JMIR Formative Research

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.

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.

Large language models (LLMs) are increasingly consulted for health advice, but they can produce fluent statements that are false or unsupported, known as hallucinations, and this risk is poorly characterised in low-resource languages such as Bangla. We measured hallucinations of locally deployable open-weight LLMs in Bangla diabetes question answering and tested whether retrieval-augmented generation (RAG) grounded in Bangladeshs National Guideline on Diabetes Mellitus reduces them. Forty patient questions spanning six clinical sub-domains were each answered twice, under zero-shot prompting, by Qwen3.5-9B, TigerLLM-9B, Qwen3.6-35B-A3B and Gemma-4-26B-A4B, and by BanglaDiaRAG, which adds hybrid dense (BGE-M3) and sparse (BM25) retrieval from a physician-reviewed Bangla guideline knowledge base to Qwen3.5-9B. Two physicians rated the 400 de-identified responses by consensus for medical accuracy, completeness, safety, readability and reliability on a 0-1 scale, and responses with accuracy below 0.70 were classified as hallucinated. Hallucination rates were 2.5% for Gemma-4-26B-A4B, 3.8% for TigerLLM-9B, 22.5% for Qwen3.6-35B-A3B and 77.5% for Qwen3.5-9B. Guideline grounding lowered the rate of Qwen3.5-9B to 11.3% (difference -66.3 percentage points; 95% CI -76.3 to -55.0) and raised every criterion by 0.24 to 0.38 points (Holm-adjusted p < 0.001). BanglaDiaRAG outscored Qwen3.6-35B-A3B on all five criteria and matched or exceeded TigerLLM-9B, but it remained below Gemma-4-26B-A4B in safety, readability and reliability, and its residual errors were specific, confident and potentially harmful. Guideline grounding can turn a weak general-purpose model into a much safer Bangla diabetes assistant, but the choice of base model mattered as much as retrieval and no configuration was free of hallucination, so clinical oversight remains essential before patient-facing use.

Background Chronic diseases are the leading causes of illness, disability, and death worldwide, with a disproportionate burden in low- and middle-income countries. Hospitalization is a critical outcome in chronic disease management, reflecting disease severity, treatment failure, and healthcare utilization. However, data on hospitalization burden and its associated factors among chronic disease patients in Ethiopia, are limited. This study aimed to determine the rate of hospitalization and its associated factors among patients with chronic diseases attending a tertiary hospital in Wolaita Zone, South Ethiopia. Methods A facility-based cross-sectional study was conducted among 321 patients with chronic diseases attending the chronic follow-up clinic at Wolaita Sodo University Comprehensive Specialized Hospital from November to December 2022. Consecutive recruitment was used, with unique medical record numbers tracked to prevent double recruitment. Data were collected through structured questionnaires and medical record review. Negative binomial regression was used to identify factors associated with hospitalization rate, with incidence rate ratios (IRR) and 95% confidence intervals (CI) reported. A p-value < 0.05 was considered statistically significant. Results Of the 321 participants, 66% reported at least one hospitalization while on treatment, with a mean of 1.70 {+/-} 1.81 hospitalizations per patient. Drug discontinuation was the most frequently documented reason for hospitalization (39.5%), followed by disease complications (22.3%) and infections (11.7%). Multimorbidity was prevalent, with the majority (59.8%) had 2-3 conditions, and a mean of 2.40 {+/-} 1.02 chronic diseases per patient. Male sex was associated with a higher hospitalization rate, while regular physical exercise was associated with a lower number of hospitalizations. Conclusion This study found a high burden of hospitalization among chronic disease patients, with drug discontinuation being the leading documented reason. Male sex and regular physical exercise were significantly associated highlighting the need for medication adherence support programs and structured exercise programs integrated into routine chronic disease care, as well as gender-sensitive interventions.

Rural patients with heart failure (HF) have poor access to care, worse outcomes, and may benefit from at-home monitoring. We evaluated whether foot temperature measured using a thermographic floor mat was associated with invasively measured cardiac output and whether longitudinal temperature changes preceded HF events. We conducted a two-part prospective, observational single-center study using the SmartMat (Podimetrics, Somerville, MA), which acquires plantar thermograms and ambient temperature. In the acute study, patients undergoing clinically indicated right heart catheterization completed 4 scans on the same day. In the chronic study, patients with a HF hospitalization in the past year and New York Heart Association Class II-III symptoms were instructed to use the device and a scale daily for 6 months. A HF event was defined as receiving at least one dose of intravenous or subcutaneous diuretic. Thirty patients were enrolled in the acute study. Mean right hallux temperature positively correlated with thermodilution cardiac output (r=0.63, p<0.001). 42 patients were enrolled in the chronic study. Among patients experiencing an HF event, ambient-adjusted hallux temperature declined in the 15 days immediately preceding the event compared with days 15-30 before the event. Plantar thermograms are associated with invasively measured cardiac output and may detect subclinical worsening before HF events. Foot temperature may provide complementary information regarding HF decompensation, supporting further evaluation of plantar thermography for remote HF monitoring.

Background: Workplace discrimination and violence against health workers including sexual harassment is a societal problem worldwide. We aimed to assess the frequency, predictive factors, and effects of sexual harassment using a nationwide survey of physicians working in Switzerland. Materials and Methods: We conducted a self-administered questionnaire survey of physicians working in Swiss healthcare institutions. The questionnaire assessed experiences of sexual harassment at work, including frequency, types, and sources of harassment. It also asked about the impact of sexual harassment, whether incidents were reported, and what actions were taken following a report. Results: Among the 1837 physicians that participated in the survey, 575 (31.3%) had experienced sexual harassment at least once during their career. Among those reporting sexual harassment, 39.1% of women and 13.2% of men had experienced harassment at least once in their job. Women reported more verbal harassment, while men reported more physical harassment. Women were also more likely to face both forms combined. The main sources of harassment were colleagues of the same profession (46.7%), patients (42.3%), and superiors (41.0%). The most frequently reported impacts included stress (60.1%), emotional exhaustion (34.7%), reduced motivation at work (30%), and considering leaving the job (7.5%). Gender-men was associated with a lower likelihood of experiencing harassment 0.24 (0.18 to 0.31), whereas working in the German-speaking region was associated with a slightly higher likelihood of having experienced sexual harassment at least once (OR 1.35, 95% CI:1.02-1.81). Conclusions: Sexual harassment is common among physicians in Swiss healthcare institutions. It adversely affects the mental health and career trajectories of both men and women yet remains widely underreported due to fears of reprisals and inaction. Institutions should effectively intervene to ensure that reported incidents result in appropriate actions, and that those who report them receive adequate protection from reprisals.

Background: Lean Healthcare has been adopted as an approach to reducing waste, enhancing process efficiency, and optimizing clinical and administrative workflows. However, empirical evaluations aimed at establishing a causal relationship between Lean Healthcare interventions and outcomes remain limited. This evidence gap is particularly pronounced in the context of large-scale implementations in low- and middle-income countries, where more efficient allocation could yield substantial improvements in population health. Methods: We conducted an impact evaluation study employing a staggered difference-in-differences approach to estimate the effects of the Lean Healthcare in Emergency Departments (ED) initiative in the Brazilian context. The intervention was implemented between January 2017 and December 2025 across 309 hospitals. We analyzed hospital-level indicators of the number of hospitalizations and the average length of stay (in days) among patients admitted through urgent or emergency pathways. Results: Our estimates suggest that the intervention was associated with an average increase of 38.6 admissions per hospital per month (p = 0.033), corresponding to approximately a 5.9% increase relative to the monthly admission volume. We also observed a reduction of 0.184 days in the average length of stay (p = 0.058), equivalent to an approximately 3.0% decrease in hospitalization time. These effects exhibited distinct temporal dynamics: the effect on the number of hospitalizations emerged gradually and increased with the duration of the intervention, whereas the reduction in length of stay became evident earlier. Conclusion: This study provides evidence of a causal effect of Lean Healthcare implementation in emergency departments on operational performance. The simultaneous increase in admissions and reduction in length of stay suggests improved bed productivity and effective capacity without expanding physical infrastructure. These findings support the use of flow-management methodologies as an effective strategy for mitigating emergency department overcrowding while increasing service capacity.

Abstract Background: Respiratory failure (RF) and atrial fibrillation/flutter (AF/AFL) often occur together in older adults, but long-term national mortality trends for the two conditions combined have not been described. We examined trends and disparities in deaths with co-listed RF and AF/AFL among U.S. adults aged 65 years and older from 1999 to 2024, and projected rates through 2035. Methodology: We used the CDC WONDER Multiple Cause of Death database to identify deaths with ICD 10 codes J96 (RF) and I48 (AF/AFL) listed together. Age-adjusted mortality rates (AAMRs) per 100,000 were standardized to the 2000 U.S. population and stratified by sex, race/ethnicity, census region, urbanization, state, and place of death. Joinpoint regression was used to estimate annual percent changes (APCs) and average annual percent changes (AAPCs). Forecasts for 2025 to 2035 were generated with an exponential smoothing (ETS) model. Results: A total of 433,598 deaths were recorded. The overall AAMR rose from 12.8 in 1999 to 66.7 in 2024 (AAPC 8.11%; 95% CI, 7.35 to 8.87; P < 0.0001). Three segments were identified: an increase from 1999 to 2018 (APC 7.73%), a sharp rise from 2018 to 2021 (APC 18.10%), and a decline from 2021 to 2024 (APC -5.25%). Males had a higher AAMR and a steeper increase than females (AAPC 8.44% vs 7.81%). Non-Hispanic White adults had the highest mean AAMR, Hispanic adults had the largest relative increase (AAPC 8.72%), and Non-Hispanic Asian/Pacific Islander adults had the smallest (AAPC 6.23%). The Midwest had the fastest regional growth (AAPC 9.35%). From 1999 to 2020, non-metropolitan areas increased faster than metropolitan areas (AAPC 10.09% vs 8.21%). State AAMRs ranged from 24.7 in Ohio to 85.1 in Louisiana. Most deaths occurred in hospital inpatient settings (64.1%), followed by nursing homes (14.1%) and the home (11.9%). The model projects an AAMR of 122.96 per 100,000 by 2035 (95% prediction interval, 85.42 to 160.50). Conclusions: Mortality involving RF with AF/AFL among older U.S. adults increased more than fivefold over 25 years, with a marked surge during the COVID 19 period and only a partial decline afterward. Differences by sex, race/ethnicity, region, and urbanization point to groups that may benefit from earlier detection and coordinated cardiopulmonary care.

Background: Although opioids are frequently prescribed for refractory chronic neuropathic pain, few data are available on baseline factors that may identify individuals more likely to respond and inform individually tailored practice. Methods: This was a post hoc, exploratory analysis of individual-level data from a placebo-controlled trial of methadone for chronic neuropathic pain (NCT05235191). Baseline clinical, psychological, psychophysical and radiological variables were tested for association with 30% or more pain reduction in the methadone group. The correlation between expectation of improvement and pain reduction was examined in both groups. Results: Of the 81 participants completing the 8-week follow-up, 28 (70%) on methadone and 14 (34.1%) on placebo achieved 30% or more pain reduction. In the methadone group, responders had higher baseline expectation of improvement (median 100 [80-100] vs. 55 [50-81]; p=0.004), which correlated moderately with percentage pain reduction (rs=0.508; p<0.001). Neither finding was observed in the placebo group (p=0.211 and p=0.161, respectively), although the correlation did not differ significantly between groups (p for interaction=0.728). Other clinical, sociodemographic, psychological and psychophysical variables were not associated with response. In the subsample with peripheral neuropathic pain and structural MRI (n=37), lower left precentral cortex volume was associated with response to methadone (p=0.041), and lower right paracentral volume with response to placebo (p=0.003). Conclusions: Baseline expectation may influence treatment outcomes, supporting its inclusion as a covariate to improve the precision of treatment effect estimates in trials, and its use in clinical practice to help tailor treatment to individual patients. These exploratory findings require confirmation in larger studies.

Purpose Prescribing to lactating women is constrained by limited evidence. Published compatibility classifications provide clinical guidance and a discrete research comparator. How often prescribing diverges from that guidance, and what characteristics are associated with divergence, is unknown at national scale. Methods Retrospective analysis of prescriptions filled during lactation, identified in the 2012-2014 MarketScan Commercial Claims Database. Prescriptions were classified as concordant (Hale's lactation risk category L1-L3) or discordant (L4-L5). Multivariable logistic regression identified factors associated with concordance. Results Of 96,484 new prescriptions, 95,710 (99.2%) were concordant and 774 (0.8%) were discordant. Discordant prescriptions were filled later after delivery (median 59 vs 33 days) and had a day supply three times longer (30 vs 10 days). In multivariable analysis (N=28,732), concordant prescriptions were associated with maternal age under 35 (adjusted odds ratio (aOR)=1.34, 95% confidence interval (CI): 1.02-1.75) and chronic-condition medications (aOR=10.94, 95% CI: 6.47-18.50), and were less likely within 14 days of delivery (aOR=0.53, 95% CI: 0.38-0.73) and with each additional day of supply (aOR=0.96, 95% CI: 0.96-0.96). Compared with family practice, concordance did not differ for obstetric, internal medicine, or emergency medicine prescriptions, but prescriptions from other provider types had lower odds of concordance (aOR=0.57, 95% CI: 0.41-0.79). Conclusions Prescribing during lactation was overwhelmingly concordant with published compatibility guidance; the rare divergence was patterned by prescription and prescriber characteristics. Because the categories are an expert classification rather than consensus guidance, divergence should not be read as inappropriate prescribing.

Background: Self-harm is a frequent and challenging occurrence in adult inpatient psychiatric units. The use of restrictive practice to manage self-harm incidents continues to be widespread within mental health environments despite increasing national and organisational guidance to minimise its use. Aim: The aim of this project was to introduce and evaluate a continuous quality improvement programme to progressively reduce incidents of self-harm and the use of restrictive practice. The project was set in a female acute mental health inpatient unit. The project sought to embed improvements in an inpatient unit interventions, procedures, and culture and staff mindset, and move towards a sustained collaborative and positive therapeutic and communication-based approach. Methods: A retrospective service evaluation design was employed to assess differences in the number of incidents of self-harm, enhanced observation, seclusion, restraint and rapid tranquillisation in the period following the introduction of a continuous whole-systems multimodal interventional initiative in 2019. Pre- and post-initiation data were compared (2017/18 compared to 2019/2025) to evaluate improvement and whether this had been sustained. Data were analysed using descriptive statistics, including frequencies, percentages, and means. Results: Self-harm incidents reduced between 2017/18 and 2019, with a downward trend sustained between 2019 and 2025 (136 to 72 incidents respectively between 2019 and 2025; 47% reduction). A sustained reduction in the use of restrictive practice was shown in all domains pre- and post-initiation (2017/18 compared to 2025). Clinical implications: No one approach will successfully reduce self-harm and the use of restrictive practice. Approaches employed have to be multi-faceted, multi-interdisciplinary, collaborative, and blend together to deliver improvement. Sustained progress requires commitment to an iterative process of continuous collaboration, improvement and adaptation.

Background: Ambient artificial-intelligence (AI) scribes are being deployed beyond medicine into allied-health practice, but comparative evidence mainly concerns physician documentation. Objective: To compare quality and transcript-grounded clinical-risk error burden of AI-generated and allied health professional notes from identical simulated consultations. Methods: Paired simulation across dietetics, physiotherapy, occupational therapy and speech and language therapy (five consultations each; 20 pairs; 40 notes). Heidi generated an unedited first-draft note and a professional documented each encounter. Professionals rated PDQI-9 quality. Professional and automated review identified unsupported statements and omissions; two language models graded clinical risk. Co-primary outcomes were PDQI-9 score and Critical+High error density. Results: Mean PDQI-9 scores were 38.45 and 35.05 for AI and professional notes (paired difference +3.40, 95% CI +0.01 to +6.79; Wilcoxon p=0.042; dz=0.47). The Bayesian estimate was +3.47 (94% equal-tailed credible interval +0.66 to +6.30; P[AI>professional]=0.989). Automated-reviewer all-error density was 3.35 vs 8.55 per note (professional-minus-AI difference 5.20, 95% CI 3.85 to 6.55); omission density was 1.45 vs 5.20 (difference 3.75, 2.80 to 4.65). Critical+High density was 0.35 vs 0.75 (difference 0.40, -0.10 to 1.00). The pooled quality result was driven principally by dietetics. Conclusions: AI-generated notes achieved modestly higher pooled quality and fewer transcript-grounded errors, chiefly omissions; serious-error evidence was inconclusive. Small discipline samples and incomplete ground truth preclude safety or discipline-wide superiority claims.

Background: Opioids remain the cornerstone of acute postoperative pain management but are associated with potentially serious adverse effects. We aimed to investigate the effect of oxycodone patient-controlled analgesia with adjunctive S-ketamine on pain after lumbar fusion surgery. Methods: In this secondary analysis, we examined oxycodone and S-ketamine exposure and PCA behaviour during the first 24 h. We assessed current pain versus subsequent PCA activity and whether the association between plasma oxycodone and adequate analgesia changed with postoperative time. Results: Plasma concentrations of administered drugs rose rapidly after PCA initiation, but variation was large. Metabolite concentrations accumulated over time. PCA attempt and delivered-dose rates decreased over postoperative time (RR 0.965 and 0.966 per hour, respectively; both P<0.001). Older age was associated with a lower delivered-dose rate (RR 0.905 per 10 years, 95% CI 0.829-0.987). Each 1-point higher current pain score was associated with more subsequent PCA attempts (RR 1.09, 95% CI 1.04-1.14) and delivered doses (RR 1.07, 95% CI 1.03-1.11). The oxycodone-analgesia association weakened over time [interaction P <0.001; odds ratios (Ors) per 10 ng/mL 1.16, 1.13, and 1.00 at 1, 4, and 24 h, respectively]. In active S-ketamine groups, including S-ketamine, norketamine, and 6-hydroxynorketamine concentrations left the pain-PCA association unchanged (RR 1.09 and 1.07) and preserved the time-dependent oxycodone association (ORs 1.30, 1.26, and 1.02). Conclusions: During PCA, drug exposure develops from patient-generated dosing histories while pain and recovery change. Current pain was associated with subsequent PCA activity, and the oxycodone-analgesia association was time-dependent.

Background. Research data enclaves such as the All of Us Research Program do not allow participant-level data, or models fitted to them, to leave, which makes it hard to deliver case-finding models, which flag patients with possibly uncoded conditions for clinician review, to patients and clinicians. Objectives. We describe and technically evaluate a system that fits models outside an enclave from released summary counts and delivers them through standard health information technology interfaces. Methods. We exported dissemination-compliant summary counts from the All of Us Researcher Workbench and, outside the enclave, fitted versioned models for Long COVID, chronic fatigue, fibromyalgia, endometriosis, Lyme disease, lupus, migraine, depression, and anxiety. A model contract (a model file and a written specification), a Python reference implementation, and synthetic Fast Healthcare Interoperability Resources (FHIR) test patients define the expected outputs. One JavaScript scorer runs in a patient demonstration, a SMART on FHIR clinician application, and a CDS Hooks service. An atlas displays aggregate results. We evaluated the system on synthetic records with automated tests, cross-implementation output comparison, launches through the public SMART sandbox, and a counterexample review of the scoring and display rules. Results. The deployed Long COVID model had an area under the receiver operating characteristic curve of 0.67 in a random validation split. The eight self-report models reached 0.61 to 0.82 in identifying self-reporters without a code. The JavaScript scorer reproduced all 162 reference outputs exactly, and all 437 automated tests passed. A counterexample review found 5 classes of failure, including scoring ineligible patients and treating unavailable data as absent, all fixed and added as tests. Conclusions. Models fitted from released summary counts can be delivered through FHIR, SMART on FHIR, and CDS Hooks with reproducible scoring on synthetic records. Real-record interoperability, usability, and prospective clinical performance remain to be evaluated.

Importance: Conditions that are hard to diagnose, several more common in women, may be missing from electronic health records (EHRs), hiding patients from research cohorts and case finding. Objective: To measure how often the EHR contains a diagnosis code for chronic conditions that participants report, and to compare this with national survey benchmarks. Design, Setting, and Participants: This cross-sectional study used the All of Us Research Program Registered Tier Dataset v9. Participants were adults with EHR visits in both 12-month periods from July 2021 to June 2023 who completed a health history survey. We analyzed the data in October 2026. Exposures: Self-reported clinician diagnosis of 10 conditions with ongoing visits or treatment, as well as sex at birth, race and ethnicity, age, and years of EHR records. Main Outcomes and Measures: Share of participants reporting current care who had a qualifying diagnosis code, alone and relative to hypertension and type 2 diabetes, and the ratio of age-standardized coded shares to national survey prevalence. Results: Of 161,480 respondents, 114,610 (73,870 female [64.5%], median [IQR] age 63 [50-72] years) had at least 5 years of EHR records before the survey. Among those reporting current care, 84.6% (95% CI, 84.2%-85.0%) with hypertension and 87.0% (95% CI, 86.5%-87.6%) with type 2 diabetes had a code, compared with 41.3% (95% CI, 38.9%-43.8%) for endometriosis, 39.4% (95% CI, 34.3%-44.8%) for Lyme disease, and 24.4% (95% CI, 23.2%-25.6%) for chronic fatigue. Counting ICD-9-CM and other mapped codes, 55.3%, 51.5%, and 28.3%, respectively, had any qualifying record. Women had lower coded shares than men for hypertension, type 2 diabetes, obesity, depression, and anxiety (crude ratios 0.93-0.96, within-health-system ratios 0.98-1.02) and higher shares for migraine and fibromyalgia (within health systems, 1.11 [95% CI, 1.07-1.15] and 1.41 [95% CI, 1.29-1.57]). Age-standardized coded shares among active patients were 1.1 to 2.4 times national prevalence of the same self-reported diagnoses. Conclusions and Relevance: In this cross-sectional study, fewer than half of participants reporting ongoing care for chronic fatigue, Lyme disease, or endometriosis had a qualifying ICD-10-CM code. Linking each participant's report to their own record measured this gap directly.

Background Chemsex, drug use before/during sex to facilitate and enhance the experience, is undertaken by a minority of gay, bisexual and other men who have sex with men (GBMSM), but evidence of chemsex prevalence in England is limited. This study describes trends in chemsex prevalence between 2022-2025 and identifies factors associated with chemsex amongst GBMSM in England. Methods Data from the RiiSH study (2022-2025), an annual online cross-sectional survey of GBMSM recruited through social networking/dating apps and community partners, were analysed. Trends in chemsex prevalence and adjusted prevalence were estimated using log-binomial regression models. The following seven variables were candidate factors associated with chemsex and were assessed through logistic regression: age, sexual orientation, education, employment, HIV status, HIV PrEP use, and sexual health service (SHS) use. Results 5728 GBMSM were included in this study. Adjusted chemsex prevalence varied from 6.1% in 2022 to 4.2% in 2023, 5.1% in 2024 and 6.8% in 2025. Chemsex was more likely in GBMSM who were: gay (aOR=1.57 versus other sexual orientations; 95%CI 1.11-2.29, p=0.01), unemployed (aOR=3.08 versus employed full-time; 95%CI 2.19-4.26, p<0.001); aged 35-59 versus 16-34 years (p<0.001), with recent HIV PrEP use (aOR=3.79 versus no recent use; 95%CI 2.63-5.56, p<0.001), SHS use (aOR=2.23 versus no recent use; 95%CI 1.76-2.83, p<0.001), and living with HIV (aOR=3.16 versus negative/unknown HIV status; 95%CI 2.43-4.09, p<0.001). Conclusions Chemsex prevalence was low and stable between 2022-2025. PrEP and SHS use were associated with chemsex, supporting integration of specialist support services within SHS.

Autistic adults face considerable challenges in receiving adequate support, from both paid/formal (such as from disability services) and unpaid/informal (for example, from friends and family) sources. Having adequate support can lead to better health and well-being outcomes. However, most instruments designed to assess adequacy of support were not made specifically for/with autistic adults. This paper presents a psychometric validation of the AASPIRE Adequacy of Supports and Information Scale (AASIS), a community-informed adaptation of a previously developed instrument (the Quality of Care and Support scale; Lucas-Carrasco et al., 2011), designed to be relevant and accessible to autistic adults. We also used the AASIS to explore correlates of perceptions of support in a sample of 798 autistic adults. Our confirmatory factor analysis supported a two factor structure for the AASIS (factor one: Supports; factor two: Information). Further testing confirmed the instrument was invariant across several groups, including race/ethnicity, gender, intellectual disability, communication fluency, and self versus proxy-report. Regression analyses suggested that the way in which participants joined the study (i.e., through community advertisements or via established healthcare/disability services) impacted whether they viewed their support as adequate. Additionally, a higher number of adverse childhood events and greater experience of discrimination were related to worse perceptions of support adequacy across both factors. Our findings support the preliminary validity of the AASIS across a variety of autistic people, though further testing is warranted. Findings also point to the importance of addressing the needs of autistic people who are minoritized and/or who have complex trauma histories.

Violence against women is a key contributor to mental health gender disparities, with greater gender gaps observed in low-and middle-income countries. This study examines state-level associations between gender-based violence against women and mental health burden in India over time. We linked 1) annual state-level age-standardised disability-adjusted life year rates (ASDR) from the Global Burden of Disease study for overall mental health disorders, depressive disorders, anxiety disorders, bipolar disorder, schizophrenia and self-harm, with 2) annual state-level gender-based violence rates from the Open Government Data Platform, India. We used descriptive ecological analyses and multilevel modelling to estimate the state-level association between gender-based violence and sex-stratified mental health outcomes over time (2001 - 2021). Results showed higher state-level gender-based violence (GBV) rates were associated with higher state-level ASDR of schizophrenia ({beta}women = 0.39 [95%CI 0.25 - 0.52]; {beta}men = 0.42 [0.29 - 0.55]) and bipolar disorder ({beta}women = 0.02 [0.01- 0.03]; {beta}men = 0.04 [0.03 - 0.05]), after adjustment for state-level social development, but not with other outcomes. Higher GBV rates were associated with a lower gender disparity in depressive disorders. Longitudinal findings revealed that the state-level association between GBV rates and ASDR of overall mental health disorders strengthened over time ({beta}women = 5.08 [3.96 - 6.20]; {beta}men = 3.38 [2.60 - 4.15]), whereas the association weakened for self-harm and schizophrenia. Lagged findings suggested that with an increase in GBV rate, ASDR of overall mental health disorders increased for men over two years and for both sexes over five years. Our findings highlight the importance of considering state-level trends in GBV and related policies, and considering impacts of GBV on both men and women's mental health.

Awareness of comorbidities in people with dementia is vital context for dementia research and clinical management. Natural Language Processing (NLP) applied to free text in the electronic health record (EHR) may help ascertain comorbidity presence. Our aim was to assess the validity of the MedCAT Comorbidity NLP app in a cohort of people with dementia. We used text from the EHR of South London and Maudsley NHS Mental Health Trust via Clinical Records Interactive Search (CRIS), linked to structured data from local primary care (Lambeth DataNet) and hospital admission data in a cohort of 3,543 people with dementia. We evaluated MedCAT Comorbidity, examining internal validity via manual review of text and external validity by cross-validation against linked primary care and hospital data; and comparison of the predictive value of comorbidity measures for mortality. Internal precision was highest for atrial fibrillation (100%) and lowest for diabetes (78%). Cross-validation with linked data varied substantially across conditions, with precision from 96% to 59% and recall 77% to 4%. The median number of comorbidities identified was two using any single source, or three when combined. MedCAT Comorbidity demonstrated predictive ability for mortality (HR 1.11 per condition), although the model fit was better using comorbidity from hospital admission data. In people with dementia, the MedCAT Comorbidity app showed high precision for most conditions during internal validation. The variability of precision and recall on external validity assessment partly reflects incomplete documentation. Results from the MedCAT comorbidity app can assist researchers in understanding comorbidity of cohorts, particularly when primary care or hospital data is not available. Our findings highlight the need to develop quality assurance protocols that include internal and external validity checks before deploying general data extraction apps for specific clinical subpopulations.

Objective: Amphetamine (AMP) and its derivates are commonly prescribed for the management of attention-deficit/hyperactivity disorder (ADHD). This study aimed to 1) quantify AMP's transfer into human milk in lactating women receiving therapeutic doses and 2) contextualize infant exposure. Methods: Milk samples and dyad health histories of thirteen lactating women treated with varying doses of either mixed AMP, dextroamphetamine, or lisdexamfetamine were released for this study from the InfantRisk Human Milk Biorepository. Milk drug concentrations were quantified by validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) and used to calculate standard relative infant dose (RIDmat) and RID standardized to the defined daily dose (RIDDDD) of 15 mg AMP base. Results: Average milk concentrations (Cavg) of AMP base ranged from 23.3 to 297.0 ng/mL and increased linearly (R^2 = 0.92) with maternal daily dose. The RIDmat ranged 3.6-10.8% and RIDDDD ranged 1.4-21.8%. The fitted regression line crossed the 10% RIDDDD threshold at 1.17x DDD, with the upper 95% prediction limit crossing at 0.84x DDD. No adverse event reported by the mother-infant dyads were attributed to AMP exposure, notably including absence of reduced milk supply. Conclusions:The levels of AMP observed in human milk followed a dose predictable manner. The average patient was predicted to remain below the 10% RID DDD safety threshold at total daily doses at or below approximately 28 mg of mixed AMP salts or 59 mg of lisdexamfetamine. Ninety-five percent of patients were predicted to remain below the threshold at or below approximately 20 mg and 40 mg, respectively.