The Telehealth chain: a framework for secure and transparent telemedicine transactions on the blockchain.

BACKGROUND: Blockchain technology provides a secure and decentralized platform for storing and transferring sensitive medical data, which can be utilized to enable remote medical consultations. AIM: A theoretical framework for creating a blockchain-based digital system created to facilitate telemedicine system. RESULTS: This paper proposes a theoretical framework based on Hyperledger fabric for creating a blockchain-based digital entity to facilitate telemedicine services. The proposed framework utilizes blockchain technology to provide a secure and reliable platform for medical practitioners to interact remotely with patient transactions. CONCLUSION: The blockchain will serve as a one-stop digital service to secure patient data, ensure privacy, and facilitate payments. The proposed framework leverages the existing Hyperledger fabric platform to build a secure blockchain-assisted telemedicine platform.

Quality of heart failure registration in primary care: observations from 1 million electronic health records in the Amsterdam Metropolitan Area.

OBJECTIVES: Proper coding of heart failure (HF) in electronic health records (EHRs) is an important prerequisite for adequate care and research towards this vulnerable patient population. We set out to evaluate the accuracy of registration of HF diagnoses in primary care EHRs. METHODS: In a routine primary care database covering the Amsterdam Metropolitan Area, we identified all episodes of care with International Classification of Primary Care (ICPC) codes K77 (decompensatio cordis) or K84.03 (cardiomyopathy) up to 31/12/2021. We also performed two text-based searches to identify HF episodes without an appropriate ICPC-code. An expert panel evaluated all ICPC and text matches for congruence between the assigned codes and notes. RESULTS: From a database of 968,433 records we identified 19,106 patients (2.0 %) with a total of 24,011 ICPC-coded HF episodes. Removal of 1,324 episodes found to concern other or uncertain diagnoses and inclusion of 4,582 validated HF episodes identified through text search led to exclusion of 909 (overregistration: 4.8 %) and inclusion of 2,266 additional patients (underregistration: 11.1 %). The inclusion of miscoded HF episodes advanced the first known date of HF diagnosis in 3.9 % of records, with a median shift of 3.45 years. Episode-level underregistration decreased significantly over time, from 23.8 % in 2006 to 10.0 % in 2021. CONCLUSIONS: While there is improvement over time, there are still substantial levels of over- and underregistration of HF, emphasizing the need for cautious interpretation of ICPC-coded data. The findings contribute to the understanding of HF registration issues in primary care and provide insights for improving registration practices.

Learning difficulties often not documented in newly diagnosed focal epilepsy.

OBJECTIVE: A previous investigation of people with newly diagnosed focal epilepsy participating in the Human Epilepsy Project 1 (HEP1) revealed an association between learning difficulties and structural brain differences, suggesting an underlying relationship prior to seizure onset. To investigate physicians' practices of documentation learning difficulties during clinical encounters, we conducted a review of initial epileptologist encounter notes from HEP1 participants who self-reported early life learning difficulties separately as part of study enrollment. METHODS: HEP1 enrolled 67 North American participants between June 2012 and November 2017 who self-reported one or more difficulties with learning (i.e., having repeated grade, receiving learning support/remediation, and/or formal diagnosis of a learning disability) prior to epilepsy diagnosis as part of the study enrollment. The epileptologist's initial encounter note was then reviewed in detail for each of these participants. Documentation of learning issues and specific diagnoses of learning disabilities was compared to participant characteristics. Regression analysis was used to test for any independent associations between participant characteristics and physician documentation of learning difficulties. RESULTS: There were significant independent relationships between age, sex, and physician documentation of learning difficulties. On average, participants ages 22 and younger were 12.12 times more likely to have their learning difficulties documented compared to those 23 years and older (95 % CI: 2.226 to 66.02, p = 0.004). Additionally, male participants had 7.2 times greater odds of having their learning difficulty documented compared to female participants (95 % CI: 1.538 to 33.717, p = 0.012). There were no significant independent associations between race, language, employment, or geographical region. SIGNIFICANCE: These findings highlight disparities in physician documentation for people with newly diagnosed focal epilepsy and a history of learning difficulties. In the HEP1 cohort, physicians were more likely to document learning difficulties in males and in younger individuals. Systematic practice standards are important for reducing healthcare disparities across populations, improving clinical care to individuals, as well as enabling more accurate retrospective study of clinical phenomenon.

Status of MyHealthWay and Suggestions for Widespread Implementation, Emphasizing the Utilization and Practical Use of Personal Medical Data.

OBJECTIVES: In the Fourth Industrial Revolution, there is a focus on managing diverse medical data to improve healthcare and prevent disease. The challenges include tracking detailed medical records across multiple institutions and the necessity of linking domestic public medical entities for efficient data sharing. This study explores MyHealthWay, a Korean healthcare platform designed to facilitate the integration and transfer of medical data from various sources, examining its development, importance, and legal implications. METHODS: To evaluate the management status and utilization of MyHealthWay, we analyzed data types, security, legal issues, domestic versus international issues, and infrastructure. Additionally, we discussed challenges such as resource and infrastructure constraints, regulatory hurdles, and future considerations for data management. RESULTS: The secure sharing of medical information via MyHealthWay can reduce the distance between patients and healthcare facilities, fostering personalized care and self-management of health. However, this approach faces legal challenges, particularly relating to data standardization and access to personal health information. Legal challenges in data standardization and access, particularly for secondary uses such as research, necessitate improved regulations. There is a crucial need for detailed governmental guidelines and clear data ownership standards at institutional levels. CONCLUSIONS: This report highlights the role of Korea's MyHealthWay, which was launched in 2023, in transforming healthcare through systematic data integration. Challenges include data privacy and legal complexities, and there is a need for data standardization and individual empowerment in health data management within a systematic medical big data framework.

Adoption of Electronic Medical Records in Healthcare Facilities in the Emirate of Dubai.

OBJECTIVES: This paper aimed to assess the adoption of electronic medical records (EMRs) in healthcare facilities in Dubai, the largest city in the United Arab Emirates (UAE) and a location where extensive healthcare services are provided. It explored the challenges, milestones, and accomplishments associated with this process. METHODS: A situation analysis was conducted by contacting 2,089 healthcare facilities in Dubai to determine whether they had implemented EMR in their medical practices and to identify the challenges they faced during this process. Additionally, the Electronic Medical Record Adoption Model (EMRAM) was utilized to measure the maturity level of hospitals in terms of EMR adoption. The EMRAM stages were rated on a scale from 0 to 7, with 0 representing the least mature stage and 7 the most mature. RESULTS: By September 2023, all hospitals (100%, n = 54) and 75% of private clinics (n = 1,460) in Dubai had implemented EMRs. Several challenges were identified, including the absence of EMRs within the healthcare facility, having an EMR with a low EMRAM score, or the lack of a unified interoperability standard. Additionally, the absence of a clear licensing program for EMR vendors, whether standalone or cloud-based, was among the other challenges noted. CONCLUSIONS: EMR implementation in healthcare facilities in Dubai is at a mature stage. However, further efforts are required at both the decision-making and technical levels. We believe that our experience can benefit other countries in the region in implementing EMRs and using EMRAM to assess their health information systems.

BCSLinker: automatic method for constructing a knowledge graph of venous thromboembolism based on joint learning.

Background: Venous thromboembolism (VTE) is characterized by high morbidity, mortality, and complex treatment. A VTE knowledge graph (VTEKG) can effectively integrate VTE-related medical knowledge and offer an intuitive description and analysis of the relations between medical entities. However, current methods for constructing knowledge graphs typically suffer from error propagation and redundant information. Methods: In this study, we propose a deep learning-based joint extraction model, Biaffine Common-Sequence Self-Attention Linker (BCSLinker), for Chinese electronic medical records to address the issues mentioned above, which often occur when constructing a VTEKG. First, the Biaffine Common-Sequence Self-Attention (BCsSa) module is employed to create global matrices and extract entities and relations simultaneously, mitigating error propagation. Second, the multi-label cross-entropy loss is utilized to diminish the impact of redundant information and enhance information extraction. Results: We used the electronic medical record data of VTE patients from a tertiary hospital, achieving an F1 score of 86.9% on BCSLinker. It outperforms the other joint entity and relation extraction models discussed in this study. In addition, we developed a question-answering system based on the VTEKG as a structured data source. Conclusion: This study has constructed a more accurate and comprehensive VTEKG that can provide reference for diagnosing, evaluating, and treating VTE as well as supporting patient self-care, which is of considerable clinical value.

The Potential for Information Sharing Between an Electronic Medical Record System and the Moroccan Transplantation and Dialysis Registry: Examining Semantic Interoperability.

Background and aim In 2005, the Moroccan Ministry of Health established Magredial, a registry to track and monitor patients with end-stage renal disease (ESRD), with the aim of improving healthcare outcomes. After achieving initial success, Magredial's activity decreased, leading to its inactivity by 2015. Currently, efforts are underway to revive Magredial's use. The main goal of this study is to investigate the feasibility of data transfer between the electronic medical records (EMRs) of Hassan II Hospital of Fes, Morocco, and the registry by achieving semantic interoperability between the two systems Materials and methods The initial phase of this study involved a detailed review of existing literature, highlighting the importance of registries, especially in nephrology. This part of the study also aims to emphasize the role of semantic interoperability in facilitating the sharing of data between EMRs and registries. Following that, the study's second phase, which centered on the case study, conducted a detailed analysis of the data architectures in both Magredial and the EMR of the nephrology department to pinpoint areas of alignment and discrepancy. This step required cooperative efforts between the nephrology and IT departments of Hassan II Hospital. Results Our findings indicate a significant interoperability gap between the two systems, stemming from differences in their data architectures and semantic frameworks. Such discrepancies severely impede the effective exchange of information between the systems. To address this challenge, a comprehensive restructuring of the EMR is proposed. This strategy is designed to align disparate systems and ensure compliance with the interoperability standards the Health Level 7 Clinical Document Architecture (HL7-CDA) set forth. Implementing the proposed medical record approach is complex and time-consuming, necessitating healthcare professional commitment, and adherence to ethical standards for patient consent and data privacy. Conclusions Implementing this strategy is expected to facilitate the seamless automation of data transfer between the EMR and Magredial. It introduces a framework that could be a foundational model for establishing a robust interoperability framework within nephrology information systems in line with international standards. Ultimately, this initiative could lead to creating a nephrologist-shared health record across the country, enhancing patient care and data management within the specialty.

Clinical Profile, Complications and Trends of Ocular Anaesthesia in a Multi-tier Ophthalmology Network in India: An Eight-Year Experience.

INTRODUCTION: To describe the clinical profile, complications and trends of ocular anaesthesia in a multi-tier ophthalmology network in India. METHODS: This retrospective hospital-based study included 417,622 patients presenting between January 2013 and December 2020. Patients who were administered either topical, local or general anaesthesia for ocular surgery in at least one eye were included as cases. The data were collected using an electronic medical record system. RESULTS: Among the 417,622 patients, local anaesthesia was administered to 280,638, (67.2%) patients and was the most commonly administered type followed by topical anaesthesia in 84,117 (20.14%) patients. The most common complication encountered in administering local anaesthesia was retrobulbar haemorrhage in 103 (0.037%) patients followed by lid haematoma in 49 (0.017%) patients. Tooth damage occurred in 40 (0.076%) patients followed by delayed recovery in 30 (0.057%) patients during general anaesthesia. The trend of local anaesthesia decreased (83.48% vs 53.36%), whereas the trend of topical anaesthesia increased (8.61% vs 32.42%) over the study period. CONCLUSION: There is a notable trend towards the adoption of less invasive anaesthetic methods, particularly in common surgeries such as cataract, intravitreal injection, and vitreoretinal surgery. However, despite this trend, a significant proportion of oculoplastic/orbital surgeries, trauma, and strabismus surgeries continue to be performed under general anaesthesia. These observations underscore the ongoing evolution of ocular anaesthesia practices, reflecting advancements in surgical techniques and patient preferences.

The Ability of Primary Care Practices to Measure and Report on Care Quality.

BACKGROUND: Creating useful clinical quality measure (CQM) reports in a busy primary care practice is known to depend on the capability of the electronic health record (EHR). Two other domains may also contribute: supportive leadership to prioritize the work and commit the necessary resources, and individuals with the necessary health information technology (IT) skills to do so. Here we describe the results of an assessment of the above 3 domains and their associations with successful CQM reporting during an initiative to improve smaller primary care practices' cardiovascular disease CQMs. METHODS: The study took place within an AHRQ EvidenceNOW initiative of external support for smaller practices across Washington, Oregon and Idaho. Practice facilitators who provided this support completed an assessment of the 3 domains previously described for each of their assigned practices. Practices submitted 3 CQMs to the study team: appropriate aspirin prescribing, use of statins when indicated, blood pressure control, and tobacco screening/cessation. RESULTS: Practices with advanced EHR reporting capability were more likely to report 2 or more CQMs. Only one-third of practices were "advanced" in this domain, and this domain had the highest proportion of practices (39.1%) assessed as "basic." The presence of advanced leadership or advanced skills did not appreciably increase the proportion of practices that reported 2 or more CQMs. CONCLUSIONS: Our findings support previous reports of limited EHR reporting capabilities within smaller practices but extend these findings by demonstrating that practices with advanced capabilities in this domain are more likely to produce CQM reports.

al-BERT: a semi-supervised denoising technique for disease prediction.

BACKGROUND: Medical records are a valuable source for understanding patient health conditions. Doctors often use these records to assess health without solely depending on time-consuming and complex examinations. However, these records may not always be directly relevant to a patient's current health issue. For instance, information about common colds may not be relevant to a more specific health condition. While experienced doctors can effectively navigate through unnecessary details in medical records, this excess information presents a challenge for machine learning models in predicting diseases electronically. To address this, we have developed 'al-BERT', a new disease prediction model that leverages the BERT framework. This model is designed to identify crucial information from medical records and use it to predict diseases. 'al-BERT' operates on the principle that the structure of sentences in diagnostic records is similar to regular linguistic patterns. However, just as stuttering in speech can introduce 'noise' or irrelevant information, similar issues can arise in written records, complicating model training. To overcome this, 'al-BERT' incorporates a semi-supervised layer that filters out irrelevant data from patient visitation records. This process aims to refine the data, resulting in more reliable indicators for disease correlations and enhancing the model's predictive accuracy and utility in medical diagnostics. METHOD: To discern noise diseases within patient records, especially those resembling influenza-like illnesses, our approach employs a customized semi-supervised learning algorithm equipped with a focused attention mechanism. This mechanism is specifically calibrated to enhance the model's sensitivity to chronic conditions while concurrently distilling salient features from patient records, thereby augmenting the predictive accuracy and utility of the model in clinical settings. We evaluate the performance of al-BERT using real-world health insurance data provided by Taiwan's National Health Insurance. RESULT: In our study, we evaluated our model against two others: one based on BERT that uses complete disease records, and another variant that includes extra filtering techniques. Our findings show that models incorporating filtering mechanisms typically perform better than those using the entire, unfiltered dataset. Our approach resulted in improved outcomes across several key measures: AUC-ROC (an indicator of a model's ability to distinguish between classes), precision (the accuracy of positive predictions), recall (the model's ability to find all relevant cases), and overall accuracy. Most notably, our model showed a 15% improvement in recall compared to the current best-performing method in the field of disease prediction. CONCLUSION: The conducted ablation study affirms the advantages of our attention mechanism and underscores the crucial role of the selection module within al-BERT.

Real-world Chinese herbal medicine for Parkinson's disease: a hospital-based retrospective analysis of electronic medical records.

Background: Parkinson's disease (PD) is a progressive neurodegenerative condition. Chinese medicine therapies have demonstrated effectiveness for PD in controlled settings. However, the utilization of Chinese medicine therapies for PD in real-world clinical practice and the characteristics of patients seeking these therapies have not been thoroughly summarized. Method: The study retrospectively analyzed initial patient encounters (PEs) with a first-listed diagnosis of PD, based on electronic medical records from Guangdong Provincial Hospital of Chinese Medicine between July 2018 and July 2023. Results: A total of 3,206 PEs, each corresponding to an individual patient, were eligible for analyses. Approximately 60% of patients made initial visits to the Chinese medicine hospital after receiving a PD diagnosis, around 4.59 years after the onset of motor symptoms. Over 75% of the patients visited the Internal Medicine Outpatient Clinic at their initial visits, while a mere 13.85% visited PD Chronic Care Clinic. Rest tremor (61.98%) and bradykinesia (52.34%) are the most commonly reported motor symptoms, followed by rigidity (40.70%). The most commonly recorded non-motor symptoms included constipation (31.88%) and sleep disturbance (25.27%). Integration of Chinese medicine and conventional medicine therapies was the most common treatment method (39.15%), followed by single use of Chinese herbal medicine (27.14%). The most frequently prescribed herbs for PD included Glycyrrhiza uralensis Fisch. (gan cao), Astragalus mongholicus Bunge (huang qi), Atractylodes macrocephala Koidz. (bai zhu), Angelica sinensis (Oliv.) Diels (dang gui), Rehmannia glutinosa (Gaertn.) DC. (di huang), Paeonia lactiflora Pall. (bai shao), Bupleurum chinense DC. (chai hu), Citrus aurantium L. (zhi qiao/zhi shi/chen pi), Panax ginseng C. A. Mey. (ren shen), and Poria cocos (Schw.) Wolf (fu ling). These herbs contribute to formulation of Bu zhong yi qi tang (BZYQT). Conclusion: Patients typically initiated Chinese medical care after the establishment of PD diagnosis, ~4.59 years post-onset of motor symptoms. The prevalent utilization of CHM decoctions and patented Chinese herbal medicine products, underscores its potential in addressing both motor and non-motor symptoms. Despite available evidence, rigorous clinical trials are needed to validate and optimize the integration of CHM, particularly BZYQT, into therapeutic strategies for PD.

Houston Methodist cardiovascular learning health system (CVD-LHS) registry: Methods for development and implementation of an automated electronic medical record-based registry using an informatics framework approach.

Objectives: To investigate the potential value and feasibility of creating a listing system-wide registry of patients with at-risk and established Atherosclerotic Cardiovascular Disease (ASCVD) within a large healthcare system using automated data extraction methods to systematically identify burden, determinants, and the spectrum of at-risk patients to inform population health management. Additionally, the Houston Methodist Cardiovascular Disease Learning Health System (HM CVD-LHS) registry intends to create high-quality data-driven analytical insights to assess, track, and promote cardiovascular research and care. Methods: We conducted a retrospective multi-center, cohort analysis of adult patients who were seen in the outpatient settings of a large healthcare system between June 2016 - December 2022 to create an EMR-based registry. A common framework was developed to automatically extract clinical data from the EMR and then integrate it with the social determinants of health information retrieved from external sources. Microsoft's SQL Server Management Studio was used for creating multiple Extract-Transform-Load scripts and stored procedures for collecting, cleaning, storing, monitoring, reviewing, auto-updating, validating, and reporting the data based on the registry goals. Results: A real-time, programmatically deidentified, auto-updated EMR-based HM CVD-LHS registry was developed with ∼450 variables stored in multiple tables each containing information related to patient's demographics, encounters, diagnoses, vitals, labs, medication use, and comorbidities. Out of 1,171,768 adult individuals in the registry, 113,022 (9.6%) ASCVD patients were identified between June 2016 and December 2022 (mean age was 69.2 ± 12.2 years, with 55% Men and 15% Black individuals). Further, multi-level groupings of patients with laboratory test results and medication use have been analyzed for evaluating the outcomes of interest. Conclusions: HM CVD-LHS registry database was developed successfully providing the listing registry of patients with established ASCVD and those at risk. This approach empowers knowledge inference and provides support for efforts to move away from manual patient chart abstraction by suggesting that a common registry framework with a concurrent design of data collection tools and reporting rapidly extracting useful structured clinical data from EMRs for creating patient or specialty population registries.

The role and limitations of electronic medical records versus patient interviews for determining symptoms, underlying comorbidities, and medication usage for patients with COVID-19.

Electronic medical records (EMR) are important for rapidly compiling information to determine disease characteristics (e.g., symptoms) and risk factors (e.g., underlying comorbidities, medications) for disease-related outcomes. To assess EMR data accuracy, agreement between EMR abstractions and patient interviews was evaluated. Symptoms, medical history, and medication usage among COVID-19 patients collected from EMR and patient interviews were compared using overall agreement (same answer in EMR and interview), reported agreement (yes answer in both EMR and interview among those who reported yes in either), and Kappa statistics. Overall, patients reported more symptoms in interviews than in EMR abstractions. Overall agreement was high (≥50% for 20/23 symptoms), but only subjective fever and dyspnea had reported agreement of ≥50%. Kappa statistics for symptoms were generally low. Reported medical conditions had greater agreement with all condition categories (10/10) having ≥50% overall agreement and half (5/10) having ≥50% reported agreement. More non-prescription medications were reported in interviews than in EMR abstractions leading to low reported agreement (28%). Discordance was observed for symptoms, medical history, and medication usage between EMR abstractions and patient interviews. Investigations utilizing EMR to describe clinical characteristics and identify risk factors should consider the potential for incomplete data, particularly for symptoms and medications.

Cashing in: cost-benefit analysis framework for digital hospitals.

BACKGROUND: For many countries, especially those outside the USA without incentive payments, implementing and maintaining electronic medical records (EMR) is expensive and can be controversial given the large amounts of investment. Evaluating the value of EMR implementation is necessary to understand whether or not, such investment, especially when it comes from the public source, is an efficient allocation of healthcare resources. Nonetheless, most countries have struggled to measure the return on EMR investment due to the lack of appropriate evaluation frameworks. METHODS: This paper outlines the development of an evidence-based digital health cost-benefit analysis (eHealth-CBA) framework to calculate the total economic value of the EMR implementation over time. A net positive benefit indicates such investment represents improved efficiency, and a net negative is considered a wasteful use of public resources. RESULTS: We developed a three-stage process that takes into account the complexity of the healthcare system and its stakeholders, the investment appraisal and evaluation practice, and the existing knowledge of EMR implementation. The three stages include (1) literature review, (2) stakeholder consultation, and (3) CBA framework development. The framework maps the impacts of the EMR to the quadruple aim of healthcare and clearly creates a method for value assessment. CONCLUSIONS: The proposed framework is the first step toward developing a comprehensive evaluation framework for EMRs to inform health decision-makers about the economic value of digital investments rather than just the financial value.

Comparison and combination of three data sources from patient medical records to determine optimal quantification of postoperative morbidity according to the Clavien Dindo Classification and the Comprehensive Complication Index. A prospective study.

INTRODUCTION: It is currently unknown which data sources from the clinical history, or combination thereof, should be evaluated to achieve the most complete calculation of postoperative complications (PC). The objectives of this study were: to analyze the morbidity and mortality of 200 consecutive patients undergoing major surgery, to determine which data sources or combination collect the maximum morbidity, and to determine the accuracy of the morbidity reflected in the discharge report. METHODS: Observational and prospective cohort study. The sum of all PC found in the combined review of medical notes, nursing notes, and a specific form was considered the gold standard. PC were classified according to the Clavien Dindo Classification and the Comprehensive Complication Index (CCI). RESULTS: The percentage of patients who presented PC according to the gold standard, medical notes, nursing notes and form were: 43.5%, 37.5%, 35% and 18.7% respectively. The combination of sources improved CCI agreement by 8%-40% in the overall series and 39.1-89.7 % in patients with PC. The correct recording of PC was inversely proportional to the complexity of the surgery, and the combination of sources increased the degree of agreement with the gold standard by 35 %-67.5% in operations of greater complexity. The CDC and CCI of the discharge report coincided with the gold-standard values in patients with PC by 46.8% and 18.2%, respectively. CONCLUSIONS: The combination of data sources, particularly medical and nursing notes, considerably increases the quantification of PC in general, most notably in complex interventions.

The clinical and genetic spectrum of paediatric speech and language disorders in 52,143 individuals.

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 (EMR) 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 twenty-six broad speech and language diagnoses. We used Natural Language Processing to assess to which degree clinical diagnosis in full-text notes were reflected in ICD-10 diagnosis codes. We found that aphasia and speech apraxia could be easily retrieved through ICD-10 diagnosis codes, while stuttering as a speech phenotype was only coded in 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 EMR analysis 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, CI=18.62-130.39) and MYO7A with speech and language development delay due to hearing loss (P=1.24 × 10-5, CI=17.46-Inf). Finally, in a sub-cohort of 726 individuals with whole exome sequencing data, we identified an enrichment of rare variants in synaptic protein and neuronal receptor pathways and associations of UQCRC1 with expressive aphasia and WASHC4 with abnormality of speech or vocalization. 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.

Crossing the digital divide: The workload of manual data entry for integration between mobile health applications and eHealth infrastructure.

Background: Many digital health interventions (DHIs), including mobile health (mHealth) apps, aim to improve both client outcomes and efficiency like electronic medical record systems (EMRS). Although interoperability is the gold standard, it is also complex and costly, requiring technical expertise, stakeholder permissions, and sustained funding. Manual data linkage processes are commonly used to "integrate" across systems and allow for assessment of DHI impact, a best practice, before further investment. For mHealth, the manual data linkage workload, including related monitoring and evaluation (M&E) activities, remains poorly understood. Methodology: As a baseline study for an open-source app to mirror EMRS and reduce healthcare worker (HCW) workload while improving care in the Nurse-led Community-based Antiretroviral therapy Program (NCAP) in Lilongwe, Malawi, we conducted a time-motion study observing HCWs completing data management activities, including routine M&E and manual data linkage of individual-level app data to EMRS. Data management tasks should reduce or end with successful app implementation and EMRS integration. Data was analysed in Excel. Results: We observed 69:53:00 of HCWs performing routine NCAP service delivery tasks: 39:52:00 (57%) was spent completing M&E data related tasks of which 15:57:00 (23%) was spent on manual data linkage workload, alone. Conclusion: Understanding the workload to ensure quality M&E data, including to complete manual data linkage of mHealth apps to EMRS, provides stakeholders with inputs to drive DHI innovations and integration decision making. Quantifying potential mHealth benefits on more efficient, high-quality M&E data may trigger new innovations to reduce workloads and strengthen evidence to spur continuous improvement.

Iraqi Population Trusts in Electronic Healthcare Records: A Cross-sectional Study.

This study investigated the perceptions of Iraqi patients regarding Electronic Healthcare Records (EHRs) in terms of trust and propensity to share and exchange medical and personal information and data within the healthcare ecosystem. During the period of April to June 2022, a researcher-assisted questionnaire was disseminated to adult Iraqi patients attending public or private healthcare facilities in a subset of Iraqi governorates. Data collection was followed by descriptive and inferential analyses. In total, 552 respondents filled out the questionnaire. The findings revealed that 71.6% of respondents were conversant with EHRs and trusted them as data collection and storage systems. In addition, 10% of respondents did not want their EHRs to be shared between healthcare professionals and institutions. However, only 3.6% of participants were willing to share all of their personal information with healthcare professionals. Female respondents were considerably more willing to share their full names with healthcare professionals than male respondents, despite the society's reputation for conservatism. The findings of this study highlighted the necessity of tailoring initiatives to enhance patients' trust in EHRs and their interactions with healthcare professionals other than medical physicians.

A Profile of Influenza Vaccine Coverage for 2019-2020: Database Study of the English Primary Care Sentinel Cohort.

BACKGROUND: Innovation in seasonal influenza vaccine development has resulted in a wider range of formulations becoming available. Understanding vaccine coverage across populations including the timing of administration is important when evaluating vaccine benefits and risks. OBJECTIVE: This study aims to report the representativeness, uptake of influenza vaccines, different formulations of influenza vaccines, and timing of administration within the English Primary Care Sentinel Cohort (PCSC). METHODS: We used the PCSC of the Oxford-Royal College of General Practitioners Research and Surveillance Centre. We included patients of all ages registered with PCSC member general practices, reporting influenza vaccine coverage between September 1, 2019, and January 29, 2020. We identified influenza vaccination recipients and characterized them by age, clinical risk groups, and vaccine type. We reported the date of influenza vaccination within the PCSC by International Standard Organization (ISO) week. The representativeness of the PCSC population was compared with population data provided by the Office for National Statistics. PCSC influenza vaccine coverage was compared with published UK Health Security Agency's national data. We used paired t tests to compare populations, reported with 95% CI. RESULTS: The PCSC comprised 7,010,627 people from 693 general practices. The study population included a greater proportion of people aged 18-49 years (2,982,390/7,010,627, 42.5%; 95% CI 42.5%-42.6%) compared with the Office for National Statistics 2019 midyear population estimates (23,219,730/56,286,961, 41.3%; 95% CI 4.12%-41.3%; P<.001). People who are more deprived were underrepresented and those in the least deprived quintile were overrepresented. Within the study population, 24.7% (1,731,062/7,010,627; 95% CI 24.7%-24.7%) of people of all ages received an influenza vaccine compared with 24.2% (14,468,665/59,764,928; 95% CI 24.2%-24.2%; P<.001) in national data. The highest coverage was in people aged ≥65 years (913,695/1,264,700, 72.3%; 95% CI 72.2%-72.3%). The proportion of people in risk groups who received an influenza vaccine was also higher; for example, 69.8% (284,280/407,228; 95% CI 69.7%-70%) of people with diabetes in the PCSC received an influenza vaccine compared with 61.2% (983,727/1,607,996; 95% CI 61.1%-61.3%; P<.001) in national data. In the PCSC, vaccine type and brand information were available for 71.8% (358,365/498,923; 95% CI 71.7%-72%) of people aged 16-64 years and 81.9% (748,312/913,695; 95% CI 81.8%-82%) of people aged ≥65 years, compared with 23.6% (696,880/2,900,000) and 17.8% (1,385,888/7,700,000), respectively, of the same age groups in national data. Vaccination commenced during ISO week 35, continued until ISO week 3, and peaked during ISO week 41. The in-week peak in vaccination administration was on Saturdays. CONCLUSIONS: The PCSC's sociodemographic profile was similar to the national population and captured more data about risk groups, vaccine brands, and batches. This may reflect higher data quality. Its capabilities included reporting precise dates of administration. The PCSC is suitable for undertaking studies of influenza vaccine coverage.

Increased Risk of New-Onset Rheumatoid Arthritis Among Osteoarthritis Patients Received Total Knee Arthroplasty: a global federated health network analysis.

Background: Complications of total knee arthroplasty (TKA) had been widely discussed. However, whether TKA influence risk of rheumatoid arthritis (RA) in osteoarthritis patients remained uncertain. We intend to evaluate the risk of RA in osteoarthritis patients underwent TKA. Methods: In this retrospective cohort study, data was retrieved from the US collaborative networks in TriNetX research network. Within the study period between 2005 and 2017, osteoarthritis patients underwent TKA were enrolled as case cohort whereas osteoarthritis patients never underwent TKA were enrolled as control cohort. Covariates were matched via propensity score matching. Risk of RA in TKA patients were valuated under various follow-up time and sensitivity models. Results: Under 1-year, 3-year and 5-year of follow-up, TKA patients were associated with significantly elevated risk of RA, especially under 1-year follow-up (HR=1.74; 95% CI, 1.39-2.18). Subgroup analysis demonstrated a significant increase in the risk of RA following TKA in the female subgroup (HR=1.42; 95% CI, 1.24-1.63), the subgroup aged 18-64 years (HR=1.48; 95% CI, 1.11-1.97), and the subgroup aged greater than 65 years old (HR=1.38; 95% CI, 1.21-1.58) based on 5-year follow-up. Conclusion: Clinicians should be concerned about uncharted association between TKA and RA reported our current study. Additional prospective studies and in-depth mechanistic inquiries were warranted to determine the causation.