall, Louise J Jackson, Niluka Gunathilaka, Alexander D'Elia, Simon George Morris, Sheila Greenfield","doi":"10.2196/71980","DOIUrl":"10.2196/71980","url":null,"abstract":"<p><strong>Background: </strong>Managing multiple long-term conditions (MLTC) is complex. Clinical management guidelines are typically focused on individual conditions and lack a robust evidence base for patients with MLTC. MLTC management is largely delivered in primary care, where health care professionals (HCPs) have identified the need for more holistic yet efficient models of care that can address patients' medical, pharmacological, social, and mental health needs. Artificial intelligence (AI) has proven effective in tackling complex, data-driven challenges in various fields, presenting significant opportunities for MLTC care. However, its role in managing patients with multifaceted psychosocial needs remains underexplored. The implementation of AI tools in this context introduces opportunities for innovation and challenges related to clinical appropriateness, trust, and ethical considerations. Understanding HCPs' experiences of MLTC management and the factors influencing their attitudes toward using AI in complex clinical decision-making is crucial for successful implementation.</p><p><strong>Objective: </strong>We aimed to explore the perspectives of primary care HCPs on managing MLTC and their attitudes toward using AI tools to support clinical decision-making in MLTC care.</p><p><strong>Methods: </strong>In total, 20 HCPs, including general practitioners, geriatricians, nurses, and pharmacists, were interviewed. A patient case study was used to explore how an AI tool might alter the way in which participants approach clinical decision-making with a patient with MLTC. We derived concepts inductively from the interview transcripts and structured them according to the 5 categories of the model by Buck exploring determinants of attitudes toward AI. These included the concerns and expectations that contributed to the minimum requirements for HCPs to consider using an AI decision-making tool, as well as the individual characteristics and environmental influences determining their attitudes.</p><p><strong>Results: </strong>HCPs' perspectives on managing MLTC were grouped into three main themes: (1) balancing multiple competing factors, including accounting for patients' social circumstances; (2) managing polypharmacy; and (3) working beyond single-condition guidelines. HCPs typically expected that AI tools would improve the safety and quality of clinical decision-making. However, they expressed concerns about the impact on the therapeutic clinician-patient relationship that is fundamental to the care of patients with MLTC. The key prerequisites for clinicians adopting AI tools in this context included improving public and patient trust in AI, saving time and integrating with existing systems, and ensuring that the rationale behind a recommendation is apparent to enable a final decision made by an experienced human clinician.</p><p><strong>Conclusions: </strong>This is the first study to examine the attitudes of HCPs toward using AI decision-mak","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e71980"},"PeriodicalIF":5.8,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12274781/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144560401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence to Improve Clinical Coding Practice in Scandinavia: Crossover Randomized Controlled Trial.","authors":"Taridzo Chomutare, Therese Olsen Svenning, Miguel Ángel Tejedor Hernández, Phuong Dinh Ngo, Andrius Budrionis, Kaisa Markljung, Lill Irene Hind, Torbjørn Torsvik, Karl Øyvind Mikalsen, Aleksandar Babic, Hercules Dalianis","doi":"10.2196/71904","DOIUrl":"10.2196/71904","url":null,"abstract":"<p><strong>Background: </strong>Clinical coding is critical for hospital reimbursement, quality assessment, and health care planning. In Scandinavia, however, coding is often done by junior doctors or medical secretaries, leading to high rates of coding errors. Artificial intelligence (AI) tools, particularly semiautomatic computer-assisted coding tools, have the potential to reduce the excessive burden of administrative and clinical documentation. To date, much of what we know regarding these tools comes from lab-based evaluations, which often fail to account for real-world complexity and variability in clinical text.</p><p><strong>Objective: </strong>This study aims to investigate whether an AI tool developed by by Norwegian Centre for E-health Research at the University Hospital of North Norway, Easy-ICD (International Classification of Diseases), can enhance clinical coding practices by reducing coding time and improving data quality in a realistic setting. We specifically examined whether improvements differ between long and short clinical notes, defined by word count.</p><p><strong>Methods: </strong>An AI tool, Easy-ICD, was developed to assist clinical coders and was tested for improving both accuracy and time in a 1:1 crossover randomized controlled trial conducted in Sweden and Norway. Participants were randomly assigned to 2 groups (Sequence AB or BA), and crossed over between coding longer texts (Period 1; mean 307, SD 90; words) versus shorter texts (Period 2; mean 166, SD 55; words), while using our tool versus not using our tool. This was a purely web-based trial, where participants were recruited through email. Coding time and accuracy were logged and analyzed using Mann-Whitney U tests for each of the 2 periods independently, due to differing text lengths in each period.</p><p><strong>Unlabelled: </strong>The trial had 17 participants enrolled, but only data from 15 participants (300 coded notes) were analyzed, excluding 2 incomplete records. Based on the Mann-Whitney U test, the median coding time difference for longer clinical text sequences was 123 seconds (P<.001, 95% CI 81-164), representing a 46% reduction in median coding time when our tool was used. For shorter clinical notes, the median time difference of 11 seconds was not significant (P=.25, 95% CI -34 to 8). Coding accuracy improved with Easy-ICD for both longer (62% vs 67%) and shorter clinical notes (60% vs 70%), but these differences were not statistically significant (P=.50and P=.17, respectively). User satisfaction ratings (submitted for 37% of cases) showed slightly higher approval for the tool's suggestions on longer clinical notes.</p><p><strong>Conclusions: </strong>This study demonstrates the potential of AI to transform common tasks in clinical workflows, with ostensible positive impacts on work efficiencies for clinical coding tasks with more demanding longer text sequences. Further studies within hospital workflows are required before these presumed impact","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e71904"},"PeriodicalIF":5.8,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12244276/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144560397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"E-Cigarette Narratives of User-Generated Posts on Xiaohongshu in China: Content Analysis.","authors":"Tingfen Ji, Zhao Liu, Zheng Su, Xin Xia, Yi Liu, Ying Xie, Zhenxiao Huang, Xinmei Zhou, Min Wang, Anqi Cheng, Qingqing Song, Yuxin Shi, Shunyi Shi, Aihemaiti Ailifeire, Jiahui He, Yingman Gao, Liang Zhao, Liyan Wu, Dan Xiao, Chen Wang","doi":"10.2196/71173","DOIUrl":"10.2196/71173","url":null,"abstract":"<p><strong>Background: </strong>Social media platforms have become influential spaces for disseminating information about electronic cigarettes (e-cigarettes). Concerns persist about the spread of misleading content, particularly among social media vulnerable groups. Xiaohongshu (RedNote), widely used by Chinese youth, plays a growing role in shaping e-cigarette perceptions. Understanding the narratives circulating on this platform is essential for identifying misinformation, assessing public perception, and guiding future health communication strategies.</p><p><strong>Objective: </strong>This study aimed to analyze the content, topics, user engagement, and sentiment trends of e-cigarette-related posts on Xiaohongshu and to assess the factors that influence engagement.</p><p><strong>Methods: </strong>E-cigarette-related posts published on Xiaohongshu between January 2020 and November 2024 were collected using web scraping, based on a predefined keyword list and a time-stratified random sampling strategy. Posts were categorized into 4 themes: advertising promotion, health hazards, usage interaction, and others. High-frequency keywords were extracted, and representative quotes were included to illustrate user perspectives across each category. Sentiment analysis was performed on posts in the usage interaction category to assess public attitudes. We defined 4 sentiment categories: positive, negative, neutral, and mixed. Logistic regression was conducted to explore the effects of post type, content length, and thematic classification on user engagement metrics such as likes, saves, and comments.</p><p><strong>Results: </strong>A total of 1729 posts were included and analyzed. Usage interaction posts were the most common (681/1729, 39.39%), with keywords such as \"experience,\" \"regulations,\" and \"quit smoking\" dominating this category. Advertising promotion posts (512/1729, 29.61%) frequently used terms like \"flavor,\" \"fashion,\" and \"design\" to attract younger users. Health hazards posts (311/1729, 17.99%) highlighted risks with keywords like \"nicotine,\" \"addiction,\" and \"secondhand smoke,\" while others included policy and industry updates. Representative quotes highlighted typical concerns about aesthetics, health risks, and cessation struggles. Health hazards posts garnered the highest engagement in terms of likes and saves, despite their limited presence (odds ratio [OR] 1.498, 95% CI 1.099-2.042, P=.01). Video posts significantly outperformed text-image posts in generating comments (OR 2.624, 95% CI 2.017-3.439, P<.001). Sentiment analysis of the usage interaction posts (n=681) revealed that 53.45% (364/681) were positive, highlighting reduced harm, convenience, or flavor preferences. Negative sentiment was observed in 33.48% (228/681) of posts, often expressing concerns about addiction and health risks. Mixed sentiments appeared in 6.90% (47/681), acknowledging both pros and cons. In addition, 6.17% (42/681) of posts were classified as neutral wit","PeriodicalId":16337,"journal":{"name":"Journal of Medical Internet Research","volume":"27 ","pages":"e71173"},"PeriodicalIF":5.8,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12244743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144560399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}