To map and synthesise existing evidence on pregnant women’s perceptions and experiences of social media communication for antenatal care (ANC).

Scoping review.

Four electronic databases (PubMed/MEDLINE, Embase, Web of Science and Google Scholar) alongside ‘grey’ and supplementary searches were conducted between December 202–January 2026.

All studies reporting pregnant women’s perceptions or experiences of social media communication for ANC.

Data were extracted independently by two reviewers using a structured charting framework. Extracted data were synthesised using a descriptive and narrative approach, with pregnant women’s perceptions and experiences analysed through reflexive thematic analysis.

Six studies met the inclusion criteria. Across platforms including WhatsApp, Facebook, Instagram and WeChat, pregnant women generally perceived social media communication as acceptable and beneficial, particularly for accessing trustworthy information, reassurance between visits, peer support and flexible engagement. Experiences varied by platform, moderation model and context. Key challenges included limited personalisation, variability in moderators’ capacity and responsiveness, digital literacy barriers, data affordability, privacy concerns and sociocultural influences. Equity-related considerations were recurrent, highlighting the potential for uneven experiences if digital communication is not carefully designed and standardised.

Social media communication is generally experienced positively by pregnant women as a complement to routine ANC, particularly when professionally moderated and responsive to women’s informational needs. However, variability in experiences and equity-related challenges underscore the need for further research and careful implementation. This scoping review provides a preliminary mapping of the evidence and identifies priorities for future qualitative synthesis, primary research and the development of inclusive, person-centred digital ANC communication strategies.

Data quality in electronic health records (EHRs) is central to data-informed healthcare. Health professionals play a key role in ensuring data quality yet the complexities of clinical data practices remain poorly understood. Previous reviews have focused on specific documentation domains or professions, leaving a gap in understanding the broader individual, organisational, technological and contextual factors influencing data quality in hospital settings. This scoping review aims to identify and map factors that promote or hinder data quality in EHRs among health professionals in hospital settings.

The review will follow the Joanna Briggs Institute (JBI) methodology for scoping reviews and be reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Scoping Reviews (PRISMA-ScR) checklist. Peer-reviewed studies will be identified through comprehensive searches in PubMed, Scopus, Web of Science, CINAHL and Google Scholar. Two independent reviewers will screen titles, abstracts and full texts and extract data using the JBI Extraction Form. Data will be charted and mapped according to the six dimensions of the Digital Health Data Quality Dimension and Outcome (DQ-DO) framework—accuracy, completeness, consistency, contextual validity, currency and accessibility—and analysed across professional groups and hospital contexts.

Ethical approval is not required for this scoping review as it is based on publicly available data. The findings will be disseminated through peer-reviewed publication and presentations at relevant academic and clinical conferences.

The protocol has been registered in the Open Science Framework: https://doi.org/10.17605/OSF.IO/YQ2DX

Healthcare professionals working in busy hospital environments are expected to make multiple back-to-back critical decisions related to patient assessment and treatment. Fatigue from a combination of complex decision-making over multiple patients can lead to less efficient care and an increased risk of error and harm. Artificial intelligence (AI) risk recommendation systems, hereafter referred to as AI risk recommenders, have the potential to reduce the impact of decision fatigue by prompting healthcare professionals with appropriate recommendations for patient care and management. A key barrier to the effective usage of such systems is the establishment of trust and subsequent acceptance among healthcare professionals. However, little is currently known about how trust and acceptance can be engendered. The aim of this review is to develop a theory explaining what influences healthcare professionals’ usage of AI risk recommenders and how trust and acceptance, facilitate their usage of such systems.

We will conduct a rapid realist review to develop a programme theory exploring how trust and acceptance of AI risk recommenders are established among healthcare professionals and how these mechanisms influence system usage. We will use the following databases—MEDLINE (Ovid), EMBASE (Elsevier), the Cumulative Index to Nursing and Allied Health Literature (CINAHL (EBSCOhost)), PubMed, The Cochrane Library, The Institute of Electrical and Electronics Engineers (IEEE) Xplore, The Association for Computing Machinery Digital Library, Scopus (Elsevier), Web of Science (Clarivate) and ProQuest Dissertation and Theses. The review will focus on identifying the resources and processes that stimulate trust and acceptance, leading to the actual use of the system in clinical practice. The review will be guided by the four steps of realist review described by Rycroft-Malone. Article searching and retrieval was conducted on 15 November 2025; full-text screening is ongoing and the review is expected to be completed by May 2026.

This study does not require formal ethics approval, as it does not involve primary research. Findings will be shared in peer-reviewed publications, conference presentations and engagement with relevant policy-makers involved in the development and integration of AI risk recommenders within hospital settings. Through these efforts, we aim to support the effective utilisation of such systems, leading to improved decision-making and patient care outcomes.

CRD420251155251

Pemphigus vulgaris is a rare autoimmune blistering disease characterised by recurrent mucocutaneous erosions, high symptom burden and unpredictable relapse. Current management relies mainly on pharmacological therapy and hospital-based follow-up, with limited real-time monitoring and individualised support in home-based disease management. To address these challenges, this trial aims to evaluate the effectiveness of an Intelligent Multimodal Symptom Assessment and Response System, integrating patient-reported outcomes, wearable physiological data and image-based lesion assessments, to improve symptom management and quality of life in pemphigus vulgaris patients. Primary objective is to evaluate the effectiveness of the Intelligent Multimodal Symptom Assessment and Response System in improving symptom alerting and symptom management outcomes in patients with pemphigus vulgaris. Secondary objectives are to enhance patients’ self-management ability in symptom monitoring and control, to improve treatment adherence throughout the follow-up period, to promote health-related quality of life among pemphigus vulgaris patients, to assess the usability and acceptability of the system from the patients’ perspective.

This is a multicentre, parallel-group, randomised controlled trial. 160 participants will be randomly assigned to either the intervention group (receiving the Intelligent Multimodal Symptom Assessment and Response System) or the control group (receiving standard care). Eligible participants will be adults aged 18 years or older with a confirmed diagnosis of pemphigus vulgaris and active skin or mucosal lesions, who are able to use digital devices and provide written informed consent. Individuals with severe comorbidities, concurrent participation in other clinical trials or cognitive impairments that may interfere with study adherence will be excluded. The intervention will be delivered via a digital platform, integrating electronic patient-reported outcomes, wearable physiological data and lesion images over a 12-week follow-up period.

Ethical approval was obtained from the Institutional Review Board of the School of Nursing, Fudan University (IRB 2025-07-13), the Medical Ethics Committee of the Institute of Dermatology, Chinese Academy of Medical Sciences (2025-KY-034) and the Ethics Committee of Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (2025-452). Approvals were granted on 10 September, 18 July and 23 July 2025, respectively. This protocol is based on V1.0, 13 July 2025 of the protocol. The results of this study will be disseminated through peer-reviewed publications and academic conferences.

ChiCTR2500109711.

Despite extensive efforts in data collection, quality and safety measurement remains a significant global challenge, with limited understanding of how and under what conditions quality and patient safety surveillance systems function effectively. With the aim of informing the development and effective functioning of quality and patient safety surveillance systems, a rapid realist review was conducted to develop a set of theories that address how, why, for whom and in what context quality and patient safety surveillance systems work.

Rapid realist review to inform recommendations and intervention design for the monitoring and evaluation phase of the QS Signals Project, reported according to Realist and Meta-narrative Evidence Syntheses: Evolving Standards (RAMESES) guidelines.

Initial programme theories were constructed based on data collected from key articles on quality and patient safety surveillance systems, consultation with an expert panel, informal meetings with a project team charged with developing a quality and patient safety surveillance system for maternal and infant health and a review of the project’s planning documents. A three-phase iterative search of PubMed, PsycInfo, CENTRAL, CINAHL and grey literature was conducted, including studies in healthcare settings across all patient groups.

Documents were assessed for relevance (alignment with the theory under test), richness (depth of insight) and rigour (trustworthiness and coherence of data).

Context–mechanism–outcome configurations were generated, iteratively refined and grouped under relevant programme theories to contribute to theory refinement.

The review process resulted in the development of 11 final programme theories, identifying mechanisms operating at organisational and national levels. Effective systems were enabled by leadership commitment, organisational readiness for change and a supportive safety culture. Clear governance structures, including defined local and national roles, strengthened accountability and coordination. The establishment of multidisciplinary clinical advisory groups facilitated the selection of meaningful safety indicators. Sustainable financial investment and adequate human and technical resources were critical for implementation. Robust data governance frameworks enhanced trust, transparency and appropriate data use. User-centred system design improved data accessibility and usability, while feedback loops supported learning and continuous improvement.

Quality and patient safety surveillance systems function most effectively when supported by strong leadership, clear governance structures, adequate resources and a learning-oriented culture that enables the meaningful use of safety data. The findings emerging from this review provide comprehensive, practical and testable systems-level programme theories to inform future research on the development of quality and patient safety surveillance systems across diverse healthcare settings and international contexts.

Perinatal mood and anxiety disorders affect more than one in five pregnant individuals. Despite the large percentage of individuals impacted by mood disorders, they continue to remain underdiagnosed and undertreated. Recent interventions such as risk prediction modelling offer opportunities to predict patients at risk prior to symptom onset. Leveraging technology platforms such as electronic health records allow for timely diagnosis and intervention. To date, there are no established clinical decision support (CDS) tools. We hypothesise that subjects who use a CDS aid will find them acceptable, appropriate and feasible.

We will conduct a pilot randomised clinical trial at three Weill Cornell Medicine clinics with randomisation at the clinician level. Patients in the control arm will receive usual care, while those in the intervention arm will receive CDS. The primary implementation outcomes are the acceptability, appropriateness and feasibility. The effectiveness outcomes are mental health service utilisation, EuroQol 5 Dimensions questionnaire (EQ-5D), Edinburgh Postnatal Depression Scale and social support measured by the perceived support scale. We hypothesise that stakeholders will find the CDS acceptable, and the intervention arm will have lower mental health service needs compared with control.

This study has been approved by the Weill Cornell Medicine Internal Review Board. IRB Protocol# 23-07026254.

NCT06818734.

The rising shift from paper-based to electronic health management information systems (EHMIS) among global health systems has shown promising strides over the past decade. Yet, most African health systems have continued to use paper records with attendant gaps and challenges. Most African governments have now started transitioning from paper to EHMIS but lack adequate guidance to support this shift. There is therefore a need for harmonised regional guidance to ensure that such transitions are carried out systematically and take into account country-specific contexts.

The objective of this study protocol is to conduct a scoping review to generate evidence that will inform the development of a comprehensive guide to support countries in the WHO African Region in transitioning from paper-based to EHMIS.

The review will follow established methodological guidance for scoping reviews as outlined by Arksey and O’Malley and further refined by Levac et al and the Joanna Briggs Institute, with reporting guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist. A search strategy will be developed to systematically identify relevant studies from both published and grey literature sources including PubMed, Cochrane Library, Scopus and African Index Medicus. Other sources will include Google Scholar, Emerald Journal, the WHO Regional Office for Africa Library and websites of WHO, ITU and Ministries of Health. Reference lists of the retrieved published articles will be manually searched to identify additional relevant studies. Descriptive qualitative content analysis will be undertaken using the policy analysis framework and key findings will be summarised and presented using tables, charts and maps.

This study does not involve the collection of primary data; therefore, ethical approval will not be required. On completion of the study, findings will be submitted for publication in a peer-reviewed scientific journal and will also be presented at national, regional and international conferences to support knowledge sharing and policy engagement.

Traditional fall risk tools are often inaccurate and burdensome. This study aims to develop a predictive model for inpatient falls and identify the most influential variables using machine learning applied to electronic health record data.

A retrospective cohort study.

A large tertiary university hospital in Türkiye.

Adult patients (≥18 years) hospitalised in a university hospital between January 2017 and June 2023.

Occurrence of inpatient falls recorded in incident reporting systems.

A total of 518 fallers were identified and compared with 3121 non-fallers. Fallers were significantly older (median 68.5 vs 64 years, p

Machine learning models using electronic health data can predict inpatient falls and reveal key risk factors. The random forest quantile classifier offers a promising approach for improving fall risk prediction in imbalanced clinical datasets.

Patient awareness of their diagnosis and management plan is crucial for improving compliance, empowering patients and enhancing outcomes. We aimed to assess surgical patients’ awareness of their diagnosis, management plans and associated factors.

A cross-sectional study was conducted from December 2024 to March 2025 on 400 adult surgical inpatients who had undergone surgery in the general surgery, gynaecology and obstetrics, and orthopaedic wards at Debre Tabor Comprehensive Specialized Hospital, Ethiopia. Data were collected using a structured written questionnaire and analysed using the SPSS V.25. Bivariate and multivariate logistic regression were used to identify factors associated with patients’ awareness of their diagnosis and care plan, with significance determined using adjusted ORs and 95% CIs.

Overall, 52% of respondents had global awareness of their clinical conditions and management plans. Awareness was highest for clinical diagnosis (78.9%), necessity of admission (78.9%) and operations performed (72.0%). However, more than 50% of respondents did not seek information on the diagnosis, possible cause and investigation related to their condition. In multivariable analysis, patients with tertiary education were 7.12 times more likely to have global awareness than those without formal education (adjusted OR, AOR=7.12; 95% CI 1.95 to 25.95), and patients living in urban areas were 3.15 times more likely to have global awareness than those in rural areas (AOR=3.15; 95% CI 1.63 to 6.10; p

Awareness of various aspects of healthcare ranged from 35.5% to 78.9%, with about half of respondents demonstrating global awareness of their diagnosis and management plans. Implementing shared decision-making models may improve patients’ understanding of their care plans.

Artificial intelligence (AI)-driven chatbots have been rapidly adopted across research, education, business, marketing and medicine. Most interactions, however, come from non-experts using chatbots like search engines, including for everyday health and medical queries.

We conducted an original study to audit chatbot responses in health and medical fields prone to misinformation.

Five popular chatbots were assessed: Gemini (Google), DeepSeek (High-Flyer), Meta AI (Meta), ChatGPT (OpenAI) and Grok (xAI). In February 2025, each chatbot was prompted with 10 questions from five categories: cancer, vaccines, stem cells, nutrition and athletic performance. We deployed an adversarial-like framework, using open- and closed-ended prompts designed to strain models toward misinformation or contraindicated advice. Two experts from each category rated responses as ‘non-problematic’, ‘somewhat problematic’ or ‘highly problematic’ using a coding matrix based on objective, predefined criteria. Citations were scored for accuracy and completeness, and each response was given a Flesch Reading Ease score.

Nearly half (49.6%) of responses were problematic: 30% somewhat problematic and 19.6% highly problematic. Response quality did not differ significantly among chatbots (p=0.566) but Grok generated significantly more highly problematic responses than would be expected under a random distribution (z-score +2.07, p=0.038). Performance was strongest in vaccines (mean z-score –2.57) and cancer (–2.12), and weakest in stem cells (+1.25), athletic performance (+3.74) and nutrition (+4.35). Chatbot outputs were consistently expressed with confidence and certainty; from 250 total questions, there were only two refusals to answer (0.8%), both from Meta AI. Reference quality was poor, with a median completeness score of 40% (Q1–Q3: 20–67%). Chatbot hallucinations and fabricated citations precluded any chatbot from producing a fully accurate reference list. All readability scores were graded as ‘Difficult’ (30–50), equivalent to college sophomore–senior level.

The audited chatbots performed poorly when answering questions in misinformation-prone health and medical fields. Continued deployment without public education and oversight risks amplifying misinformation.

To develop and evaluate an explainable machine learning framework enhanced with synthetic data generation to predict unplanned 30-day hospital readmissions among patients with chronic obstructive pulmonary disease (COPD), heart failure (HF) and type 2 diabetes mellitus (T2DM), and to identify key clinical and social predictors of readmission.

A retrospective cohort study using electronic health record data incorporating both structured variables and information extracted from unstructured clinical notes. Synthetic data were generated using advanced resampling and deep learning-based techniques to address outcome imbalance and improve model training.

Intensive care unit and general ward admissions at a single tertiary academic medical centre included in the MIMIC-IV (Medical Information Mart for Intensive Care IV) database.

Adult patients (≥18 years) were admitted with a primary diagnosis of COPD (n=14 050), HF (n=7097) or T2DM (n=12 735) between 2008 and 2019, with complete 30-day follow-up and no in-hospital mortality during the index admission.

The primary outcome was unplanned all-cause hospital readmission within 30-days of discharge. Predictors were drawn from six domains, including demographics, comorbidities, clinical acuity, therapies, behavioural factors and care continuity. Predictive performance was evaluated using multiple machine learning methods and fivefold cross-validation, with model interpretability assessed using established goal and local explanation approaches.

Ensemble-based machine learning models demonstrated the strongest predictive performance across all three disease cohorts. Key predictors of readmission included higher illness severity, greater comorbidity burden, medication non-adherence, gaps in preventive care and limited social support. Models incorporating synthetic data augmentation showed improved discrimination compared with models trained on original data alone.

An explainable synthetic-data driven framework incorporating clinical, behavioural and social data can support prediction of 30-day readmissions among patients with common chronic conditions using routinely available electronic health record data.

Non-communicable diseases (NCDs) have become the leading cause of mortality globally, with a sharp rise in Iran due to lifestyle changes and urbanisation. Although many NCD risk factors are modifiable, limited understanding of their determinants hinders effective prevention. To address this, the Prospective Epidemiological Research Studies in Iran (PERSIAN) Cohort was established in 2014 to study NCDs nationwide. The Sabzevar PERSIAN Cohort Study (SPECS) is the first in northeastern Iran, aiming to investigate environmental and social factors influencing NCDs in a unique regional context.

SPECS enrolled 5174 adults (aged 35–70 years) in northeastern Iran between January 2018 and January 2019 through a census and an online registration process. The baseline data collection included demographic verification, informed consent, health questionnaires, anthropometric measurements and biological samples (blood, urine, hair, nails). The annual follow-up began in April 2019, with full reassessments every 5 years over a 15-year period. The data is gathered via an active and passive follow-up, supported by trained staff and registry linkages.

Of the 5174 participants, 4241 (81%) remained in the study. Among the cohort, 54.5% were female, with a mean age of 50.5 years. The majority were married (93.5%), and nearly half had at least high-school education (46.5%) and moderate socioeconomic status (49.4%). Drug abuse history (smoking/drugs) was reported by about 15% of the sample. The mean body mass index was 26.9 kg/m², and the average blood pressure was higher in males (118.1/74.0 mm Hg) than in females (111.5/70.2 mm Hg). The common conditions included hypertension (22.8%), kidney stones (22.4%), fatty liver (15.4%) and diabetes (13.8%). Cancer had the highest treatment rate (100%), while fatty liver had the lowest (70.1%). Stroke had the highest mean age of onset (51.2 years), and epilepsy the lowest (23.7 years). All health data were self-reported.

SPECS, part of the national PERSIAN cohort initiative, is the only adult NCD-focused study in Khorasan Razavi. Its 15-year follow-up aims to generate region-specific insights into the incidence of NCDs and their risk factors. The ethnically homogeneous sample enhances statistical power, and the findings may inform culturally tailored health policies. While self-reported data have limitations due to bias, high initial participation and access to free healthcare support long-term engagement, especially among lower-income groups.

Clinical documentation is a significant driver of burnout among physicians. Ambient artificial intelligence (AI) scribes, which leverage generative large language models to automate the creation of clinical notes from patient–physician conversations, are rapidly emerging as a potential solution. While these tools promise to enhance efficiency and reduce administrative tasks, concerns about the quality, accuracy and potential biases persist. There is now a need for a systematic synthesis of evidence to evaluate the impact of these technologies in clinical practice. To assess the effects of ambient AI scribes on physicians’ clinical documentation, the specific objectives are to: (1) evaluate the effectiveness of these tools on documentation, including accuracy and completeness; (2) synthesise evidence on the impact on physician efficiency after adoption, including time spent on documentation and (3) examine physicians’ satisfaction with these tools, including physicians’ perceived burden.

A systematic review of quantitative or mixed-method studies as well as preprints will be conducted. We will perform a comprehensive search of four electronic databases (PubMed, IEEE Xplore, APA PsycInfo and Web of Science, along with medRix and ClinicalTrials.gov for preprints) for empirical studies published between January 2023 and March 2026. The review will synthesise studies comparing physicians’ use of ambient AI scribes with traditional documentation approaches. Given the anticipated heterogeneity of the studies, a narrative synthesis will be employed to summarise the findings. Where common quantitative outcomes exist, effect sizes will be calculated using Hedges’ g, mean differences or risk ratios/odds ratios as appropriate. The overall quality of evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework.

As no patient data are involved in the data collection, no ethical approval is acquired. Results will be disseminated in a peer-reviewed, open-access journal, and presented at relevant academic conferences.

CRD420251149086.

Medication errors pose a significant threat to public health. Despite efforts by health agencies and the implementation of various interventions, such as staff training, medication reconciliation and automation, the persistence of these incidents highlights the need for more effective, scalable solutions. In recent years, machine learning (ML) has emerged as a promising approach in healthcare, offering potential to detect and predict medication errors through data-driven insights. This scoping review aims to systematically map the existing literature on ML-based approaches to predict or detect medication errors across all stages of the medication use process. The review seeks to identify the range of ML applications in this domain, characterise methodological trends and highlight current knowledge gaps. The findings will provide a structured and accessible overview for both clinicians and researchers, supporting the development of safer, more data-informed medication practices.

The review will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guideline. Structured searches will be performed in PubMed, Embase and Web of Science, covering publications from 1 January 2015 to 28 April 2025. Predefined inclusion and exclusion criteria will be used to identify eligible studies. Key information—including ML models, data sources and type, evaluation methods and clinical contexts—will be extracted and analysed using descriptive statistics, visualisations, thematic analysis and narrative synthesis.

This study involves a review of existing literature and does not involve human participants, personal data or unpublished secondary data. As such, ethical approval was not required. All data analysed were obtained from publicly available sources. Findings of the scoping review will be disseminated through professional networks, conference presentations and publications in scientific journals.

This protocol has been registered on the Open Science Framework (https://doi.org/10.17605/OSF.IO/38SFY).

To identify barriers and facilitators to implementing an electronic shared decision-making tool for managing anticoagulant-related drug-drug interactions that affect bleeding risk in routine clinical care.

Preimplementation qualitative study using semistructured interviews.

Three academic medical centres in the southeastern and western USA. Interviews were conducted between 27 March and 25 September 2024.

36 participants, including 19 clinicians involved in prescribing or managing anticoagulants and seventeen patients prescribed anticoagulants, were recruited using purposive and convenience sampling.

Participants identified multiple barriers and facilitators to tool implementation. Common barriers included limited visit time, challenges integrating the tool into existing workflows, role and scope-of-practice constraints, and variation in patient digital literacy. Facilitators included clear visualisation of bleeding risk, access to supporting evidence, familiar interface design and perceived potential to support patient engagement and shared decision-making. Several determinants functioned as both barriers and facilitators, depending on clinical context and user role.

This preimplementation qualitative study identified context-specific determinants that influence the adoption of an electronic shared decision-making tool for anticoagulant-related drug–drug interactions. Findings highlight the importance of early attention to workflow integration, role alignment and usability to support uptake in routine care. Addressing these factors during design and implementation may inform strategies to support adoption and future evaluation in real-world clinical settings.

Global ageing populations require accessible, non-invasive tools for early detection and monitoring of neurological chronic and neurodegenerative diseases. Current diagnostic methods face limitations including invasiveness, high costs and infrequent clinical assessments. The human voice has emerged as a promising digital biomarker, with vocal characteristics reflecting physiological and cognitive changes associated with conditions like dementia and Parkinson’s disease. While artificial intelligence (AI) and machine learning have enabled sophisticated vocal analysis, the literature remains fragmented without comprehensive synthesis. This scoping review protocol delineates a systematic approach to collate and synthesise existing research on the application of AI-driven audio biomarkers for the detection and management of neurological diseases (eg, cognitive decline, Parkinson’s disease, Alzheimer’s, dementia and depression) in older adults aged 65 years and above.

This scoping review will be conducted in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and the methodological framework proposed by Arksey and O’Malley, incorporating recent methodological advancements. The eligibility criteria for study selection will be formulated using the PCC (Population, Concept, Context) framework. A comprehensive literature search will be performed across several electronic databases, including PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, Embase, Compendex, CINAHL, Scientific Information Database (SID), Magiran, IranMedex and Barakat Knowledge Network System (BKNS). The search will encompass peer-reviewed articles published in Persian and English from 1 January 2012 to 31 March 2026. Two independent reviewers will screen titles, abstracts and full texts and extract data according to the predefined PCC-based eligibility criteria. Discrepancies will be resolved through discussion or, if necessary, by consultation with a third reviewer. The results will be synthesised and presented narratively, accompanied by summary tables, charts and figures to address each research question.

The Research Ethics Committee of Tabriz University of Medical Sciences approved the protocol for this scoping review (approval number: IR.TBZMED.VCR.REC.1404.223). They concluded that since the review involves only analysis of existing literature without direct patient involvement or clinical procedures, it meets the relevant ethical standards. Results from the review will be shared through peer-reviewed journals and conference presentations to provide valuable insights for researchers, clinicians and policymakers on the use of audio-based biomarkers in older adults.

Not registered.

Healthcare logistics involves the coordination of resources, services and infrastructure to ensure timely and efficient care delivery. Process mining offers data-driven insights into logistical workflows such as patient transport, inventory management and scheduling. This systematic review aims to synthesise evidence on the application of process mining in healthcare logistics, focusing on its impact on operational efficiency, resource utilisation and service delivery.

A systematic search will be conducted in MEDLINE, Embase, Google Scholar, Web of Science and ABI/Inform for studies published from 1999 onward. Eligible studies will include observational studies, case reports, conference papers and meta-analyses focusing on process mining applications to logistical processes in healthcare settings. Studies screening, data extraction and methodological quality assessment will be conducted using the Mixed Methods Appraisal Tool. Data will be extracted on key dimensions and performance indicators and will be presented in a structured format. A narrative synthesis will be conducted, and findings will be categorised and thematically analysed where appropriate. Primary outcomes include improvements in logistical efficiency, traceability, resource utilisation and sustainability. Secondary outcomes include implementation challenges, data integration issues and limitations in applying process mining techniques to logistical workflows.

The results of the systematic review will be disseminated via publication in a peer-reviewed journal and presented at a relevant conference. The data we will use do not include individual patient data, so ethical approval is not required.

CRD420251164812.

This study investigates the potential of digital health technologies (DHTs), such as wearable devices and smartphones, to complement traditional submaximal functional capacity tests, such as the 6 min walk test (6MWT) and the timed up and go test (TUG). While these traditional tests are widely used due to their simplicity and relevance to daily living activities, they have limitations, including infrequent administration and the need for clinical observation. DHT offers continuous, real-world monitoring, which may accurately reflect patients’ health status and effectively inform clinical decisions. However, there is a need to establish the validity of the data and metrics computed through DHT and understand patient perspectives on using such technology.

This is an observational pilot study (Synergy Digital Health study) that aims at linking wearable data with traditional test outcomes and assessing participants’ acceptance and usage of such DHT. A cohort of 30 cardiovascular patients from Oxford University Hospitals, UK, and 30 community-dwelling elderly people from social centres in Helsingborg, Sweden, will use wearable devices for 2 months in free-living conditions, they will fill out technology acceptance questionnaires (AQs), have baseline assessments and perform physical tests such as the 6MWT and TUG using the Mobistudy smartphone app. Subgroups will participate in codesign workshops to identify experience-based design recommendations for the technology. Quantitative and qualitative methods will be adopted to analyse the collected data.

The study protocol received ethical approval in Sweden from the Etikprövningsmyndigheten (2024-04886-01) and in the UK from the National Health Service (NHS) Research Ethics Committees (Iras project ID: 340870), in accordance with local regulations. All participants are asked for written informed consent. The results of the study will be shared via scientific journals and conferences.

To determine if communication disorders (1) increase the risk for common mental and physical health conditions and (2) if risk varies by age of onset (≤25 years (developmental) or >25 years (acquired)) by using the large-scale All of Us Research Program participant-reported survey data to electronic health records (EHR) data. We hypothesised that adults with a communication disorder would have a higher risk of mental and physical health conditions.

A retrospective cross-sectional study.

Secondary analysis of EHR and online surveys conducted in the USA.

We assessed 410 360 US adults enrolled in the All of Us Research Program from August 2023 to May 2024 for study eligibility. We used medical diagnosis of a communication disorder from EHR data to group participants into communication disorder (CD) and typical communication (TC) groups, and age of first diagnosis to assign to age of onset (≤25 years (developmental) or >25 years (acquired)) groups. 234 519 participants (median (IQR) age 57.00 (41.00, 68.00); 3700 (1.6%) qualified for the CD group) were included in the analyses.

Primary outcome measures were diagnosis of 11 common mental and physical health conditions from EHR data.

Multiple logistic regression models with propensity score weighting revealed that participants with CD had higher odds for attention deficit hyperactivity disorder, anxiety, asthma, cancer, chronic kidney disease, cardiovascular disease, depression, diabetes and hypertension. Estimates for chronic kidney disease (acquired: adjusted OR (AOR), 1.89 (1.62, 2.20); developmental: AOR, 1.26 (0.42, 3.82)), diabetes (acquired: AOR, 1.64 (1.49, 1.81); developmental: AOR, 1.51 (0.95, 2.41)), hypertension (acquired: AOR, 2.02 (1.85, 2.19); developmental: AOR, 1.16 (0.80, 1.68)) and substance use (acquired: AOR, 1.76 (1.47, 2.12); developmental: AOR, 1.08 (0.65, 1.82)) varied by age of onset. Confounding factors are controlled in the analysis, such as age, income, employment, enrolment, sex at birth, gender identity and US census division.

Our study demonstrates that adults with CD experience health disparities compared with adults with TC, and that these disparities vary by age of onset of CD.

Research has increasingly underscored the impact of factors such as socioeconomic status, education, healthcare access, housing and environmental conditions in shaping population health outcomes. These factors, collectively called social determinants of health (SDOH), provide crucial context for understanding drivers of health outcomes. In sub-Saharan Africa (SSA), the study of SDOH is critical due to the region’s unique sociocultural and economic conditions. Understanding how SDOH interacts with health systems and capturing SDOH in data is crucial for informing modelling efforts and policies improving population health more effectively. This scoping review aims to map the types of data used to capture SDOH in research conducted in SSA, to identify research gaps and to summarise key findings.

This scoping review will follow the Arksey and O’Malley methodological framework, enhanced by Levac et al, providing best practices for identifying, selecting and analysing eligible studies. Key steps include (1) identifying the research question, (2) identifying relevant studies, (3) selecting eligible studies via a locally curated search, (4) extracting information, (5) collating, summarising and reporting results and (6) consultation with stakeholders.

Ethical approval is not required, as this review relies solely on published literature. Findings will be disseminated across academic channels (journals, conferences) and through targeted stakeholder engagement efforts, such as policy briefs and public health workshops, to reach policymakers, healthcare practitioners and community health organisations. This dissemination strategy aims to inform health policy and drive programme development in SSA.