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Background: Identifying key variables is essential for developing clinical outcome prediction models based on high-dimensional electronic medical records (EMR). However, despite the abundance of feature selection (FS) methods available, challenges remain in choosing the most appropriate method, deciding how many top-ranked variables to include, and ensuring these selections are meaningful from a medical perspective. Methods: We developed a practical multi-step feature selection (FS) framework that integrates data-driven statistical inference with a knowledge verification strategy. This framework was validated using two distinct EMR datasets targeting different clinical outcomes. The first cohort, sourced from the Medical Information Mart for Intensive Care III (MIMIC-III), focused on predicting acute kidney injury (AKI) in ICU patients. The second cohort, drawn from the MIMIC-IV Emergency Department (MIMIC-IV-ED), aimed to estimate in-hospital mortality (IHM) for patients transferred from the ED to the ICU. We employed various machine learning (ML) methods and conducted a comparative analysis considering accuracy, stability, similarity, and interpretability. The effectiveness of our FS framework was evaluated using discrimination and calibration metrics, with SHAP applied to enhance the interpretability of model decisions. Results: Cohort 1 comprised 48,780 ICU encounters, of which 8,883 (18.21%) developed AKI. Cohort 2 included 29,197 transfers from the ED to the ICU, with 3,219 (11.03%) resulting in IHM. Among the ten ML methods evaluated, the tree-based ensemble method achieved the highest accuracy. As the number of top-ranking features increased, the models' accuracy began to stabilize, while feature subset stability (considering sample variations) and inter-method feature similarity reached optimal levels, confirming the validity of the FS framework. The integration of interpretative methods and expert knowledge in the final step further improved feature interpretability. The FS framework effectively reduced the number of features (e.g., from 380 to 35 for Cohort 1, and from 273 to 54 for Cohort 2) without significantly affecting prediction performance (Delong test, p > 0.05). Conclusion: The multi-step FS method developed in this study successfully reduces the dimensionality of features in EMR while preserving the accuracy of clinical outcome prediction. Furthermore, it improves the interpretability of risk factors by incorporating expert knowledge validation. [ABSTRACT FROM AUTHOR]

Background: Despite substantial progress in maternal and neonatal health, Rwanda's mortality rates remain high, necessitating innovative approaches to meet health related Sustainable Development Goals (SDGs). By leveraging data collected from Electronic Medical Records, this study explores the application of machine learning models to predict adverse pregnancy outcomes, thereby improving risk assessment and enhancing care delivery. Methods: This study utilized retrospective cohort data from the electronic medical record (EMR) system of 25 hospitals in Rwanda from 2020 to 2023. The independent variables included socioeconomic status, health status, reproductive health, and pregnancy-related factors. The outcome variable was a binary composite feature that combined adverse pregnancy outcomes in both the mother and the newborn. Extensive data cleaning was performed, with missing values addressed through various strategies, including the exclusion of variables and instances, imputation techniques using K-Nearest Neighbors and Multiple Imputation by Chained Equations. Data imbalance was managed using a synthetic minority oversampling technique. Six machine learning models—Logistic Regression, Decision Trees, Support Vector Machine, Gradient Boosting, Random Forest, and Multilayer Perceptron—were trained using 10-fold cross-validation and evaluated on an unseen dataset with–70 − 30 training and evaluation splits. Results: Data from 117,069 women across 25 hospitals in Rwanda were analyzed, leading to a final dataset of 32,783 women after removing entries with significant missing values. Among these women, 5,424 (16.5%) experienced adverse pregnancy outcomes. Random Forest and Gradient Boosting Classifiers demonstrated high accuracy and precision. After hyperparameter tuning, the Random Forest model achieved an accuracy of 90.6% and an ROC-AUC score of 0.85, underscoring its effectiveness in predicting adverse outcomes. However, a recall rate of 46.5% suggests challenges in detecting all the adverse cases. Key predictors of adverse outcomes identified in this study included gestational age, number of pregnancies, antenatal care visits, maternal age, vital signs, and delivery methods. Conclusions: This study recommends enhancing EMR data quality, integrating machine learning into routine practice, and conducting further research to refine predictive models and address evolving pregnancy outcomes. In addition, this study recommends the design of AI-based interventions for high-risk pregnancies. Clinical trial number: Not applicable. [ABSTRACT FROM AUTHOR]

Background: Self-management education is integral for proper asthma management. However, there is an accessibility gap to self-management education following asthma hospitalizations. Most pediatric patients and their families receive suboptimal or no education. Objective: To implement a comprehensive pediatric asthma education program and evaluate subsequent self-management knowledge in patients as well as behavior change outcomes reflected in the frequency of asthma related repeat emergency department visits and hospitalization. The program implementation was informed by the Knowledge to Translation Action Framework and the i-PARIHS model for quality improvement and involved several iterative stages. Methods: We implemented a comprehensive asthma education program for the families of all children 0-18 years old who had been admitted for an asthma exacerbation to the Children's Hospital of Eastern Ontario (CHEO), beginning on April 1, 2018. The program was adapted to the stages of the Knowledge Translation to Action Framework including undertaking an environmental scan, expert stakeholder feedback, reviews, addressing barriers, and tailoring the intervention, along with evaluating knowledge and health outcomes. Education was delivered over 1-2 h in personalized individual or small group settings, within 4 wk of hospital discharge. All education was provided by registered nurses or respiratory therapists who were also certified asthma educators. The EPIC electronic medical record was used to facilitate referral and scheduling of asthma education sessions, and to track subsequent acute asthma visits. We compared the frequency of a repeat asthma emergency department (ED) visit or hospitalization within 1-year following an initial asthma hospitalization for children who would have received comprehensive asthma education, to a historical cohort of children who were hospitalized between April 9, 2017 – Apr 8, 2018, and did not receive asthma education. Results: The program had a high enrollment, capturing nearly 75% of the target population. Most families found the program to be acceptable and reported increased knowledge of how to manage asthma. We identified a crude overall 54% reduction in repeat hospitalizations among children 1 year after implementation of the asthma education program (i.e. 10.2% (23/225) repeat hospitalization rate pre- implementation versus 4.8% (11/227) post-implementation). In adjusted time-to event analysis, this reduction was prominent at 3 months among those who received comprehensive asthma education, relative to those who did not, but this improvement was not sustained by 1 year (HR =1.1, 95% CI =0.55- 2.05; p-value = 0.6). Discussion: Although we did not find long-term improvements in ED visits, or hospitalizations, in children of caregivers who participated in comprehensive asthma education, the asthma education program holds potential given that most patients found it to be acceptable and that it increased asthma management knowledge. A future asthma education program should include multiple sessions to ensure that the knowledge and behavior change will be sustained, leading ultimately to long-term reductions in repeat ED visits and hospitalizations. [ABSTRACT FROM AUTHOR]

Speech and language disorders are known to have a substantial genetic contribution. Although frequently examined as components of other conditions, research on the genetic basis of linguistic differences as separate phenotypic subgroups has been limited so far. Here, we performed an in-depth characterization of speech and language disorders in 52 143 individuals, reconstructing clinical histories using a large-scale data-mining approach of the electronic medical records from an entire large paediatric healthcare network. The reported frequency of these disorders was the highest between 2 and 5 years old and spanned a spectrum of 26 broad speech and language diagnoses. We used natural language processing to assess the degree to which clinical diagnoses in full-text notes were reflected in ICD-10 diagnosis codes. We found that aphasia and speech apraxia could be retrieved easily through ICD-10 diagnosis codes, whereas stuttering as a speech phenotype was coded in only 12% of individuals through appropriate ICD-10 codes. We found significant comorbidity of speech and language disorders in neurodevelopmental conditions (30.31%) and, to a lesser degree, with epilepsies (6.07%) and movement disorders (2.05%). The most common genetic disorders retrievable in our analysis of electronic medical records were STXBP1 (n = 21), PTEN (n = 20) and CACNA1A (n = 18). When assessing associations of genetic diagnoses with specific linguistic phenotypes, we observed associations of STXBP1 and aphasia (P = 8.57 × 10−7, 95% confidence interval = 18.62–130.39) and MYO7A with speech and language development delay attributable to hearing loss (P = 1.24 × 10−5, 95% confidence interval = 17.46–infinity). Finally, in a sub-cohort of 726 individuals with whole-exome sequencing data, we identified an enrichment of rare variants in neuronal receptor pathways, in addition to associations of UQCRC1 and KIF17 with expressive aphasia, MROH8 and BCHE with poor speech, and USP37 , SLC22A9 and UMODL1 with aphasia. In summary, our study outlines the landscape of paediatric speech and language disorders, confirming the phenotypic complexity of linguistic traits and novel genotype–phenotype associations. Subgroups of paediatric speech and language disorders differ significantly with respect to the composition of monogenic aetiologies. [ABSTRACT FROM AUTHOR]

Objective Event capture in clinical trials is resource-intensive, and electronic medical records (EMRs) offer a potential solution. This study develops algorithms for EMR-based death and hospitalization capture and compares them with traditional event capture methods. Materials and Methods We compared the effectiveness of EMR-based event capture and site-captured events adjudicated by a clinical endpoint committee in the multi-center INfluenza Vaccine to Effectively Stop cardio Thoracic Events and Decompensated heart failure (INVESTED) trial for participants from the Veterans Affairs healthcare system. Varying time windows around event dates were used to optimize events matching. The algorithms were externally validated for heart failure hospitalizations in the Medical Information Mart for Intensive Care (MIMIC)-IV database. Results We observed 100% sensitivity for death events with a 1-day window. Sensitivity for cardiovascular, heart failure, pulmonary, and nonspecific cardiopulmonary hospitalizations using discharge diagnosis codes varied between 75% and 95%. Including Centers for Medicare & Medicaid Services data improved sensitivity with no meaningful decrease in specificity. The MIMIC-IV analysis showed 82% sensitivity and 99% specificity for heart failure hospitalizations. Discussion EMR-based method accurately identifies all-cause mortality and demonstrates high accuracy for cardiopulmonary hospitalizations. This study underscores the importance of optimal time windows, data completeness, and domain variability in EMR systems. Conclusion EMR-based methods are effective strategies for capturing death and hospitalizations in clinical trials; however, their effectiveness may be influenced by the complexity of events and domain variability across different EMR systems. Nonetheless, EMR-based methods can serve as a valuable complement to traditional methods. [ABSTRACT FROM AUTHOR]

Background: Effective interventions to reduce drowning incidents require accurate and reliable data for scientific analysis. However, the lack of high-quality evidence and the variability in drowning terminology, definitions, and outcomes present significant challenges in assessing studies to inform drowning guidelines. Many drowning reports use inappropriate classifications for drowning incidents, which significantly contributes to the underreporting of drowning. In particular, non-fatal drowning incidents are underreported because many countries do not routinely collect this data. The Danish Drowning Cohort: The Danish Drowning Cohort was established in 2016 to facilitate research to improve preventative, rescue, and treatment interventions to reduce the incidence, mortality, and morbidity of drowning. The Danish Drowning Cohort contains nationwide data on all fatal and non-fatal drowning incidents treated by the Danish Emergency Medical Services. Data are extracted from the Danish prehospital electronic medical record using a text-search algorithm (Danish Drowning Formula) and a manual validation process. The WHO definition of drowning, supported by the clarification statement for non-fatal drowning, is used as the case definition to identify drowning. All drowning patients are included, including unwitnessed incidents, non-conveyed patients, patients declared dead prehospital, or patients with obvious clinical signs of irreversible death. This method allows syndromic surveillance and monitors a nationwide cohort of fatal and non-fatal drowning incidents in near-real time to inform future prevention strategies. The Danish Drowning Cohort complies with the Utstein style for drowning reporting guidelines. The 30-day mortality is obtained through the Civil Personal Register to differentiate between fatal and non-fatal drowning incidents. In addition to prehospital data, new data linkages with other Danish registries via the patient's civil registration number will enable the examination of various additional factors associated with drowning risk. Conclusion: The Danish Drowning Cohort contains nationwide prehospital data on all fatal and non-fatal drowning incidents treated by the Danish Emergency Medical Service. It is a basis for all research on drowning in Denmark and may improve preventative, rescue, and treatment interventions to reduce the incidence, mortality, and morbidity of drowning. Plain Language Summary: The Danish Drowning Cohort includes data on fatal and non-fatal drowning incidents treated by the Emergency Medical Services from 2016 and onwards and serves as the foundation for drowning research in Denmark. Data are extracted from the Danish Prehospital Electronic Medical Record using the Danish Drowning Formula and manual validation. The research data can advance prevention, rescue, and treatment interventions, aiming to decrease drowning incidence, mortality, and morbidity. The research data follows the Utstein style for drowning reporting guidelines linked with 30-day survival. [ABSTRACT FROM AUTHOR]

Objective To construct a machine learning model to predict the risk of all-cause mortality in intensive care unit (ICU) patients. Methods Based on the intensive care medical information market Ⅲ (MIMIC-Ⅲ) database, the machine learning method was used to integrate the structured and unstructured data in the electronic medical record (EHR) to create a mortality risk prediction model for ICU patients. Results The machine learning model combined with structured and unstructured data improved the accuracy of clinical outcome prediction of ICU patients. The AUROC value of the optimized gradient enhancement model was 0. 88,indicating that the patient′s life state could be accurately predicted. Conclusion Using machine learning models, based on a small number of easily collected structured variables combined with unstructured data, can significantly improve the prediction performance of ICU patients′ mortality risk prediction models. [ABSTRACT FROM AUTHOR]

Background: Electronic medical records (EMR) can be utilized to understand the impact of the disruption in care provision caused by the pandemic. We aimed to develop and validate an algorithm to identify persons with epilepsy (PWE) from our EMR and to use it to explore the effect of the pandemic on outpatient service utilization. Methods: EMRs from the neurology specialty, covering the period from January 2018 to December 2023, were used. An algorithm was developed using an iterative approach to identify PWE with a critical lower bound of 0.91 for negative predictive value. Manual internal validation was performed. Outpatient visit data were extracted and modeled as a time series using the autoregressive integrated moving average model. All statistical analyses were performed using STATA version 14.2 (Statacorp, USA). Results: Four iterations resulted in an algorithm, with a negative predictive value 0.98 (95% CI: 0.95–0.99), positive predictive value of 0.98 (95% CI: 0.85–0.99), and an F-score accuracy of 0.96, which identified 4474 PWE. The outpatient service utilization was abruptly reduced by the pandemic, with a change of -902.1 (95%CI: -936.55 to -867.70), and the recovery has also been slow, with a decrease of -5.51(95%CI: -7.00 to -4.02). Model predictions aligned closely with actual visits with median error of -3.5%. Conclusions: We developed an algorithm for identifying people with epilepsy with good accuracy. Similar methods can be adapted for use in other resource-limited settings and for other diseases. The COVID pandemic appears to have caused a lasting reduction of service utilization among PWE. [ABSTRACT FROM AUTHOR]

The deployment of Health Information Systems (HIS) in Sri Lanka has been low in adoption compared to developed countries. There has been a dearth of studies to identify the factors that improve the adoption of HIS in developing countries. Thus, this study investigates the factors influencing the acceptance of HIS among public healthcare staff. A survey was administered among 170 medical professionals, including nurses and doctors. Partial Least Squares Structural Equation Modelling (PLS-SEM) was applied to the dataset with 5000 bootstrap subsamples. The research model was developed based on the prior literature and by extending the Technology Acceptance Model (TAM) to the context of public healthcare. A positive relationship was observed between the actual use of HIS and constructs such as perceived usefulness, perceived ease of use, attitude, behavioural intention, prior use of HIS by supervisors, computer anxiety and facilitating conditions. These findings confirm the applicability of the proposed extended TAM in the public healthcare system of a developing country. Furthermore, HIS practitioners and policymakers in the healthcare sector would find these results valuable. [ABSTRACT FROM AUTHOR]

Background Antibiotic durations for uncomplicated skin/soft tissue infections (SSTI) often exceed the guideline-recommended 5–7 days. We assessed the effectiveness of a default duration order panel in the Electronic Health Record to reduce long prescriptions. Methods Cluster randomized trial of an SSTI order panel with default antibiotic durations (implemented 12/2021), compared to a control panel (no decision support) in 14 pediatric primary care clinics. We assessed long prescription rates from 23 months before to 12 months after order panel implementation (1/2020–12/2022). Antibiotic duration was considered long if >5 days for cellulitis or drained abscess, or >7 days for undrained abscess, impetigo, or other SSTI. Results We included 1123 and 511 encounters in intervention and control clinics, respectively. In a piecewise generalized linear model, the long prescription rate decreased from 63.8% to 54.6% (absolute difference, −9.2%) in the intervention group and from 70.0% to 54.9% (absolute difference, −15.1%) in the control group. The relative change in trajectories from pre-panel to post-panel periods did not differ significantly between intervention and control groups (P  = .488). Although used in only 29.4% of eligible encounters, intervention panel use had lower odds of long prescription compared to all other prescriptions (odds ratio 0.18). Conclusions We did not detect an overall impact of an order panel with default durations in reducing long antibiotic prescriptions for SSTIs. When ordered from the intervention panel, prescriptions were usually guideline-concordant. Effective strategies to make choosing a default duration more automatic are necessary to further reduce long prescriptions. [ABSTRACT FROM AUTHOR]

Purpose: In the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. Methods: We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance. Results: Based on the annotated data, the BERT model pre-trained with the medical corpus showed a significant performance improvement to the BiLSTM-CRF model with a micro-recall of 0.979 and an F1 value of 0.976, which indicates that the model has promising performance in identifying private information in Chinese clinical texts. Conclusions: The BERT-based BiLSTM-CRF model excels in identifying privacy information in Chinese clinical texts, and the application of this model is very effective in protecting patient privacy and facilitating data sharing. [ABSTRACT FROM AUTHOR]

Introduction Traumatic brain injury (TBI) is among the most common conditions in the military. VA Caribbean Healthcare System (VACHS) patients with Traumatic Brain Injury (TBI) have a higher mortality rate than Veterans in other VA health care systems in the United States. The main goal of this study was to develop sociodemographic profiles and outline health characteristics of Hispanic patients with TBI treated at the VA Caribbean Healthcare System in a search for potential explanations to account for the higher mortality rate. This study advocates for equity in health services provided for minorities inside the militia. Materials and Methods Data collected from electronic medical records and VA databases were used to create sociodemographic and health characteristics profiles, in addition to survival models. The population of the study were post 911 Veteran soldiers who had been diagnosed with TBI. Adjusted models were created to provide hazard ratios (HR) for mortality risk. Results Out of the 16,549 files available from all 10 selected VA sites, 526 individuals were identified as treated at the VACHS. Of 526 subjects screened, 39 complied with the inclusion/exclusion criteria. Results include: 94.4% male, 48.7% between the ages of 21 and 41 years, 89.7% have depression, 66.7% have post-traumatic stress disorder (PTSD), 82.1% receive occupational therapy, 94.9% have severe headaches, 100% suffer from pain, 94.9% have memory problems, and 10.3% have had suicidal thoughts. Over 60% had a first-hand explosion experience, be it just the explosion or with another type of injury. Data showed that 33% of our patients had a Magnetic Resonance Imaging (MRI), 31% had a CT, 15.4% had a SPECT, and 2.6% had PET scan. Significant associations were found between MRIs and speech therapies, and MRIs and total comorbidities. The Cox proportional-hazards model for survival adjusted for age, gender, race/ethnicity, and comorbidities shows that VACHS Veterans diagnosed with a TBI had a higher mortality risk rate (HR 1.23 [95% CI 1.10, 1.37]) when compared to the other 9 health centers with the highest percentage of Hispanic Veterans. Conclusions Since explosions were the most common mechanism of injury, further research is needed into the experiences of Veterans in connection with this specific variable. A high percentage of the patients suffered from depression and PTSD. Additionally, over half of the patients had an unmeasured TBI severity. The effects these aspects have on symptomatology and how they hinder the recovery process in Hispanic patients should be examined in further detail. It is also important to highlight that family and friends' support could be key for injury treatment. This study highlights the use of the 4 types of scans (MRI, CT, PET/CT, and SPECT/CT) as ideal diagnosis tools. The alarming number of patients with suicidal thoughts should be a focus in upcoming studies. Future studies should aim to determine whether increased death rates in TBI Veterans can be linked to other United States islander territories. Concepts, such as language barriers, equal resource allocation, and the experiences of Veterans with TBIs should be further explored in this Veteran population. [ABSTRACT FROM AUTHOR]

Introduction Low back pain (LBP) is highly prevalent after lower limb amputation (LLA) and contributes to substantial reductions in quality of life and function. Towards understanding pathophysiological mechanisms underlying LBP after LLA, this article compares lumbar spine pathologies and muscle morphologies between individuals with LBP, with and without LLA. Materials and Methods We queried electronic medical records of Service members with and without LLA who sought care for LBP at military treatment facilities between January 2002 and May 2020. Two groups with cLBP, one with (n  = 15) and one without unilateral transtibial LLA (n  = 15), were identified and randomly chosen from a larger sample. Groups were matched by age, mass, and sex. Lumbar muscle morphology, Pfirrmann grades, Modic changes, facet arthrosis, Meyerding grades, and lordosis angle were determined from radiographs and magnetic resonance images available in the medical record. Independent t -tests compared variables between cohorts while multiple regression models determined if intramuscular fat influenced Pfirrmann grades. Chi-square determined differences in presence of spondylolysis and facet arthrosis. Results Lordosis angle was larger with LLA (P  = 0.01). Spondylolysis was more prevalent with LLA (P  = 0.008; 40%) whereas facet arthrosis was similar between cohorts (P  = 0.3). Muscle area was not different between cohorts, yet intramuscular fat was greater with LLA (P  ≤ 0.05). Intramuscular fat did not influence Pfirrmann grades (P  > 0.15). Conclusions Despite similar lumbar muscle size, those with unilateral LLA may be predisposed to progress to symptomatic spondylolisthesis and intramuscular fat. Surgical and/or rehabilitation interventions may mitigate long-term effects of diminished spinal health, decrease LBP-related disability, and improve function for individuals with LLA. [ABSTRACT FROM AUTHOR]

The Chikungunya virus (CHIKV) presents substantial public health challenges in the Eastern Mediterranean Region (EMR), with its prevalence and interaction with other arboviruses (ABVs) remaining poorly understood. This systematic review and meta-analysis aimed to assess the prevalence of CHIKV and its association with other ABVs, such as dengue virus (DENV), Rift Valley fever virus (RVFV), malaria, and yellow fever virus (YFV), in the EMR. We systematically searched databases including PubMed, Embase, Web of Science, Scopus, Cochrane Library, CINAHL, PsycINFO, and ScienceDirect to identify epidemiological studies that report CHIKV prevalence and provide odds ratios (ORs) for CHIKV compared to other ABVs. Data analysis was performed using a random-effects model. Heterogeneity was evaluated using the χ2 test and I 2 statistic. The GRADE approach was used to evaluate the quality of the studies while the AXIS tool, NOS tool, and AHRQ checklist assessed the risk of bias. The meta-analysis revealed a significant prevalence of CHIKV in the EMR. However, the studies exhibited heterogeneity, indicating variability in the results. A comparison of CHIKV with other ABVs did not show any statistically significant differences in prevalence. The meta-analysis found a notable prevalence of CHIKV in the EMR. The results also indicated that the prevalence of CHIKV is comparable to that of other ABVs in the region. These findings provide an overview of the burden of CHIKV in the EMR. [ABSTRACT FROM AUTHOR]

Bartonella is a vector-borne zoonotic pathogen, which could also be transmitted directly and cause a variety of clinical illnesses. This study aimed to investigate the prevalence of Bartonella in countries in the WHO Eastern Mediterranean Region (WHO-EMR) region. We searched using the keywords Bartonella and the name of each country in the WHO-EMR in databases such as PubMed, ISI (Web of Science), Scopus, and Google Scholar, with a publication date range of 1990–2022 and limited to English articles. We evaluated the quality of the studies using the STROBE 6-item checklist and used the random effects model to integrate the findings of the included studies. A total of 45 papers out of 240 were included in the analysis. The results showed the prevalence of Bartonella infection among endocarditis patients was 3.8% (95% CI: 0.2–7.4) and the seroprevalence of Bartonella among other people was 27.5% (95% CI: 13.5–41.5). The overall prevalence of Bartonella spp. among animals, as determined by molecular, serological, and culture methods, was 11.9% (95% CI: 5.7–18.2), 38.9% (95% CI: 27.5–50.2), and 1.7% (95% CI: 0.5–2.9), respectively. Furthermore, the prevalence of Bartonella spp. in ectoparasites was 3.9% (95% CI: 3.5–5.2), with fleas (6.2%) showing a higher prevalence compared to lice (4.9%) and ticks (1.0%). The detection of Bartonella in all animal and ectoparasites species and human populations in the WHO-EMR with prevalence ranging from 0.3% to 23% is concerning, emphasizes the importance of conducting more comprehensive studies to gain a deeper understanding of the spread of Bartonella in these areas. [ABSTRACT FROM AUTHOR]

Background: Electronic medical records (EMRs) have the potential to improve and streamline the quality and safety of patient care. Harnessing the full benefits of EMR implementation depends on the utilisation of advanced features, defined as "mature usage." At present, little is known about the maturity of EMR usage by allied health professionals (AHPs). Objective: To examine current maturity of EMR use by AHPs and explore perceived barriers to mature EMR utilisation and optimisation. Method: AHPs were recruited from three health services. Participants completed a 27-question electronic questionnaire based on the EMR Adoption Framework, which measures clinician EMR utilisation (0 = paper chart, 5 = theoretical maximum) across 10 EMR feature categories. Interviews were conducted with both clinicians and managers to explore the nature of current EMR utilisation and perceived facilitators and barriers to mature usage. Results: Questionnaire responses were obtained from 193 participants AHPs. The majority of questions (74%) showed a mean score of <3, indicating a lack of mature EMR use. Pockets of mature usage were identified in the categories of health information, referrals and administration processes. Interviews with 21 clinicians and managers revealed barriers to optimisation across three themes: (1) limited understanding of EMR opportunities; (2) complexity of the EMR change process and (3) end-user and environmental factors. Conclusion: Mature usage across EMR feature categories of the EMR Adoption Framework was low. However, questionnaire and qualitative interview data suggested pockets of mature utilisation. Implications: Achieving mature allied health EMR use will require strategies implemented at the clinician, EMR support, and service levels. [ABSTRACT FROM AUTHOR]

Background: Primary ciliary dyskinesia (PCD) is a rare, genetically heterogeneous disease. Due to difficulty accessing diagnostic services and a lack of awareness of the syndrome, clinicians often fail to recognize the classic phenotype, leading to missed diagnoses. Methods: Relevant medical records were accessed through The BIG DATA QUERY AND ANALYSIS SYSTEM of Peking Union Medical College Hospital from September 1, 2012 to March 31, 2024. The search strategy included the following key terms: (bronchiectasis OR atelectasis OR recurrent cough OR recurrent expectoration OR hemoptysis) AND (sinusitis OR nasal polyps OR otitis media OR neonatal pneumonia OR neonatal respiratory distress OR ectopic pregnancy OR infertility OR artificial insemination OR assisted reproduction OR hydrocephalus OR congenital heart disease OR organ laterality defect OR right‐sided heart OR semen OR consanguineous marriage). Patients were filtered according to inclusion and exclusion criteria, and those with clinical suspicion of PCD were invited for screening, which included nasal nitric oxide and whole exome sequencing. Results: A total of 874 medical records were retrieved. After filtering based on inclusion and exclusion criteria, 65 patients with clinical suspicion of PCD were identified, 21 of whom accepted our invitation to complete PCD‐related screening. Among them, four were diagnosed with PCD, one was diagnosed with cystic fibrosis, and one was diagnosed with immunodeficiency‐21. Conclusions: This is the first study to use an electronic medical record retrieval system to identify missed diagnoses PCD. We believe that the methods used in this study can be extended to other rare diseases in the future. [ABSTRACT FROM AUTHOR]

Canada's healthcare system is undergoing rapid technological changes, alongside increasing demand for services, clinician burnout, and rising costs. Achieving interoperability between primary care electronic medical records (EMRs) is critical to addressing these challenges by enabling real-time data sharing and informed decision-making. This study analyzes stakeholder requirements for interoperability, utilizing an adapted House of Quality matrix to map the contributions needed from healthcare providers, vendors, government, researchers, and technology deployment partners. The findings emphasize the necessity of crosssector collaboration, with vendors playing a central role in providing technological solutions and providers and the government ensuring alignment with clinical standards and policies. The study highlights the importance of coordinated governance and identifies key areas for stakeholder collaboration to drive value for all interest holders in the healthcare ecosystem. [ABSTRACT FROM AUTHOR]

Introduction: While an association between hidradenitis suppurativa (HS) and inflammatory arthritis has been reported in clinical studies, the potential link between HS and gout remains uncertain. As HS and gout share common immunological pathways, we conducted a retrospective cohort study to determine whether HS patients are at an increased risk of developing gout in the future. Methods: This retrospective multicenter cohort study obtained information through the US collaborative network, a subset of the TriNetX research network. Patients diagnosed with HS between January 01, 2005, and December 31, 2017, were recruited, and a 1:1 propensity score matching was conducted to identify appropriate controls. The hazard ratio (HR) for the new-onset gout in HS patients was subsequently calculated. Results: Compared to individuals without HS, those with HS were associated with a 1.39-fold higher risk (95% confidence interval [CI], 1.20, 1.62) of developing new-onset gout within 5 years after the index date. This association remained significant in shorter follow-up times and sensitivity analyses utilizing different matching models. For both male and female HS patients, the risk of developing new-onset gout within 5 years after the index date was statistically significant, with respective HRs of 1.61 (95% CI, 1.28, 2.02) for males and 1.41 (95% CI, 1.11,1.78) for females. Conclusion: HS patients are at a high risk of developing gout within 5 years after an HS diagnosis while comparing with non-HS controls. [ABSTRACT FROM AUTHOR]

Patient privacy data security is a pivotal area of research within the burgeoning field of smart healthcare. This study proposes an innovative hybrid blockchain-based framework for the secure sharing of electronic medical record (EMR) data. Unlike traditional privacy protection schemes, our approach employs a novel tripartite blockchain architecture that segregates healthcare data across distinct blockchains for patients and healthcare providers while introducing a separate social blockchain to enable privacy-preserving data sharing with authorized external entities. This structure enhances both security and transparency while fostering collaborative efforts across different stakeholders. To address the inherent complexity of managing multiple blockchains, a unique cross-chain signature algorithm is introduced, based on the Boneh-Lynn-Shacham (BLS) signature aggregation technique. This algorithm not only streamlines the signature process across chains but also strengthens system security and optimizes storage efficiency, addressing a key challenge in multi-chain systems. Additionally, our external sharing algorithm resolves the prevalent issue of medical data silos by facilitating better data categorization and enabling selective, secure external sharing through the social blockchain. Security analyses and experimental results demonstrate that the proposed scheme offers superior security, storage optimization, and flexibility compared to existing solutions, making it a robust choice for safeguarding patient data in smart healthcare environments. [ABSTRACT FROM AUTHOR]

Patient privacy data security is a pivotal area of research within the burgeoning field of smart healthcare. This study proposes an innovative hybrid blockchain-based framework for the secure sharing of electronic medical record (EMR) data. Unlike traditional privacy protection schemes, our approach employs a novel tripartite blockchain architecture that segregates healthcare data across distinct blockchains for patients and healthcare providers while introducing a separate social blockchain to enable privacy-preserving data sharing with authorized external entities. This structure enhances both security and transparency while fostering collaborative efforts across different stakeholders. To address the inherent complexity of managing multiple blockchains, a unique cross-chain signature algorithm is introduced, based on the Boneh-Lynn-Shacham (BLS) signature aggregation technique. This algorithm not only streamlines the signature process across chains but also strengthens system security and optimizes storage efficiency, addressing a key challenge in multi-chain systems. Additionally, our external sharing algorithm resolves the prevalent issue of medical data silos by facilitating better data categorization and enabling selective, secure external sharing through the social blockchain. Security analyses and experimental results demonstrate that the proposed scheme offers superior security, storage optimization, and flexibility compared to existing solutions, making it a robust choice for safeguarding patient data in smart healthcare environments.

• EMR-LIP offers a preprocessing workflow for longitudinal irregular data that is more aligned with clinical practice than previous pipelines. • EMR-LIP provides automated preprocessing tools for longitudinal irregular data, which are universally applicable across EMR databases. • Across multiple large databases, data processed by EMR-LIP has demonstrated optimal performance in several benchmark clinical prediction tasks. Longitudinal data from Electronic Medical Records (EMRs) are increasingly utilized to construct predictive models for various clinical tasks, offering enhanced insights into patient health. However, significant discrepancies exist in preprocessing the irregular and intricate EMR data across studies due to the absence of universally accepted tools and standardization methods. This study introduces the E lectronic M edical R ecord L ongitudinal I rregular Data P reprocessing (EMR-LIP) framework, a lightweight approach for optimizing the preprocessing of longitudinal, irregular EMR data, aiming to enhance research efficiency, consistency, reproducibility, and comparability. EMR-LIP modularizes the preprocessing of longitudinal irregular EMR data, offering tools with a low level of encapsulation. Compared to other pipelines, EMR-LIP categorizes variables in a more granular manner, designing specific preprocessing techniques for each type. To demonstrate its versatility, EMR-LIP was applied in an empirical study to two public EMR databases, MIMIC-IV and eICU-CRD. Data processed with EMR-LIP was then used to test several renowned deep learning models on a range of commonly used benchmark tasks. In both the MIMIC-IV and eICU-CRD databases, models based on EMR-LIP showed superior baseline performance compared to previous studies. Interestingly, using data preprocessed by EMR-LIP, traditional models such as LSTM and GRU outperformed more complex models, achieving an AUROC of up to 0.94 for in-hospital death prediction. Additionally, models based on EMR-LIP showed stable performance across various resampling intervals and exhibited better fairness in performance across different ethnic groups. EMR-LIP streamlines the preprocessing of irregular longitudinal EMR data, offering an end-to-end solution for model-ready data creation, and has been open-sourced for collaborative refinement by the research community. [Display omitted] [ABSTRACT FROM AUTHOR]

Dementia rates are projected to increase significantly by 2050, posing considerable challenges for healthcare systems worldwide. Developing efficient diagnostic tools is critical, and machine learning (ML) algorithms have shown potential for improving the accuracy of cognitive impairment classification. This study aims to address challenges in current systems by leveraging readily available electronic medical record (EMR) data to simplify and enhance the classification of cognitive impairment. The analysis includes 283 older adults, categorized into three groups: 144 individuals with mild cognitive impairment (MCI), 38 with dementia, and 101 healthy controls. Various ML techniques are evaluated to classify cognitive performance levels based on input features such as sociodemographic variables, lab results, comorbidities, Body Mass Index (BMI), and functional scales. Key predictors for distinguishing healthy controls from individuals with MCI are identified. These are history of myocardial infarction, vitamin D3 levels, the Instrumental Activities of Daily Living (IADL) scale, age, and sodium levels. The nonlinear Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel achieve the best performance for MCI classification, with an accuracy of 69%, an AUC of 0.75, and a Matthews Correlation Coefficient (MCC) of 0.43. For distinguishing healthy controls from those with dementia, the most influential factors include the IADL scale, the Activities of Daily Living (ADL) scale, education, vitamin D3 levels, and age. Here, the Random Forest algorithm demonstrates superior performance, achieving 84% accuracy, an AUC of 0.96, and an MCC of 0.71. These two models consistently outperform other ML techniques, such as K-Nearest Neighbors, Multi-Layer Perceptron, linear SVM, Naive Bayes, Quadratic Discriminant Analysis, Linear Discriminant Analysis, AdaBoost, and Gaussian Process Classifiers. The findings suggest that EMR data can be an effective resource for the initial classification of cognitive impairments. Integrating these ML-driven approaches into primary care settings may facilitate the early identification of older patients who could benefit from further cognitive assessments., Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Ethical approval: The study was conducted in accordance with the guidelines and regulations of the Declaration of Helsinki and was approved by the Committee of Bioethics for Scientific Research of the Jerzy Kukuczka Academy of Physical Education in Katowice (Resolutions No. 2/1/2015 and No. 1/2015). All participants provided their informed consent prior to participating in the study., (© 2025. The Author(s).)

In this retrospective observational study, we aimed to investigate the potential of natural language processing (NLP) for drug repositioning by analyzing the preventive effects of cardioprotective drugs against anthracycline-induced cardiotoxicity (AIC) using electronic medical records. We evaluated the effects of angiotensin II receptor blockers/angiotensin-converting enzyme inhibitors (ARB/ACEIs), beta-blockers (BBs), statins, and calcium channel blockers (CCBs) on AIC using signals extracted from clinical texts via NLP. The study included 2935 patients prescribed anthracyclines at a single hospital, with concomitant prescriptions of ARB/ACEIs, BBs, statins, and CCBs. Upon propensity score matching, groups with and without these medications were compared, and expressions suggestive of cardiotoxicity, extracted via NLP, were considered as the outcome. The hazard ratios for ARB/ACEIs, BBs, statins, and CCBs were 0.58 [95% CI: 0.38-0.88], 0.71 [95% CI: 0.35-1.44], 0.60 [95% CI 0.38-0.95], and 0.63 [95% CI: 0.45-0.88], respectively. ARB/ACEIs, statins, and CCBs significantly suppressed AIC, whereas BBs did not demonstrate statistical significance, possibly due to limited statistical power. NLP-extracted signals from clinical texts reflected the known effects of these medications, demonstrating the feasibility of NLP-based drug repositioning. Further investigation is needed to determine if similar results can be replicated using electronic medical records from other institutions., Competing Interests: Declarations. Competing interests: YK, KS, and ES belong to the Artificial Intelligence and Digital Twin in Healthcare, Graduate School of Medicine, University of Tokyo, which is an endowment department supported by an unrestricted grant from EM Systems, EPNextS, MRP Co., Ltd., SHIP HEALTHCARE HOLDINGS, Inc., SoftBank Corp., and NEC Corporation; these organizations had no control over the interpretation, writing, or publication of this work. All authors declare no financial or non-financial competing interests., (© 2025. The Author(s).)

Background: Healthcare is rapidly leveraging machine learning to enhance patient care, streamline operations, and address complex medical issues. Though ethical issues, model efficiency, and algorithmic bias exist, the COVID-19 pandemic highlighted its usefulness in disease outbreak prediction and treatment optimization., Aim: This article aims to discuss machine learning applications, benefits, and the ethical and practical challenges in healthcare., Discussion: Machine learning assists in diagnosis, patient monitoring, and epidemic prediction but faces challenges like algorithmic bias and data quality. Overcoming these requires high-quality data, impartial algorithms, and model monitoring., Conclusion: Machine learning might revolutionize healthcare by making it more efficient and better for patients. Full acceptance and the advancement of technologies to improve health outcomes on a global scale depend on resolving ethical, practical, and technological concerns., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

While telehealth may improve access to healthcare for some, it may also widen gaps in access across different economic groups. Using electronic health records for outpatient mental health care of patients with depression in a large US academic health system, we assessed changes in mental health care utilization from 2016 to 2024 (primary care: n = 42,640 patients, 270,754 visits; psychiatry: n = 12,846 patients, 336,918 visits) and odds of using telehealth relative to in-person care from 2020 to 2024, across national area deprivation index (ADI) percentiles. We found that over 3 years prepandemic (July 2016-June 2019), the volume of mental health care delivered to patients from low-deprivation areas (1st-25th national ADI percentile) was increasing at a steeper rate than for high-deprivation areas (76th-100th national ADI percentile). Visit volume changed rapidly at the onset of the COVID-19 pandemic, and by July 2021 it was increased relative to prepandemic levels. From July 2021 to June 2024, volume of care declined for all deprivation groups, but at a more rapid rate for the high-deprivation group than the low-deprivation group. Further, on average from July 2020 to June 2024, the odds of receiving telehealth relative to in-person care were significantly higher for patients living in low deprivation rather than high-deprivation areas in both primary care and psychiatry. We did not find evidence of telehealth improving access to care for patients in high-deprivation areas. Differences in telehealth use may contribute to sustained disparities in access to mental health care across economic groups., (© The Author(s) 2025. Published by Oxford University Press on behalf of National Academy of Sciences.)

Background: The adoption of digital systems requires processes for quality assurance and uptake of standards to achieve universal health coverage. The World Health Organization developed the Digital Adaptation Kits (DAKs) within the SMART (Standards-based, Machine-readable, Adaptive, Requirements-based, and Testable) guidelines framework to support the uptake of standards and recommendations through digital systems. DAKs are a software-neutral mechanism for translating narrative guidelines to support the design of digital systems. However, a systematic process is needed to implement and ensure the impact of DAKs in country contexts., Objective: This paper details the structured process and stepwise approach to customize the DAKs to the national program and digital context in 5 countries in Africa with diverse program guideline uptake and significant digital health investments: Ethiopia, Ghana, Malawi, Zambia, and Zimbabwe. All these countries have existing digital systems, which have the potential to be updated with the DAKs., Methods: A DAK assessment tool was developed and used to assess guideline digitization readiness and opportunities for system uptake in each country. Multistakeholder teams were established to conduct the content review and alignment of the generic DAK to national guidelines and protocols through a series of stakeholder consultations, including stakeholder orientation, content review and alignment, content validation, and software update meetings., Unlabelled: Country adaptation processes identified requirements for national-level contextualization and highlighted opportunities for refinement of DAKs. Quality assurance of the content during the content review and validation processes ensured alignment with national protocols. Adaptation processes also facilitated the adoption of the DAKs approach into national guidelines and strategic documents for sexual and reproductive health., Conclusions: Country experiences offered early insights into the opportunities and benefits of a structured approach to digitalizing primary health care services. They also highlighted how this process can be continuously refined and sustained to enhance country-level impact., (© Rosemary K Muliokela, Kuwani Banda, Abdulaziz Mohammed Hussen, Sarai Bvulani Malumo, Andrew Kashoka, Angel Mwiche, Innocent Chiboma, Maria Barreix, Muyereka Nyirenda, Zvanaka Sithole, Natschja Ratanaprayul, Berhanu Fikadie Endehabtu, Hanna Abayneh Telake, Adane Weldeab, William J M Probert, Ӧzge Tunçalp, Ernest Maya, Mulatu Woldetsadik, Binyam Tilahun, Chris Guure, Kafui Senya, Lale Say, Tigest Tamrat. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Pharmacogenetic testing provides patient genotype information which could influence medication selection and dosing for optimal patient care. Insurance coverage for pharmacogenetic testing varies widely. A better understanding of the commonly used medications with clinically important pharmacogenetic recommendations can inform which medications and/or genes should be prioritized for coverage and reimbursement in the context of finite healthcare resources. The aim of this scoping review was to collate previous studies that investigated the utilization rate of medications that could be guided by pharmacogenetic testing. Included studies utilized electronic medical records or claims data to assess pharmacogenetic medication prescription rates for older adults (≥ 65 years old). Identified pharmacogenetic medications were classified according to therapeutic class and assessed for actionability based on the Clinical Pharmacogenetics Implementation Consortium guidelines. Across the 31 included studies, analgesic (n = 29), psychotropic (n = 29), and cardiovascular (n = 27) therapeutic classes were most commonly investigated. Study populations were primarily generalized (48%); however, some studies focused on specific populations, such as, cancer (n = 6), mental health (n = 1), and nursing home (n = 2) cohorts. A total of 215 unique pharmacogenetic medications were reported, of which, 82 were associated with actionable pharmacogenetic recommendations. The most frequent genes implicated in potential drug-gene interactions with these actionable pharmacogenetic drugs were CYP2D6 (25.6%), CYP2C19 (18.3%), and CYP2C9 (11%). Medications most frequently prescribed included pantoprazole (range 0%-49.6%), simvastatin (range 0%-54.9%), and ondansetron (range 0.1%-62.6%). Overall, the frequently prescribed medications and associated genes identified in this review could guide pharmacogenetic testing implementation into clinical practice, including insurer subsidization., (© 2025 The Author(s). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Purpose: This study assesses success and methodological implications of linking IQVIA's Electronic Medical Records (EMR) of type 2 diabetes (T2D) patients with the National Health Data System (SNDS) database, a cornerstone process in healthcare research., Methods: The OREOT cohort was constituted by T2D patients identified in the IQVIA EMR from 2014 to 2018 and linked indirectly to SNDS database. The EMR database contains clinical records from general practitioner consultations, representing ~2.8% of the French population and the SNDS claims database covers over 99% of the French population's healthcare activities. Linkage success was evaluated by the linkage rate. Baseline patients' characteristics were described for both linked and non-linked patients., Results: Of the 291 408 T2D patients identified in the EMR, 244 656 (84%) were successfully linked. After technical data cleaning, 239 141 (82%) were finally linked. Linked and non-linked patients (n = 52,267) were aged 65 years and more frequently male (57% and 59%); half were obese, and most of comorbidities were consistent. Linked patients had more EMR consultations (median 32 vs 16), and more cardiovascular events (12% vs 7%) or chronic kidney disease (10% vs 7%)., Conclusions: The successful linkage of EMR and SNDS databases provides valuable insights for future research in T2D and other chronic diseases requiring clinical data. This study demonstrates the feasibility of such data alignments, particularly in patients with complex health profiles or extensive medical records, and linkage potential to enhance real-world research quality. Despite higher prevalence of baseline comorbidities among linked patients, patients' characteristics were consistent with French T2D population., (© 2025 John Wiley & Sons Ltd.)

Rationale, Aims, and Objectives: Medical device-integrated electronic medical records (MDI-EMR) pose significant challenges in ensuring effective usage, data security and patient safety. The complexities of MDI-EMR necessitate applying various security mechanisms to safeguard against cyber threats. Therefore, we evaluated cyber threats to MDI-EMR and the effectiveness of applied security controls using a proposed framework from sociotechnical and risk assessment perspectives., Method: We conducted a qualitative case study evaluation in a general hospital in Saudi Arabia using interviews, observation, and document analysis from the perspectives of major MDI-EMR stakeholders, including healthcare providers, IT professionals and cybersecurity specialists., Results: The results showed the interplay among physical, technical and administrative security controls that maintained a secure posture of MDI-EMR. The effectiveness of security controls is highly influenced by the staff's cybersecurity awareness and training. The perceived effectiveness of security controls varied among users, with some expressing satisfaction with the ease of use and reliability, while others highlighting challenges such as password complexity and access procedures. Understanding these diverse perspectives is crucial for tailoring security measures to meet the needs of different stakeholders effectively., Conclusion: Collaboration among the key stakeholders is crucial for implementing security controls for MDI-EMR. Balancing security measures with usability concerns is essential, as highlighted by challenges in implementing technical controls. A comprehensive approach encompassing physical, technical and administrative controls, continuous education and awareness initiatives are significant to empower staff in recognising and mitigating cyber threats effectively to safeguard medical data and ensure the integrity of healthcare systems., (© 2024 John Wiley & Sons Ltd.)

Objective: This study aimed to compare imputation approaches to identify the likely undocumented patient population in electronic health record (EHRs). EHR are a promising source of information on undocumented immigrants' medical needs and care utilization, but there is no verified way to identify immigration status in the data. Different approaches to approximating immigration status in EHR introduce unique biases, which in turn has major implications on our understanding of undocumented immigrant patients., Study Setting and Design: We used a dataset of all emergency department (ED) visits from 2016 to 2019 in the Los Angeles Department of Health Services (LADHS) merged across patient medical records, demographic data, and claims data. We included all ED visits from our patient groups of interest and limited to patients at or over the age of 18 years at the time of their ED visit and excluded empty encounter records (n = 1,106,086 ED encounters)., Data Sources and Analytic Sample: We created three patient groups: (1) US-born, (2) foreign-born documented, and (3) undocumented using two different imputation approaches: a logical approach versus statistical assignment. We compared predicted probabilities for two outcomes: an ED visit related to a behavioral health (BH) disorder and inpatient admission/transfer to another facility., Principal Findings: Both approaches provide comparable estimates among the three patient groups for ED encounters for a BH disorder and inpatient admission/transfer to another facility. Undocumented immigrants are less likely to have a BH diagnosis in the ED and are less likely to be admitted or transferred compared to the US-born., Conclusions: Researchers should consider expanding EHR with administrative data when studying the undocumented patient population and may prefer a logical approach to estimate immigration status. Researchers who rely on payer status alone (i.e., restricted Medicaid) as a proxy for undocumented immigrants in EHR should consider how this may bias their results. As Medicaid expands for undocumented immigrants, statistical assignment may become the preferred method., (© 2024 The Author(s). Health Services Research published by Wiley Periodicals LLC on behalf of Health Research and Educational Trust.)

Background: Over the past decade, there has been a significant shift from paper-based to digital medical record management, driven largely by advances in digital technology. This transition has led to widespread adoption of Electronic Medical Records (EMRs), with the expectation that paper documentation will soon be fully replaced. In response, the European Medicines Agency's "Guideline on Computerised Systems in Clinical Trials" outlines essential criteria for validated EMR systems to ensure data integrity and security, and sets standards for electronic source documents in clinical trials., Methods: From December 2023 to March 2024, the Italian Group of Data Managers and Clinical Research Coordinators (GIDMcrc) conducted an online survey across clinical research sites in Italy, France and Belgium to assess the characteristics of medical records and source documents., Results: The survey was completed by 37 centres: 70.3% from Italy, 16.2% from France and 13.5% from Belgium. Most sites use a mixed paper/electronic Source Document (SD) system (72.3%), with fewer centres having fully electronic SD systems (13.5%) or fully paper-based systems (16.2%). EMR systems are used in 70.3% of sites, but only 23.8% comply with EMA guidelines for computerised systems. A country-specific analysis was also conducted to further explore the situations in Italy and France/Belgium., Conclusion: Despite the widespread use of electronic medical records (EMRs) in Italy, France and Belgium, Italy lags behind the other two countries in terms of digitization. Despite the presence of an EMR, many centres still use a mixed system of paper and electronic source documents. There is also a lack of awareness regarding EMA and GCP standards, particularly concerning training and system testing. The higher response rate from Italian centres highlights the need for a larger sample in France and Belgium, and a follow-up survey would be beneficial for assessing progress and refining corrective actions., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Background: Patient portals, or secure websites linked to electronic medical records, have emerged as tools to provide patients with timely access to their health information. To support the potential benefits of patient portals such as improved engagement in health care, it is essential to understand how patients and caregivers experience these portals., Objective: This study aimed to explore patient and caregiver experiences, facilitators, and barriers to accessing and using a patient portal called MyChart during the initial stages of its implementation., Methods: We applied explanatory sequential mixed methods to conduct a web-based questionnaire and semistructured interviews with MyChart users and nonusers at a large community hospital in Ontario, Canada. Among users, we explored user satisfaction with MyChart, its impact on care, and areas for improvement. For nonusers, we explored barriers to MyChart access and willingness to use it in the future. Descriptive statistics and thematic analysis were used for data analysis., Results: A total of 5651 patients and caregivers completed the web-based questionnaire and 18 (12 users and 6 nonusers) participated in interviews. MyChart users primarily learned about the portal through email (n=1288, 39%), after-visit summaries (n=953, 29%), and hospital staff (n=408, 12%). Nonusers cited lack of awareness (n=1291, 59%) and registration difficulties (n=707, 32%) as some barriers to activation and adoption, but the majority would consider activating and using MyChart if they could learn more about it (n=1126, 54%). Users valued MyChart for preparing for health care encounters but expressed dissatisfaction with limited features and access to medical history and test results, whereas nonusers tended to be unsure about the benefits of using MyChart, especially if they were infrequent health care users., Conclusions: Patient portals offer benefits, but barriers to access and limited functionality can hinder widespread use. To enhance the adoption and potential benefits of patient portals, targeted outreach and comprehensive access to health information are essential to promote positive and seamlessly integrated health care experiences., (©Shelley Vanderhout, Shipra Taneja, Kamini Kalia, Walter P Wodchis, Terence Tang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.01.2025.)

Background: Limited data have described the testing patterns and outcomes of adults (≥18 years) with acute respiratory illness (ARI) in the emergency department setting., Methods: This prospective cohort study includes patients with ARI from a program sponsored by the Centers for Disease Control and Prevention entitled Respiratory Virus Laboratory Emergency Department Network Surveillance (RESP-LENS) from August 2021 until March 2024 (91 hospitals). Patients with ARIs were identified weekly by electronic surveillance for 1 or more of 130 ICD-10 codes that defined ARI. Patients were followed for 30 days for the primary outcomes of hospitalization and mortality. Testing for RSV with nasopharyngeal swabbing followed by reverse transcription polymerase chain reaction was done as part of usual care. Risk of 30-day mortality and RSV positivity was tested in a generalized estimating equation., Results: From 1 210 394 patients with ARI, 345 185 (28.5%) adults underwent RSV testing, which was positive in 2.4%. In adults who were RSV+, the overall mortality rate was 1.9% as compared with 2.9% in adults who were RSV-. Mortality with RSV+ status increased with age ≥65 years to 3.8% (95% CI, 3.1%-4.5%). However, in the generalized estimating equation, RSV+ status was not associated with a higher rate of hospitalization (adjusted odds, 0.79; 95% CI, .75-.84) or 30-day mortality (odds, 0.62; 95% CI, .53-.74) relative to those who were RSV-. Age ≥65 years, incremental worsening of vital signs, male sex, and heart failure were independently associated with death., Conclusions: Among adults with ARI presenting to an emergency department who were tested for RSV as part of their usual care, laboratory-confirmed RSV positivity was not associated with increased risk, including hospitalization, intensive care unit requirement, or death., Competing Interests: Potential conflicts of interest. All authors: No reported conflicts., (© The Author(s) 2025. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Background: Electronic medical records (EMRs) can minimize mistakes, enhance the comprehensiveness, legibility, and overall comprehension of medical records. However, nurses' limited familiarity with advanced technology lowers their confidence in utilizing EMRs. We aimed to collect and synthesize the most credible evidence on nurses' perception of EMRs, along with the barriers and facilitators that influence their acceptance., Methods: Searching for relevant studies was carried out across three electronic databases, namely PubMed, Scopus and ProQuest in Dec 2023. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was followed in this study report. The selected studies were then analyzed narratively and organized thematically for presentation., Results: Out of the 4,382 articles identified through comprehensive database searches, only 19 met the criteria for inclusion and reviewed. Through the synthesis of findings, two primary themes emerged, including nurses' perceptions and experiences with EMRs and facilitators & barriers for nurses' in utilizing EMRs., Conclusion: Nurses' perspectives are shaped by their computer skills, confidence in their abilities, and training. Despite obstacles such as nurse stress, EMRs present advantages such as enhanced patient care and decreased errors. Augmenting computer competency, delivering training, and guaranteeing support are essential for effective EMRs integration, leading to enhanced healthcare provision and better patient results., (Copyright© 2025 Maawati et al. Published by Tehran University of Medical Sciences.)

Background: Recent studies suggest a potential link between HS and renal dysfunction. Our objective is to assess the correlation between hidradenitis suppurativa (HS) and renal consequences, specifically focusing on acute kidney injury (AKI), chronic kidney disease (CKD), and end-stage renal disease (ESRD). Methods: This study was performed based on retrospective cohort design. Electronic medical records of participants were retrieved from the US collaborative network in the TriNetX research network. Information from 46,561 individuals with HS was examined alongside an equivalent number of matched controls. Propensity matching was performed for matching confounders. The study spanned from January 1, 2005, to December 31, 2017. Primary outcomes were set as renal dysfunction, including AKI, CKD, and ESRD. Results: Over the 1-year follow-up, people with HS presented a 1.84-fold higher risk of AKI (95% CI, 1.34-2.53) and a 1.37-fold higher risk of CKD (95% CI, 1.02-1.85) than non-HS individuals. Elevated risks persisted over the longer follow-up periods for AKI at 1.51-fold (95% CI, 1.28-1.77) for 3-years-follow-up and 1.47-fold (95% CI, 1.30-1.65) for 5-years-follow-up, respectively. Stratification by sex revealed higher risks in males, and comparison with psoriasis patients indicated increased AKI and CKD risks in HS patients. Conclusion: This study highlights a significant association between HS and renal dysfunction, emphasizing the need for further exploration of shared pathophysiological mechanisms. The findings could offer potential insights into HS-related comorbidities., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)