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Background: Routinely collected electronic health records (EHR) offer a valuable opportunity to carry out research on immunization uptake, effectiveness, and safety, using large and representative samples of the population. In contrast to other drugs, vaccines do not require electronic prescription in many settings, which may lead to ambiguous coding of vaccination status and timing., Methodology: We propose a comprehensive algorithm to identifying childhood immunizations in routinely collected EHR. In order to deal with ambiguous coding, over-recording, and backdating in EHR, we suggest an approach combining a wide range of medical codes in combination to identify vaccination events and using appropriate wash-out periods and quality checks. We illustrate this approach on a cohort of children born between 2006 and 2014 followed up to the age of five in the Clinical Practice Research Datalink (CPRD) Aurum, a UK primary care dataset of EHR, and validate the results against national estimates of vaccine coverage by NHS Digital and Public Health England., Results: Our algorithm reproduced estimates of vaccination coverage, which are comparable to official national estimates and allows to approximate the age at vaccination. Electronic prescription data only do not cover vaccination events sufficiently., Conclusion: Our new proposed method could be used to provide a more accurate estimation of vaccination coverage and timing of vaccination for researchers and policymakers using EHR. As with all observational research using real-world data, it is important that researchers understand the context of the used dataset used and the clinical practice of recording., (© 2024 The Author(s). Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Purpose: Bleeding is an important health outcome of interest in epidemiological studies. We aimed to develop and validate rule-based algorithms to identify (1) major bleeding and (2) all clinically relevant bleeding (CRB) (composite of major and all clinically relevant nonmajor bleeding) within real-world electronic healthcare data., Methods: We took a random sample (n = 1630) of inpatient admissions to Singapore public healthcare institutions in 2019 and 2020, stratifying by hospital and year. We included patients of all age groups, sex, and ethnicities. Presence of major bleeding and CRB were ascertained by two annotators through chart review. A total of 630 and 1000 records were used for algorithm development and validation, respectively. We formulated two algorithms: sensitivity- and positive predictive value (PPV)-optimized algorithms. A combination of hemoglobin test patterns and diagnosis codes were used in the final algorithms., Results: During validation, diagnosis codes alone yielded low sensitivities for major bleeding (0.16) and CRB (0.24), although specificities and PPV were high (>0.97). For major bleeding, the sensitivity-optimized algorithm had much higher sensitivity and negative predictive values (NPVs) (sensitivity = 0.94, NPV = 1.00), however false positive rates were also relatively high (specificity = 0.90, PPV = 0.34). PPV-optimized algorithm had improved specificity and PPV (specificity = 0.96, PPV = 0.52), with little reduction in sensitivity and NPV (sensitivity = 0.88, NPV = 0.99). For CRB events, our algorithms had lower sensitivities (0.50-0.56)., Conclusions: The use of diagnosis codes alone misses many genuine major bleeding events. We have developed major bleeding algorithms with high sensitivities, which can ascertain events within populations of interest., (© 2024 John Wiley & Sons Ltd.)

Bipolar disorder is a chronic and complex polygenic disease with high rates of comorbidity. However, the independent contribution of either diagnosis or genetic risk of bipolar disorder to the medical comorbidity profile of individuals with the disease remains unresolved. Here, we conducted a multi-step phenome-wide association study (PheWAS) of bipolar disorder using phenomes derived from the electronic health records of participants enrolled in the Mayo Clinic Biobank and the Mayo Clinic Bipolar Disorder Biobank. First, we explored the conditions associated with a diagnosis of bipolar disorder by conducting a phenotype-based PheWAS followed by LASSO-penalized regression to account for correlations within the phenome. Then, we explored the conditions associated with bipolar disorder polygenic risk score (BD-PRS) using a PRS-based PheWAS with a sequential exclusion approach to account for the possibility that diagnosis, instead of genetic risk, may drive such associations. 53,386 participants (58.7% women) with a mean age at analysis of 67.8 years (SD = 15.6) were included. A bipolar disorder diagnosis (n = 1479) was associated with higher rates of psychiatric conditions, injuries and poisonings, endocrine/metabolic and neurological conditions, viral hepatitis C, and asthma. BD-PRS was associated with psychiatric comorbidities but, in contrast, had no positive associations with general medical conditions. While our findings warrant confirmation with longitudinal-prospective studies, the limited associations between bipolar disorder genetics and medical conditions suggest that shared environmental effects or environmental consequences of diagnosis may have a greater impact on the general medical comorbidity profile of individuals with bipolar disorder than its genetic risk., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

To date, symptom documentation has mostly relied on clinical notes in electronic health records or patient-reported outcomes using disease-specific symptom inventories. To provide a common and precise language for symptom recording, assessment, and research, a comprehensive list of symptom codes is needed. The International Classification of Diseases, Ninth Revision or its clinical modification ( International Classification of Diseases, Ninth Revision, Clinical Modification ) has a range of codes designated for symptoms, but it does not contain codes for all possible symptoms, and not all codes in that range are symptom related. This study aimed to identify and categorize the first list of International Classification of Diseases, Ninth Revision, Clinical Modification symptom codes for a general population and demonstrate their use to characterize symptoms of patients with type 2 diabetes mellitus in the Cerner database. A list of potential symptom codes was automatically extracted from the Unified Medical Language System Metathesaurus. Two clinical experts in symptom science and diabetes manually reviewed this list to identify and categorize codes as symptoms. A total of 1888 International Classification of Diseases, Ninth Revision, Clinical Modification symptom codes were identified and categorized into 65 categories. The symptom characterization using the newly obtained symptom codes and categories was found to be more reasonable than that using the previous symptom codes and categories on the same Cerner diabetes cohort., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Synthetic Electronic Health Records (EHRs) are becoming increasingly popular as a privacy enhancing technology. However, for longitudinal EHRs specifically, little research has been done into how to properly evaluate synthetically generated samples. In this article, we provide a discussion on existing methods and recommendations when evaluating the quality of synthetic longitudinal EHRs., Methods: We recommend to assess synthetic EHR quality through similarity to real EHRs in low-dimensional projections, accuracy of a classifier discriminating synthetic from real samples, performance of synthetic versus real trained algorithms in clinical tasks, and privacy risk through risk of attribute inference. For each metric we discuss strengths and weaknesses, next to showing how it can be applied on a longitudinal dataset., Results: To support the discussion on evaluation metrics, we apply discussed metrics on a dataset of synthetic EHRs generated from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) repository., Conclusions: The discussion on evaluation metrics provide guidance for researchers on how to use and interpret different metrics when evaluating the quality of synthetic longitudinal EHRs., (© 2024. The Author(s).)

Background: Patients' online record access (ORA) enables patients to read and use their health data through online digital solutions. One such solution, patient-accessible electronic health records (PAEHRs) have been implemented in Estonia, Finland, Norway, and Sweden. While accumulated research has pointed to many potential benefits of ORA, its application in mental healthcare (MHC) continues to be contested. The present study aimed to describe MHC users' overall experiences with national PAEHR services., Methods: The study analysed the MHC-part of the NORDeHEALTH 2022 Patient Survey, a large-scale multi-country survey. The survey consisted of 45 questions, including demographic variables and questions related to users' experiences with ORA. We focused on the questions concerning positive experiences (benefits), negative experiences (errors, omissions, offence), and breaches of security and privacy. Participants were included in this analysis if they reported receiving mental healthcare within the past two years. Descriptive statistics were used to summarise data, and percentages were calculated on available data., Results: 6,157 respondents were included. In line with previous research, almost half (45%) reported very positive experiences with ORA. A majority in each country also reported improved trust (at least 69%) and communication (at least 71%) with healthcare providers. One-third (29.5%) reported very negative experiences with ORA. In total, half of the respondents (47.9%) found errors and a third (35.5%) found omissions in their medical documentation. One-third (34.8%) of all respondents also reported being offended by the content. When errors or omissions were identified, about half (46.5%) reported that they took no action. There seems to be differences in how patients experience errors, omissions, and missing information between the countries. A small proportion reported instances where family or others demanded access to their records (3.1%), and about one in ten (10.7%) noted that unauthorised individuals had seen their health information., Conclusions: Overall, MHC patients reported more positive experiences than negative, but a large portion of respondents reported problems with the content of the PAEHR. Further research on best practice in implementation of ORA in MHC is therefore needed, to ensure that all patients may reap the benefits while limiting potential negative consequences., (© 2024. The Author(s).)

Background: Improving hypertension control is a public health priority. However, consistent identification of uncontrolled hypertension using computable definitions in electronic health records (EHR) across health systems remains uncertain., Methods: In this retrospective cohort study, we applied two computable definitions to the EHR data to identify patients with controlled and uncontrolled hypertension and to evaluate differences in characteristics, treatment, and clinical outcomes between these patient populations. We included adult patients (≥ 18 years) with hypertension (based on either ICD-10 codes of hypertension or two elevated blood pressure [BP] measurements) receiving ambulatory care within Yale-New Haven Health System (YNHHS; a large US health system) and OneFlorida Clinical Research Consortium (OneFlorida; a Clinical Research Network comprised of 16 health systems) between October 2015 and December 2018. We identified patients with controlled and uncontrolled hypertension based on either a single BP measurement from a randomly selected visit or all BP measurements recorded between hypertension identification and the randomly selected visit)., Results: Overall, 253,207 and 182,827 adults at YNHHS and OneFlorida were identified as having hypertension. Of these patients, 83.1% at YNHHS and 76.8% at OneFlorida were identified using ICD-10-CM codes, whereas 16.9% and 23.2%, respectively, were identified using elevated BP measurements (≥ 140/90 mmHg). A total of 24.1% of patients at YNHHS and 21.6% at OneFlorida had both diagnosis code for hypertension and elevated blood pressure measurements. Uncontrolled hypertension was observed among 32.5% and 43.7% of patients at YNHHS and OneFlorida, respectively. Uncontrolled hypertension was disproportionately higher among Black patients when compared with White patients (38.9% versus 31.5% in YNHHS; p < 0.001; 49.7% versus 41.2% in OneFlorida; p < 0.001). Medication prescription for hypertension management was more common in patients with uncontrolled hypertension when compared with those with controlled hypertension (overall treatment rate: 39.3% versus 37.3% in YNHHS; p = 0.04; 42.2% versus 34.8% in OneFlorida; p < 0.001). Patients with controlled and uncontrolled hypertension had similar incidence rates of deaths, CVD events, and healthcare visits at 3, 6, 12, and 24 months. The two computable definitions generated consistent results., Conclusions: While the current EHR systems are not fully optimized for disease surveillance and stratification, our findings illustrate the potential of leveraging EHR data to conduct digital population surveillance in the realm of hypertension management., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

Pompe disease (OMIM #232300), a rare genetic disorder, leads to glycogen buildup in the body due to an enzyme deficiency, particularly harming the heart and muscles. Infantile-onset Pompe disease (IOPD) requires urgent treatment to prevent mortality, but the unavailability of these methods often delays diagnosis. Our study aims to streamline IOPD diagnosis in the UAE using electronic health records (EHRs) for faster, more accurate detection and timely treatment initiation. This study utilized electronic health records from the Abu Dhabi Healthcare Company (SEHA) healthcare network in the UAE to develop an expert rule-based screening approach operationalized through a dashboard. The study encompassed six diagnosed IOPD patients and screened 93,365 subjects. Expert rules were formulated to identify potential high-risk IOPD patients based on their age, particular symptoms, and creatine kinase levels. The proposed approach was evaluated using accuracy, sensitivity, and specificity. The proposed approach accurately identified five true positives, one false negative, and four false positive IOPD cases. The false negative case involved a patient with both Pompe disease and congenital heart disease. The focus on CHD led to the overlooking of Pompe disease, exacerbated by no measurement of creatine kinase. The false positive cases were diagnosed with Mitochondrial DNA depletion syndrome 12-A (SLC25A4 gene), Immunodeficiency-71 (ARPC1B mutation), Niemann-Pick disease type C (NPC1 gene mutation leading to frameshift), and Group B Streptococcus meningitis. The proposed approach of integrating expert rules with a dashboard facilitated efficient data visualization and automated patient screening, which aids in the early detection of Pompe disease. Future studies are encouraged to investigate the application of machine learning methodologies to enhance further the precision and efficiency of identifying patients with IOPD., (© 2024. The Author(s).)

Background: Risk prediction plays a crucial role in planning for prevention, monitoring, and treatment. Electronic Health Records (EHRs) offer an expansive repository of temporal medical data encompassing both risk factors and outcome indicators essential for effective risk prediction. However, challenges emerge due to the lack of readily available gold-standard outcomes and the complex effects of various risk factors. Compounding these challenges are the false positives in diagnosis codes, and formidable task of pinpointing the onset timing in annotations., Objective: We develop a Semi-supervised Double Deep Learning Temporal Risk Prediction (SeDDLeR) algorithm based on extensive unlabeled longitudinal Electronic Health Records (EHR) data augmented by a limited set of gold standard labels on the binary status information indicating whether the clinical event of interest occurred during the follow-up period., Methods: The SeDDLeR algorithm calculates an individualized risk of developing future clinical events over time using each patient's baseline EHR features via the following steps: (1) construction of an initial EHR-derived surrogate as a proxy for the onset status; (2) deep learning calibration of the surrogate along gold-standard onset status; and (3) semi-supervised deep learning for risk prediction combining calibrated surrogates and gold-standard onset status. To account for missing onset time and heterogeneous follow-up, we introduce temporal kernel weighting. We devise a Gated Recurrent Units (GRUs) module to capture temporal characteristics. We subsequently assess our proposed SeDDLeR method in simulation studies and apply the method to the Massachusetts General Brigham (MGB) Biobank to predict type 2 diabetes (T2D) risk., Results: SeDDLeR outperforms benchmark risk prediction methods, including Semi-parametric Transformation Model (STM) and DeepHit, with consistently best accuracy across experiments. SeDDLeR achieved the best C-statistics ( 0.815, SE 0.023; vs STM +.084, SE 0.030, P-value .004; vs DeepHit +.055, SE 0.027, P-value .024) and best average time-specific AUC (0.778, SE 0.022; vs STM + 0.059, SE 0.039, P-value .067; vs DeepHit + 0.168, SE 0.032, P-value <0.001) in the MGB T2D study., Conclusion: SeDDLeR can train robust risk prediction models in both real-world EHR and synthetic datasets with minimal requirements of labeling event times. It holds the potential to be incorporated for future clinical trial recruitment or clinical decision-making., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Electronic health records (EHR) are seen as a promising endeavour, in spite of policies, designs, user rights and types of health data varying across countries. In many European countries, including Austria, EHR usage has fallen short when compared to the deployment plans., Objective: By adopting a qualitative approach, this research aimed to explore facilitators and barriers experienced by patients and physicians across the entire EHR usage process in Austria., Method: Two studies were conducted: In Study 1, discussions were held with four homogeneously composed groups of patients ( N  = 30). In Study 2, eight expert semi-structured interviews were conducted with physicians to gain insights into potential facilitators and barriers Austrian physicians face when utilising personal EHR., Results: A wide range of barriers and facilitators were identified along the entire EHR usage spectrum, emerging on three different levels: the micro-level (individual level), the meso-level (level of the EHR system) and the macro-level (level of the health system). EHR literacy was identified as a booster to support EHR adherence. Health providers were identified as crucial gatekeepers with regard to EHR usage., Conclusion: The implications for mutual benefits arising out of EHR usage among the triad of health policymakers, providers and patients for both theory and practice are discussed., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Introduction and Objectives: Despite the huge clinical burden of MASLD, validated tools for early risk stratification are lacking, and heterogeneous disease expression and a highly variable rate of progression to clinical outcomes result in prognostic uncertainty. We aimed to investigate longitudinal electronic health record-based outcome prediction in MASLD using a state-of-the-art machine learning model., Patients and Methods: n = 940 patients with histologically-defined MASLD were used to develop a deep-learning model for all-cause mortality prediction. Patient timelines, spanning 12 years, were fully-annotated with demographic/clinical characteristics, ICD-9 and -10 codes, blood test results, prescribing data, and secondary care activity. A Transformer neural network (TNN) was trained to output concomitant probabilities of 12-, 24-, and 36-month all-cause mortality. In-sample performance was assessed using 5-fold cross-validation. Out-of-sample performance was assessed in an independent set of n = 528 MASLD patients., Results: In-sample model performance achieved AUROC curve 0.74-0.90 (95 % CI: 0.72-0.94), sensitivity 64 %-82 %, specificity 75 %-92 % and Positive Predictive Value (PPV) 94 %-98 %. Out-of-sample model validation had AUROC 0.70-0.86 (95 % CI: 0.67-0.90), sensitivity 69 %-70 %, specificity 96 %-97 % and PPV 75 %-77 %. Key predictive factors, identified using coefficients of determination, were age, presence of type 2 diabetes, and history of hospital admissions with length of stay >14 days., Conclusions: A TNN, applied to routinely-collected longitudinal electronic health records, achieved good performance in prediction of 12-, 24-, and 36-month all-cause mortality in patients with MASLD. Extrapolation of our technique to population-level data will enable scalable and accurate risk stratification to identify people most likely to benefit from anticipatory health care and personalized interventions., Competing Interests: Conflicts of interest ID and BS are employees of Bering Limited. ID is a shareholder at Bering Limited. The funder (Innovate UK) provided support in the form of salaries for authors ID and BS but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Views expressed are those of the authors and not necessarily those of Innovate UK or Bering. IAR serves as a consultant or has received speakers’ fees from Novo Nordisk, Boehringer Ingelheim, Bayer, Roche, and Norgine. TJK serves as a consultant for or has received speakers’ fees from Resolution Therapeutics, Clinnovate Health, Perspectum, Servier Laboratories, Kynos Therapeutics, Concept Life Sciences, HistoIndex, Fibrofind, and Incyte Corporation. JAF serves as a consultant or advisory board member for Resolution Therapeutics, Kynos Therapeutics, Ipsen, River 2 Renal Corp., Stimuliver, Global Clinical Trial Partners and Guidepoint and has received research grant funding from GlaxoSmithKline, Intercept Pharmaceuticals and Genentech., (Copyright © 2024 Fundación Clínica Médica Sur, A.C. Published by Elsevier España, S.L.U. All rights reserved.)

Heart Failure (HF) is a life-threatening condition. It affects more than 64 million people worldwide. Early diagnosis of HF is extremely crucial. In this study, we propose utilization of machine learning (ML) models to predict severity of HF from primary Electronic Health Records (EHRs). We used a public dataset of 2008 HF patients for the study. Gaussian Naive Bayes, Random Forest and CatBoost methods were used for prediction. The study shows that CatBoost works best for the goal. In addition to that, the largest contributors for tree-based models harmonize well with clinically important parameters, which exhibits the trustworthiness of these models. Hence, we conclude that utilization of ML methods on primary EHRs is a promising step for time-efficient diagnosis of HF patients.

Cardiovascular diseases are the leading cause of death globally. Timely health services are fundamental to the appropriate prevention, identification, care and rehabilitation of these diseases. This study aimed to explore the potential of using electronic health records as a data source to help identify health system -related delays in care processes of cardiac patients. This retrospective registry study is based on a sample of electronic health records of 200 cardiac patients admitted to one out of twenty wellbeing services counties in Finland during the years 2021-2022. A total of 426 health system -related delays were identified. All expressions were found in unstructured format and most of these (58.7%) were generated by nurses. These results show that the electronic health records contained a variety of information on health system -related patient care delays, and that most delays were associated with difficulties in finding a bed for the patient in a post-acute care facility (49.8%), but also in-hospital process delays were common (27.7%). These findings show great potential for exploring electronic health record data with natural language processing methods in the future for the development of tools to better identify and monitor different types of delays in care processes. Such tools may support leadership to respond to organisational procedures in need of improvement.

Emergency departments (EDs) are pivotal in detecting child abuse and neglect, but this task is often complex. Our study developed a machine learning model using structured and unstructured electronic health record (EHR) data to predict when children in EDs might need intervention from child protective services. We used a case-control study design, analyzing data from a pediatric ED. Clinical notes were processed with natural language processing (NLP) techniques to identify suspected cases and matched in a 1:9 ratio to ensure dataset balance. The features from these notes were combined with structured EHR data to construct a model using the XGBoost algorithm. The model achieved a precision of 0.95, recall of 0.88, and F1-score of 0.92, with improvements seen from integrating NLP-derived data. Key indicators for abuse included hospital admissions, extended ED stays, and specific clinical orders. The model's accuracy and the utility of NLP suggest the potential for EDs to better identify at-risk children. Future work should validate the model further and explore additional features while considering ethical implications to aid healthcare providers in safeguarding children.

Background: Electronic health records (EHRs) contain patients' health information over time, including possible early indicators of disease. However, the increasing amount of data hinders clinicians from using them. There is accumulating evidence suggesting that machine learning (ML) and deep learning (DL) can assist clinicians in analyzing these large-scale EHRs, as algorithms thrive on high volumes of data. Although ML has become well developed, studies mainly focus on engineering but lack medical outcomes., Objective: This study aims for a scoping review of the evidence on how the use of ML on longitudinal EHRs can support the early detection and prevention of disease. The medical insights and clinical benefits that have been generated were investigated by reviewing applications in a variety of diseases., Methods: This study was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A literature search was performed in 2022 in collaboration with a medical information specialist in the following databases: PubMed, Embase, Web of Science Core Collection (Clarivate Analytics), and IEEE Xplore Digital Library and computer science bibliography. Studies were eligible when longitudinal EHRs were used that aimed for the early detection of disease via ML in a prevention context. Studies with a technical focus or using imaging or hospital admission data were beyond the scope of this review. Study screening and selection and data extraction were performed independently by 2 researchers., Results: In total, 20 studies were included, mainly published between 2018 and 2022. They showed that a variety of diseases could be detected or predicted, particularly diabetes; kidney diseases; diseases of the circulatory system; and mental, behavioral, and neurodevelopmental disorders. Demographics, symptoms, procedures, laboratory test results, diagnoses, medications, and BMI were frequently used EHR data in basic recurrent neural network or long short-term memory techniques. By developing and comparing ML and DL models, medical insights such as a high diagnostic performance, an earlier detection, the most important predictors, and additional health indicators were obtained. A clinical benefit that has been evaluated positively was preliminary screening. If these models are applied in practice, patients might also benefit from personalized health care and prevention, with practical benefits such as workload reduction and policy insights., Conclusions: Longitudinal EHRs proved to be helpful for support in health care. Current ML models on EHRs can support the detection of diseases in terms of accuracy and offer preliminary screening benefits. Regarding the prevention of diseases, ML and specifically DL models can accurately predict or detect diseases earlier than current clinical diagnoses. Adding personally responsible factors allows targeted prevention interventions. While ML models based on textual EHRs are still in the developmental stage, they have high potential to support clinicians and the health care system and improve patient outcomes., (©Laura Swinckels, Frank C Bennis, Kirsten A Ziesemer, Janneke F M Scheerman, Harmen Bijwaard, Ander de Keijzer, Josef Jan Bruers. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.08.2024.)

Introduction: Deployment-limiting medical conditions (DLMCs) such as debilitating injuries and conditions may interfere with the ability of military service members (SMs) to deploy. SMs in the United States (U.S.) Department of the Navy (DoN) with DLMCs who are not deployable should be placed in the medically restricted status of limited duty (LIMDU) or referred to the Physical Evaluation Board (PEB) for Service retention determination. It is critical to identify SMs correctly and promptly with DLMCs and predict their return-to-duty (RTD) to ensure the combat readiness of the U.S. Military. In this study, an algorithmic approach was developed to identify DoN SMs with previously unidentified DLMCs and predict whether SMs on LIMDU will be able to RTD., Materials and Methods: Five years of historical data (2016-2022) were obtained from inpatient and outpatient datasets across direct and purchased care from the Military Health System (MHS) Data Repository (MDR). Key fields included International Classification of Diseases diagnosis and procedure codes, Current Procedure Terminology codes, prescription medications, and demographics information such as age, rank, gender, and service. The data consisted of 44,580,668 medical encounters across 1,065,224 SMs. To identify SMs with unidentified DLMCs, we developed an ensemble model combining outputs from multiple machine learning (ML) algorithms. When the ML ensemble model predicted a SM to have high risk scores, despite appearing healthy on administrative reports, their case was reviewed by expert clinicians to investigate for previously unidentified DLMCs; and such feedback served to validate the developed algorithms. In addition, leveraging 1,735,422 encounters (60,433 SMs) from LIMDU periods, we developed four separate ML models to estimate RTD probabilities for SMs after each medical encounter and predict the final LIMDU outcome., Results: The ensemble model had 0.91 area under the receiver operating characteristic curve (AUROC). Out of 236 (round one) and 314 (round two) SMs reviewed by clinicians, 127 (54%) and 208 (66%) SMs were identified with a previously unidentified or undocumented DLMC, respectively. Regarding predicting RTD for SMs placed on LIMDU, the best performing ML model achieved 0.76 AUROC, 68% sensitivity, and 71% specificity., Conclusion: Our research highlighted potential benefits of using predictive analytics in a medical assessment to identify SMs with DLMCs and to predict RTD outcomes once placed on LIMDU. This capability is being deployed for real-time clinical decision support to enhance health care provider's deployability assessment capability, improve accuracy of the DLMC population, and enhance combat readiness of the U.S Military., (© The Association of Military Surgeons of the United States 2024. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site–for further information please contact journals.permissions@oup.com.)

Background: Surgical site infection (SSI) is an important cause of disease burden and healthcare costs. Fully manual surveillance is time-consuming and prone to subjectivity and inter-individual variability, which can be partly overcome by semi-automated surveillance. Algorithms used in orthopaedic SSI semi-automated surveillance have reported high sensitivity and important workload reduction. This study aimed to design and validate different algorithms to identify patients at high risk of SSI after hip or knee arthroplasty., Methods: Retrospective data from manual SSI surveillance between May 2015 and December 2017 were used as gold standard for validation. Knee and hip arthroplasty were included, patients were followed up for 90 days and European Centre for Disease Prevention and Control SSI classification was applied. Electronic health records data was used to generate different algorithms, considering combinations of the following variables: ≥1 positive culture, ≥ 3 microbiological requests, antimicrobial therapy ≥ 7 days, length of hospital stay ≥ 14 days, orthopaedics readmission, orthopaedics surgery and emergency department attendance. Sensitivity, specificity, negative and predictive value, and workload reduction were calculated., Results: In total 1631 surgical procedures were included, of which 67.5% (n = 1101) in women; patients' median age was 69 years (IQR 62 to 77) and median Charlson index 2 (IQR 1 to 3). Most surgeries were elective (92.5%; n = 1508) and half were hip arthroplasty (52.8%; n = 861). SSI incidence was 3.8% (n = 62), of which 64.5% were deep or organ/space infections. Positive culture was the single variable with highest sensitivity (64.5%), followed by orthopaedic reintervention (59.7%). Twenty-four algorithms presented 90.3% sensitivity for all SSI types and 100% for deep and organ/space SSI. Workload reduction ranged from 59.7 to 67.7%. The algorithm including ≥ 3 microbiological requests, length of hospital stay ≥ 14 days and emergency department attendance, was one of the best options in terms of sensitivity, workload reduction and feasibility for implementation., Conclusions: Different algorithms with high sensitivity to detect all types of SSI can be used in real life, tailored to clinical practice and data availability. Emergency department attendance can be an important variable to identify superficial SSI in semi-automated surveillance., (© 2024. The Author(s).)

Background: Despite the frequent diagnostic delays of rare neurologic diseases (RND), it remains difficult to study RNDs and their comorbidities due to their rarity and hence the statistical underpowering. Affecting one to two in a million annually, stiff person syndrome (SPS) is an RND characterized by painful muscle spasms and rigidity. Leveraging underutilized electronic health records (EHR), this study showcased a machine-learning-based framework to identify clinical features that optimally characterize the diagnosis of SPS., Methods: A machine-learning-based feature selection approach was employed on 319 items from the past medical histories of 48 individuals (23 with a diagnosis of SPS and 25 controls) with elevated serum autoantibodies against glutamic-acid-decarboxylase-65 (anti-GAD65) in Dartmouth Health's EHR to determine features with the highest discriminatory power. Each iteration of the algorithm implemented a Support Vector Machine (SVM) model, generating importance scores-SHapley Additive exPlanation (SHAP) values-for each feature and removing one with the least salient. Evaluation metrics were calculated through repeated stratified cross-validation., Results: Depression, hypothyroidism, GERD, and joint pain were the most characteristic features of SPS. Utilizing these features, the SVM model attained precision of 0.817 (95% CI 0.795-0.840), sensitivity of 0.766 (95% CI 0.743-0.790), F-score of 0.761 (95% CI 0.744-0.778), AUC of 0.808 (95% CI 0.791-0.825), and accuracy of 0.775 (95% CI 0.759-0.790)., Conclusions: This framework discerned features that, with further research, may help fully characterize the pathologic mechanism of SPS: depression, hypothyroidism, and GERD may respectively represent comorbidities through common inflammatory, genetic, and dysautonomic links. This methodology could address diagnostic challenges in neurology by uncovering latent associations and generating hypotheses for RNDs., (© 2024. The Author(s).)

Sepsis refers to a potentially life-threatening situation where the immune system of the human body has an extreme response to an infection. In the presence of underlying comorbidities, the situation can become even worse and result in death. Employing unsupervised machine learning techniques, such as clustering, can assist in providing a better understanding of patient phenotypes by unveiling subgroups characterized by distinct sepsis progression and treatment patterns. More concretely, this study introduces M-ClustEHR, a clustering approach that utilizes medical data of multiple modalities by employing a multimodal autoencoder for learning comprehensive sepsis patient representations. M-ClustEHR consistently outperforms traditional clustering approaches in terms of several internal clustering performance metrics, as well as cluster stability in identifying phenotypes in the sepsis cohort. The unveiled patterns, supported by existing medical literature and clinicians, highlight the importance of multimodal clustering for advancing personalized sepsis care., Competing Interests: Declaration of competing interest We declare no Conflict of Interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Irregular sampling of time series in electronic health records (EHRs) is one of the main challenges for developing machine learning models. Additionally, the pattern of missing values in certain clinical variables is not at random but depends on the decisions of clinicians and the state of the patient. Point process is a mathematical framework for analyzing event sequence data consistent with irregular sampling patterns. Our model, TEE4EHR, is a transformer event encoder (TEE) with point process loss that encodes the pattern of laboratory tests in EHRs. The utility of our TEE has been investigated in various benchmark event sequence datasets. Additionally, we conduct experiments on two real-world EHR databases to provide a more comprehensive evaluation of our model. Firstly, in a self-supervised learning approach, the TEE is jointly learned with an existing attention-based deep neural network, which gives superior performance in negative log-likelihood and future event prediction. Besides, we propose an algorithm for aggregating attention weights to reveal the events' interactions. Secondly, we transfer and freeze the learned TEE to the downstream task for the outcome prediction, where it outperforms state-of-the-art models for handling irregularly sampled time series. Furthermore, our results demonstrate that our approach can improve representation learning in EHRs and be useful for clinical prediction tasks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Breastfeeding information stored within electronic health records (EHR) has recently been used for pharmacoepidemiological research, however the data are primarily collected for clinical care., Objectives: To characterise breastfeeding information recorded in structured fields in EHR during infant and postpartum health care visits, and to assess the validity of lactation status based on EHR data versus maternal report at research study visits., Methods: We assessed breastfeeding information recorded in structured fields in EHR from one health system for a subset of 211 patients who were also enrolled in a study on breast milk composition between 2014 and 2017 that required participants to exclusively breastfeed their infants until at least 1 month of age. We assessed the frequency of breastfeeding information in EHR during the first 12 months of age and compared lactation status based on EHR with maternal report at 1 and 6-month study visits (reference standard)., Results: The median number of breastfeeding records in the EHR per infant was six (interquartile range 3) with most observations clustering in the first few weeks of life and around well-infant visits. At the 6-month study visit, 93.8% of participants were breastfeeding and 80.1% were exclusively breastfeeding according to maternal report. Sensitivity of EHR data for identifying ever breastfeeding was at or near 100%, and sensitivity for identifying ever exclusive breastfeeding was 98.0% (95% CI: 95.0%, 99.2%). Sensitivities were 97.3% (95% CI: 93.9%, 98.9%) for identifying any breastfeeding and 94.4% (95% CI: 89.7%, 97.0%) for exclusive breastfeeding, and positive predictive values were 99.5% (95% CI: 97.0%, 99.9%) for any breastfeeding and 95.0% (95% CI: 90.4%, 97.4%) for exclusive breastfeeding., Conclusions: Breastfeeding information in structured EHR fields have the potential to accurately classify lactation status. The validity of these data should be assessed in populations with a lower breastfeeding prevalence., (© 2024 John Wiley & Sons Ltd.)

Objectives: In 2018, the Centers for Disease Control and Prevention introduced the Adult Sepsis Event (ASE) definition, using electronic health records (EHRs) data for surveillance and sepsis quality improvement. However, data regarding ASE outside the United States remain limited. We therefore aimed to validate the diagnostic accuracy of the ASE and to assess the prevalence and mortality of sepsis using ASE., Design: Retrospective cohort study., Setting: A single center in South Korea, with 2732 beds including 221 ICU beds., Patients: During the validation phase, adult patients who were hospitalized or visiting the emergency department between November 5 and November 11, 2019, were included. In the subsequent phase of epidemiologic analysis, we included adult patients who were admitted from January to December 2020., Interventions: None., Measurements and Main Results: ASE had a sensitivity of 91.6%, a specificity of 98.3%, a positive predictive value (PPV) of 57.4%, and a negative predictive value of 99.8% when compared with the Sepsis-3 definition. Of 126,998 adult patient hospitalizations in 2020, 6,872 cases were diagnosed with sepsis based on the ASE (5.4% per year), and 893 patients were identified as having sepsis according to the International Classification of Diseases , 10th Edition (ICD-10) (0.7% per year). Hospital mortality rates were 16.6% (ASE) and 23.5% (ICD-10-coded sepsis). Monthly sepsis prevalence and hospital mortality exhibited less variation when diagnosed using ASE compared with ICD-10 coding (coefficient of variation [CV] for sepsis prevalence: 0.051 vs. 0.163, Miller test p < 0.001; CV for hospital mortality: 0.087 vs. 0.261, p = 0.001)., Conclusions: ASE demonstrated high sensitivity and a moderate PPV compared with the Sepsis-3 criteria in a Korean population. The prevalence of sepsis, as defined by ASE, was 5.4% per year and was similar to U.S. estimates. The prevalence of sepsis by ASE was eight times higher and exhibited less monthly variability compared with that based on the ICD-10 code., Competing Interests: Drs. Lim and Lee received support for article research from the Korea Disease Control and Prevention Agency (fund code 2019E280500, 2020E280700, 2021-10-026 to Dr. Lim). They disclosed government work. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Society of Critical Care Medicine and Wolters Kluwer Health, Inc.)

Objectives: Negative symptoms in schizophrenia are associated with significant illness burden. We sought to investigate clinical outcomes for patients with schizophrenia who present with predominant negative symptoms (PNS) vs without PNS., Design: Retrospective analysis of electronic health record (EHR) data., Setting: 25 US providers of mental healthcare., Participants: 4444 adults with schizophrenia receiving care between 1999 and 2020., Exposure: PNS defined as ≥3 negative symptoms and ≤3 positive symptoms recorded in EHR data at the time of the first recorded schizophrenia diagnosis (index date). Symptom data were ascertained using natural language processing applied to semistructured free text records documenting the mental state examination. A matched sample (1:1) of patients without PNS was used to compare outcomes. Follow-up data were obtained up to 12 months following the index date., Primary Outcome Measure: Mean number of psychiatric hospital admissions., Secondary Outcome Measures: Mean number of outpatient visits, estimated treatment costs, Clinical Global Impression - Severity score and antipsychotic treatments (12 months before and after index date)., Results: 360 (8%) patients had PNS and 4084 (92%) did not have PNS. Patients with PNS were younger (36.4 vs 39.7 years, p<0.001) with a greater prevalence of psychiatric comorbidities (schizoaffective disorders: 25.0 vs 18.4%, p=0.003; major depressive disorder: 17.8 vs 9.8%, p<0.001). During follow-up, patients with PNS had fewer days with an antipsychotic prescription (mean=111.8 vs 140.9 days, p<0.001). Compared with matched patients without PNS, patients with PNS were more likely to have a psychiatric inpatient hospitalisation (76.1% vs 59.7%, p<0.001) and had greater estimated inpatient costs ($16 893 vs $13 732, p=0.04)., Conclusions: Patients with PNS were younger and presented with greater illness severity and more psychiatric comorbidities compared with patients without PNS. Our findings highlight an unmet need for novel therapeutic approaches to address negative symptoms to improve clinical outcomes., Competing Interests: Competing interests: CD, CZ and AP are employees of Sunovion Pharmaceuticals Inc. AK was an employee of Sunovion Pharmaceuticals Inc. at the time of this research. YW and XH are employees of Holmusk Technologies Inc., which received funding from Sunovion to conduct this analysis. RP has received grant funding from Janssen, and consulting fees from Holmusk, Akrivia Health, Columbia Data Analytics, Clinilabs, Boehringer Ingelheim, Bristol Myers Squibb, Teva and Otsuka., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Background: An outbreak of acute severe hepatitis of unknown aetiology (AS-Hep-UA) in children during 2022 was subsequently linked to infections with adenovirus-associated virus 2 and other 'helper viruses', including human adenovirus. It is possible that evidence of such an outbreak could be identified at a population level based on routine data captured by electronic health records (EHR)., Methods: We used anonymised EHR to collate retrospective data for all emergency presentations to Oxford University Hospitals NHS Foundation Trust in the UK, between 2016-2022, for all ages from 18 months and older. We investigated clinical characteristics and temporal distribution of presentations of acute hepatitis and of adenovirus infections based on laboratory data and clinical coding. We relaxed the stringent case definition adopted during the AS-Hep-UA to identify all cases of acute hepatitis with unknown aetiology (termed AHUA). We compared events within the outbreak period (defined as 1st Oct 2021-31 Aug 2022) to the rest of our study period., Results: Over the study period, there were 903,433 acute presentations overall, of which 391 (0.04%) were classified as AHUA. AHUA episodes had significantly higher critical care admission rates (p < 0.0001, OR = 41.7, 95% CI:26.3-65.0) and longer inpatient admissions (p < 0.0001) compared with the rest of the patient population. During the outbreak period, significantly more adults (≥ 16 years) were diagnosed with AHUA (p < 0.0001, OR = 3.01, 95% CI: 2.20-4.12), and there were significantly more human adenovirus (HadV) infections in children (p < 0.001, OR = 1.78, 95% CI:1.27-2.47). There were also more HAdV tests performed during the outbreak (p < 0.0001, OR = 1.27, 95% CI:1.17-1.37). Among 3,707 individuals who were tested for HAdV, 179 (4.8%) were positive. However, there was no evidence of more acute hepatitis or increased severity of illness in HadV-positive compared to negative cases., Conclusions: Our results highlight an increase in AHUA in adults coinciding with the period of the outbreak in children, but not linked to documented HAdV infection. Tracking changes in routinely collected clinical data through EHR could be used to support outbreak surveillance., (© 2024. The Author(s).)

Background: Information on registered adverse drug reactions (ADRs) in hospitals may provide a large real-world data source that can be used to ensure patients' safety. This study aimed to assess the potential contribution of hospital registration of ADRs in electronic health records (EHR) to pharmacovigilance., Research Design and Methods: An observational retrospective descriptive study using data from the Jeroen Bosch Hospital in the Netherlands in 2019. 'Serious and/or severe' and 'previously unknown' ADRs registered systematically in the corresponding field of EHRs were assessed., Results: ADR data concerning 1010 patients were included. In total, 1630 ADRs were registered in EHRs. Fifty-eight serious and/or severe ADRs (5.2%) were registered. Tubulointerstitial nephritis was the most frequently registered severe ADR and was mainly associated with antibacterials for systemic use. A total of 82 previously unknown ADRs (5%) were registered. 'Migraine' and 'chest pain' were the most frequently registered unknown ADRs. Additionally, 25 ADRs (1.5%) were registered that may be attributable to 10 drugs 'under additional monitoring.', Conclusions: Hospital registrations of ADRs in EHRs provide information on ADRs, which are challenging to assess during clinical trials. However, improvements are required to optimize this registration before it can serve as a valuable data source for pharmacovigilance purposes.

Introduction: Electronic Health Records (EHR) are a useful data source for research, but their usability is hindered by measurement errors. This study investigated an automatic error detection algorithm for adult height and weight measurements in EHR for the All of Us Research Program (All of Us)., Methods: We developed reference charts for adult heights and weights that were stratified on participant sex. Our analysis included 4,076,534 height and 5,207,328 wt measurements from ∼ 150,000 participants. Errors were identified using modified standard deviation scores, differences from their expected values, and significant changes between consecutive measurements. We evaluated our method with chart-reviewed heights (8,092) and weights (9,039) from 250 randomly selected participants and compared it with the current cleaning algorithm in All of Us., Results: The proposed algorithm classified 1.4 % of height and 1.5 % of weight errors in the full cohort. Sensitivity was 90.4 % (95 % CI: 79.0-96.8 %) for heights and 65.9 % (95 % CI: 56.9-74.1 %) for weights. Precision was 73.4 % (95 % CI: 60.9-83.7 %) for heights and 62.9 (95 % CI: 54.0-71.1 %) for weights. In comparison, the current cleaning algorithm has inferior performance in sensitivity (55.8 %) and precision (16.5 %) for height errors while having higher precision (94.0 %) and lower sensitivity (61.9 %) for weight errors., Discussion: Our proposed algorithm outperformed in detecting height errors compared to weights. It can serve as a valuable addition to the current All of Us cleaning algorithm for identifying erroneous height values., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Pulmonary hypertension is a progressive disease for which early treatment interventions are essential. Traditionally, patients undergo periodic clinical assessments. However, recent advances in wearable technology could improve the quality and efficiency of follow-up monitoring in patients with pulmonary hypertension., Trial Design: To our knowledge, this is the first study describing direct data transmission from a smartwatch to patients' electronic health records. It implements a novel update and customised program to continuously and automatically transmit data from a smartwatch to the patient's electronic healthcare records. It will evaluate continuous monitoring in patients with pulmonary hypertension and monitor their physical activity time, heart rate variability, and heart rate at rest and during physical activity via a smartwatch. It will also evaluate the data transmission method, and its data will be assessed by the treating physicians supplemental to clinical practice. Smartwatch integration promises numerous advantages: comprehensive cardiovascular monitoring and improved patient experience. Our continuous smartwatch monitoring approach offers a solution for earlier detection of clinical worsening and could be included as a combined endpoint in future clinical trials. It could improve patient empowerment, enhance precision medicine, and reduce hospitalisations. The user-friendly smartwatch is designed to minimise disruption in daily life., Conclusion: The ability to transfer real-time data from wearable devices to electronic health records could help to transform the treatment of patients with pulmonary hypertension and their follow-up monitoring outside a clinical setting, enhancing the efficiency of healthcare delivery., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Mads Ørbæk Andersen has nothing to declare. Jørn Carlsen is a member of an advisory board for Janssen-Cilag and has received institutional research grants and speaker fees., (Copyright © 2023. Published by Elsevier Inc.)

Introduction: Rapid identification of individuals developing a psychotic spectrum disorder (PSD) is crucial because untreated psychosis is associated with poor outcomes and decreased treatment response. Lack of recognition of early psychotic symptoms often delays diagnosis, further worsening these outcomes., Methods: The proposed study is a cross-sectional, retrospective analysis of electronic health record data including clinician documentation and patient-clinician secure messages for patients aged 15-29 years with ≥ 1 primary care encounter between 2017 and 2019 within 2 Kaiser Permanente regions. Patients with new-onset PSD will be distinguished from those without a diagnosis if they have ≥ 1 PSD diagnosis within 12 months following the primary care encounter. The prediction model will be trained using a trisourced natural language processing feature extraction design and validated both within each region separately and in a modified combined sample., Discussion: This proposed model leverages the strengths of the large volume of patient-specific data from an integrated electronic health record with natural language processing to identify patients at elevated chance of developing a PSD. This project carries the potential to reduce the duration of untreated psychosis and thereby improve long-term patient outcomes., Competing Interests: Conflicts of Interest None declared

Type 2 diabetes mellitus (T2DM) is a prevalent health challenge faced by countries worldwide. In this study, we propose a novel large language multimodal models (LLMMs) framework incorporating multimodal data from clinical notes and laboratory results for diabetes risk prediction. We collected five years of electronic health records (EHRs) dating from 2017 to 2021 from a Taiwan hospital database. This dataset included 1,420,596 clinical notes, 387,392 laboratory results, and more than 1505 laboratory test items. Our method combined a text embedding encoder and multi-head attention layer to learn laboratory values, and utilized a deep neural network (DNN) module to merge blood features with chronic disease semantics into a latent space. In our experiments, we observed that integrating clinical notes with predictions based on textual laboratory values significantly enhanced the predictive capability of the unimodal model in the early detection of T2DM. Moreover, we achieved an area greater than 0.70 under the receiver operating characteristic curve (AUC) for new-onset T2DM prediction, demonstrating the effectiveness of leveraging textual laboratory data for training and inference in LLMs and improving the accuracy of new-onset diabetes prediction., (© 2024. The Author(s).)

Background: Polypharmacy, prescription of multiple medications to a patient, is a major challenge for health systems. There have been no peer-reviewed studies of polypharmacy prevalence and medication cost at a population level in England., Aims: To determine prevalence and medication cost of polypharmacy, by patient characteristics. Design and setting: Retrospective cohort study of North West London electronic health records., Method: We quantified prevalence and direct cost of polypharmacy (five or more regular medications), stratified by demographics and frailty. We fitted a mixed-effects logistic regression for polypharmacy., Results: Of 1.7 million adults, 167,665 (9.4%) were on polypharmacy. Age and socio-economic deprivation were associated with polypharmacy (OR 9.24 95% CI 8.99 to 9.50, age 65-74 compared with 18-44; OR 0.68 95% CI 0.65 to 0.71, least deprived compared with most). Polypharmacy prevalence increased with frailty (OR 1.53 95% CI 1.53 to 1.54 per frailty component, for White women). Men had higher odds of polypharmacy than women at average frailty (OR 1.26 95% CI 1.24 to 1.28) and with additional frailty components (OR 1.10 95% CI 1.09 to 1.10). Black people had lower odds of polypharmacy at average frailty (OR 0.82 95% CI 0.79 to 0.85, compared with White), but along with other ethnicities, saw greater odds increases with increasing frailty (OR 1.02 95% CI 1.01 to 1.03). Annual medication cost 8.2 times more for those on polypharmacy compared with not (£370.89 and £45.31)., Conclusion: Demographic characteristics are associated with polypharmacy, after adjusting for frailty. Further research should explore why, to reduce health inequities and optimise cost associated with polypharmacy., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Woodcock et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: The reuse of Electronic Health Records (EHR) information models (e.g., templates and archetypes) may bring various benefits, including higher standardization, integration, interoperability, increased productivity in developing EHR systems, and unlock potential Artificial Intelligence applications built on top of medical records. The literature presents recent advances in standards for modeling EHR, in Knowledge Organization Systems (KOS) and EHR data reuse. However, methods, development processes, and frameworks to improve the reuse of EHR information models are still scarce. This study proposes a software engineering framework, named BioFrame, and analyzes how the reuse of EHR information models can be improved during the development of EHR systems., Methods: EHR standards and KOS, including ontologies, identified from systematic reviews were considered in developing the BioFrame framework. We used the structure of the OpenEHR to model templates and archetypes, as well as its relationship to international KOS used in the oncology domain. Our framework was applied in the context of pediatric oncology. Three data entry forms concerning nutrition and one utilized during the first pediatric oncology consultations were analyzed to measure the reuse of information models., Results: There was an increase in the adherence rate to international KOS of 18% to the original forms. There was an increase in the concepts reused in all 12 scenarios analyzed, with an average reuse of 6.55% in the original forms compared to 17.1% using BioFrame, resulting in significant differences., Conclusions: Our results point to higher reuse rates achieved due to an engineering process that provided greater adherence to EHR standards combined with semantic artifacts. This reveals the potential to develop new methods and frameworks aimed at EHR information model reuse. Additional research is needed to evaluate the impacts of the reuse of the EHR information model on interoperability, EHR data reuse, and data quality and assess the proposed framework in other health domains., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Rodrigo Bonacin reports financial support was provided by State of Sao Paulo Research Foundation. Rodrigo Bonacin reports financial support was provided by National Council for Scientific and Technological Development. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Understanding the latent disease patterns embedded in electronic health records (EHRs) is crucial for making precise and proactive healthcare decisions. Federated graph learning-based methods are commonly employed to extract complex disease patterns from the distributed EHRs without sharing the client-side raw data. However, the intrinsic characteristics of the distributed EHRs are typically non-independent and identically distributed (Non-IID), significantly bringing challenges related to data imbalance and leading to a notable decrease in the effectiveness of making healthcare decisions derived from the global model. To address these challenges, we introduce a novel personalized federated learning framework named PEARL, which is designed for disease prediction on Non-IID EHRs. Specifically, PEARL incorporates disease diagnostic code attention and admission record attention to extract patient embeddings from all EHRs. Then, PEARL integrates self-supervised learning into a federated learning framework to train a global model for hierarchical disease prediction. To improve the performance of the client model, we further introduce a fine-tuning scheme to personalize the global model using local EHRs. During the global model updating process, a differential privacy (DP) scheme is implemented, providing a high-level privacy guarantee. Extensive experiments conducted on the real-world MIMIC-III dataset validate the effectiveness of PEARL, demonstrating competitive results when compared with baselines.

Background: Diagnosis of alcohol use disorder (AUD) in primary care is critical for increasing access to alcohol treatment. However, AUD is underdiagnosed and may be inequitably diagnosed due to societal structures that determine access to resources (e.g., structural racism that limits opportunities for some groups and influences interpersonal interactions in and beyond health care). This study described patterns of provider-documented AUD in primary care across intersections of race, ethnicity, sex, and community-level socioeconomic status (SES)., Methods: This cross-sectional study used EHR data from a regional healthcare system with 35 primary care clinics that included adult patients who completed alcohol screenings between 3/1/2015 and 9/30/2020. The prevalence of provider-documented AUD in primary care based on International Classification of Diseases-9 (ICD-9) and ICD-10 diagnoses was compared across intersections of race, ethnicity, sex, and community-level SES., Results: Among 439,375 patients, 6.6% were Latine, 11.0% Asian, 5.4% Black, 1.3% Native Hawaiian/Pacific Islander (NH/PI), 1.5% American Indian/Alaska Native (AI/AN), and 74.2% White, and 58.3% women. The overall prevalence of provider-documented AUD was 1.0% and varied across intersecting identities. Among women, the prevalence was highest for AI/AN women with middle SES, 1.5% (95% CI 1.0-2.3), and lowest for Asian women with middle SES, 0.1% (95% CI 0.1-0.2). Among men, the prevalence was highest for AI/AN men with high and middle SES, 2.0% (95% CI 1.1-3.4) and 2.0% (95% CI 1.2-3.2), respectively, and lowest for Asian men with high SES, 0.5% (95% CI 0.3-0.7). Black and Latine patients tended to have a lower prevalence of AUD than White patients, across all intersections of sex and SES except for Black women with high SES. There were no consistent patterns of the prevalence of AUD diagnosis that emerged across SES., Conclusion: The prevalence of provider-documented AUD in primary care was highest in AI/AN men and women and lowest in Asian men and women. Findings of lower prevalence of provider-documented AUD in Black and Hispanic than White patients across most intersections of sex and SES differed from prior studies. Findings may suggest that differences in access to resources, which vary in effects across these identity characteristics and lived experiences, influence the diagnosis of AUD in clinical care., (© 2024. The Author(s).)

Can Electronic Health Records (EHR) predict opioid misuse in general patient populations? This research trained three backpropagation neural networks to explore EHR predictors using existing patient data. Model 1 used patient diagnosis codes and was 75.5% accurate. Model 2 used patient prescriptions and was 64.9% accurate. Model 3 used both patient diagnosis codes and patient prescriptions and was 74.5% accurate. This suggests patient diagnosis codes are best able to predict opioid misuse. Opioid misusers have higher rates of drug abuse/mental health disorders than the general population, which could explain the performance of diagnosis predictors. In additional testing, Model 1 misclassified only 1.9% of negative cases (non-abusers), demonstrating a low type II error rate. This suggests further clinical implementation is viable. We hope to motivate future research to explore additional methods for universal opioid misuse screening., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Vega et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Clozapine is the only recommended antipsychotic medication for individuals diagnosed with treatment-resistant schizophrenia. Unfortunately, its wider use is hindered by several possible adverse effects, some of which are rare but potentially life threatening. As such, there is a growing interest in studying clozapine use and safety in routinely collected healthcare data. However, previous attempts to characterise clozapine treatment have had low accuracy., Aim: To develop a methodology for identifying clozapine treatment dates by combining several data sources and implement this on a large clinical database., Methods: Non-identifiable electronic health records from a large mental health provider in London and a linked database from a national clozapine blood monitoring service were used to obtain information regarding patients' clozapine treatment status, blood tests and pharmacy dispensing records. A rule-based algorithm was developed to determine the dates of starting and stopping treatment based on these data, and more than 10% of the outcomes were validated by manual review of de-identified case note text., Results: A total of 3,212 possible clozapine treatment periods were identified, of which 425 (13.2%) were excluded due to insufficient data to verify clozapine administration. Of the 2,787 treatments remaining, 1,902 (68.2%) had an identified start-date. On evaluation, the algorithm identified treatments with 96.4% accuracy; start dates were 96.2% accurate within 15 days, and end dates were 85.1% accurate within 30 days., Conclusions: The algorithm produced a reliable database of clozapine treatment periods. Beyond underpinning future observational clozapine studies, we envisage it will facilitate similar implementations on additional large clinical databases worldwide., (© 2024. The Author(s).)

Background: The NORDeHEALTH project studies patient-accessible electronic health records (PAEHRs) in Estonia, Finland, Norway, and Sweden. Such country comparisons require an analysis of the sociotechnical context of these services. Although sociotechnical analyses of PAEHR services have been carried out in the past, a framework specifically tailored to in-depth cross-country analysis has not been developed., Objective: This study aims to develop and evaluate a method for a sociotechnical analysis of PAEHRs that advances a framework for sociotechnical analysis of eHealth solutions first presented by Sittig and Singh. This first article in a series presents the development of the method and a cross-country comparison of the contextual factors that enable PAEHR access and use., Methods: The dimensions of the framework for sociotechnical analysis were thoroughly discussed and extended in a series of workshops with international stakeholders, all being eHealth researchers focusing on PAEHRs. All countries were represented in the working group to make sure that important national perspectives were covered. A spreadsheet with relevant questions related to the studied services and the various dimensions of the sociotechnical framework was constructed and distributed to the 4 participating countries, and the project participants researched various national sources to provide the relevant data for the comparisons in the 10 sociotechnical dimensions., Results: In total, 3 dimensions were added to the methodology of Sittig and Singh to separate clinical content from features and functions of PAEHRs and demonstrate basic characteristics of the different countries regarding national and regional steering of health care and information and communications technology developments. The final framework contained the following dimensions: metadata; hardware and software computing infrastructure; features and functions; clinical content shared with patients; human-computer interface; people; workflow and communication; the health care organization's internal policies, procedures, and culture; national rules, regulations, and incentives; system measurement and monitoring; and health care system context. The dimensions added during the study mostly concerned background information needed for cross-country comparisons in particular. Several similarities were identified among the compared countries, especially regarding hardware and software computing infrastructure. All countries had, for example, one national access point, and patients are provided a PAEHR automatically. Most of the differences could be identified in the health care system context dimension. One important difference concerned the governing of information and communications technology development, where different levels (state, region, and municipality) were responsible in different countries., Conclusions: This is the first large-scale international sociotechnical analysis of services for patients to access their electronic health records; this study compared services in Estonia, Finland, Norway, and Sweden. A methodology for such an analysis was developed and is presented to enable comparison studies in other national contexts to enable future implementations and evaluations of PAEHRs., (©Jonas Moll, Isabella Scandurra, Annika Bärkås, Charlotte Blease, Maria Hägglund, Iiris Hörhammer, Bridget Kane, Eli Kristiansen, Peeter Ross, Rose-Mharie Åhlfeldt, Gunnar O Klein. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.08.2024.)

The Valkyrie project aims to develop a demonstration Federated Electronic Health Record for the use of mental health practitioners in Norway. Information for the record is drawn from existing records in Source Systems operating across primary and secondary care. Recording of information in any such system, in response to a healthcare event, triggers the generation of an Encrypted Token, containing summary metadata about the event, clinical coding indicating its clinical context and a locator that can be used to retrieve the full record of the event from the original Source System. The Valkyrie architecture consists of a number of interlinked Security Domains, each with its own private and public keys, through which the Encrypted Tokens are passed. Each Security Domain performs a specific function on a set of Tokens and only has access to the information within each Token that is necessary to perform that function. This paper describes the structure of the Encrypted Token, the function of each Security Domain and the orchestration of the flow of Tokens through the Domains. Together this allows a user to run a Valkyrie Session, in which they can view the content of a patient record, where all content has been drawn in real-time from heterogenous Source Systems (ISO13606- and openEHR-based) and is destroyed when the session terminates.

Computer-mediated clinical consultation, involving clinicians, electronic health record (EHR) systems, and patients, yield rich narrative data. Despite advancements in Natural Language Processing (NLP), these narratives remain underutilised. Free text recording in EHRs allows expressivity, complements structured data from clinical coding systems, and facilitates collaborative care. Large language models (LLMs) excel in understanding and generating natural language, enabling complex dialogue processing. Integrating LLM tools into consultations could harness the untapped potential of free text to identify patient safety concerns, support diagnosis and provide content to enhance clinical-patient interactions. Tailoring LLMs for specific consultation tasks through pre-training and fine-tuning is viable. This paper outlines approaches for adopting LLMs in primary care and suggests that using fine-tuned LLMs with prompt engineering could enhance computer-mediated clinical consultation cost-effectively.

Low-Density Lipoprotein (LDL) cholesterol is one of the main target for cardiovascular (CV) prevention and therapy. In the last years, Proprotein Convertase Subtilisin-Kexin type 9 inhibitors (PCSK9-i) has emerged as a key therapeutic target to lower LDL and were introduced for prevention of CV events. Recently (June 2022) the Italian Medicines Agency (AIFA) modified the eligibility criteria for the use of PCSK9-i. We designed an observational study to estimate the prevalence of eligible subjects and evaluate the effectiveness of PCSK9-i applying a Target Trial Emulation (TTE) approach based on Electronic Health Records (EHR). Subjects meeting the eligibility criteria were identified from July 2017 (when PCSK9-i became available) to December 2020. Outcomes were all-cause death and the first hospitalization. Among eligible subjects, we identified those treated at date of the first prescription. Inverse Probability of Treatment Weights (IPTW) were estimated including demographic and clinical covariates, history of treatment with statins and the month/year eligibility date. Competing risk models on weighted cohorts were used to derive the Average Treatment Effect (ATE) and the Conditional Average Treatment Effect (CATE) in subgroups of interest. Out of 1976 eligible subjects, 161 (8%) received treatment with PCSK9-i. Treated individuals were slightly younger, predominantly male, had more severe CV conditions, and were more often treated with statin compared to the untreated subjects. The latter exhibited a higher prevalence of non-CV comorbidities. A significant absolute and relative risk reduction of death and a lower relative risk for the first hospitalization was observed. The risk reduction for death was confirmed in CATE analysis. PCSk9-i were prescribed to a minority of eligible subjects. Within the TTE framework, the analysis confirmed the association between PCSK9-i and lower risk of events, aligning with findings from randomized clinical trials (RCTs). In our study, PCSK9-i provided protection specifically against all-cause death, expanding upon the evidence from RCTs that had primarily focused on composite CV outcomes., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Barbati et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Electronic health records (EHRs) are currently gaining popularity in emerging economies because they provide options for exchanging patient data, increasing operational efficiency, and improving patient outcomes. This study examines how service providers at Ghana's Komfo Anokye Teaching Hospital adopt and use an electronic health records (EHRs) system. The emphasis is on identifying factors impacting adoption and the problems that healthcare personnel encounter in efficiently using the EHRs system., Method: A quantitative cross-sectional technique was utilised to collect data from 234 trauma and emergency department staff members via standardised questionnaires. The participants were selected using the purposive sampling method. The Pearson Chi-square Test was used to examine the relationship between respondents' acceptability and use of EHRs., Results: The study discovered that a sizable number of respondents (86.8%) embraced and actively used the EHRs system. However, other issues were noted, including insufficient system training and malfunctions (35.9%), power outages (18.8%), privacy concerns (9.4%), and insufficient maintenance (4.7%). The respondents' comfortability in using the electronic health record system (X 2 =11.30, p=0.001), system dependability (X 2 =30.74, p=0.0001), and EHR's ability to reduce patient waiting time (X 2 =14.39, p=0.0001) were all strongly associated with their degree of satisfaction with the system. Furthermore, respondents who said elects increase patient care (X 2 = 75.59, p = 0.0001) and income creation (X 2 = 8.48, p = 0.004), which is related to the acceptability of the electronic health records system., Conclusion: The study revealed that comfort, reliability, and improved care quality all had an impact on the EHRs system's acceptability and utilization. Challenges, including equipment malfunctions and power outages, were found. Continuous professional training was emphasized as a means of increasing employee confidence, as did the construction of a power backup system to combat disruptions. Patient data privacy was highlighted. In conclusion, this study highlights the relevance of EHRs system adoption and usability in healthcare. While the benefits are obvious, addressing obstacles through training, technical support, and infrastructure improvements is critical for increasing system effectiveness., (© 2024. The Author(s).)

Background: In the United Kingdom, 15-min appointments with the general practitioner (GP) are recommended for people with complex health conditions, including intellectual disabilities and health needs, but we do not know whether this happens., Aims: We compared number and length of primary care consultations (GP, nurse, other allied health, other) for people with and without intellectual disabilities and health needs., Methods: Linked primary care data from the Clinical Practice Research Datalink (CPRD) in England were used to investigate face-to-face and telephone primary care consultations in 2017-2019. Health needs investigated were: epilepsy; incontinence; severe visual/hearing impairments; severe mobility difficulties; cerebral palsy; and percutaneous endoscopic gastrostomy feeding. Age and gender-standardized consultation rates per year (Poisson), duration of consultations, and the proportion of "long consultations" (≥15 min) were reported., Results: People with intellectual disabilities (n = 7,794) had 1.9 times as many GP consultations per year as those without (n = 176,807; consultation rate ratio = 1.87 [95% confidence interval 1.86-1.89]). Consultation rates with nurses and allied healthcare professionals were also twice as high. Mean GP consultation time was 9-10 min regardless of intellectual disability/health need status. Long GP consultations were less common in people with intellectual disabilities (18.2% [17.8-18.7] vs. 20.9% [20.8-21.0]). Long consultations with practice nurses were more common in people with health needs, particularly severe visual loss., Conclusions: People with intellectual disabilities and/or health needs tend to have more, rather than longer, GP consultations compared with the rest of the population. We recommend further investigation into the role of practice nurses to support people with intellectual disabilities and health needs., (© The Author(s) 2022. Published by Oxford University Press.)

Objective: There are established inequities in the monitoring and management of hypertension in England. The COVID-19 pandemic had a major impact on primary care management of long-term conditions such as hypertension. This study investigated the possible disproportionate impact of the pandemic across patient groups., Design: Open cohort of people with diagnosed hypertension., Settings: North East London primary care practices from January 2019 to October 2022., Participants: All 224 329 adults with hypertension registered in 193 primary care practices., Outcomes: Monitoring and management of hypertension were assessed using two indicators: (i) blood pressure recorded within 1 year of the index date and (ii) blood pressure control to national clinical practice guidelines., Results: The proportion of patients with a contemporaneous blood pressure recording fell from a 91% pre-pandemic peak to 62% at the end of the pandemic lockdown and improved to 77% by the end of the study. This was paralleled by the proportion of individuals with controlled hypertension which fell from a 73% pre-pandemic peak to 50% at the end of the pandemic lockdown and improved to 60% by the end of the study. However, when excluding patients without a recent blood pressure recording, the proportions of patients with controlled hypertension increased to 81%, 80% and 78% respectively.Throughout the study, in comparison to the White ethnic group, the Black ethnic group was less likely to achieve adequate blood pressure control (ORs 0.81 (95% CI 0.78 to 0.85, p<0.001) to 0.87 (95% CI 0.84 to 0.91, p<0.001)). Conversely, the Asian ethnic group was more likely to have controlled blood pressure (ORs 1.09 (95% CI 1.05 to 1.14, p<0.001) to 1.28 (95% CI 1.23 to 1.32, p<0.001)). Men, younger individuals, more affluent individuals, individuals with unknown or unrecorded ethnicity or those untreated were also less likely to have blood pressure control to target throughout the study., Conclusion: The COVID-19 pandemic had a greater impact on blood pressure recording than on blood pressure control. Inequities in blood pressure control persisted during the pandemic and remain outstanding., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.)

Objectives: Variation exists in the capabilities of electronic healthcare records (EHRs) systems and the frequency of their use by primary care physicians (PCPs) from different settings. We aimed to examine the factors associated with everyday EHRs use by PCPs, characterise the EHRs features available to PCPs, and to identify the impact of practice settings on feature availability., Study Design: Cross-sectional study., Methods: PCPs from 20 countries completed cross-sectional online survey between June and September 2020. Responses which reported frequency of EHRs use were retained. Associations between everyday EHRs use and PCP and practice factors (country, urbanicity, and digital maturity) were explored using multivariable logistic regression analyses. The effect of practice factors on the variation in availability of ten EHRs features was estimated using Cramer's V., Results: Responses from 1520 out of 1605 PCPs surveyed (94·7%) were retained. Everyday EHRs use was reported by 91·2% of PCPs. Everyday EHRs use was associated with PCPs working >28 h per week, having more years of experience using EHRs, country of employment, and higher digital maturity. EHRs features concerning entering, and retrieving data were available to most PCPs. Few PCPs reported having access to tools for 'interactive patient education' (37·3%) or 'home monitoring and self-testing of chronic conditions' (34·3%). Country of practice was associated with availability of all EHRs features (Cramer's V range: 0·2-0·6), particularly with availability of tools enabling patient EHRs access (Cramer's V: 0·6, P < 0.0001). Greater feature availability of EHRs features was observed with greater digital maturity., Conclusions: EHRs features intended for patient use were uncommon across countries and levels of digital maturity. Systems-level research is necessary to identify the country-specific barriers impeding the implementation of EHRs features in primary care, particularly of EHRs features enabling patient interaction with EHRs, to develop strategies to improve systems-wide EHRs use., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Introduction: There is limited epidemiologic evidence on keloids using real-world data, especially in the United States (US) across race and ethnicity., Methods: We conducted a retrospective cohort study using Cerner Real-World Data, between 2015 and 2021, to describe the demographic and clinical characteristics of US adults with keloids. Keloids were identified using a combination of ICD-10 and (Systemized Nomenclature of Medicine-Clinical Terms [SNOMED] codes). Demographics (including race and ethnicity), clinical characteristics, treatment patterns, and healthcare utilization were compared across keloid and non-keloid populations., Results: Among 5,457 keloid patients identified in the study, the majority were female (61.8%) with a mean age of 34.2 years and of non-Hispanic Black, Hispanic, and Asian descent (P < 0.001). Relative to non-keloid cohorts, patients with keloids had significantly higher rates of integumentary, cardiorespiratory, general, auditory, and ocular surgeries and burns (all P < 0.05). Patients with keloids were also more likely to have comorbidities like obesity, hypertension, hyperlipidemia, and diabetes (P < 0.05) when compared to those with no keloids. A large proportion of keloids were untreated; among those treated, the most common keloid treatments were medication therapy (51.5%) and surgical excision (10.6%). Non-Hispanic Black and Hispanic keloid patients were significantly more likely to receive medication therapy and surgical excision (P < 0.001) compared to keloid patients of other races or ethnicities., Conclusions: This study provided real-world insights into the keloid population in the US. Our findings emphasize the high burden of keloids and its substantial impact on ethnic minorities. Given high keloid recurrence rates and limited standardized treatments for keloids, further research into keloids is crucial to the development of keloid-specific therapeutic options., (© 2024 the International Society of Dermatology.)