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.

The literature examining direct-to-consumer (DTC) commercial virtual care has expanded rapidly over the past decade. Our objective was to synthesise the nature and range of evidence on DTC commercial virtual care.

Scoping review.

MEDLINE ALL, EMBASE Classic+Embase, CINAHL, HealthSTAR, PsycINFO, CENTRAL and grey literature sources.

We included original research studies published in English or French between 1 January 2016 and 30 April 2025 that assessed DTC commercial virtual care in all contexts and in all populations.

Screening titles and abstracts, and full-text manuscripts, and extracting data was done in duplicate. We analysed quantitative data using descriptive statistics and reported findings in tables. We provided a narrative summary of textual data.

After excluding duplicates, we identified 8055 studies for title and abstract screening; 691 articles for full-text screening; and 103 studies meeting our inclusion criteria. 32 studies (31.1%) reported financial ties to the virtual care industry. 67 (65.0%) studies were conducted in the USA. Studies were largely quantitative (87/103 (84.5%)) or mixed methods (8/103 (7.8%)) studies and used cross-sectional (85/95 (89.5%)) designs. Most quantitative studies were descriptive, reporting on quality of care, health outcomes, platform characteristics and patient views, with only 24 of the 95 quantitative studies (25.3%) including a control or comparison group. 18 of these 24 studies (75.0%) compared the quality of care, costs and/or utilisation to other models of care and reported variable findings. The rest compared patient characteristics. Few studies assessed clinician perspectives or addressed privacy-related ethical concerns.

Despite a large number of studies assessing DTC commercial virtual care, we have little insight into impacts on quality of care, health outcomes, health system utilisation and privacy-related ethical concerns. The financial ties with industry suggest that there may be bias in the body of research literature.

Statins are a cornerstone of cardiovascular disease prevention yet remain underused among eligible patients. Clinical decision support systems embedded in electronic health records (EHRs) are commonly used to encourage guideline-concordant prescribing. Interruptive reminders (eg, pop-ups) may be effective but interfere with clinical workflows and contribute to alert fatigue. Non-interruptive alerts are less intrusive, but their effectiveness remains unclear. The Interruptive versus Non-Interruptive Reminders for Statin tHerApy in Primary Care (INIRSHA-PC) trial is designed to evaluate the comparative effectiveness of interruptive and non-interruptive reminders on statin-prescribing rates.

INIRSHA-PC is a single-centre, pragmatic, three-arm, parallel-group randomised controlled trial embedded in the EHR at Vanderbilt University Medical Center. The trial will enrol adults aged 18–74 seen in primary care who are eligible for, but not currently prescribed, statin therapy. The planned sample size is 3000 patients (1000 per arm). Enrolled patients will be randomised 1:1:1 to (1) interruptive reminder, (2) non-interruptive reminder or (3) no reminder (usual care). The primary outcome is statin prescription within 24 hours of enrolment. Secondary outcomes are statin prescribing within 12 months and low-density lipoprotein cholesterol levels measured between 30 days and 12 months after enrolment. Enrolment began on 14 August 2024. The study is expected to be completed on 19 November 2025.

The trial has been approved by the Vanderbilt University Medical Center Institutional Review Board with waiver of patient informed consent (IRB number: 240419). Results will be disseminated through peer-reviewed publication and presentation at scientific conferences.

NCT06456658.

Persons with serious mental illness (SMI) often have coexisting medical conditions and experience a significantly reduced life expectancy compared with the general population. Peer support is considered an effective care approach for this population, and with rapid technological advancements, digital peer support, such as the DigiPer mobile application, can be a feasible self-management tool for persons with SMI. The study aims to assess the feasibility of DigiPer for persons with SMI in the Norwegian community mental health service settings.

This feasibility study will incorporate both qualitative and quantitative methods. The study consists of three phases: (1) simulation-based training among peer support workers using qualitative individual interviews; (2) pre–post study of DigiPer among peer support workers and service users using quantitative questionnaires and (3) process evaluation for peer support workers and service users using qualitative individual interviews. Peer support workers (n=5) and service users with SMI (n=15) will be recruited to evaluate the feasibility of DigiPer.

Ethics approval was granted from the Regional Committee for Medical Research Ethics (reference no. 853041), along with an assessment of processing of personal data by the Norwegian Agency for Shared Services in Education and Research (reference no. 810990). Findings will be disseminated through peer-reviewed publications and presentations at relevant national and international scientific conferences.

Digital documentation in patients’ electronic medical records (EMRs) places new demands on perioperative nurses, increasing workload and cognitive strain, with subsequent technostress. While new EMR systems are implemented, they are not always adapted to users’ needs.

This study aimed to explore how perioperative nurses and nursing assistants describe their experiences with electronic documentation during surgery and its impact on their work environment. Additionally, it examined the emotional reactions these experiences triggered, and the adaptive strategies used to manage their effects.

A qualitative study was conducted. Data were analysed using Braun and Clarke’s thematic analysis.

Two university hospitals and one county hospital in Sweden were included.

15 women and 5 men, including 9 specialist nurses in anaesthesia care, 9 nurses in operating room (OR) care and 2 nursing assistants in OR care in Sweden participated.

Two main themes emerged: A—Introducing digital systems without a clear aim undermines work performance and B—Digital systems were embraced when automation and comfort were present. Subthemes included leadership and management, possibilities to develop and influence digital systems and EMRs not adapted to clinical needs. Automation from digital systems made work easier, and digital systems within one’s comfort zone were appreciated. However, frustration and stress arose when aforementioned preconditions were not fulfilled, leading to adjustments to manage these challenges.

Digital documentation is appreciated when fundamental conditions are met. A lack of clarity on how, what and why to document, along with insufficient training and limited ability to have an influence, triggers negative emotional reactions and unhealthy coping strategies. To enhance digital literacy, a standardised process of digital systems including digital documentation through educational efforts in which knowledge control in educational purposes is included could be tested as a potential solution.

There is interest in using predictive models to address non-attendance of healthcare appointments without prior notification. Although several National Health Service (NHS) hospital trusts have piloted predictive models for non-attendance, there is a lack of published evidence in clinical settings.

This mixed-methods evaluation of the pilot of a predictive model intervention in outpatient services aimed to examine (1) the effect of the intervention on patient non-attendance and (2) staff engagement in the delivery of the intervention.

A mixed-methods study across two pilot phases. Quantitative data explored the use and impact of the predictive model on non-attendance. Z-tests were conducted to assess changes to non-attendance rates prepilot and in the two phases. Qualitative ethnographic work included 30 periods of observation and interviews with staff.

Nine outpatient services in an NHS Foundation Trust that volunteered to pilot the predictive model intervention. Qualitative participants were NHS clerical and administrative staff delivering the intervention and service managers.

An off-the-shelf predictive model, consisting of a cloud-based, random forest algorithm, produced a risk score of non-attendance by drawing on information from patients’ electronic health records. Staff in the pilot sites attempted to phone patients with a risk score to remind them of upcoming appointments.

Quantitative analysis showed that in phase 1, there were low volumes of intervention calls made across services, but three of nine outpatient services significantly reduced their non-attendance rate. There was a lower overall call rate in phase 2 among the four remaining participating services. One significantly reduced its non-attendance rate from 20.4% to 19.1% (p

The predictive model intervention was positioned as a simple solution to address a complex problem; however, there were challenges inherent in deployment within a dynamic healthcare setting. The sustainability of the intervention and its impact on patient experience warrants further exploration.

To identify Clinical Decision Support Software (CDSS) that have been implemented in hospital which aim to influence empirical antibiotic prescribing, and to establish their impact on antibiotic prescribing and patient outcomes.

Systematic review & meta-analysis.

MEDLINE, Cochrane Central Register of Controlled Trials and Embase were searched from their inception to February 2024.

Studies evaluating the impact of digital CDSS with the primary purpose of influencing initial empirical antibiotic prescribing for patients with acute infection in hospital.

Study characteristics, intervention characteristics and outcome data were extracted independently by two reviewers. Outcomes were grouped into four domains including clinical outcomes (mortality, length of stay, readmission rates), antibiotic appropriateness (guideline adherence, coverage of causative organism), antimicrobial stewardship and health economics. Risk of bias assessment was conducted using Risk of Bias In Non-randomised Studies - of Interventions for non-randomised studies and Cochrane Risk of Bias 2 for randomised studies. Outcome data with sufficient reporting and homogeneity were synthesised quantitatively using a random-effects meta-analysis; other outcomes were synthesised qualitatively.

15 full texts met the eligibility criteria after screening 7984 unique studies. Low-quality evidence suggested that implementation of CDSS was associated with lower mortality (OR 0.76, 95% CI 0.57 to 1.01) and improved adherence to antibiotic prescribing guidelines (OR 1.75, 95% CI 1.26 to 2.43). No change in length of stay or readmission rates were observed. Coverage of the causative organism was similar after CDSS implementation (OR 1.26, 95% CI 0.97 to 1.63). High-quality evidence supported the association between CDSS implementation and reduced broad-spectrum antibiotic prescribing.

CDSS can be used to reduce the unnecessary prescribing of broad-spectrum antibiotics. Further high-quality studies are required to establish whether their implementation also results in improvements in other outcomes.

CRD42024501185.

To develop and psychometrically evaluate a multidimensional Disaster Health Literacy Questionnaire (DHLQ) for diabetic patients in Iran, using advanced item response theory approaches. The questionnaire was designed in the Persian (Farsi) language.

A sequential mixed-methods study incorporating qualitative (scoping review and interviews) and quantitative (psychometric validation) phases.

Diabetes clinics and healthcare centres across Iran (2022–2023).

The study enrolled 570 patients with diabetes (56% female, mean age 45.57±16.33 years) for quantitative validation; 15 experts and 15 patients for qualitative validation.

The psychometric properties evaluated included content validity (using content validity ratio (CVR) and content validity index (CVI)), construct validity (assessed via multidimensional item response theory (MIRT)), and reliability (measured by Cronbach’s alpha and test-retest Kappa). Additionally, item parameters (multidimensional difficulty (MDIFF) and multidimensional discrimination (MDISC)) and model fit indices (RMSEA, CFI and TLI) were examined.

The final 30-item DHLQ demonstrated excellent content validity (scale-level CVI=1; item-level CVI>0.79; CVR>0.49). Cronbach’s alpha for the total scale was 0.606; test-retest reliability showed significant agreement (Kappa=0.35–1, p

The DHLQ is a rigorously validated tool for assessing disaster health literacy in diabetic populations. Its multidimensional structure and strong psychometric properties support its use in clinical and emergency planning contexts to identify literacy gaps and tailor interventions.

Telehealth has the potential to address challenges faced by the healthcare industry. To achieve the intended goals of telehealth programmes, stakeholders should engage with these services. Prior research demonstrates that perceived value influences stakeholder engagement in a system-based service. Therefore, this review aims to synthesise the value perceptions of telehealth stakeholders.

The review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Articles published between 1 January 2013 and 31 December 2024 were identified through SCOPUS, PubMed and Association for Computing Machinery (ACM) digital library database search, and screening relevant article reference and citation lists.

Articles examining a single-specific telehealth application, proving evidence of post-use value by one or more stakeholder groups were selected.

Two independent reviewers used standardised methods to search, screen and code included studies. Information was recorded related to telehealth type, stakeholders, reported perceived value from the articles and codes were developed successively from specific perceived outcomes.

140 articles were included in the review. The selected studies assessed various types of telehealth applications with a balanced representation of the types of care, telehealth modality and service. The stakeholders were patients and/or healthcare providers; the majority (82.85%) focusing on patients’ view. The reported perceived value outcomes were diverse and categorised into six themes: access to care, care effectiveness and efficiency, quality of care, affective outcomes and human capital. None of the studies reported all these value dimensions and there wasn’t a single value dimension reported by all studies.

The review demonstrates the diversity and fragmentation in perceived value of telehealth. Within each theme, there were variances in how different stakeholders defined their meaning. These insights highlight the multi-dimensional and context-specific nature of perceived value. This comprehensive view of value can inform the design of telehealth programmes to motivate the engagement of all stakeholders.