JMIR Formative Research

Pain assessment remains challenging in clinical practice. Automated pain assessment using artificial intelligence (AI) and facial expression analysis has emerged as a potential approach to support more objective and continuous pain monitoring, particularly for patients who are unable to communicate effectively. However, successful implementation depends on clinician acceptance. We conducted a cross-sectional survey of physicians, advanced practice providers (APPs), and nurses at a tertiary academic medical center to evaluate current pain assessment practices and perceptions of a facial-analysis-based pain assessment system. Eighty-seven eligible clinicians participated. For current assessment practices, clinicians commonly relied on observational indicators of pain, including vital signs (88.5%), physical examination findings (85.1%), and facial expressions (79.3%). Attitudes toward automated pain assessment were generally neutral to positive. Half of respondents agreed that such a system would be useful for monitoring patients over time (50.6%), and many perceived value for nonverbal patients (86.5%), patients with dementia or cognitive impairment (78.4%), and patients with language barriers (62.2%). Most clinicians reported requiring high levels of accuracy before routine use, with nearly 60% requiring at least 90% accuracy. Key concerns included alert fatigue and unequal performance across patient populations. These findings suggest clinicians view automated facial-analysis systems as potentially valuable adjuncts for pain monitoring, particularly in communication-impaired populations, while emphasizing the importance of accuracy, fairness, explainability, and workflow integration during system development and implementation.

BackgroundPatient education is critical to safe and effective postoperative bariatric surgery care. Large language models (LLMs) may improve access to personalized patient education but evidence regarding direct patient use in clinical settings remains limited. We evaluated BariBot, an LLM-powered educational application for patients undergoing bariatric surgery. MethodsWe conducted a prospective, single-arm mixed-methods feasibility study at a tertiary academic medical center from May 2024 to May 2025. Adult patients undergoing bariatric surgery used BariBot independently on postoperative day 1. The application provided conversational postoperative education and adapted language complexity to participants educational attainment. Outcomes included feasibility, usability, acceptability, perceived trustworthiness, and anticipated future use. Usability was assessed using the System Usability Scale (SUS). Immediately after BariBot use, participants completed a semi-structured qualitative interview assessing acceptability, perceived trustworthiness, and anticipated future use. Interview transcripts underwent thematic analysis. ResultsSixteen participants completed the BariBot use session, SUS assessment, and post-intervention interview. No technical failures prevented application use, and no participant requested early discontinuation. The median SUS score was 91.3 (IQR, 81.3-98.1), consistent with excellent perceived usability. Qualitative analysis showed that participants valued the applications immediacy, structured responses, conversational context, ability to address follow-up questions, judgement-free nature, and future availability between clinical encounters. Trust was strengthened by alignment with prior clinical guidance and perceived institutional association, although participants emphasized the need for transparency, clinician oversight, and use as an adjunct rather than a replacement of clinician guidance. ConclusionsBariBot was feasible to implement and was associated with high perceived usability and acceptability postoperatively after bariatric surgery. Larger, longitudinal, and comparative studies are needed to evaluate safety during unsupervised use and determine whether LLM-based educational applications improve patient understanding, engagement, and clinical outcomes.

The explosive growth of ambient AI scribes in healthcare has focused almost exclusively on relieving clinician documentation burdens and enhancing clinician efficiency and reducing burnout. While most research has focused on the clinician experience, these same types of tools, such as a patient held AI Personal Health Assistant, can be put in the hands of patients to facilitate communication with healthcare providers, to provide greater autonomy and activation for patients in managing their own healthcare, and to facilitate the task of receiving and retaining complex new information. However, to ensure these tools serve as more than just administrative aids, we partnered with a large technology company to conceptually codesign how such a tool might be studied in real world settings in a way that reflected outcomes of greatest importance to patients and families. We used the Nominal Group Technique and engaged thirty three patients and caregivers and ten multidisciplinary clinicians in structured virtual sessions to identify the outcomes on which subsequent studies should hinge. Our findings reveal a significant misalignment between current industry trends and patient priorities. While the market emphasizes the scribe function for billing, stakeholders overwhelmingly prioritized the AIPHAs role in longitudinal care management and post visit coordination. The most critical outcome identified was the impact on communication and respect, suggesting that patients view AI as a mechanism to improve the human to human dynamic and ensure a shared understanding of the care plan. Our findings further highlighted that the primary hurdle for the use of patient held AI is not technical utility but clinician trust and the need for formal consent processes. Importantly, as this technology is new and integration into the clinical experience is still being explored, patients cannot assume they will have access to the transcript of their own encounter when that record is created and held by the health system. This gap highlights the potential value of a patient held tool, preserving discussions from their visit and follow up steps in clearer detail than they may receive in their clinical note or traditional visit summary. From a system perspective, this transition requires a strategic shift from measuring workflow focused efficiency to measuring patient side advocacy and engagement. By centering the evaluation of AI on these patient defined metrics and fundamentally designing subsequent AI investigations using a partnership model, health systems can move beyond simple automation toward a model of care delivery that empowers patients to navigate complex clinical journeys with greater autonomy and clarity.

Depression is one of the most prevalent mental health disorders globally. Social media content can reflect emotional states, offering real-time signals of depressive symptoms. This study aimed to develop a deep learning model to predict depression using Twitter/X data, and to characterize potential causes of depression using a large language model (LLM). Twitter/X posts from April 20, 2023, to July 24, 2024, were obtained using the key phrase ("I" or "me") and "diagnosed with depression". After data cleaning and GPT-4o labelling, 2,275 depressive Twitter users, and an additional 1,661 non-depressive users from tweets using the keyword "today", were identified. A deep learning RoBERTa model for predicting depression was built on tweets from both user groups, with an 80/20 training-testing split. GPT-4o models were subsequently employed to analyze depressive users posts to understand potential causes for depression. The RoBERTa model achieved strong performance in predicting depression among Twitter users from their tweets, with an accuracy of 0.822, an F1 score of 0.855, and an AUC of 0.809. Common reasons for depression identified by GPT-4o models included societal pressure, low self-esteem, cultural influences, and identity-related challenges. These findings highlight the potential of deep learning models in early screening of depression using social media data. Insights into potential reasons for depression may inform targeted prevention strategies, public health interventions, and improved mental health support for at-risk populations.

Chronic pain and insomnia are common, each has serious consequences and they often coexist [1-5]. This novel study employed a phenomenological method to explore adults lived experiences of the core and meaning of comorbid insomnia and pain, here conceptualized as COMIP. The empirical data is based on 22 semi-structured interviews with adults with COMIP. Each interview was analysed, developing themes and analytical frameworks for all participants, which were then compared to construct one main analytical framework. The consolidated criteria for reporting qualitative research were used [6]. The essence of COMIP was: A vicious cycle of insomnia and pain driven by four intertwined primary factors with cross effects, which reduced sleep quality and quality of life. Four main themes emerged: Firstly, insomnia with hyperarousal like racing thoughts, and difficulties with finding comfortable sleeping positions due to pain, disturbed sleep and decreased sleep quality. Secondly, daytime symptoms like reduced energy, with reduced daytime function and reduced quality of life. Thirdly, 24-hour hyperarousal, stress and distressing experiences such as suffering. Finally, contextual factors like treatment reduced and/or increased COMIP. The main contribution of this research is three-fold; mapping of the experiences of living with COMIP, the conceptualization of COMIP, and introduction of novel concepts. Our findings indicate that, when evaluating patients with COMIP, there is value in exploring the four COMIP factors. Although, it varies between individuals which factor/s is central to their COMIP experience, we emphasize that COMIP is distressing, and patients are likely to need emotional support and validation. Keywords: COMIP, sleep, phenomenology, disability, rehabilitation, ICF.

Background: Patient Decision aids (PtDAs) are important tools that support shared decision-making (SDM) between clinicians and patients, enabling patients to be more informed and engaged in decisions regarding their care. The use of PtDAs can increase patient knowledge, reduce decisional regret, and engage the clinician and patient in meaningful dialogue. Shared decision-making relies on decision aids, but how patients receive the PtDA can vary. This qualitative study elucidated patient familiarity with PtDAs and the barriers, needs, and requirements of patients to access and confidently use DAs, including when received via a clinician-facing EHR-integrated Decision Aid Navigator System (DEANS). Methods: We conducted six focus groups (FG) with participants who were recruited from two academic medical centers. Focus groups included an introduction to what a PtDA is and an exploration of how patients or family members, i.e., people who had not made a medical decision for themselves using a PtDA, but who have helped a family member, had previously used PtDAs in their healthcare. The FGs focused on PtDA-users, affordances and barriers to using PtDAs, and PtDA-user needs and requirements of a proposed DEAN patient portal. We applied Rapid Qualitative Analysis (RQA) to summarize the data, using a consensus making approach to identify emerging themes. Results: We identified three overarching themes: 1) Patients had varied familiarity with PtDAs and often confused them with general health tools, but found value in SDM, 2) Patients perceived technology accessibility, PtDA credibility, and time as barriers potentially impeding PtDA use, and 3) Patients valued flexible, user-friendly, and accessible PtDAs that support patient-led decisions, particularly when introduced by clinicians and delivered via a patient portal. Conclusion: This study provides patient-derived insights into factors that influence PtDA uptake, highlighting common misconceptions about PtDAs and identifying key patient priorities such as accessibility, flexibility, clear content, and clinician-guided usage. Patient also expected clinicians to be familiar with PtDAs, especially when the PtDA covers a complex decision. These findings can inform the development of more patient-centered SDM infrastructure and strategies to support PtDA use in clinical practice, including clinician-supported delivery, accessibility, and integration into routine care to strengthen shared decision-making.

Many pathological conditions, including chronic pain (CP), can impair cognitive functions. These functions are essential for adapting and evolving in concordance with the environment, which are key processes for coping with CP. However, in CP centers, cognitive assessment is rarely included in CP patient follow-up due to a lack of time, appropriate tools, or trained health professionals. To fill this gap, our team developed EasyCog, a digital tool that enables rapid self-assessment of cognitive functions. It enables cognitive evaluation without an experimenter's help, thanks to speech recognition powered by an artificial neural network. In the initial validation steps, we established normative values for 7 cognitive functions on a cohort of 76 healthy subjects. Because EasyCog includes tests comparable to the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE), we assessed its reliability against available normative values. To assess its specificity, we compared data from healthy subjects with 15 subjects with mild cognitive impairment (MCI) and 15 age-matched CP patients selected from a medical center. Finally, we tested EasyCog's test-retest reliability by repeating sessions at different time intervals. EasyCog showed excellent sensitivity to age-related decline, similar to MoCA normative values. Several cognitive functions were significantly and differentially impaired in MCI subjects and CP patients compared to normative values in healthy subjects, confirming good specificity. Finally, test-retest analysis showed no effect at a 4-month interval. In conclusion, EasyCog appears to be a convenient and reliable numeric tool for rapidly assessing and monitoring cognitive functions in patients.

Background: Since Michigan's legalization of recreational adult-use cannabis in December 2018, health care providers in the city of Kalamazoo, Michigan, have informally observed an increase in cannabinoid hyperemesis syndrome (CHS) presentations, although the extent of this increase has not been formally characterized locally. CHS is frequently underrecognized and misdiagnosed, often resulting in repeated emergency department (ED) visits and substantial diagnostic costs before a diagnosis is reached. Despite this clinical and financial burden, it is unclear whether consumers are adequately informed of this risk through point-of-sale or public health resources. Methods: A cross-sectional environmental scan was conducted to identify publicly available educational content about CHS on the websites of licensed cannabis retailers in Kalamazoo and relevant public health agencies. Provisioning centers and public health websites were screened using a structured coding rubric developed specifically for the environmental scan to assess the presence, depth, web page placement, and intended audience of CHS-related content. Additionally, the electronic medical records at a local teaching hospital in Kalamazoo were queried to identify ED visits with a primary diagnosis of CHS from January 1, 2010 to December 31, 2025. Results: Before the legalization of marijuana, the hospital recorded six ED encounters with the primary diagnosis of CHS between January 2010 and November 2018, and 274 ED encounters between December 2018 and December 2025 (mean age: 29 years; 157 females, 123 males). The incidence rate of CHS-related ED encounters was 57.2 times higher during the post-legalization period than during the pre-legalization period (95% CI, 26.0-157.3; exact Poisson rate-ratio test, p < 0.0001). Among the licensed Kalamazoo provisioning centers reviewed, only one mentioned CHS as a warning but did not describe symptoms or offer medical guidance. CHS content was not identified on the websites of the state or local health departments. Conclusion: Despite the growing clinical concern regarding CHS, consumer-facing educational resources remain scarce. Although this study cannot establish a causal relationship between the availability of CHS-related educational content and the observed increase in ED encounters following legalization, it identifies a substantial lack of CHS-specific warnings in the evaluated consumer-facing resources. Given the clinical and potential economic burden associated with delayed CHS recognition, we recommend that Michigans existing point-of-sale pamphlet requirement be amended to include CHS-specific warning language.

Background: Patient-facing AI intake chatbots can enter clinical use without regulatory review, so their errors may first surface with actual patients. Objective: To develop and pilot a remote, voice-based standardized patient (SP) method for evaluating an intake chatbot's history-taking, documentation, and patient-centeredness. Methods: Four primary care cases had Easy and Difficult variants. In Phase 1, five SPs completed 40 encounters with internal medicine residents rating portrayal fidelity. In Phase 2, SPs completed 80 encounters with a ChatGPT 4o chatbot, recorded whether critical elements were asked about and addressed satisfactorily, and rated summaries; four debriefing interviews were analyzed. In Phase 3, two blinded clinicians rated transcripts and summaries from 101 encounters (32 resident-led, 69 chatbot-led). Results: Portrayal fidelity averaged 4.52 of 5. The chatbot asked about 59.3% of critical elements in Easy versus 50.2% in Difficult variants (adjusted OR 1.49; 95% CI, 1.15 to 1.93) and addressed 73.3% versus 64.6% (OR 1.47; 1.12 to 1.93); near-ceiling summary quality did not differ. SPs reported missed information and rushed pacing. Reviewer agreement was moderate to substantial for element judgments (AC1 0.49 to 0.70) but poor for totals (ICC 0.10 and 0.21). In exploratory comparisons, residents asked about more elements (adjusted difference 12.5 percentage points; 95% CI, 7.0 to 18.1) and satisfactorily addressed more (9.2 points; 3.8 to 14.6); totals did not differ. Conclusions: SP portrayal was consistent enough for pre-clinical chatbot evaluation, and the method detected lower coverage in harder cases. Documentation ratings alone may miss conversational gaps, which require direct measurement.

Background: Diabetes poses a major global health and economic burden, and effective self management is essential for preventing complications. The National Health Service in England has recently announced plans to shift from analogue to digital services in their 10-year plan. However, digital health technology may also risk widening existing inequalities. MyWay Diabetes is a digital self-management intervention implemented in three regions across England. Understanding the acceptability of online and digital tools to support diabetes self-management, particularly among non users and underserved groups, is essential to ensure equitable engagement and implementation. Objectives: To (1) explore barriers to using online and digital support across diverse demographic groups with type 1 and type 2 diabetes who had not used the MyWay Diabetes platform, and (2) identify factors that could improve uptake and continued engagement. Design: Cross-sectional qualitative study. Methods: We conducted six focus groups with n=35 people living with diabetes (27F, 8M; 40% Asian British Pakistani) across three regions in England. The Theoretical Framework of Acceptability (TFA) informed data collection and analysis. Data were transcribed and analysed thematically using template analysis, mapped onto constructs of the TFA. Results: Digital engagement in diabetes self management was shaped by intersecting personal, social, and healthcare system factors. Participants' confidence with navigating digital environments was constrained by the emotional and practical burden of living with diabetes. Social and structural barriers, including cultural mismatch, accessibility challenges, financial constraints, and reliance on family further limited meaningful use of digital tools. Engagement was strongly influenced by trust in healthcare professionals and services; digital tools were considered acceptable when they complemented person-centred care, but resisted when perceived as replacing personalised, face to face support. Conclusions: Digital support for diabetes self management is more likely to be acceptable when it aligns with people's capabilities, social circumstances, and trusted healthcare relationships. Therefore, to prevent widening inequalities, digital tools should be accessible, culturally relevant, and embedded within supportive, person centred models of care. Lived Experience or Public Contribution: Public contributors with lived experience of diabetes (3F, 3M) reviewed participant materials and pseudonymised focus group transcripts, providing feedback on recruitment, data collection, and interpretation. Their insights informed analysis and theme development, ensuring a lived-experience perspective. Our public co-applicant is a co-author on this manuscript.

Background: Adolescents frequently consult online resources regarding sensitive health topics, including suicide. Although content analyses demonstrate that online platforms contain both suicide-preventive and pro-suicide material, they rarely incorporate subjective data from adolescents at elevated suicide risk. Aims: This exploratory study examined associations between reasons for seeking suicide-related information online and the digital media used to access such content among adolescents with suicidal ideation. Method: Over one year, 131 adolescents aged 13 to 18 with current or past suicidal ideation were recruited from the Emergency Department of an urban pediatric hospital to complete a cross-sectional survey. Participants reported whether they had sought suicide-related information online, the type of information sought, and the online locations used. Mixed-effects regression models examined associations between reasons for online searches and digital media used. Results: Of 131 enrolled adolescents, 113 completed the survey; 69 (61.1%) reported ever seeking suicide-related information online. Searches were more often intended to assist with acting on suicidal thoughts than to reduce suicidal thoughts (63.7% vs. 55.1%). Social media were used for help-seeking searches slightly more often than professional health organization websites (42.1% vs. 31.6%). Among adolescents who used social media for their searches, discussion forums were significantly more likely to be used when seeking information to act on suicidal thoughts (OR=7.0, p=.02). Conclusion: The majority of adolescents with suicidal ideation may seek out suicide-related content online, particularly to facilitate suicide planning. Social media may be used as frequently as professional mental health websites like 988 or The Trevor Project for suicide-related searches.

Introduction: Safety parameters for non-invasive neuromodulation (NINM) remain scattered across consensus documents that differ in format, units, and scope, so pre-session parameter screening stays error-prone and difficult to audit. Objectives: To describe the NINM Safety Parameter Explorer, a browser-based tool that consolidates safety thresholds for six NINM modalities (e.g., TMS, tDCS, peripheral magnetic stimulation, TENS, transcranial ultrasound, and transcutaneous vagus nerve stimulation) into a single rule engine in which every numeric boundary carries an explicit, auditable provenance classification as literature-derived, literature-anchored, or operational default. Methods and Materials: We encoded the tool's rule corpus from published international consensus guidelines and peer-reviewed literature (82 modality threshold rules: 29 literature-derived, 36 literature-anchored, 17 operational defaults; complemented by 5 target-site caps for 87 total evaluative rules, alongside 15 informational reference values), then evaluated it in a simulated, automated internal verification suite of 38 example protocols spanning all six modalities and three expected verdicts, with automated static integrity assertions, regression guards, and behavioural and accessibility unit checks, all under a single pinned software version (v1.1.0-beta). Results: All 38 examples agreed with their declared verdicts (19 safe, 8 caution, 11 unsafe), and all static guards and supporting checks passed. Conclusion: The Explorer shows complete internal self-consistency between its rule corpus, engine, and interface, and its per-rule provenance labelling renders safety reasoning transparent and auditable for research and educational use. The tool is an in-silico protocol planning aid; it is not a medical device and is not intended for clinical decision-making or use in humans.

Lines, drains, and airway devices are commonly used in surgical and inpatient care to support treatment and patient monitoring. While these devices are essential, prolonged device retention is associated with complications such as infection, tissue injury, thrombosis, and extended hospital stay. Understanding how long devices remain in place and how duration varies across device types is important for improving patient safety and guiding device management practices. This study analyzes device duration patterns using Patient Lines, Drains, and Airways (LDA) data containing information on device type, placement time, and removal time across multiple patient encounters. Exploratory data analysis and machine learning approaches are used to examine duration distributions, distinguish short and prolonged device retention, and identify structural patterns across device categories. The findings indicate notable variation in device duration patterns across categories, with identifiable differences in device retention behavior across device groups. This study highlights the value of observational electronic health record data for characterizing device exposure patterns and provides a foundation for future data-driven device management and risk assessment.

Objective: To examine changes in function and pain interference among patients utilizing a digital exercise therapy application (DETA) for knee and shoulder pain. Methods: We conducted a retrospective analysis of data from patients utilizing a DETA for non-operative knee and shoulder pain. Primary outcomes for the study were 8-week changes in physical function, measured using the PROMIS-Physical Function (knee pain) and PROMIS-Upper Extremity Function (shoulder pain); and pain interference, measured using the PROMIS-Pain Interference. Data on patient characteristics was used to examine outcomes among specific patient subgroups. Results: We observed a 3.5 point (SD: 7.8) increase in function and a 3.9 point (SD: 7.5) decrease in pain interference overall (p<0.001), with changes in function and pain interference surpassing the minimal clinically important difference for patients with knee and shoulder pain. Patients with knee pain experienced greater improvements in function than those with shoulder pain (p=0.02), as did patients who report moderate-high levels of exertion during exercise compared to those reporting low levels of exertion (p=0.05). We saw greater reductions in pain interference among those with higher education (p=0.03), those with more acute symptoms (p=0.01) and those reporting moderate-high levels of exercise exertion (p=0.02). Conclusion: We observed a clinically meaningful improvement in pain interference and function among patients utilizing a DETA for knee and shoulder pain. Patients with knee pain, higher levels of education, more acute symptoms, and those who reported higher levels of exertion with exercise experienced the greatest changes in function and pain. Impact Statement: This study indicates that self-guided interventions, such as those delivered through a DETA, may be beneficial for patients with knee and shoulder pain. These results may also inform the development of patient profiles that can be used to identify patients most appropriate to receive care through a mobile application.

Background: Antimicrobial resistance (AMR) is a growing global health threat, and India's high antibiotic consumption makes public knowledge, attitudes, and practices (KAP) a critical determinant of stewardship outcomes. Existing Indian KAP evidence is drawn from small, localised cohorts, leaving a gap in nationwide, digitally-sourced data. This study aimed to describe antibiotic-related knowledge, attitudes and self-reported practices among users of a digital healthcare platform in India, examine demographic and regional variation, and identify potential areas for future stewardship interventions. Methods: A cross-sectional survey was conducted among adult users of the Tata 1mg platform (December 2025-February 2026), with participants invited via an in-app banner. Knowledge and attitude were each scored on a 0-4 scale (4 items); practice was scored as a percentage of applicable items answered favourably, with scoring adjusted for branching logic based on antibiotic-access route. Of 10,726 respondents who started the survey, 4,883 completed it (45.5%). City of residence was mapped to one of six regions (India's five statutory Zonal Council zones plus a North-Eastern region) for 4,187 respondents. Associations with demographic characteristics were assessed using multivariable linear regression, and regional differences were assessed using one-way ANOVA. A parallel survey of 256 physicians was conducted to enable patient-provider comparison. Results: Mean scores were: knowledge 2.28 (SD 1.19), attitude 0.91 (SD 1.14), practice 44.9% (SD 33.4); attitude, not knowledge, was the weakest domain. Older age was associated with higher scores across all domains (practice: B=12.52, p<0.001 for age [&ge;]65 vs. 18-34); city tier was independently associated with lower knowledge and attitude (Tier 3 vs. Tier 1: p=0.010 and p<0.001, respectively). Regional differences were non-significant for knowledge and practice but significant for attitude (F=2.56, p=0.026, ^2=0.003), driven by lower scores in the North-Eastern region. In the parallel physician survey, 55.5% of respondents estimated that more than half of their patients self-medicated before consultation. Conclusion: In this platform-based sample, attitudes were the weakest domain, not knowledge, and 42.0% said their last antibiotic did not come on a doctor's recommendation. The findings may inform digital stewardship touchpoints, such as point-of-purchase guidance and prescription verification, in line with India's National Action Plan on Antimicrobial Resistance 2.0. As a cross-sectional survey of platform users, it cannot establish the relative contributions of knowledge, beliefs and access pathways to antibiotic use.

Objectives: To investigate whether the PneumoRator wearable sensor could accurately assess Respiratory Rate (RR) and signs of air hunger (SAH) compared with clinician-observed assessment in adults with and without breathing pattern disorder (BrPD). Design: A pilot cross-sectional observational study. Setting: A research laboratory on a university campus. Participants: Healthy adults aged 18-40, who were not pregnant, no smokers or history of cardiovascular, respiratory or neurological illness. Thirty consented to the study, Twenty-nine had full data for analysis. Interventions: Participants wore the PneumoRator chest-worn sensor continuously for 10 minutes while seated at rest. Main outcome measures: Respiratory Rate was compared between PneumoRator-derived measurements and randomised 1-minute observer manual counts. Clinician-observed SAH and BrPD were assessed using the Breathing Pattern Assessment Tool (BPAT). Participants also completed the Nijmegen Questionnaire. PneumoRator signals were analysed to identify large-volume breaths as potential SAH events. Results: Thirty participants were recruited. Twelve participants met BPAT criteria for BrPD and four met Nijmegen Questionnaire criteria. There was no significant difference between manual and PneumoRator RR measurements (p=0.89). No significant difference was observed between methods for SAH detection (p=0.17). Participants with clinician-observed SAH demonstrated significantly higher PneumoRator signal amplitudes than those without SAH (p=0.035). Conclusion: The PneumoRator produced RR measurements comparable to clinician assessment and demonstrated potential for identifying signs of air hunger. Findings from this pilot study indicate that the PneumoRator device shows promise as a tool for continuous RR monitoring and as an adjunct to BrPD assessment. Larger clinical validation studies are now warranted.

Objectives: Health professions students often receive limited training on how to adjust their communication for patients whose first language is not English (LX), despite the risks for diagnosis, treatment, and patient trust. Based on Communication Accommodation Theory, we developed a brief module to improve students' awareness and knowledge of strategic communication adjustments for LX patients. Methods: We tested feasibility in a single-group, pre-post pilot study guided by the Kirkpatrick framework with undergraduate (n=22) and graduate (n=41) speech-language pathology and psychology students in Massachusetts and North Carolina. The module included instructional content, video and audio examples, interactive activities, and a podcast. Learning was assessed with knowledge checks, and self-reported facilitative and resistant accommodation with the Communication Accommodation Behavior Scale (CABS). After training, students rated their confidence and likelihood of using accommodation strategies. Results: At both sites, facilitative accommodation increased and resistant accommodation decreased significantly, with a larger decrease at the undergraduate site. Knowledge-check accuracy was high at both time points, suggesting a ceiling effect. Students reported moderate- to-high confidence in adapting their speech and high likelihood of using a learned strategy in future clinical encounters. Conclusions: A brief module can be feasibly delivered across institutions and is associated with short-term improvements in self-reported communication accommodation. Practice implications: We recommend future work evaluate whether these changes translate into clinical behavior long-term and refine knowledge-check items to better capture learning gains.

Background: Clinical decision support systems (CDSS) are increasingly embedded in national telemedicine services, however how different stakeholders who use them experience these tools, particularly in low- and middle-income countries, is less explored. In the assisted model of India's eSanjeevani service, spoke health workers enter structured symptoms and hub clinicians review a symptom summary and an artificial intelligence (AI)-generated differential diagnosis. Objective: To describe role-specific experiences of eSanjeevani's CDSS functions and to estimate how acceptance coexists with specific reported difficulties within each role. Methods: We analyzed a program feedback web survey completed between 11 July and 29 July, 2025 by hub clinicians and spoke providers. Prespecified item profiles covered review of summaries and suggested diagnoses, the perceived relation of suggestions to the clinician's own diagnosis, ease of use and intention to use (hub), and symptom-capture utility, question quantity, question relevance, summary presentation and satisfaction (spoke). Within-role contrasts were reported as absolute differences in acceptance with Newcombe 95% CIs. Short open comments underwent content analysis. Results: The cohort comprised 190 hub and 662 spoke respondents from 13 and 15 states (21.4% and 19.3% of eligible providers). Among hub respondents, 76.8% regularly reviewed the symptom summary and 58.9% the suggested diagnoses; 72.6% judged suggestions matched (32.6%) or appropriate but different (40.0%), 7.9% mostly inappropriate, and 11.1% reported that suggestions were mostly not generated. Overall, 85.3% intended to keep using the tools. Affirmative intention was 98.4% when suggestions matched, 66.7% when they were mostly inappropriate (difference vs matched or appropriate -23.2 percentage points, 95% CI -48.5 to -4.0) and 61.9% when mostly absent (-25.7, 95% CI -47.1 to -7.3); technical issues, reported by 38.4%, made little difference (-2.8, 95% CI -14.1 to 7.3). Among spoke respondents, 63.4% rated question quantity appropriate, with miscalibration in both directions (15.6% too many; 16.6% too few); 89.9% found the questions relevant and 91.8% found the summary clear. Satisfaction was 75.3% and did not differ materially between those reporting too many questions and those reporting an appropriate number (-2.7, 95% CI -12.8 to 6.0). Comments located spoke difficulties in question burden and missing complaints, and hub difficulties in freezing and in the scope and availability of suggestions. Estimates were stable across cohort, ambiguity and state-exclusion sensitivity analyses. Conclusions: Acceptance of eSanjeevani's CDSS was high in both roles. For hub clinicians it tracked the perceived quality and availability of AI suggestions rather than technical friction; spoke workers accepted the question burden, but its two-directional miscalibration points toward complaint-specific, adaptive questioning. These provider reports do not measure diagnostic accuracy. They identify log-based and audit-based indicators that the next evaluation cycle should measure directly.

Objective: The Alimetry Gut-Brain Wellbeing (AGBW) Survey is a validated 10-item scale developed to assess mental health in patients with chronic gastroduodenal symptoms. While the original scale demonstrated strong psychometric properties overall, as part of an ongoing commitment to continuous instrument optimization, this study evaluated a revised, unidirectional version of the scale (AGBW-II) to enhance measurement precision. Methods: The psychometric properties of the revised AGBW-II were evaluated via an anonymous online questionnaire in a sample of 161 patients with chronic gastroduodenal symptoms. Score distributions, factor analysis, reliability, and validity were analyzed and compared with data from the validation study of the original scale (n= 311). Results: The AGBW-II total and subscale scores demonstrated robust reliability and validity, outperforming the original AGBW Survey. Notable improvements in the reliability and validity of the stress subscale were observed following item rewording. Confirmatory factor analysis verified the three-factor model separating the depression, stress, and anxiety subscales, with the AGBW-II showing improved model fit indices. Conclusions: The revised, unidirectional AGBW-II demonstrated improved reliability and validity, while maintaining the underlying psychometric properties of the original scale. Consequently, the AGBW-II provides a reliable, valid, and brief mental health assessment for patients with chronic gastroduodenal symptoms.

Objective To evaluate the information quality, reliability, readability, and content deficits of ChatGPT, DeepSeek, and Doubao in answering frequently asked questions about bee stings. Methods Twenty-five high-frequency patient questions were selected based on Google Trends, domestic health platforms, and allergy guidelines, and submitted to the three LLMs, yielding 75 responses. Two emergency physicians independently assessed quality using GQS, mDISCERN, QAMAI, EQIP, and PEMAT-U/A. Readability was evaluated using six indices (ARI, FRES, GFI, FKGL, CLI, SMOG). Content deficits were classified qualitatively. Twenty emergency department patients evaluated the comprehensibility and credibility of AI responses. Results Significant differences were found among the three models on all quality metrics (all P < 0.01). DeepSeek achieved the highest mDISCERN score (median 21.00); ChatGPT led on QAMAI (25.00) and EQIP (75.00%); Doubao ranked lowest on all quality indicators but highest on PEMAT-U (73.00%). No model met the sixth-grade reading level standard. Eleven content deficits were identified: outdated medical advice (45.45%), omissions (36.36%), and hallucinations (18.18%). Doubao received the highest patient preference (45.0%). Conclusion The three LLMs exhibited distinct strengths: DeepSeek in source reliability, ChatGPT in overall quality, and Doubao in understandability. All models had excessively high readability levels, and outdated medical advice was the predominant content deficit. Objective To evaluate the information quality, reliability, readability, and content deficits of ChatGPT, DeepSeek, and Doubao in answering frequently asked questions about bee stings. Methods Twenty-five high-frequency patient questions were selected based on Google Trends, domestic health platforms, and allergy guidelines, and submitted to the three LLMs, yielding 75 responses. Two emergency physicians independently assessed quality using GQS, mDISCERN, QAMAI, EQIP, and PEMAT-U/A. Readability was evaluated using six indices (ARI, FRES, GFI, FKGL, CLI, SMOG). Content deficits were classified qualitatively. Twenty emergency department patients evaluated the comprehensibility and credibility of AI responses. Results Significant differences were found among the three models on all quality metrics (all P < 0.01). DeepSeek achieved the highest mDISCERN score (median 21.00); ChatGPT led on QAMAI (25.00) and EQIP (75.00%); Doubao ranked lowest on all quality indicators but highest on PEMAT-U (73.00%). No model met the sixth-grade reading level standard. Eleven content deficits were identified: outdated medical advice (45.45%), omissions (36.36%), and hallucinations (18.18%). Doubao received the highest patient preference (45.0%). Conclusion The three LLMs exhibited distinct strengths: DeepSeek in source reliability, ChatGPT in overall quality, and Doubao in understandability. All models had excessively high readability levels, and outdated medical advice was the predominant content deficit.