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Background: Antibiotic resistance in uropathogens has rapidly escalated over time, complicating treatment and increasing morbidity and mortality. Few studies have explored how the social determinants of health may be associated with patients' risks for acquiring antibiotic-resistant (AR) uropathogens. Methods: We identified urine cultures collected from outpatients presenting to Tufts Medical Center Primary Care Practices between 2015 and 2020. Specimens were included if patients' age, sex, and residential address were recorded in the electronic medical record (EMR) and if their urine culture yielded Enterococcus spp. or one or more gram-negative bacterial organism(s) or for which antibiotic susceptibility profiling and species identification was conducted. We abstracted patients' sociodemographic characteristics from the EMR and used US Census Bureau data to identify characteristics about patients' census tracts of residence. We evaluated associations between individual- and neighborhood-level characteristics and patients' risk of having a urine culture resistant to (1) three or more antibiotic classes (i.e., multidrug resistant [MDR]), (2) first-line treatments, (3) fluoroquinolones, (4) aminoglycosides, or (5) ceftriaxone using logistic regression models and a Bonferroni correction to account for multiple hypothesis testing. Results: We included urine cultures from 1,306 unique outpatients, most of whom were female (89%). Patients largely self-identified as Non-Hispanic White (36%), Asian (15%), or Non-Hispanic Black (11%). Over 60% lived in an environmental justice-designated census tract. Most included isolates were Escherichia coli (76%) or Klebsiella pneumoniae (7%). Using public insurance increased patients' odds of having a uropathogen resistant to first-line antibiotics, but living in a limited-income neighborhood reduced patients' odds of having a MDR uropathogen by 47%. We noted a strong but non-significant positive trend between speaking a language other than English and having an aminoglycoside-resistant uropathogen (p-value = 0.02). Most notably, after controlling for other factors, we observed no statistically significant associations between race or ethnicity and AR uropathogens. Conclusion: The social determinants of health may play important and intersecting roles in determining a patient's risk of having a resistant uropathogens that is more challenging or expensive to treat. It is crucial to acknowledge how race is likely to be a proxy for other factors affecting health, and to consider that some groups may be disproportionately impacted by antibiotic resistance. [ABSTRACT FROM AUTHOR]

Background: The mechanism for recording International Classification of Diseases (ICD) and diagnosis related groups (DRG) codes in a patient's chart is through a certified medical coder who manually reviews the medical record at the completion of an admission. High-acuity ICD codes justify DRG modifiers, indicating the need for escalated hospital resources. In this manuscript, we demonstrate that value of rules-based computer algorithms that audit for omission of administrative codes and quantifying the downstream effects with regard to financial impacts and demographic findings did not indicate significant disparities. Methods: All study data were acquired via the UCLA Department of Anesthesiology and Perioperative Medicine's Perioperative Data Warehouse. The DataMart is a structured reporting schema that contains all the relevant clinical data entered into the EPIC (EPIC Systems, Verona, WI) electronic health record. Computer algorithms were created for eighteen disease states that met criteria for DRG modifiers. Each algorithm was run against all hospital admissions with completed billing from 2019. The algorithms scanned for the existence of disease, appropriate ICD coding, and DRG modifier appropriateness. Secondarily, the potential financial impact of ICD omissions was estimated by payor class and an analysis of ICD miscoding was done by ethnicity, sex, age, and financial class. Results: Data from 34,104 hospital admissions were analyzed from January 1, 2019, to December 31, 2019. 11,520 (32.9%) hospital admissions were algorithm positive for a disease state with no corresponding ICD code. 1,990 (5.8%) admissions were potentially eligible for DRG modification/upgrade with an estimated lost revenue of $22,680,584.50. ICD code omission rates compared against reference groups (private payors, Caucasians, middle-aged patients) demonstrated significant p-values < 0.05; similarly significant p-value where demonstrated when comparing patients of opposite sexes. Conclusions: We successfully used rules-based algorithms and raw structured EHR data to identify omitted ICD codes from inpatient medical record claims. These missing ICD codes often had downstream effects such as inaccurate DRG modifiers and missed reimbursement. Embedding augmented intelligence into this problematic workflow has the potential for improvements in administrative data, but more importantly, improvements in administrative data accuracy and financial outcomes. [ABSTRACT FROM AUTHOR]

Background: It is a challenge for the ambulance service to identify which patients to convey to the emergency department (ED). Ideally this would be the patients with serious conditions requiring further care in the ED. However, patients presenting with non-specific chief complaints are difficult to prioritize and typically have normal or near normal vital signs despite up to one third having underlying serious conditions. The proportion of patients with serious conditions among non-conveyed patients with non-specific chief complaints (NSC) as assessed by ambulance clinicians is not known. Therefore, the primary aim was to study the proportion of serious conditions among conveyed and non-conveyed patients presenting to the ambulance service with NSCs. The secondary aim was to study the mortality rate among patients with NSCs in relation to conveyance and serious and nonserious conditions. Method: A retrospective cohort study of patients ≥ 18 years of age presenting with NSCs to the ambulance service in Stockholm Region between January 1st, 2013 and December 31st, 2013. Patients were identified via the ambulance service electronic health record and followed via records from the the National Patient Register and Causes of Death Registry at Sweden's National Board for Health and Welfare. Descriptive statistics as well as regression analyses were used. Results: A total of 4744 patients were included with a median age of 76 years. A serious condition was present in 1398 (29.5%) of the patients. After index assessment by the ambulance service, 3780 (79.7%) were conveyed of which 1334 (35.3%) had serious conditions, compared to 964 (20.3%) who were non-conveyed of which 64 (6.6%) had serious conditions. 30-day mortality was 372 (9.8%) in the conveyance group compared to 32 (3.3%) in the non-conveyance group. If serious conditions were present, the mortality rates were 269 (20.2%) in the conveyance group compared to 11 (17.2%) in the non-conveyance group. Conclusion: The results show that serious conditions were more than five times more common among conveyed as compared to non-conveyed patients. Mortality rates were three times higher in the conveyance group as compared to the non-conveyance group. Our results suggest that the ambulance personnel play an important role in identifying patients with serious conditions, hence, likely to be in need of treatment in the ED. Trial registration: N/A [ABSTRACT FROM AUTHOR]

Study design: Retrospective analysis of difficult airway alerts in a major tertiary centre. Objective: To investigate the completeness, utility and efficacy of difficult airway alerts and their impact on future airway management episodes. Design, data sources and methods: Patients who had a "difficult airway" entry on electronic medical records (EMR) from 2011 to 2021 were included. Each alert was analyzed by a team of anesthetists with expertise in difficult airway management for its quality, appropriateness, and impact on future airway management episodes. Alert quality was defined as to whether the content of the alert contained pertinent information for emergent airway management. Alert appropriateness was defined as to whether the experts would be unhappy to perform a rapid sequence induction and intubation, if required, following review of all available documentation. Results: 141 patients were included for this study, with a mean age of 58.6 +/- 15.3 years. Ninety-three (66%) alerts were created by medical staff, of which 52 were recorded by consultant anesthetists. 117 alerts (83%) were deemed to be appropriate by the airway expert team, but only 40 alerts (28%) were found to have sufficient quality to be helpful in emergent airway management. Sixty-five patients (47%) had at least one subsequent airway management episode, of which 35 patients (56%) underwent a change of management following alert creation. We proceeded to modify 103 alerts (73%) to improve their quality to aid future encounters. Conclusion: Difficult Airway encounters are an uncommon event in anesthesia, but clear, comprehensive and effectively communicated documentation is required to minimize the risk in future encounters. In our institution, while most difficult airway alerts were appropriate, we found significant heterogeneity in the quality of this documentation, which limits the clinical utility of the alert system. We have taken measures to improve local processes of difficult airway documentation and considered the implications of our project for the broader airway management community. Trial registration: Not applicable. [ABSTRACT FROM AUTHOR]

Background: The transformational potential of electronic medical records (EMRs) in global healthcare is evident, but their adoption in low- and middle-income countries, including Nigeria, remains low. Objective: This study aimed to understand the perceptions of southwestern Nigerian medical students toward EMRs given the limited literature on this topic. Methods: A descriptive, cross-sectional online survey was conducted among 412 medical students from four institutions in southwestern Nigeria. Data on the students' demographics, exposure to, and perspectives on EMR was collected. Data was analyzed using R Version 3.6.1. Results: Majority (79%) of respondents were in their clinical years. We found that 68.4% of the respondents had good perception (scores ≥ 16) about EMRs while 31.6% were deemed as having fair level of perception (scores < 16). Interestingly, 54.9% had never used an electronic medical record (EMR). Although 46.4% felt very comfortable with EMRs' postgraduation use, 16% felt very uncomfortable. Notably, 94.5% believed that health technology solutions could enhance Nigerian health outcomes. Furthermore, 81.8% felt that their medical education would benefit from greater EMR exposure, while 80.9% believed that EMRs are essential for adequate patient care. Males exhibited significantly more EMR experience and more positive attitudes toward EMRs. However, there was no significant association between the level of study, location of medical school, and respondents' experience or perceptions of EMRs. Conclusion: More than half of students surveyed reported that they have never used EMRs. Despite this, many students felt comfortable with the idea of using EMRs in their future practice. Observed gender differences in EMR use and attitudes highlight the need for targeted strategies to prepare all students, regardless of gender, for the digital transition in healthcare. [ABSTRACT FROM AUTHOR]

Background: Triage nurses play a vital role in emergency departments (Eds), with the accuracy of the triage nurse significantly impacting patient care and departmental efficiency. However, there is a lack of exploration into whether the time it takes for triage nurses to triage patients affects accuracy. Method: This study analyzed the electronic medical records of 787 patients and the characteristics of triage nurses, using statistical methods to determine factors affecting classification accuracy. Data were collected from a single general hospital between November 1 and November 30, 2023. Results: Findings revealed an 84.9% accuracy rate. Longer clinical experience (p =.001, CI = 1.094–2.052), as well as extended classification time (p =.002, CI = 1.338–2.916), significantly improved accuracy. Age and gender had no notable effect. Conclusion: Enhancing triage nurse experience and allowing adequate classification time can improve accuracy, optimizing patient care and ED operations. [ABSTRACT FROM AUTHOR]

Background: Chronic osteomyelitis is a highly prevalent and severe orthopaedic complication, representing a critical unresolved issue. The clinical symptoms of osteomyelitis are influenced by various factors, including geography, lifestyle, and pre-existing medical conditions.This study aims to provide theoretical basis for treatment and prevention of osteomyelitis by investigating and analyzing clinical features and pathogen distribution among 282 patients with chronic tibial osteomyelitis in xinjiang. Methods: A total of 282 patients with chronic tibial osteomyelitis from January 1, 2012 to January 1, 2022 in the First Affiliated Hospital of Xinjiang Medical University were retrospectively analyzed. All data were collected from electronic medical record (EMR) system including demographics, etiology, risk factors, osteomyelitis location and clinical classification. Results: Farmers, students, unemployed and retirees accounted for a relatively large proportion of the 282 patients. There were 233 males and 49 females with a gender ratio of 4.75:1. The average age was 40.21 ± 15.68 years and was mainly concentrated in 41–50 years, specifically, the mean age of females was slightly older than that of males. Education level was mostly primary and secondary school education, and illiteracy. Risk factors of chronic tibial osteomyelitis included history of smoking and drinking, history of multiple repeated surgeries, and impaired immunity. Frequent clinical symptoms were in the order of pain, local swelling, pus discharge and skin ulceration. Among all inflammatory markers, proportion of positive results were 30.85%, 59.93% and 53.90% for white blood cell (WBC), erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), respectively. Positive rate of pathogenic microorganism culture was low and the three most common bacteria were Staphylococcus aureus (S. aureus), Pseudomonas aeruginosa (P. aeruginosa) and Escherichia coli (E. coli). The most frequent site of infection was middle tibia. According to Cierny-Mader osteomyelitis classification, the most common types were type IIIA, IVA and IIA. Conclusion: Number of visits due to chronic osteomyelitis increased year by year, with young and middle-aged male farmers and low education level as the main groups. Smoking and drinking were two considerable risk factors that should be attached to a great importance. No significant increase was found in inflammatory markers and lower positive rate of pathogenic microorganism culture was observed. Multi-drug resistant bacteria were common and S. aureus remained the most frequent pathogen. Elevated ESR had certain diagnostic value for osteomyelitis. Type III and type IV osteomyelitis accounted for a large proportion which posed great challenges for clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Background: Missing data in confounding variables present a frequent challenge in generating evidence using real-world data, including electronic health records (EHR). Our objective was to apply a recently published toolkit for characterizing missing data patterns and based on the toolkit results about likely missingness mechanisms, illustrate the decision-making process for analyses in an empirical case example. Methods: We utilized the Structural Missing Data Investigations (SMDI) toolkit to characterize missing data patterns in the context of a pharmacoepidemiology study comparing cardiovascular outcomes of initiating sodium-glucose-cotransporter-2 inhibitors (SGLT2i) and dipeptidyl peptidase‐4 inhibitors (DPP‐4i) among older adults. The study used a linked EHR-Medicare claims dataset from Duke Health patients (2015–2017), focusing on partially observed confounders from EHR data (HbA1c lab and body mass index [BMI] values). Our analysis incorporated SMDI's descriptive functions and diagnostic tests to explore missingness patterns and determine missingness mitigation approaches. We used findings from these investigations to inform estimation of adjusted hazard ratios comparing the two classes of medications. Results: High levels of missingness were noted for important confounding variables including HbA1c (63.6%) and BMI (16.5%). Diagnostic tests resulted in output that described: 1) the distributions of patient characteristics, exposure, and outcome between patients with or without an observed value of the partially observed covariate, 2) the ability to predict missingness based on observed covariates, and 3) estimate if the missingness of a partially observed covariate is differential with respect to the outcome. There was evidence that missingness could be sufficiently described using observed data, which allowed multiple imputation by chained equations using random forests to address missing confounder data in estimating treatment effects. Multiple imputation resulted in improved alignment of effect estimates with previous studies. Conclusions: We were able to demonstrate the practical application of the SMDI toolkit in a real-world setting. Application of the SMDI toolkit and the resulting insights of potential missingness patterns can inform the choice of appropriate analytic methods and increase transparency of research methods in handling missing data. This type of approach can inform analytic decision making and may increase our ability to generate evidence from real-world data. [ABSTRACT FROM AUTHOR]

Background: With the recent surge in the utilization of electronic health records for cognitive decline, the research community has turned its attention to conducting fine-grained analyses of dementia onset using advanced techniques. Previous works have mostly focused on machine learning-based prediction of dementia, lacking the analysis of dementia progression and its associations with risk factors over time. The black box nature of machine learning models has also raised concerns regarding their uncertainty and safety in decision making, particularly in sensitive domains like healthcare. Objective: We aimed to characterize the progression of health conditions, such as chronic diseases and neuropsychiatric symptoms, of the participants in Mayo Clinic Study of Aging (MCSA) from initial mild cognitive impairment (MCI) diagnosis to dementia onset through network analysis. Methods: We used the data from the MCSA, a prospective population-based cohort study of cognitive aging, and examined the changing association among variables (i.e., participants' health conditions) from the first visit of MCI diagnosis to the visit of dementia onset using network analysis. The number of participants for this study are 97 with the number of visits ranging from 2 visits (30 months) to 7 visits (105 months). We identified the network communities among variables from three-fold collection of instances: (i) the first MCI diagnosis, (ii) progression to dementia, and (iii) dementia diagnosis. We determine the variables that play a significant role in the dementia onset, aiming to identify and prioritize specific variables that prominently contribute towards developing dementia. In addition, we explore the sex-specific impact of variables in relation to dementia, aiming to investigate potential differences in the influence of certain variables on dementia onset between males and females. Results: We found correlation among certain variables, such as neuropsychiatric symptoms and chronic conditions, throughout the progression from MCI to dementia. Our findings, based on patterns and changing variables within specific communities, reveal notable insights about the time-lapse before dementia sets in, and the significance of progression of correlated variables contributing towards dementia onset. We also observed more changes due to certain variables, such as cognitive and functional scores, in the network communities for the people who progressed to dementia compared to those who does not. Most changes for sex-specific analysis are observed in clinical dementia rating and functional activities questionnaire during MCI onset are followed by chronic diseases, and then by NPI-Q scores. Conclusions: Network analysis has shown promising potential to capture significant longitudinal changes in health conditions, spanning from the MCI diagnosis to dementia progression. It can serve as a valuable analytic approach for monitoring the health status of individuals in cognitive impairment assessment. Furthermore, our findings indicate a notable sex difference in the impact of specific health conditions on the progression of dementia. [ABSTRACT FROM AUTHOR]

Background: Many hospitalised patients require rehabilitation during recovery from acute illness. We use routine data from Electronic Health Records (EHR) to report the quantity and intensity of rehabilitation required to achieve hospital discharge, comparing patients with and without COVID-19. Methods: We performed a retrospective cohort study of consecutive adults in whom COVID-19 testing was undertaken between March 2020 and August 2021 across three acute hospitals in Scotland. We defined rehabilitation contacts (physiotherapy, occupational therapy, dietetics and speech and language therapy) from timestamped EHR data and determined contact time from a linked workforce planning dataset. Our aim was to clarify rehabilitation required to achieve hospital discharge and so we excluded patients who died during their admission, and those who did not require rehabilitation (fewer than two specialist contacts). The primary outcome was total rehabilitation time. Secondary outcomes included the number of contacts, admission to first contact, and rehabilitation minutes per day. A multivariate regression analysis for identifying patient characteristics associated with rehabilitation time included age, sex, comorbidities, and socioeconomic status. Results: We included 11,591 consecutive unique patient admissions (76 [63,85] years old, 56% female), of which 651 (6%) were with COVID-19, and 10,940 (94%) were admissions with negative testing. There were 128,646 rehabilitation contacts. Patients with COVID-19 received more than double the rehabilitation time compared to those without (365 [165, 772] vs 170 [95, 350] mins, p<0.001), and this was delivered over more specialist contacts (12 [6, 25] vs 6 [3, 11], p<0.001). Admission to first rehabilitation contact was later in patients with COVID-19 (3 [1, 5] vs 2 [1, 4] days from admission). Overall, patients with COVID-19 received fewer minutes of rehabilitation per day of admission (14.1 [9.8, 18.7] vs 15.6 [10.6, 21.3], p<0.001). In our regression analyses, older age and COVID-19 were associated with increased rehabilitation time. Conclusions: Patients with COVID received more rehabilitation contact time than those without COVID, but this was delivered less intensively and was commenced later in an admission. Rehabilitation data derived from the EHR represents a novel measure of delivered hospital care. [ABSTRACT FROM AUTHOR]

Background: Integrating advanced machine-learning (ML) algorithms into clinical practice is challenging and requires interdisciplinary collaboration to develop transparent, interpretable, and ethically sound clinical decision support (CDS) tools. We aimed to design a ML-driven CDS tool to predict opioid overdose risk and gather feedback for its integration into the University of Florida Health (UFHealth) electronic health record (EHR) system. Methods: We used user-centered design methods to integrate the ML algorithm into the EHR system. The backend and UI design sub-teams collaborated closely, both informed by user feedback sessions. We conducted seven user feedback sessions with five UF Health primary care physicians (PCPs) to explore aspects of CDS tools, including workflow, risk display, and risk mitigation strategies. After customizing the tool based on PCPs' feedback, we held two rounds of one-on-one usability testing sessions with 8 additional PCPs to gather feedback on prototype alerts. These sessions informed iterative UI design and backend processes, including alert frequency and reappearance circumstances. Results: The backend process development identified needs and requirements from our team, information technology, UFHealth, and PCPs. Thirteen PCPs (male = 62%, White = 85%) participated across 7 user feedback sessions and 8 usability testing sessions. During the user feedback sessions, PCPs (n = 5) identified flaws such as the term "high risk" of overdose potentially leading to unintended consequences (e.g., immediate addiction services referrals), offered suggestions, and expressed trust in the tool. In the first usability testing session, PCPs (n = 4) emphasized the need for natural risk presentation (e.g., 1 in 200) and suggested displaying the alert multiple times yearly for at-risk patients. Another 4 PCPs in the second usability testing session valued the UFHealth-specific alert for managing new or unfamiliar patients, expressed concerns about PCPs' workload when prescribing to high-risk patients, and recommended incorporating the details page into training sessions to enhance usability. Conclusions: The final backend process for our CDS alert aligns with PCP needs and UFHealth standards. Integrating feedback from PCPs in the early development phase of our ML-driven CDS tool helped identify barriers and facilitators in the CDS integration process. This collaborative approach yielded a refined prototype aimed at minimizing unintended consequences and enhancing usability. [ABSTRACT FROM AUTHOR]

Background: Management of rheumatoid arthritis (RA) relies on symptoms reported by patients during infrequent outpatient clinic visits. These reports are often incomplete and inaccurate due to poor recall, leading to suboptimal treatment decisions and outcomes. Asking people to track symptoms in-between visits and integrating the data into clinical pathways may improve this. However, knowledge on how to implement this into practice and its impact on services and outcomes remains scarce in RA. Therefore, we evaluate the comparative effectiveness and cost-effectiveness of integrated symptom tracking in people with RA over and above usual care, while generating insights on factors for successful implementation. Methods: In this superiority stepped wedge cluster-randomized controlled trial with continuous recruitment short exposure design, 16 rheumatology outpatient departments (clusters) recruit a total of 732 people with active RA. They initially offer clinic visits according to standard of care before switching in pairs to visits with integrated symptom tracking. Clusters switch in randomized order every 3 weeks. Integrated symptom tracking consists of (1) a mobile app for patients to track their symptoms daily and other RA aspects weekly/monthly, and (2) an interactive dashboard visualizing the app data, which healthcare professionals access from their electronic health record system. Clinic visits happen according to usual practice, with tracked symptom data only reviewed during visits. Our primary outcome is a difference in marginal mean disease activity score at 12 ± 3 months between standard of care and integrated symptom tracking, after accounting for baseline values, cluster, and other covariates. Secondary outcomes include patient-reported disease activity, quality of life and quality-adjusted life-years, medication/resource use, consultation and decision-making experience, self-management, and illness perception. We also conduct interviews and observations as part of a parallel process evaluation to gather information on implementation. Discussion: Our trial will generate high-quality evidence of comparative and cost-effectiveness of integrated symptom tracking compared to standard of care in people with RA, with our process evaluation delivering knowledge on successful implementation. This optimizes the chances of integrated symptom tracking being adopted more widely if we find it is (cost-) effective. Trial registration: Registered 4-Jun-2024 on https://www.isrctn.com/, ISRCTN51539448. Trial open science framework repository: https://osf.io/sj9ha/. [ABSTRACT FROM AUTHOR]

Background: The increasing availability of large volumes of personal data from diverse sources such as electronic health records, research programmes, commercial genetic testing, national health surveys and wearable devices presents significant opportunities for advancing public health, disease surveillance, personalized medicine and scientific research and innovation. However, this potential is hampered by a lack of clarity related to the processing and sharing of personal health data, particularly across varying national regulatory frameworks. This often leaves researcher stakeholders uncertain about how to navigate issues around secondary data use, repurposing data for different research objectives and cross-border data sharing. Method: We analysed 37 data protection legislation across Africa to identify key principles and requirements for processing and sharing of personal health and genetic data in scientific research. On the basis of this analysis, we propose strategies that data science research initiatives in Africa can implement to ensure compliance with data protection laws while effectively reusing and sharing personal data for health research and scientific innovation. Results: In many African countries, health and genetic data are categorized as sensitive and subject to stricter protection. Key principles guiding the processing of personal data include confidentiality, non-discrimination, transparency, storage limitation, legitimacy, purpose specification, integrity, fairness, non-excessiveness, accountability and data minimality. The rights of data subjects include the right to be informed, the right of access, the right to rectification, the right to erasure/deletion of data, the right to restrict processing, the right to data portability and the right to seek compensation. Consent and adequacy assessments were the most common legal grounds for cross-border data transfers. However, considerable variation exists in legal requirements for data transfer across countries, potentially creating barriers to collaborative health research across Africa. Conclusions: We propose several strategies that data science research initiatives can adopt to align with data protection laws. These include developing a standardized module for safe data flows, using trusted data environments to minimize cross-border transfers, implementing dynamic consent mechanisms to comply with consent specificity and data subject rights and establishing codes of conduct to govern the secondary use of personal data for health research and innovation. [ABSTRACT FROM AUTHOR]

Background: As digital healthcare services handle increasingly more sensitive health data, robust access control methods are required. Especially in emergency conditions, where the patient's health situation is in peril, different healthcare providers associated with critical cases may need to be granted permission to acquire access to Electronic Health Records (EHRs) of patients. The research objective of this work is to develop a proactive access control method that can grant emergency clinicians access to sensitive health data, guaranteeing the integrity and security of the data, and generating trust without the need for a trusted third party. Methods: A contextual and blockchain-based mechanism is proposed that allows access to sensitive EHRs by applying prognostic procedures where information based on context, is utilized to identify critical situations and grant access to medical data. Specifically, to enable proactivity, Long Short Term Memory (LSTM) Neural Networks (NNs) are applied that utilize patient's recent health history to prognose the next two-hour health metrics values. Fuzzy logic is used to evaluate the severity of the patient's health state. These techniques are incorporated in a private and permissioned Hyperledger-Fabric blockchain network, capable of securing patient's sensitive information in the blockchain network. Results: The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient's well-being. Integrating this predictive mechanism within the blockchain network proved to be a robust tool to enhance the performance of the access control mechanism. Furthermore, the blockchain network of this work can record the history of who and when had access to a specific patient's sensitive EHRs, guaranteeing the integrity and security of the data, as well as recording the latency of this mechanism, where three different access control cases are evaluated. This access control mechanism is to be enforced in a real-life scenario in hospitals. Conclusions: The proposed mechanism informs proactively the emergency team of professional clinicians about patients' critical situations by combining fuzzy and predictive machine learning techniques incorporated in the private and permissioned blockchain network, and it exploits the distributed data of the blockchain architecture, guaranteeing the integrity and security of the data, and thus, enhancing the users' trust to the access control mechanism. [ABSTRACT FROM AUTHOR]

Background: Paroxysmal Nocturnal Haemoglobinuria (PNH) is an ultra-rare, acquired disorder that is challenging to diagnose due to varied symptoms, heterogeneous patient presentations, and lack of awareness among healthcare professionals. This leads to frequent misdiagnosis and delays in diagnosis. This study evaluated the feasibility of a machine learning model to identify undiagnosed PNH patients using structured electronic health records. Methods: The study used data from the Optimum Patient Care Research Database, which contains electronic health records from general practitioner (GP) practices across the United Kingdom. PNH patients were identified by the presence, and control patients by the absence of a PNH diagnosis code in their records. Clinical features (symptoms, diagnoses, healthcare utilisation) from 131 patients in the PNH group and 593,838 patients in the control group, were inputted to a tree-based XGBoost machine learning model to classify patients as either "positive" or "negative" for PNH suspicion. The algorithm was finalised after additional exclusions and inclusions applied. Performance was assessed using positive predictive value (PPV), recall and specificity. As the sample used to develop the algorithm was not representative of the true population prevalence, PPV was additionally adjusted to reflect performance in the wider population. Results: Of all the patients in the PNH group, 27% were classified as positive (recall). 99.99% of the control group were classified as negative (specificity). Of all the patients classified as positive, 60.4% had a diagnosis of PNH in their record (PPV). The PPV adjusted for the population prevalence of PNH was 19.59 suggesting nearly 1 in 5 patients flagged may warrant further PNH investigation. The key clinical features in the model were aplastic anaemia, pancytopenia, haemolytic anaemia, myelodysplastic syndrome, and Budd-Chiari syndrome. Conclusion: This is the first study to combine clinical understanding of PNH with machine learning, demonstrating the ability to discriminate between PNH and control patients in retrospective electronic health records. With further investigation and validation, this algorithm could be deployed on live health data, potentially leading to earlier diagnosis for patients who currently experience long diagnostic delays or remain undiagnosed. [ABSTRACT FROM AUTHOR]

Background: Pneumocystis jirovecii pneumonia (PcP) is an opportunistic infection associated with immunocompromised patients. The development of novel immunotherapies has promoted the incidence of PcP. This study describes the clinical course and outcome of PcP in chimeric antigen receptor (CAR) T cell recipients with hematological malignancies. Methods: This is a retrospective case series of CAR-T recipients diagnosed with PcP in our center. The cases were all confirmed by metagenomic next-generation sequencing of clinical samples. The demographic, clinical, and outcome data were retrieved from the patients' medical charts and electronic medical record system. Results: In total, 8 cases of PcP were identified. The underlying malignancies included T-acute lymphoblastic leukemia (ALL) (n = 1), diffuse large B cell lymphoma (DLBCL) (n = 4), and B-ALL (n = 3). One patient received short-term sulfamethoxazole-trimethoprim (SMZ-TMP) while the others had no prophylaxis. Four patients had neutropenia/lymphopenia at the diagnosis of PcP, and two patients had immunosuppressants within one month before PcP manifestation. The median time from CAR-T infusion to PcP diagnosis was 98.5 days (range 52–251). Seven patients recovered from PcP after proper management while one died of septic shock. Conclusion: PcP can occur after different CAR-T product, and the long-term depletion of immune cells seems to be related to PcP. SMZ-TMP is effective in this setting. More real-world experience of CAR-T therapy is required to assess the incidence and outcome of PcP in this population. [ABSTRACT FROM AUTHOR]

Background: Warfarin and Direct Oral Anticoagulant (DOAC) users may have more frequent antibiotic prescriptions than non-users. The aim of this study was to estimate rates of common and resistant infections, and antibiotic prescribing amongst warfarin and DOAC users versus non-users. Methods: This matched retrospective cohort study used data from patients registered with General practices in England contributing to the Clinical Practice Research Datalink GOLD. We included 61,750 adults who initiated warfarin or a DOAC between 1st January 2011 and 31st December 2019, matched 1:1 to non-users. We estimated Incident Rate Ratios (IRR) and 95% Confidence Intervals for three common infections and all-cause antibiotic prescribing. We estimated hazard ratios (HRs) and 95% CIs for the risk of methicillin resistance Staphylococcus aureus (MRSA), other antibiotic-resistant organisms, or Clostridium difficile. We assessed the extent to which any of the effect of warfarin and DOAC use on antibiotic resistant infections or Clostridium difficile was mediated by antibiotic prescribing patterns. Results: 37,143 warfarin users and 24,607 DOAC users were matched 1:1 to non-users. Warfarin and DOAC users had greater relative consultation rates for respiratory, urinary, and skin infections. All-cause antibiotic prescribing was greater in warfarin and DOAC users (warfarin; adjusted IRR 1.47, 95% CI 1.45–1.50, DOAC; adjusted IRR 1.66, 95% CI 1.63–1.69). Largest effect sizes were observed for flucloxacillin (adjusted IRR 2.11, 95% CI 2.01–2.20), and erythromycin (adjusted IRR 2.32, 95% CI 2.00–2.70). Warfarin users had significantly higher risk of MRSA (adjusted HR 1.68, 95% CI 1.38–2.05) and hospital admission with antibiotic resistant infections (adjusted HR 1.91, 95% CI 1.11–3.30). DOAC users had significantly higher risk of MRSA (adjusted HR 1.57, 95% CI 1.20–2.06), hospital admission with antibiotic resistant infections (adjusted HR 2.13, 95% CI 1.61–2.82), and Clostridium difficile (adjusted HR 1.45, 95% CI 1.10–1.92). We found little evidence to suggest that the increased risks of studied outcomes were mediated by rates of antibiotic prescription. Conclusion: Warfarin and DOAC use was associated with greater rates of infection consultations, all-cause antibiotic prescribing, antibiotic resistant infections, and Clostridium difficile, but there was little evidence that antibiotic prescribing rates mediated risk of resistant infections or Clostridium difficile. [ABSTRACT FROM AUTHOR]

The assessment and adaptation of remote symptom analysis remains a significant challenge for researchers. Qualitative evaluation in real-time clinical settings is crucial for the formative adaptation of remote symptom analysis. Remote patient monitoring and cloud integration unlock opportunities for affordability, personalization, and transparency. Despite facing barriers, the authors skilfully showcase how the integration of Natural language processing (NLP) and Deep learning (DL) can lead to impactful interventions. The study identifies Bi-directional long short term memory (Bi-LSTM) and contextual embedding techniques as pivotal in improving model accuracy. By combining patient symptom sequences with specialist recommendations, the research aims to facilitate the smooth transition of patients from the hospital to home, ensuring they receive the necessary attention. The 96% accuracy rate discovered by the researchers is a compelling indicator of the potential to drive the development of future remote palliative care units. This paper emphasizes the importance of capturing patient symptom notes and highlights the extensive pilot studies conducted by the researcher before to developing this innovative adaptation. [ABSTRACT FROM AUTHOR]

Background: The widespread adoption of Hospital Information Systems (HIS) has brought significant benefits in healthcare quality and workflow efficiency. However, downtimes for system maintenance are inevitable and pose a considerable challenge to continuous patient care. Existing strategies, including manual prescription methods, are no longer effective due to increasing reliance on digital systems. Method: This study implemented two main strategies to mitigate the impact of scheduled downtimes. First, we created an "Emergency query program" that switches to a read-only backup server during downtimes, allowing clinicians to view essential patient data. Second, an "Emergency prescription system" was developed based on the Microsoft Power Platform and integrated into Microsoft Teams. This allows clinicians to perform digital prescriptions even during downtimes. Results: During a planned 90-minute downtime, 282 users accessed the Emergency Prescription System, resulting in 22 prescriptions from various departments. Average times for prescription confirmation and completion were 8 min and 3 s, and 18 min and 40 s, respectively. A post-downtime evaluation revealed high user satisfaction. Conclusion: Essential maintenance-induced HIS downtimes are inherently disruptive to patient care process. Our deployment of an emergency query program and a Microsoft Teams-integrated emergency prescription system demonstrated robust care continuity during HIS downtime. [ABSTRACT FROM AUTHOR]

Background: Social and behavioral determinants of health (SBDH) are associated with a variety of health and utilization outcomes, yet these factors are not routinely documented in the structured fields of electronic health records (EHR). The objective of this study was to evaluate different machine learning approaches for detection of SBDH from the unstructured clinical notes in the EHR. Methods: Latent Semantic Indexing (LSI) was applied to 2,083,180 clinical notes corresponding to 46,146 patients in the MIMIC-III dataset. Using LSI, patients were ranked based on conceptual relevance to a set of keywords (lexicons) pertaining to 15 different SBDH categories. For Generative Pretrained Transformer (GPT) models, API requests were made with a Python script to connect to the OpenAI services in Azure, using gpt-3.5-turbo-1106 and gpt-4-1106-preview models. Prediction of SBDH categories were performed using a logistic regression model that included age, gender, race and SBDH ICD-9 codes. Results: LSI retrieved patients according to 15 SBDH domains, with an overall average PPV ≥ 83%. Using manually curated gold standard (GS) sets for nine SBDH categories, the macro-F1 score of LSI (0.74) was better than ICD-9 (0.71) and GPT-3.5 (0.54), but lower than GPT-4 (0.80). Due to document size limitations, only a subset of the GS cases could be processed by GPT-3.5 (55.8%) and GPT-4 (94.2%), compared to LSI (100%). Using common GS subsets for nine different SBDH categories, the macro-F1 of ICD-9 combined with either LSI (mean 0.88, 95% CI 0.82-0.93), GPT-3.5 (0.86, 0.82-0.91) or GPT-4 (0.88, 0.83-0.94) was not significantly different. After including age, gender, race and ICD-9 in a logistic regression model, the AUC for prediction of six out of the nine SBDH categories was higher for LSI compared to GPT-4.0. Conclusions: These results demonstrate that the LSI approach performs comparable to more recent large language models, such as GPT-3.5 and GPT-4.0, when using the same set of documents. Importantly, LSI is robust, deterministic, and does not have document-size limitations or cost implications, which make it more amenable to real-world applications in health systems. [ABSTRACT FROM AUTHOR]

Background: In Jordan, the confluence of traffic congestion and overcrowding in public hospitals poses a significant challenge for patients to collect their medications timely. This challenge was further intensified during the COVID-19 pandemic. Recognizing this issue, the Ministry of Health (MOH) and Electronic Health Solutions (EHS) intend to establish a Medication Delivery System (MDS), designed to provide patients with home delivery of medications and ensure proper treatment. This paper outlines a comprehensive framework to guide requirements engineers in devising an effective MDS framework, with a focus on expediting the development and testing processes and mitigating the risks associated with constructing such a system. Method: The proposed methodology entails a robust, structured approach to requirements development for an MDS that integrates an electronic health record system, billing system, pharmacy application, the patient-oriented My Hakeem app, and a delivery tracking system. The requirements elicitation and analysis processes were undertaken by a multidisciplinary committee from MOH and EHS teams, ensuring a diverse understanding of stakeholder needs and expectations. The requirement specifications were meticulously documented via a data dictionary, unified modeling language (UML), and context diagrams. The quality and accuracy of the requirements were verified through an extensive validation process, involving thorough review by various EHS teams and the MOH committee. Results: The MDS was implemented across numerous MOH facilities within a timeline that was a third of the original projection, leveraging the same level of resources and expertise. Post the requirements development phase, there were no changes requested by any stakeholders, indicating a high level of requirement accuracy and satisfaction. Conclusion: The study illustrates that our proposed methodology significantly results in a comprehensive, well-documented, and validated set of requirements, which streamlines the development and testing phases of the project and effectively eliminates requirement errors at an early stage of the requirements development process. [ABSTRACT FROM AUTHOR]

Background: We aimed to investigate the longitudinal impact of COVID-19 and the effects of clinical and psychosocial factors, accounting for post-COVID conditions (PCC), on the mental and physical aspect of health-related quality of life (HRQoL) of patients diagnosed with COVID-19. Methods: Data from the Nivel Corona Cohort were used, which includes individuals with an established SARS-CoV-2 infection that received four questionnaires over a year's time with questions regarding HRQoL (SF-12), symptoms and social characteristics. PCC was determined based on questionnaire data. Data on medical history and healthcare utilization were obtained from electronic health records from general practice. A repeated measures linear mixed model was used to explore associations between clinical and social characteristics, and the course of mental and physical HRQoL after a SARS-CoV-2 infection, taking PCC into account. Results: One hundred fifty-eight individuals of whom it was possible to determine whether they had PCC or not were included in this study. Seventy-six (48.1%) developed PCC, which was associated with a persistent reduction in both physical and mental HRQoL. Hospitalization during the acute phase of the infection had a negative impact on the physical HRQoL, which decreased over time. Females, people older than 53, and those with increased resilience and mental HRQoL before infection were more likely to report a more positive mental HRQoL over time. Conclusion: The negative association PCC has with both mental and physical HRQoL for at least six months, calls for more research to support patients with PCC. [ABSTRACT FROM AUTHOR]

Background: Growth references play a crucial role in the screening, evaluation, and surveillance of children, aiding in the early identification of the requirement for diverse growth-promoting interventions. Variations in human growth across distinct ethnic cohorts arise from genetic disparities, lifestyle variances, nutritional diversity, and diverse social and environmental contexts. Consequently, the selection of growth references markedly influences the prevalence of developmental disorders and nutritional imbalances in children. The study aims to assess the growth percentile of children in the north-east of Iran and establish population-specific reference charts for length, weight, and head circumference spanning from birth to 24 months. Methods: This cross-sectional population-based research conducted in the north-east of Iran, from 2016 to 2023. The Data extracted from the electronic health records of Mashhad University of Medical Sciences. All apparently healthy children aged from birth to 24 months who were measured at least once by health staff at the ages of birth,1,2,4,6,7,9,12,15,18,24 months were included. The target population of the study were 479,089 children (96.21%), encompassing 233,565 girls (48.75%) and 245,524 boys (51.25%). Gender-specific percentile curves for length, weight, and head circumference concerning age, as well as weight concerning length, were derived using the GAMLSS approach. Results: From the anthropometric information of 479,089 children (245,524 boys and 233,565 girls), growth charts were constructed. In comparison to the standard WHO chart, Iranian neonates displayed lower weight across all percentiles during the first month after birth, exhibited decreased head circumference at the 3rd percentile, and boys showed reduced length across all percentiles. After this age, Iranian children demonstrated increased weight, length, and head circumference. Conclusions: This research introduces the inaugural large-scale endeavor for indigenous reference charts. Through the noted distinctions from the international reference, the utilization of this novel resource offers the potential to enhance the surveillance of children's growth within the area. Moreover, by accurately assessing growth anomalies such as underweight, stunting, and wasting, it expands the domain of impactful policies in this sphere. Simultaneously, it enables the exploration of the secular trend of children's growth in the forthcoming years. [ABSTRACT FROM AUTHOR]

Purpose: Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valuable information for identifying rare diseases, but manual curation is time-consuming and prone to subjectivity. This study aims to develop a hybrid approach combining dictionary-based natural language processing (NLP) tools with large language models (LLMs) to improve rare disease identification from unstructured clinical reports. Methods: We propose a novel hybrid framework that integrates the Orphanet Rare Disease Ontology (ORDO) and the Unified Medical Language System (UMLS) to create a comprehensive rare disease vocabulary. SemEHR, a dictionary-based NLP tool, is employed to extract rare disease mentions from clinical notes. To refine the results and improve accuracy, we leverage various LLMs, including LLaMA3, Phi3-mini, and domain-specific models like OpenBioLLM and BioMistral. Different prompting strategies, such as zero-shot, few-shot, and knowledge-augmented generation, are explored to optimize the LLMs' performance. Results: The proposed hybrid approach demonstrates superior performance compared to traditional NLP systems and standalone LLMs. LLaMA3 and Phi3-mini achieve the highest F1 scores in rare disease identification. Few-shot prompting with 1-3 examples yields the best results, while knowledge-augmented generation shows limited improvement. Notably, the approach uncovers a significant number of potential rare disease cases not documented in structured diagnostic records, highlighting its ability to identify previously unrecognized patients. Conclusion: The hybrid approach combining dictionary-based NLP tools with LLMs shows great promise for improving rare disease identification from unstructured clinical reports. By leveraging the strengths of both techniques, the method demonstrates superior performance and the potential to uncover hidden rare disease cases. Further research is needed to address limitations related to ontology mapping and overlapping case identification, and to integrate the approach into clinical practice for early diagnosis and improved patient outcomes. [ABSTRACT FROM AUTHOR]

Background: Evaluating healthcare information systems, such as the Electronic Health Records (EHR), is both challenging and essential, especially in resource-limited countries. This study aims to psychometrically develop and validate an instrument (questionnaire) to assess the factors influencing the successful adoption of the EHR system by healthcare professionals in Moroccan university hospitals. Methods: The questionnaire validation process occurred in two main stages. Initially, data collected from a pilot sample of 164 participants underwent analysis using exploratory factor analysis (EFA) to evaluate the validity and reliability of the retained factor structure. Subsequently, the validity of the overall measurement model was confirmed using confirmatory factor analysis (CFA) in a sample of 368 healthcare professionals. Results: The structure of the modified HOT-fit model, comprising seven constructs (System Quality, Information Quality, Information technology Service Quality, User Satisfaction, Organization, Environment, and Clinical Performance), was confirmed through confirmatory factor analysis. Absolute, incremental, and parsimonious fit indices all indicated an appropriate level of acceptability, affirming the robustness of the measurement model. Additionally, the instrument demonstrated adequate reliability and convergent validity, with composite reliability values ranging from 0.75 to 0.89 and average variance extracted (AVE) values ranging from 0.51 to 0.63. Furthermore, the square roots of AVE values exceeded the correlations between different pairs of constructs, and the heterotrait-monotrait ratio of correlations (HTMT) was below 0.85, confirming suitable discriminant validity. Conclusions: The resulting instrument, due to its rigorous development and validation process, can serve as a reliable and valid tool for assessing the success of information technologies in similar contexts. [ABSTRACT FROM AUTHOR]

Background: Medication reconciliation is essential for optimizing medication use. In part to promote effective medication reconciliation, the Department of Veterans Affairs (VA) invested substantial resources in health information exchange (HIE) technologies. The objectives of this qualitative study were to characterize VA clinicians' use of HIE tools for medication reconciliation in their clinical practice and to identify facilitators and barriers. Methods: We recruited inpatient and outpatient prescribers (physicians, nurse practitioners, physician assistants) and pharmacists at four geographically distinct VA medical centers for observations and interviews. Participants were observed as they interacted with HIE or medication reconciliation tools during routine work. Participants were interviewed about clinical decision-making pertaining to medication reconciliation and use of HIE tools, and about barriers and facilitators to use of the tools. Qualitative data were analyzed via inductive and deductive approaches using a priori codes. Results: A total of 63 clinicians participated. Over half (58%) were female, and the mean duration of VA clinical experience was 7 (range 0–32) years. Underlying motivators for clinicians seeking data external to their VA medical center were having new patients, current patients receiving care from an external institution, and clinicians' concerns about possible medication discrepancies among institutions. Facilitators for using HIE software were clinicians' familiarity with the HIE software, clinicians' belief that medication information would be available within HIE, and their confidence in the ability to find HIE medication-related data of interest quickly. Six overarching barriers to HIE software use for medication coordination included visual clutter and information overload within the HIE display; challenges with HIE interface navigation; lack of integration between HIE and other electronic health record interfaces, necessitating multiple logins and application switching; concerns with the dependability of HIE medication information; unfamiliarity with HIE tools; and a lack of HIE data from non-VA facilities. Conclusions: This study is believed to be the first to qualitatively characterize clinicians' HIE use with respect to medication reconciliation. Results inform recommendations to optimize HIE use for medication management activities. We expect that healthcare organizations and software vendors will be able to apply the findings to develop more effective and usable HIE information displays. [ABSTRACT FROM AUTHOR]

Background: COVID-19 Ethnic Inequalities in Mental health and Multimorbidities (COVEIMM) is a mixed methods study to explore whether COVID-19 exacerbated ethnic health inequalities in adults with serious mental and physical health conditions. We analysed data from electronic health records for England and conducted interviews in Birmingham and Solihull, Manchester, and South London. Sites were selected because they were pilot sites for the Patient and Carer Race Equality Framework being introduced by NHS England to tackle race inequalities in mental health. Prior to the pandemic people in England with severe mental illnesses (SMIs) faced an 11–17-year reduction in life expectancy, mostly due to preventable, long-term, physical health conditions. During the pandemic there was a marked increase in deaths of those living with an SMI. Aims: This qualitative interview study aimed to understand the reasons underlying ethnic inequalities in mortality and service use during the COVID-19 pandemic for adult service users and carers of Black African, Black Caribbean, Indian, Pakistani, and Bangladeshi backgrounds living with serious multiple long-term mental and physical health conditions. Methods: We took a participatory action research approach and qualitative interviews undertaken by experts-by-experience and university researchers Participants were purposively sampled by ethnicity, diagnoses, and comorbidities across three geographically distinct sites in England. Transcriptions were coded inductively and deductively and analysed thematically. Results: Findings indicated multiple points along primary and secondary health pathways for mental and physical health that have the potential to exacerbate the unjust gap in mortality that exists for Black and Asian people with SMIs. Issues such as timely access to care (face-to-face and remote), being treated in a culturally appropriate manner with empathy, dignity and respect, and being able to use services without experiencing undue force, racism or other forms of intersectional discrimination were important themes arising from interviews. Conclusion: These poor experiences create systemic and enduring healthcare harms for racialised groups with SMIs that need to be addressed. Our findings suggest a need to address these, not only in mental health providers, but across the whole health and care system and a need to ensure more equitable healthcare partnerships with service users, carers, and communities from racialised backgrounds who are often excluded. [ABSTRACT FROM AUTHOR]

Background: Long term oxygen therapy (LTOT) is prescribed for hypoxemia in pulmonary disease. Like other medical therapies, LTOT requires a prescription documenting the dosage (flow rate) and directions (at rest, with activity) which goes to a supplier. Communication with patients regarding oxygen prescription (flow rate, frequency, directions), monitoring (pulse oximetry) and dosage adjustment (oxygen titration) differs in comparison with medication prescriptions. We examined the communication of oxygen management plans in the electronic health record (EHR), and their consistency with patient-reported LTOT use. Study design and methods: A cross-sectional study was conducted in 71 adults with chronic lung disease on LTOT. Physician communication regarding oxygen management was obtained from the EHR. Participants were interviewed on their LTOT management plan. The information from each source was compared. Results: The study population was, on average, 64 years, two-thirds women, and most used oxygen for over 3 years. Only 45% of both at-rest and with-activity oxygen prescriptions were documented in the Electronic Health Record (EHR). Less than 20% of prescriptions were relayed to the patient in the after-visit summary. Of those with EHR-documented oxygen prescriptions, 44% of patients adhered to prescribed oxygen flow rates. Nearly all patients used a pulse oximeter (96%). Interpretation: We identified significant gaps in communication of oxygen management plans from provider to patient. Even when the oxygen prescription was clearly documented, there were differences in patient-reported oxygen management. Critical gaps in oxygen therapy result from the lack of consistent documentation of oxygen prescriptions in the EHR and patient-facing documents. Addressing these issues systematically may improve home oxygen management. [ABSTRACT FROM AUTHOR]

Background: Acute deteriorations of health status are common in hospitalized patients and are often preceded by changes in their vital signs. Events such as heart attacks, death or admission to the intensive care unit can be averted by early detection, therefore so-called Early Warning Scores (EWS) such as the National Early Warning Score 2 (NEWS2), including basic vital parameters such as heart rate, blood pressure, respiratory rate, temperature and level of consciousness, have been developed for a systematic approach. Although studies have shown that EWS have a positive impact on patient outcomes, they are often limited by issues such as calculation errors, time constraints, and a shortage of human resources. Therefore, development of tools for automatic calculation of EWS could help improve quality of EWS calculation and may improve patient outcomes. The aim of this study is to analyze the feasibility of wearable devices for the automatic calculation of NEWS2 compared to conventional calculation using vital signs measured by health care professionals. Methods: We conducted a prospective trial at a large tertiary hospital in Switzerland. Patients were given a wristband with a photoplethysmogram (PPG) sensor that continuously recorded their heart rate and respiratory rate for 3 consecutive days. Combined with data from the electronic health record (EHR), NEWS2-score was calculated and compared to NEWS2 score calculated from vital parameters in the EHR measured by medical staff. The main objective of our study was to assess the agreement between NEWS2 scores calculated using both methods. This analysis was conducted using Cohen's Kappa and Bland–Altman analysis. Secondary endpoints were compliance concerning the medical device, patient acceptance, data quality analysis and data availability and signal quality for all time stamps needed for accurate calculation. Results: Of 210 patients enrolled in our study, NEWS2 was calculated in 904 cases, with 191 cases being directly compared to conventional measurements. Thirty-three of these measurements resulted in a NEWS2 ≥ 5, 158 in a NEWS2 < 5. Comparing all 191 measurements, accordance was substantial (K = 0.76) between conventional and automated NEWS2. No adverse effects due to the device were recorded. Patient acceptance was high. Conclusions: In conclusion, the study found strong agreement between automated and conventional NEWS2 calculations using wearable devices, with high patient acceptance despite some data quality challenges. To maximize the potential of continuous monitoring, further research into fully automated EWS calculations without relying on spot measurements is suggested, as this could provide a reliable alternative to traditional methods. Trial registration: January 26, 2023, NCT05699967. [ABSTRACT FROM AUTHOR]

Background: Primary health care professionals are held accountable for various quality measures in the treatment of patients with chronic diseases such as diabetes. Uncontrolled type 2 diabetes (T2D) remains a considerable health problem; thus, further studying patients with this condition is important for delivering effective interventions. Social determinants of health (SDoH) have been shown to affect various aspects of diabetes care in different subpopulations. We studied the association of SDoH with uncontrolled T2D in a population of adult primary care patients. Methods: We retrospectively searched our electronic health record for adult patients (≥18 years) with a diagnosis of T2D and a hemoglobin A1c (HbA1c) level of 8% or higher. Patients were empaneled to 2 primary care clinic sites between January 1, 2021, and January 31, 2022. Patients were grouped by HbA1c level to stratify patients according to the extent of uncontrolled T2D. Patient characteristics were compared among groups. Unadjusted and adjusted multinomial logistic regression analysis was used to estimate the odds of various SDoH factors among patient groups with different levels of uncontrolled T2D. Results: The study cohort included 1,596 patients. Most patients were White (79%), and the median age was 58.8 years. The median HbA1c level was 8.9%, and approximately 68% of patients were obese (body mass index [BMI] ≥30). When the study population was grouped by HbA1c level (8% to < 9% [n = 806], ≥9% to < 12% [n = 684], and ≥12% [n = 106]), significant differences among groups were observed in age group (P <.001), marital status (P <.001), race (P <.001), ethnicity (P =.001), and BMI category (P =.01). In groups with higher HbA1c levels, we noticed a higher percentage of patients who were aged 51 to 65 years or single. Among patients with uncontrolled HbA1c levels, more patients were obese than overweight. Patients in the intermediate HbA1c group had increased odds of food insecurity and some decreased social connections, even after adjusting for age, sex, race, ethnicity, and marital status. Conclusions: Among patients with uncontrolled T2D, higher HbA1c levels were associated with decreased social connections and increased food insecurity. Our findings provide insight into the role of these SDoH in managing T2D and have important implications for primary care practice. [ABSTRACT FROM AUTHOR]

Background: Use of electronic health records (EHR) to provide real-world data for research is established, but using EHR to deliver randomised controlled trials (RCTs) more efficiently is less developed. The Allergy AntiBiotics And Microbial resistAnce (ALABAMA) RCT evaluated a penicillin allergy assessment pathway versus usual clinical care in a UK primary care setting. The aim of this paper is to describe how EHRs were used to facilitate efficient delivery of a large-scale randomised trial of a complex intervention embracing efficient participant identification, supporting minimising GP workload, providing accurate post-intervention EHR updates of allergy status, and facilitating participant follow up and outcome data collection. The generalisability of the EHR approach and health economic implications of EHR in clinical trials will be reported in the main ALABAMA trial cost-effectiveness analysis. Methods: A descriptive account of the adaptation of functionality within SystmOne used to deliver/facilitate multiple trial processes from participant identification to outcome data collection. Results: An ALABAMA organisation group within SystmOne was established which allowed sharing of trial functions/materials developed centrally by the research team. The 'ALABAMA unit' within SystmOne was also created and provided a secure efficient environment to access participants' EHR data. Processes of referring consented participants, allocating them to a trial arm, and assigning specific functions to the intervention arm were developed by adapting tools such as templates, reports, and protocols which were already available in SystmOne as well as pathways to facilitate allergy de-labelling processes and data retrieval for trial outcome analysis. Conclusions: ALABAMA is one of the first RCTs to utilise SystmOne EHR functionality and data across the RCT delivery, demonstrating feasibility and applicability to other primary care RCTs. Trial registration: ClinicalTrials.gov: NCT04108637, registered 05/03/2019. ISRCTN: ISRCTN20579216. [ABSTRACT FROM AUTHOR]

Background: Cardiopulmonary resuscitation (CPR) is an emergency procedure performed to restore heart function to minimize anoxic injury to the brain following cardiac arrest. Despite the establishment of emergency department and training on Pediatric Advanced Life Support (PALS) at Muhimbili National Hospital (MNH) the outcomes of pediatric in-hospital cardiac arrest have not been documented. We ought to determine the outcomes and factors associated with 24-h survival after pediatric in-hospital cardiac arrests at MNH in Tanzania. Methods: We conducted a retrospective study of all patients aged 1 month to 18 years who had in-hospital cardiac arrests (IHCA) prompting CPR in the Emergency Medicine Department (EMD) at MNH, Tanzania from January 2016 to December 2019. Data was collected from electronic medical record (Wellsoft) system using a standardized and pretested data collection form that recorded clinical baseline, pre-arrest, arrest, and post-arrest parameters. Bivariate and multivariable logistic regression analyses were performed to assess the influence of each factor on 24-h survival. Results: A total of 11,951 critically ill patients were screened, and 257 (2.1%) had cardiac arrest at EMD. Among 136 patients enrolled, the median age was 1.5 years (interquartile range: 0.5–3 years) years, and the majority 108 (79.4%) aged ≤ 5 years, and 101 (74.3%) had been referred from peripheral hospitals. Overall stained return of spontaneous circulation was achieved in 70 (51.5%) patients, 24-h survival was attained in 43 (31.3%) of patients, and only 7 patients (5.2%) survived to hospital discharge. Factors independently associated with 24-h survival were CPR event during the day/evening (p = 0.033), duration of CPR ≤ 20 min (p = 0.000), reversible causes of cardiac arrest being identified (p = 0.001), and having assisted/mechanical ventilation after CPR (p = 0.002). Conclusion: In our cohort of children with cardiac arrest, survival to hospital discharge was only 5%. Factors associated with 24-h survival were CPR events during the daytime, short duration of CPR, recognition of reversible causes of cardiac arrest, and receiving mechanical ventilation. Future studies should explore the detection of decompensation, the quality of CPR, and post-cardiac arrest care on the outcomes of IHCA. [ABSTRACT FROM AUTHOR]

Background: The incidence of infective endocarditis (IE) in patients undergoing redo mitral valve (MV) surgery was evaluated. The outcomes of all the patients and the patients' specific characteristics were recorded. The patients were analyzed to further the research of IE in this population. Method: This was a retrospective review of patients admitted for redo MV surgery with a prospective follow-up of electronic medical records at King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia, from 2009 to 2019. Pre/intra/post-operative factors contributing to mortality, morbidity, and freedom of adverse events were analyzed. Result: A total of 211 patients underwent redo MV surgery, and 41 patients (19.4%) had IE; and 51% of this subset of patients, 21 individuals, developed IE after the initial MV surgery. MV stenosis was moderate/severe in 50 patients. Furthermore, MV regurgitation was present in 89 patients. Multivariate analysis of the data revealed multiple factors influencing mortality: age, peripheral vascular disease, concomitant procedures, peripheral vascular disease, red blood cell transfusions, preoperative mechanical valves, and active IE. In-hospital Mortality was 10.9%. The one-, five-, and ten-year survival was 88%, 79%, and 69% across all patients. Conclusion: Although redo MV surgery has acceptable outcomes; the presence of IE or concomitant procedures is a significant health detriment in these patients. Our study highlights the need for careful patient management and more in-depth research in this area to improve patient outcomes. [ABSTRACT FROM AUTHOR]

Introduction: Advance Care Planning (ACP) refers to a process that includes Advance Care Directives (ACD) and Goals of Care (GOC), a practice widely used for over three decades. Following the findings of an audit and a cross-sectional study in 2019 and 2021 respectively, we implemented several educational and other interventional strategies aimed at enhancing staff awareness and emphasizing the importance of recognizing and documenting of ACD/GOC. The aim of this study was to evaluate the acknowledgement and use of ACD and GOC by Emergency Department (ED) staff following these interventions. Method: We used a mixed methods approach, incorporating both observational and cross-sectional designs with reflexive thematic analysis. Data extraction for the observational study took place between 1st April and 30th June 2023 focusing on a target population of randomly sampled adults aged ≥ 65 years. Demographics and other ACD and GOC related patients' clinical data were collected. Data collection for the cross-sectional study occurred between 19th July and 13th September 2023 targeting all ED staff. Information gathered included demographics, awareness about ACD and GOC, including storage location and implementation, as well as knowledge of Medical Treatment decision Makers (MTDM), a jurisdictional term identifying a person legally appointed to make healthcare decisions on behalf of someone who lacks decision-making capacity and other Victorian State legislative requirements were collected. Results: In the observational period, 22,335 patients attended the ED and 19% (n = 6546) qualified for inclusion from which a sample of 308 patients were randomly extracted. We found ACD documents were noted in the medical records of 6.5% of the sample, fewer than 8% identified in our previous study. There was no correlation between ACD record availability and age (p = 0.054; CI ranging from − 0.065 to 7.768). The response rate for the cross-sectional survey was 12% (n = 340) in contrast to earlier study with 28% (n = 476) respondents. Staff knowledge and familiarity with ACD was 25% and GOC 45%. Conclusion: After implementing interventions in staff education and ACP awareness, we found that ACD documentation did not improve. However, GOC documentation increased in the context of heightened institutional awareness and integration into the Electronic Medical Records (EMR). [ABSTRACT FROM AUTHOR]

Background: Substantial variability in response to lifestyle interventions has been recognized for many years, and researchers have begun to disentangle sources of error from inherent differences in individual responsiveness. The objective of this secondary analysis of an intensive lifestyle intervention (diet and exercise) for metabolic syndrome (MetS) was to identify potentially important differences among study completers grouped by treatment response as measured by change in a continuous metabolic syndrome score (Gurka/MetS). Methods: All study completers from a 12-month primary care study were categorized into one of five groups according to change in the Gurka/MetS score. A change of 0.4 in z-score defined clinically relevant change in line with results of previous studies. Repeated measures analysis of variance was used to examine cardiovascular disease risk and individual clinical indicators of MetS over 12 months, looking for differences in response over time by the five groups. Results: Of 176 participants, 50% (n = 88) had stable scores, 10% (n = 18) had relevant change scores in the first 3 months only and reverted toward baseline, 20% (n = 35) achieved meaningful change over the whole study, 11% (n = 20) had a delayed response at 3–12 months, and 9% (n = 15) demonstrated worsening scores. Significant differential patterns were noted for groups over the duration of the intervention (p <.001). Improvement in diet quality and fitness scores were similar across all groups. Other available variables were tested and did not account for the differences. Conclusion: Work is needed to identify key factors that account for differences in responses to lifestyle interventions that can be used to guide treatment decisions for intensive lifestyle interventions for this common condition. Trial Registration: ClinicalTrials.gov Identifier: NCT01616563; first registered June 12, 2012. [ABSTRACT FROM AUTHOR]

Background: Hereditary angioedema with normal C1-inhibitor function (HAE nC1-INH) and idiopathic angioedema of unknown etiology (AE-UNK) are rare conditions that cause recurrent subcutaneous and submucosal swelling. The characteristics and clinical outcomes of patients with these conditions in Canada have not been studied. Methods: The aim of this study was to extract real-world evidence from the electronic health records of patients with HAE nC1-INH or AE-UNK who were managed in selected practices of Canadian HAE-treating specialist physicians between 01-Jan-2012 and 01-Jan-2022, to examine case numbers, treatment, clinical outcomes, and healthcare utilization. Results: Of 60 patients (37 with HAE nC1-INH, 23 with AE-UNK), median (range) age at symptom onset was 21.5 (5.0–57.0) and 23.0 (10.0–54.0) years, respectively. Time to diagnosis from onset of symptoms was 7.0 (0.0–43.0) and 2.0 (− 10.0 to 50.0) years. Significant differences were observed in terms of the predominant triggers for angioedema attacks between patients with HAE nC1-INH and AE-UNK: stress (65% vs. 26%, p = 0.007) and estrogen therapy (35% vs. 9%, p = 0.031). Before diagnosis, most patients received antihistamines (50% of HAE nC1-INH and 61% of AE-UNK patients). Post-diagnosis, 73% and 74% of HAE nC1-INH and AE-UNK patients received long-term prophylaxis (LTP), with the most common LTP treatments being subcutaneous pdC1-INH (43% of HAE nC1-INH patients and 39% of AE-UNK patients) and tranexamic acid (41% of HAE nC1-INH patients and 35% of AE-UNK patients). Of patients with HAE nC1-INH, and patients with AE-UNK, 22% and 13%, respectively, were taking more than one LTP treatment concurrently. Before HAE treatment initiation, significantly fewer patients with AE-UNK compared to patients with HAE nC1-INH had angioedema attacks affecting their extremities (13% vs. 38%, p = 0.045) and GI system (22% vs. 57%, p = 0.015). In the three months following treatment initiation, patients with AE-UNK experienced significantly fewer angioedema attacks compared to patients with HAE nC1-INH (median 2.0 attacks [0.0–48.0] vs. 6.0 attacks [0.0–60.0], p = 0.044). Additionally, fewer patients with AE-UNK compared to HAE nC1-INH experienced attacks affecting their GI system (26% vs. 57%, p = 0.032). Attack duration and frequency significantly decreased for patients with HAE nC1-INH from a median of 1.00 day (range: 0.00–7.00) to 0.29 day (range: 0.02–4.00; p = 0.001) and from 10.50 attacks (range: 0.00–90.00) to 6.00 attacks (range: 0.00–60.00; p = 0.004) in the three months following HAE treatment initiation. Conclusions: Using Canadian real-world evidence, these data demonstrate differing clinical trajectories between patients with HAE nC1-INH and AE-UNK, including diagnostic delays, varied attack characteristics, treatment responses and healthcare utilization. Despite treatment response, many patients still experienced frequent angioedema attacks. These results suggest an unmet need for treatment guidelines and therapies specifically for patients with HAE nC1-INH and AE-UNK and better understanding of the pathophysiology accounting for the reported differences between the two. [ABSTRACT FROM AUTHOR]

Background: Intraoperative hemorrhage in cardiac surgery increases risk of morbidity and mortality. Low pre-operative and perioperative levels of fibrinogen, a key clotting factor, are associated with severity of hemorrhage and increased transfusion of blood components. The ability to supplement fibrinogen during hemorrhagic resuscitation is delayed 45–60 min because cryoprecipitated antihemophilic factor (cryo AHF) is stored frozen, due to a short post-thaw shelf life. Pathogen Reduced Cryoprecipitated Fibrinogen Complex (INTERCEPT Fibrinogen Complex, IFC) can be kept thawed, at room temperature, for up to 5 days, making it possible to be immediately available for hemorrhaging patients. This trial will investigate if earlier correction of acquired hypofibrinogenemia with IFC in hemorrhaging cardiac surgery patients reduces the total number of perioperatively transfused allogeneic blood products (red blood cells, plasma, and platelets) as compared to cryo AHF. Methods: This is a single center, prospective, cluster randomized trial with an adaptive design. Acquired hypofibrinogenemia will be assessed by rotational thromboelastometry (ROTEM) and the threshold for cryo AHF/IFC transfusion defined as FIBTEM A10 ≤ 10 mm in bleeding patients. IFC/cryo AHF will be randomized by 1-month blocks. Cardiac surgery patients will be enrolled in the study if they have an eligible procedure and at least one dose of a cryo AHF/IFC product (approximately 2 g fibrinogen) is transfused. Data from the electronic health record, including the blood bank and lab information systems, will be prospectively collected from the health system's data warehouse. Discussion: This trial aims to provide evidence of the clinical efficacy of utilizing readily available thawed IFC during acute bleeding in the cardiac surgery setting compared to traditional cryo AHF. Trial registration: ClinicalTrials.gov NCT05711524. Feb 3, 2023. [ABSTRACT FROM AUTHOR]

Purpose: This study explored potential categories of dyadic disease appraisal differences among patients hospitalized with chronic heart failure (CHF) in China and analyzed the main factors influencing these categories. Methods: A survey was conducted using various tools and scales, including the Chinese version of the Memorial Heart Failure Symptom Appraisal Scale, Heart failure self-care index scale, Social Support Rating Scale, Zarit burden interview, and Self-rating anxiety scale. The data was collected from patients who were hospitalized with CHF in the cardiology department of one of two tertiary hospitals in Nanchong City, China. The dyadic disease appraisal categories were identified using latent profile analysis (LPA). Multiple logistic regression analysis was also employed to analyze the factors influencing the formation of potential categories of differences in dyadic disease appraisal in CHF patients. Results: A total of 262 pairs of hospitalized CHF patients and their caregivers participated in this study. The dyadic disease appraisal of CHF patients was potentially categorized as the "negative difference group" (28 individuals, 10.7%) and the "positive or convergence group" (234 persons, 89.3%). The results showed that the factors influencing the categorization of dyadic disease appraisal differences included the patient's social support, disease progression, and Caregivers anxiety level, burden, gender, educational attainment, and age (p < 0.05). Conclusion: The study findings demonstrated heterogeneity between the two groups of CHF patients in the dyadic disease appraisal. Therefore, it is necessary to focus on patients who have a brief duration of illness and limited social support. Specifically, it is important to prioritize support for female caregivers who are 65 years or older, have lower levels of educational attainment, and experience a significant burden and anxiety. Regular implementation of support person-bilateral co-management strategies can effectively reduce differences in how the disease is perceived and enhance the overall well-being of both caregivers and patients. [ABSTRACT FROM AUTHOR]

Background: China accounts for 24% of newly diagnosed cancer cases and 30% of cancer-related deaths worldwide. Comprehensive analyses of the economic burden on patients across different cancer treatment phases, based on empirical data, are lacking. This study aims to estimate the financial burden borne by patients and analyze the cost compositions of the leading cancers with the highest number of new cases in China. Methods: This cross-sectional cost-of-illness study analyzed patients diagnosed with lung, breast, colorectal, esophageal, liver, or gastric cancer, identified through electronic health records (EHRs) from 84 hospitals across 17 provinces in China. Patients completed any one of the initial treatment phase, follow-up phase, and relapse/metastasis phase were recruited by trained attending physicians through a stratified sampling procedure to ensure enough cases for each cancer progression stage and cancer treatment phase. Direct and indirect costs by treatment phase were collected from the EHRs and self-reported surveys. We estimated per case cost for each type of cancer, and employed subgroup analyses and multiple linear regression models to explore cost drivers. Results: We recruited a total of 13,745 cancer patients across three treatment phases. The relapse/metastasis phase incurred the highest per case costs, varying from $8,890 to $14,572, while the follow-up phase was the least costly, ranging from $1,840 to $4,431. Being in the relapse/metastasis phase and having an advanced clinical stage of cancer at diagnosis were associated with significantly higher cost, while patients with low socioeconomic status borne lower costs. Conclusions: There were substantial financial burden on patients with six leading cancers in China. Health policymakers should emphasize comprehensive healthcare coverage for marginalized populations such as the uninsured, less educated, and those living in underdeveloped regions. [ABSTRACT FROM AUTHOR]

Introduction: This study analyzed and discussed the characteristics of peripheral blood lymphocytes (PBLs) in patients with endometrial carcinoma (EC) to explore the PBLs' clinical application value. Methods: This single-center case‒control study analyzed the clinical data of patients with EC and uterine fibroids who underwent surgery at the First Affiliated Hospital of Chongqing Medical University between October 2018 and October 2023 retrospectively. The Center for Clinical Molecular Medical Detection of our hospital performed PBLs detection using flow cytometry, and recorded the detection results in the electronic medical records system. Between-group and subgroup comparisons of PBLs in patients with EC were analyzed using t-test or Mann–Whitney U test. The effect of surgery on PBLs in patients with EC was assessed using a paired t-test or the Wilcoxon signed rank test. Results: The immune function of patients with EC was significantly lower than that of healthy people (P < 0.05) and those with benign uterine diseases (P < 0.05) and was related to body mass index (BMI), hypertension, diabetes, and blood lipids (P < 0.05). In patients with EC, the PBLs counts decreased significantly after surgery (P < 0.05) and more kinds of lymphocytes were affected in the laparoscopic surgery group than in the open surgery group. Conclusions: With the decrease of PBLs counts, the immune status of patients with EC is impaired. Metabolic syndrome (Mets), including obesity, hypertension, diabetes, and high blood lipids, also affects the immune function of patients with EC. And for EC patients, the effect of laparoscopic surgery is greater than that of open surgery. PBLs has the potential to be one of indicator during the diagnosis and treatment of EC. Trial registration: This study was retrospectively registered by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (approval number K2023-578). [ABSTRACT FROM AUTHOR]

Background: Recognizing the limitations of pre-market clinical data, regulatory authorities have embraced total product lifecycle management with post-market surveillance (PMS) data to assess medical device safety and performance. One method of proactive PMS involves the analysis of real-world data (RWD) through retrospective review of electronic health records (EHR). Because EHRs are patient-centered and focused on providing tools that clinicians use to determine care rather than collecting information on individual medical products, the process of transforming RWD into real-world evidence (RWE) can be laborious, particularly for medical devices with broad clinical use and extended clinical follow-up. This study describes a method to extract RWD from EHR to generate RWE on the safety and performance of embolization coils. Methods: Through a partnership between a non-profit data institute and a medical device manufacturer, information on implantable embolization coils' use was extracted, linked, and analyzed from clinical data housed in an electronic data warehouse from the state of Indiana's largest health system. To evaluate the performance and safety of the embolization coils, technical success and safety were defined as per the Society of Interventional Radiology guidelines. A multi-prong strategy including electronic and manual review of unstructured (clinical chart notes) and structured data (International Classification of Disease codes), was developed to identify patients with relevant devices and extract data related to the endpoints. Results: A total of 323 patients were identified as treated using Cook Medical Tornado, Nester, or MReye embolization coils between 1 January 2014 and 31 December 2018. Available clinical follow-up for these patients was 1127 ± 719 days. Indications for use, adverse events, and procedural success rates were identified via automated extraction of structured data along with review of available unstructured data. The overall technical success rate was 96.7%, and the safety events rate was 5.3% with 18 major adverse events in 17 patients. The calculated technical success and safety rates met pre-established performance goals (≥ 85% for technical success and ≤ 12% for safety), highlighting the relevance of this surveillance method. Conclusions: Generating RWE from RWD requires careful planning and execution. The process described herein provided valuable longitudinal data for PMS of real-world device safety and performance. This cost-effective approach can be translated to other medical devices and similar RWD database systems. [ABSTRACT FROM AUTHOR]

Background: In recent years, eHealth has received much attention as an opportunity to increase efficiency within healthcare organizations. Adoption of eHealth might consequently help to solve perceived health workforce challenges, including labor shortages and increasing workloads among primary care professionals, who serve as the first point of contact for healthcare in many countries. The purpose of this systematic review was to investigate the impact of general eHealth use and specific eHealth services use on general practice workload in the pre-COVID-19 era. Methods: The databases of CINAHL, Cochrane, Embase, IEEE Xplore, Medline ALL, PsycINFO, Web of Science, and Google Scholar were searched, using combinations of keywords including 'eHealth', 'workload', and 'general practice'. Data extraction and quality assessment of the included studies were independently performed by at least two reviewers. Publications were included for the period 2010 – 2020, before the start of the COVID-19 pandemic. Results: In total, 208 studies describing the impact of eHealth services use on general practice workload were identified. We found that two eHealth services were mainly investigated within this context, namely electronic health records and digital communication services, and that the largest share of the included studies used a qualitative study design. Overall, a small majority of the studies found that eHealth led to an increase in general practice workload. However, results differed between the various types of eHealth services, as a large share of the studies also reported a reduction or no change in workload. Conclusions: The impact of eHealth services use on general practice workload is ambiguous. While a small majority of the effects indicated that eHealth increased workload in general practice, a large share of the effects also showed that eHealth use reduced workload or had no impact. These results do not imply a definitive conclusion, which underscores the need for further explanatory research. Various factors, including the study setting, system design, and the phase of implementation, may influence this impact and should be taken into account when general practices adopt new eHealth services. Study registration number: PROSPERO (International Prospective Register of Systematic Reviews) CRD42020199897; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=199897. [ABSTRACT FROM AUTHOR]

Background: The Brazilian Ministry of Health has developed and provided the Citizen's Electronic Health Record (PEC e-SUS APS), a health information system freely available for utilization by all municipalities. Given the substantial financial investment being made to enhance the quality of health services in the country, it is crucial to understand how users evaluate this product. Consequently, this scoping review aims to map studies that have evaluated the PEC e-SUS APS. Methods: This scoping review is guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA-P) framework, as well as by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Checklist extension for scoping reviews (PRISMA-ScR). The research question was framed based on the "CoCoPop" mnemonic (Condition, Context, Population). The final question posed is, "How has the Citizen's Electronic Health Record (PEC e-SUS APS) been evaluated?" The search strategy will be executed across various databases (LILACS, PubMed/MEDLINE, Scopus, Web of Science, ACM Digital Library, and IEEE Digital Library), along with gray literature from ProQuest Dissertation and Theses Global and Google Scholar, with assistance from a professional healthcare librarian skilled in supporting systematic reviews. The database search will encompass the period from 2013 to 2024. Articles included will be selected by three independent reviewers in two stages, and the findings will undergo a descriptive analysis and synthesis following a "narrative review" approach. Independent reviewers will chart the data as outlined in the literature. Discussion: The implementation process for the PEC e-SUS APS can be influenced by the varying characteristics of the over 5500 Brazilian municipalities. These factors and other challenges encountered by health professionals and managers may prove pivotal for a municipality's adoption of the PEC e-SUS APS system. With the literature mapping to be obtained from this review, vital insights into how users have evaluated the PEC will be obtained. Systematic review registration: The protocol has been registered prospectively at the Open Science Framework platform under the number 10.17605/OSF.IO/NPKRU. [ABSTRACT FROM AUTHOR]

Background: Postoperative complications of pancreaticoduodenectomy (PD) are still a thorny problem. This study aims to verify the preventative impact of T-tube on them. Methods: The electronic medical records and follow-up data of patients who received pancreaticoduodenectomy in our center from July 2016 to June 2020 were reviewed. According to whether T tube was placed during the operation, the patients were divided into T-tube group and not-T-tube group. Propensity score matching analysis was performed to minimize selection bias. Results: A total of 330 patients underwent PD (Not-T-tube group =226, T-tube group=104). Propensity score matching resulted in 222 patients for further analysis (Not-T-tube group =134, T-tube group=88). Patients' demographics were comparable in the matched cohorts. Significantly higher incidences of clinically relevant postoperative pancreatic fistula (CR-POPF) ((14 (10.45%) VS 20 (22.73%)), P=0.013) were observed in the T-tube group. The total incidence of biliary anastomotic stricture (BAS) was 3.15%. The incidence was slightly lower in the T-tube group, but there was no statistically significant differentiation (6 (4.48%) VS 1 (1.14%), P=0.317). Conclusions: It is not feasible to prevent postoperative complications with the application of a T-tube in PD. [ABSTRACT FROM AUTHOR]

Background: Over 80% of people who experience a stroke present with residual impairment of the upper extremity, such as the shoulder, elbow, wrist, and hand. However, rarely do stroke rehabilitation practitioners (e.g., occupational therapists) use standardized outcome measures to objectively evaluate upper extremity function. Accordingly, the purpose of this project was to develop a bundle of implementation strategies that supports practitioners' adoption of the Fugl-Meyer Assessment of the Upper Extremity in stroke rehabilitation practice. Methods: We used tenets of Implementation Mapping to guide the development of our implementation strategy bundle. We partnered with one, large academic health system serving over 200 stroke patients annually through intensive rehabilitation care. Strategies were selected and developed through a multi-method process that included a review of the literature, qualitative input from our health system's practitioners and managers, and expert consultation. We also specified the hypothesized implementation "mechanisms" our strategies intended to change. Practitioners' adoption (yes/no) of the Fugl-Meyer Assessment was calculated by analyzing electronic health record documentation of the 6-month time frame before strategies were deployed compared to the 6-month time frame after deployment. Results: Practitioners were exposed to the following implementation strategies to support Fugl-Meyer adoption: conduct educational meetings, prepare outcome measure champions, provide equipment, develop training materials, and adapt documentation systems. In the 6-months before deployment of our implementation strategies, practitioners implemented the Fugl-Meyer with 14.8% of stroke patients. In the six months after deployment, adoption of the Fugl-Meyer increased to 73.8% (p <.001). Conclusions: When systematically developed in collaboration with health system partners, a bundle of implementation strategies may support outcome measure adoption in stroke rehabilitation. Improving the use of standardized outcome measures is of paramount importance in stroke rehabilitation to objectively monitor patients' progress or decline, to demonstrate the value of rehabilitation services for enhancing patients' recovery, and to advocate for continued reimbursement for rehabilitation care. Future opportunities lie in further specifying the mechanisms through which implementation strategies are intended to work and how those mechanisms contribute to strategy effectiveness. Trial registration: NCT registration: NCT04888416; May 06, 2021. [ABSTRACT FROM AUTHOR]

Background: The objectives of this study were to examine the association of psychiatric comorbidities and patient characteristics with treatment change and response as well as to assess the association between treatment change and healthcare resource utilization (HCRU) among adult patients with attention-deficit/hyperactivity disorder (ADHD) and psychiatric comorbidities. Methods: De-identified electronic health records from the NeuroBlu Database (2002–2021) were used to select patients ≥ 18 years with ADHD who were prescribed ADHD-specific medication. The index date was set as the first prescription of ADHD medication. The outcomes were treatment change (discontinuation, switch, add-on, or drop) and HCRU (inpatient, outpatient, composite) within 12 months of follow-up. Cox proportional-hazard model was used to assess the association between clinical and demographic patient characteristics and treatment change, while generalized linear model with negative binomial distribution and log link function was used to assess the association between key risk factors linked to treatment change and HCRU rates. Results: A total of 3,387 patients with ADHD were included (ADHD only: 1,261; ADHD + major depressive disorder (MDD): 755; ADHD + anxiety disorder: 467; ADHD + mood disorder: 164). Nearly half (44.8%) of the study cohort experienced a treatment change within the 12-month follow-up period. Treatment switch and add-on were more common in patients with ADHD and comorbid MDD and anxiety disorder (switch: 18.9%; add-on: 20.5%) compared to other cohorts (range for switch: 8.5–13.6%; range for add-on: 8.9–12.1%) Survival analysis demonstrated that the probability of treatment change within 12 months from treatment initiation in the study cohort was estimated to be 42.4%. Outpatient visit rates statistically significantly increased from baseline (mean [SD] 1.03 [1.84] visits/month) to 3 months post-index (mean [SD] 1.62 [1.91] visits/month; p < 0.001), followed by a gradual decline up to 12 months post-index. Being prescribed both a stimulant and a non-stimulant at index date was statistically significantly associated with increased risk of treatment change (adjusted hazard ratio: 1.64; 95% CI: 1.13, 2.38; p = 0.01). Conclusions: This real-world study found that treatment change was common among patients with ADHD and psychiatric comorbidities. These findings support the need for future studies to examine the unmet medical and treatment needs of this complex patient population. [ABSTRACT FROM AUTHOR]

Background: The high complexity of systemic autoimmune diseases (SADs) has hindered precise management. This study aims to investigate heterogeneity in SADs. Methods: We applied a joint cluster analysis, which jointed multiple correspondence analysis and k-means, to immunomarkers and measured the heterogeneity of clusters by examining differences in immunomarkers and clinical manifestations. The electronic health records of patients who received an antinuclear antibody test and were diagnosed with SADs, namely systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and Sjögren's syndrome (SS), were retrieved between 2001 and 2016 from hospitals in Taiwan. Results: With distinctive patterns of immunomarkers, a total of 11,923 patients with the three SADs were grouped into six clusters. None of the clusters was composed only of a single SAD, and these clusters demonstrated considerable differences in clinical manifestation. Both patients with SLE and SS had a more dispersed distribution in the six clusters. Among patients with SLE, the occurrence of renal compromise was higher in Clusters 3 and 6 (52% and 51%) than in the other clusters (p < 0.001). Cluster 3 also had a high proportion of patients with discoid lupus (60%) than did Cluster 6 (39%; p < 0.001). Patients with SS in Cluster 3 were the most distinctive because of the high occurrence of immunity disorders (63%) and other and unspecified benign neoplasm (58%) with statistical significance compared with the other clusters (all p < 0.05). Conclusions: The immunomarker-driven clustering method could recognise more clinically relevant subgroups of the SADs and would provide a more precise diagnosis basis. [ABSTRACT FROM AUTHOR]

Background: Electronic Health Record systems (EHRs) offer significant benefits and have transformed healthcare in developed countries. However, their implementation and adoption in low- and middle-income countries (LMICs) remains low due to challenges and competing interests. Health professionals' perception of EHRs can influence their adoption and continued use. The objectives of this study are to explore the perception of health professionals regarding implemented EHR systems in three hospitals in Ghana and identify factors influencing their perception and satisfaction. Methods: In this study, we employed a concurrent mixed method design to collect data from study participants from May to June 2023. The quantitative part employed a descriptive-survey and the qualitative (in-depth interview) techniques was applied. After obtaining written informed consent from each respondent, a structured survey questionnaire was filled out by the health professionals from three hospitals. An a priori power calculation was used to determine the sample size for the quantitative component. Two hundred and sixty-three (263) health professionals completed the questionnaire from the three facilities. A purposive sampling technique was used to select fifteen [1] participants for the interviews. A semi-structured interview guide was used for the in-depth interviews. The interviews were audio recorded, transcribed, and coded into themes using QSR Nvivo 12 software before thematic content analysis. Results: Our findings revealed that 213 (80.99%) health professionals perceived the EHRs as beneficial to patients and were generally satisfied. An overwhelming majority, 197 (74.90%) of the health professionals, were satisfied with its use and expressed interest in continuing to use the system. The majority of health professionals viewed the EHRs to have improved their work and workflow processes and provided the desired results. However, few other health professionals were dissatisfied with the system because they viewed the EHRs as frustrating due to unstable internet connectivity and power supply. Other concerns were related to the privacy and confidentiality of patient information. They believe access to patient information should be on a need-to-know basis, and patient information should not be accessible to all other clinicians except those involved directly in their care processes. Conclusion: The study revealed that health professionals have a positive perception of the implemented EHRs, are highly satisfied with them, and are interested in continuing to use them. However, health professionals' concerns about the unstable power supply, poor internet connectivity, security, and confidentiality of patient's information need attention, to mitigate their frustrations and boost their confidence in the system. [ABSTRACT FROM AUTHOR]

Purpose: The European health data space promises an efficient environment for research and policy-making. However, this data space is dependent on high data quality. The implementation of electronic medical record systems has a positive impact on data quality, but improvements are not consistent across empirical studies. This study aims to analyze differences in the changes of data quality and to discuss these against distinct stages of the electronic medical record's adoption process. Methods: Paper-based and electronic medical records from three surgical departments were compared, assessing changes in data quality after the implementation of an electronic medical record system. Data quality was operationalized as completeness of documentation. Ten information that must be documented in both record types (e.g. vital signs) were coded as 1 if they were documented, otherwise as 0. Chi-Square-Tests were used to compare percentage completeness of these ten information and t-tests to compare mean completeness per record type. Results: A total of N = 659 records were analyzed. Overall, the average completeness improved in the electronic medical record, with a change from 6.02 (SD = 1.88) to 7.2 (SD = 1.77). At the information level, eight information improved, one deteriorated and one remained unchanged. At the level of departments, changes in data quality show expected differences. Conclusion: The study provides evidence that improvements in data quality could depend on the process how the electronic medical record is adopted in the affected department. Research is needed to further improve data quality through implementing new electronical medical record systems or updating existing ones. [ABSTRACT FROM AUTHOR]

Background: Back pain is very common and a leading cause of disability worldwide. Due to health care system inequalities, Indigenous communities have a disproportionately higher prevalence of injury and acute and chronic diseases compared to the general Canadian population. Indigenous communities, particularly in northern Canada, have limited access to evidence-based spine care. Strategies established in collaboration with Indigenous peoples are needed to address unmet healthcare needs, including spine care (chiropractic and movement program) services. This study aimed to understand the views and perspectives of Cross Lake community leaders and clinicians working at Cross Lake Nursing Station (CLNS) in northern Manitoba regarding the implementation of the Global Spine Care Initiative (GSCI) model of spine care (MoC) and related implementation strategies. Method: A qualitative exploratory design using an interpretivist paradigm was used. Twenty community partners were invited to participate in semi-structured interviews underpinned by the Theoretical Domains Framework (TDF) adapted to capture pertinent information. Data were analyzed deductively and inductively, and the interpretation of findings were explored in consultation with community members and partners. Results: Community leaders (n = 9) and physicians, nurses, and allied health workers (n = 11) emphasized: 1) the importance of contextualizing the MoC (triaging and care pathway) and proposed new services through in-person community engagement; 2) the need and desire for local non-pharmacological spine care approaches; and 3) streamlining patient triage and CLNS workflow. Recommendations for the streamlining included reducing managerial/administrative duties, educating new incoming clinicians, incorporating follow-up appointments for spine pain patients, and establishing an electronic medical record system along with a patient portal. Suggestions regarding how to sustain the new spine care services included providing transportation, protecting allocated clinic space, resolving insurance coverage discrepancies, addressing misconceptions about chiropractic care, instilling the value of physical activity for self-care and pain relief, and a short-term (30-day) incentivised movement program which considers a variety of movement options and offers a social component after each session. Conclusion: Community partners were favorable to the inclusion of a refined GSCI MoC. Adapting the TDF to unique Indigenous needs may help understand how best to implement the MoC in communities with similar needs. [ABSTRACT FROM AUTHOR]