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In recent years, Electronic health records (EHR) has gradually become the mainstream in the healthcare field. However, due to the fact that EHR systems are provided by different vendors, data is dispersed and stored, which leads to the phenomenon of data silos, making medical information too fragmented and bringing some challenges to current medical services. Therefore, in view of the difficulties in sharing EHR between medical institutions, the risk of privacy leakage, and the lack of EHR usage control by patients, an EHR sharing model based on consortium blockchain is proposed in this paper. Firstly, the Interplanetary File System is combined with consortium blockchain, which forms a hybrid storage scheme of EHR, this technology effectively improves data security, privacy protection, and operational efficiency. Secondly, the model combines unidirectional multi-hop conditional proxy re-encryption based on type and identity with distributed key generation technology to achieve secure EHR sharing with fine grained control. At the same time, users are required to link the operation records of EHR, so as to realize the traceability of EHR usage. A dynamic Byzantine fault-tolerant algorithm based on reputation and clustering is then proposed to solve the problems of arbitrary master node selection, high latency and low throughput of PBFT, enabling the nodes to reach consensus more efficiently. Finally, the model is analyzed in terms of security and user control, showing that the model is less energy intensive in terms of communication overhead and time consumption, and can effectively achieve secure sharing between medical data., Competing Interests: Declarations Competing Interests The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Objective: Occupational and environmental medicine (OEM) clinicians require specialized electronic health records (EHRs) to address the privacy, data governance, interoperability, and medical surveillance concerns that are specific to occupational health., Methods: The American College of Occupational and Environmental Medicine (ACOEM) Section of Health Informatics evaluated clinical workflow concerns, assessed health information requirements, and developed informatics recommendations through iterative consultation with ACOEM members., Results: The ACOEM presents 10 recommendations that specialized occupational EHR systems (OEHRs) should meet to serve the information needs and practice requirements of OEM clinicians. Common challenges in OEM practice and potential informatics solutions are used to illustrate each recommendation., Conclusions: The recommendations serve as a framework for occupational health clinicians to consider in their adoption of OEHRs and provide software engineers a set of requirements to facilitate the development and improvement of OEHRs., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 American College of Occupational and Environmental Medicine.)

Background and Aims: Opioid use disorder (OUD) and opioid dependence lead to significant morbidity and mortality, yet treatment retention, crucial for the effectiveness of medications like buprenorphine-naloxone, remains unpredictable. Our objective was to determine the predictability of 6-month retention in buprenorphine-naloxone treatment using electronic health record (EHR) data from diverse clinical settings and to identify key predictors., Design: This retrospective observational study developed and validated machine learning-based clinical risk prediction models using EHR data., Setting and Cases: Data were sourced from Stanford University's healthcare system and Holmusk's NeuroBlu database, reflecting a wide range of healthcare settings. The study analyzed 1800 Stanford and 7957 NeuroBlu treatment encounters from 2008 to 2023 and from 2003 to 2023, respectively., Measurements: Predict continuous prescription of buprenorphine-naloxone for at least 6 months, without a gap of more than 30 days. The performance of machine learning prediction models was assessed by area under receiver operating characteristic (ROC-AUC) analysis as well as precision, recall and calibration. To further validate our approach's clinical applicability, we conducted two secondary analyses: a time-to-event analysis on a single site to estimate the duration of buprenorphine-naloxone treatment continuity evaluated by the C-index and a comparative evaluation against predictions made by three human clinical experts., Findings: Attrition rates at 6 months were 58% (NeuroBlu) and 61% (Stanford). Prediction models trained and internally validated on NeuroBlu data achieved ROC-AUCs up to 75.8 (95% confidence interval [CI] = 73.6-78.0). Addiction medicine specialists' predictions show a ROC-AUC of 67.8 (95% CI = 50.4-85.2). Time-to-event analysis on Stanford data indicated a median treatment retention time of 65 days, with random survival forest model achieving an average C-index of 65.9. The top predictor of treatment retention identified included the diagnosis of opioid dependence., Conclusions: US patients with opioid use disorder or opioid dependence treated with buprenorphine-naloxone prescriptions appear to have a high (∼60%) treatment attrition by 6 months. Machine learning models trained on diverse electronic health record datasets appear to be able to predict treatment continuity with accuracy comparable to that of clinical experts., (© 2024 The Author(s). Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.)

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.)

Introduction: Asthma exacerbations or 'attacks' can vary in severity from mild worsening of symptoms to life-threatening changes that require urgent hospital care. Understanding these exacerbations is crucial to improving treatment and support for patients. Electronic health records (EHR) using anonymised data from people with asthma in primary and secondary care can be used to understand exacerbations and outcomes. However, previous studies found significant heterogeneity in the algorithms used to define asthma exacerbations. Validating definitions of asthma exacerbations in EHR will lead to more robust and comparable evidence in future research., Methods and Analysis: Medline and Embase will be searched for the key concepts relating to asthma exacerbations, EHR and validation. All studies that validate exacerbations of asthma in EHR and administrative claims databases published before 30 May 2024 and written in English will be considered. Validated algorithms for asthma exacerbations or attacks must be compared against a reference or gold standard definition, and a measure of validity must be included. Articles will be screened for inclusion by two independent reviewers with any disagreements resolved by consensus or arbitration by a third reviewer. Study details will be extracted, and the risk of bias will be assessed using a QUADAS-2 tailored to this review., Ethics & Dissemination: No ethical approval is required as this is a review of previously published literature. Results will be disseminated in a peer-reviewed journal with the aim of being used in future research to help identify asthma exacerbation in EHR., Prospero Registration Number: CRD42024545081., Competing Interests: Competing interests: The author JKQ has been supported by institutional research grants from the Medical Research Council, NIHR, Health Data Research, GSK, BI, AZ, Insmed and Sanofi and received personal fees for advisory board participation, consultancy or speaking fees from GlaxoSmithKline, Chiesi and AstraZeneca., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Multicenter electronic health records can support quality improvement and comparative effectiveness research in stroke. However, limitations of electronic health record-based research include challenges in abstracting key clinical variables, including stroke severity, along with missing data. We developed a natural language processing model that reads electronic health record notes to directly extract the National Institutes of Health Stroke Scale score when documented and predict the score from clinical documentation when missing., Methods and Results: The study included notes from patients with acute stroke (aged ≥18 years) admitted to Massachusetts General Hospital (2015-2022). The Massachusetts General Hospital data were divided into training/holdout test (70%/30%) sets. We developed a 2-stage model to predict the admission National Institutes of Health Stroke Scale, obtained from the GWTG (Get With The Guidelines) stroke registry. We trained a model with the least absolute shrinkage and selection operator. For test notes with documented National Institutes of Health Stroke Scale, scores were extracted using regular expressions (stage 1); when not documented, least absolute shrinkage and selection operator was used for prediction (stage 2). The 2-stage model was tested on the holdout test set and validated in the Medical Information Mart for Intensive Care (2001-2012) version 1.4, using root mean squared error and Spearman correlation. We included 4163 patients (Massachusetts General Hospital, 3876; Medical Information Mart for Intensive Care, 287); average age, 69 (SD, 15) years; 53% men, and 72% White individuals. The model achieved a root mean squared error of 2.89 (95% CI, 2.62-3.19) and Spearman correlation of 0.92 (95% CI, 0.91-0.93) in the Massachusetts General Hospital test set, and 2.20 (95% CI, 1.69-2.66) and 0.96 (95% CI, 0.94-0.97) in the MIMIC validation set, respectively., Conclusions: The automatic natural language processing-based model can enable large-scale stroke severity phenotyping from the electronic health record and support real-world quality improvement and comparative effectiveness studies in stroke.

Introduction: A nationwide pay-for-performance (P4P) scheme was introduced in the Netherlands between 2018 and 2023 to incentivize appropriate prescribing in general practice. Appropriate prescribing was operationalised as adherence to prescription formularies and measured based on electronic health records (EHR) data. We evaluated this P4P scheme from a learning health systems perspective., Methods: We conducted semi-structured interviews with 15 participants representing stakeholders of the scheme: general practitioners (GPs), health insurers, pharmacists, EHR suppliers and formulary committees. We used a thematic approach for data analysis., Results: Using EHR data showed several benefits, but lack of uniformity of EHR systems hindered consistent measurements. Specific indicators were favoured over general indicators as they allow GPs to have more control over their performance. Most participants emphasized the need for GPs to jointly reflect on their performance. Communication to GPs appeared to be challenging. Partly because of these challenges, impact of the scheme on prescribing behaviour was perceived as limited. However, several unexpected positive effects of the scheme were mentioned, such as better EHR recording habits., Conclusions: This study identified benefits and challenges useful for future P4P schemes in promoting appropriate care with EHR data. Enhancing uniformity in EHR systems is crucial for more consistent quality measurements. Future P4P schemes should focus on high-quality feedback, peer-to-peer learning and establish a single point of communication for healthcare providers., Competing Interests: Declaration of competing interest None of the authors had a conflict of interest., (Copyright © 2024. Published by Elsevier B.V.)

Objectives: Well-designed electronic health records (EHRs) training programs for clinical practice are known to be valuable. Training programs should be role-specific and there is a need to identify key implementation factors of EHR training programs for nurses. This scoping review (1) characterizes the EHR training programs used and (2) identifies their implementation facilitators and barriers., Materials and Methods: We searched MEDLINE, CINAHL, PsycINFO, and Web of Science on September 3, 2023, for peer-reviewed articles that described EHR training program implementation or delivery to nurses in inpatient settings without any date restrictions. We mapped implementation factors to the Consolidated Framework for Implementation Research. Additional themes were inductively identified by reviewing these findings., Results: This review included 30 articles. Healthcare systems' approaches to implementing and delivering EHR training programs were highly varied. For implementation factors, we observed themes in innovation (eg, ability to practice EHR skills after training is over, personalizing training, training pace), inner setting (eg, availability of computers, clear documentation requirements and expectations), individual (eg, computer literacy, learning preferences), and implementation process (eg, trainers and support staff hold nursing backgrounds, establishing process for dissemination of EHR updates). No themes in the outer setting were observed., Discussion: We found that multilevel factors can influence the implementation and delivery of EHR training programs for inpatient nurses. Several areas for future research were identified, such as evaluating nurse preceptorship models and developing training programs for ongoing EHR training (eg, in response to new EHR workflows or features)., Conclusions: This scoping review highlighted numerous factors pertaining to training interventions, healthcare systems, and implementation approaches. Meanwhile, it is unclear how external factors outside of a healthcare system influence EHR training programs. Additional studies are needed that focus on EHR retraining programs, comparing outcomes of different training models, and how to effectively disseminate updates with the EHR to nurses., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: Leveraging "big data" to improve care requires that clinical concepts be operationalized using available data. Electronic health record (EHR) data can be used to evaluate asthma care, but relying solely on diagnosis codes may misclassify asthma-related encounters. We created streamlined, feasible and transparent prototype algorithms for EHR data to classify emergency department (ED) encounters and hospitalizations as "asthma-related.", Methods: As part of an asthma program evaluation, expert clinicians conducted a multi-phase iterative chart review to evaluate 467 pediatric ED encounters and 136 hospitalizations with asthma diagnosis codes from calendar years 2017 and 2019, rating the likelihood that each encounter was actually asthma-related. Using this as a reference standard, we developed rule-based algorithms for EHR data to classify visits. Accuracy was evaluated using sensitivity, specificity, and positive and negative predictive values (PPV, NPV)., Results: Clinicians categorized 38% of ED encounters as "definitely" or "probably" asthma-related; 13% as "possibly" asthma-related; and 49% as "probably not" or "definitely not" related to asthma. Based on this reference standard, we created two rule-based algorithms to identify "definitely" or "probably" asthma-related encounters, one using text and non-text EHR fields and another using non-text fields only. Sensitivity, specificity, PPV, and NPV were >95% for the algorithm using text and non-text fields and >87% for the algorithm using only non-text fields compared to the reference standard. We created a two-rule algorithm to identify asthma-related hospitalizations using only non-text fields., Conclusions: Diagnostic codes alone are insufficient to identify asthma-related visits, but EHR-based prototype algorithms that include additional methods of identification can predict clinician-identified visits with sufficient accuracy., 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 Academic Pediatric Association. Published by Elsevier Inc. All rights reserved.)

Currently, the development of deep learning-based multimodal learning is advancing rapidly, and is widely used in the field of artificial intelligence-generated content, such as image-text conversion and image-text generation. Electronic health records are digital information such as numbers, charts, and texts generated by medical staff using information systems in the process of medical activities. The multimodal fusion method of electronic health records based on deep learning can assist medical staff in the medical field to comprehensively analyze a large number of medical multimodal data generated in the process of diagnosis and treatment, thereby achieving accurate diagnosis and timely intervention for patients. In this article, we firstly introduce the methods and development trends of deep learning-based multimodal data fusion. Secondly, we summarize and compare the fusion of structured electronic medical records with other medical data such as images and texts, focusing on the clinical application types, sample sizes, and the fusion methods involved in the research. Through the analysis and summary of the literature, the deep learning methods for fusion of different medical modal data are as follows: first, selecting the appropriate pre-trained model according to the data modality for feature representation and post-fusion, and secondly, fusing based on the attention mechanism. Lastly, the difficulties encountered in multimodal medical data fusion and its developmental directions, including modeling methods, evaluation and application of models, are discussed. Through this review article, we expect to provide reference information for the establishment of models that can comprehensively utilize various modal medical data.

Background: Many hospitalised patients require rehabilitation during recovery from acute illness. We use routine data from Electronic Health Records (EHR) to report the quantity and intensity of rehabilitation required to achieve hospital discharge, comparing patients with and without COVID-19., Methods: We performed a retrospective cohort study of consecutive adults in whom COVID-19 testing was undertaken between March 2020 and August 2021 across three acute hospitals in Scotland. We defined rehabilitation contacts (physiotherapy, occupational therapy, dietetics and speech and language therapy) from timestamped EHR data and determined contact time from a linked workforce planning dataset. Our aim was to clarify rehabilitation required to achieve hospital discharge and so we excluded patients who died during their admission, and those who did not require rehabilitation (fewer than two specialist contacts). The primary outcome was total rehabilitation time. Secondary outcomes included the number of contacts, admission to first contact, and rehabilitation minutes per day. A multivariate regression analysis for identifying patient characteristics associated with rehabilitation time included age, sex, comorbidities, and socioeconomic status., Results: We included 11,591 consecutive unique patient admissions (76 [63,85] years old, 56% female), of which 651 (6%) were with COVID-19, and 10,940 (94%) were admissions with negative testing. There were 128,646 rehabilitation contacts. Patients with COVID-19 received more than double the rehabilitation time compared to those without (365 [165, 772] vs 170 [95, 350] mins, p<0.001), and this was delivered over more specialist contacts (12 [6, 25] vs 6 [3, 11], p<0.001). Admission to first rehabilitation contact was later in patients with COVID-19 (3 [1, 5] vs 2 [1, 4] days from admission). Overall, patients with COVID-19 received fewer minutes of rehabilitation per day of admission (14.1 [9.8, 18.7] vs 15.6 [10.6, 21.3], p<0.001). In our regression analyses, older age and COVID-19 were associated with increased rehabilitation time., Conclusions: Patients with COVID received more rehabilitation contact time than those without COVID, but this was delivered less intensively and was commenced later in an admission. Rehabilitation data derived from the EHR represents a novel measure of delivered hospital care., (© 2024. The Author(s).)

Background: Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes., Objective: This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes., Methods: We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains., Results: A substantial proportion of studies (>85%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited., Conclusions: This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support., (©Abolfazl Mollalo, Bashir Hamidi, Leslie A Lenert, Alexander V Alekseyenko. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 15.10.2024.)

Background: As digital healthcare services handle increasingly more sensitive health data, robust access control methods are required. Especially in emergency conditions, where the patient's health situation is in peril, different healthcare providers associated with critical cases may need to be granted permission to acquire access to Electronic Health Records (EHRs) of patients. The research objective of this work is to develop a proactive access control method that can grant emergency clinicians access to sensitive health data, guaranteeing the integrity and security of the data, and generating trust without the need for a trusted third party., Methods: A contextual and blockchain-based mechanism is proposed that allows access to sensitive EHRs by applying prognostic procedures where information based on context, is utilized to identify critical situations and grant access to medical data. Specifically, to enable proactivity, Long Short Term Memory (LSTM) Neural Networks (NNs) are applied that utilize patient's recent health history to prognose the next two-hour health metrics values. Fuzzy logic is used to evaluate the severity of the patient's health state. These techniques are incorporated in a private and permissioned Hyperledger-Fabric blockchain network, capable of securing patient's sensitive information in the blockchain network., Results: The developed access control method provides secure access for emergency clinicians to sensitive information and simultaneously safeguards the patient's well-being. Integrating this predictive mechanism within the blockchain network proved to be a robust tool to enhance the performance of the access control mechanism. Furthermore, the blockchain network of this work can record the history of who and when had access to a specific patient's sensitive EHRs, guaranteeing the integrity and security of the data, as well as recording the latency of this mechanism, where three different access control cases are evaluated. This access control mechanism is to be enforced in a real-life scenario in hospitals., Conclusions: The proposed mechanism informs proactively the emergency team of professional clinicians about patients' critical situations by combining fuzzy and predictive machine learning techniques incorporated in the private and permissioned blockchain network, and it exploits the distributed data of the blockchain architecture, guaranteeing the integrity and security of the data, and thus, enhancing the users' trust to the access control mechanism., (© 2024. The Author(s).)

Background: Paroxysmal Nocturnal Haemoglobinuria (PNH) is an ultra-rare, acquired disorder that is challenging to diagnose due to varied symptoms, heterogeneous patient presentations, and lack of awareness among healthcare professionals. This leads to frequent misdiagnosis and delays in diagnosis. This study evaluated the feasibility of a machine learning model to identify undiagnosed PNH patients using structured electronic health records., Methods: The study used data from the Optimum Patient Care Research Database, which contains electronic health records from general practitioner (GP) practices across the United Kingdom. PNH patients were identified by the presence, and control patients by the absence of a PNH diagnosis code in their records. Clinical features (symptoms, diagnoses, healthcare utilisation) from 131 patients in the PNH group and 593,838 patients in the control group, were inputted to a tree-based XGBoost machine learning model to classify patients as either "positive" or "negative" for PNH suspicion. The algorithm was finalised after additional exclusions and inclusions applied. Performance was assessed using positive predictive value (PPV), recall and specificity. As the sample used to develop the algorithm was not representative of the true population prevalence, PPV was additionally adjusted to reflect performance in the wider population., Results: Of all the patients in the PNH group, 27% were classified as positive (recall). 99.99% of the control group were classified as negative (specificity). Of all the patients classified as positive, 60.4% had a diagnosis of PNH in their record (PPV). The PPV adjusted for the population prevalence of PNH was 19.59 suggesting nearly 1 in 5 patients flagged may warrant further PNH investigation. The key clinical features in the model were aplastic anaemia, pancytopenia, haemolytic anaemia, myelodysplastic syndrome, and Budd-Chiari syndrome., Conclusion: This is the first study to combine clinical understanding of PNH with machine learning, demonstrating the ability to discriminate between PNH and control patients in retrospective electronic health records. With further investigation and validation, this algorithm could be deployed on live health data, potentially leading to earlier diagnosis for patients who currently experience long diagnostic delays or remain undiagnosed., (© 2024. The Author(s).)

The use of data derived from electronic health records (EHRs) to describe racial and ethnic health disparities is increasingly common, but there are challenges. While the number of patients covered by EHRs can be quite large, such patients may not be representative of a source population. One way to evaluate the extent of this limitation is by linking EHRs to an external source, in this case with the American Community Survey (ACS). Relying on a stratified random sample of about 200,000 patient records from a large, public, integrated health delivery system in North Carolina (2016-2019), we assess linkages to restricted ACS microdata (2001-2017) by race and ethnicity to understand the strengths and weaknesses of EHR-derived data for describing disparities. The results in this research note suggest that Black-White comparisons will benefit from standard adjustments (e.g., weighting procedures) but that misestimation of health disparities may arise for Hispanic patients because of differential coverage rates for this group., (Copyright © 2024 The Authors.)

Background: Clinical trials and registry studies are essential for advancing research and developing novel treatments. However, these studies rely on manual entry of thousands of variables for each patient. Repurposing real-world data can significantly simplify the data collection, reduce transcription errors, and make the data entry process more efficient, consistent, and cost-effective., Methods: We developed an open-source computational pipeline to collect laboratory and medication information from the electronic health record (EHR) data and populate case report forms. The pipeline was developed and validated with data from two independent pediatric hospitals in the US as part of the Long-terM OUtcomes after Multisystem Inflammatory Syndrome In Children (MUSIC) study. Our pipeline allowed the completion of two of the most time-consuming forms. We compared automatically extracted results with manually entered values in one hospital and applied the pipeline to a second hospital, where the output served as the primary data source for case report forms., Findings: We extracted and populated 51,845 laboratory and 4913 medication values for 159 patients in two hospitals participating in a prospective pediatric study. We evaluated pipeline performance against data for 104 patients manually entered by clinicians in one of the hospitals. The highest concordance was found during patient hospitalization, with 91.59% of the automatically extracted laboratory and medication values corresponding with the manually entered values. In addition to the successfully populated values, we identified an additional 13,396 laboratory and 567 medication values of interest for the study., Interpretation: The automatic data entry of laboratory and medication values during admission is feasible and has a high concordance with the manually entered data. By implementing this proof of concept, we demonstrate the quality of automatic data extraction and highlight the potential of secondary use of EHR data to advance medical science by improving data entry efficiency and expediting clinical research., Funding: NIH Grant 1OT3HL147154-01, U24HL135691, UG1HL135685., Competing Interests: Declaration of interests The authors declare no competing interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

The use of clinical prediction models to produce individualized risk estimates can facilitate the implementation of precision psychiatry. As a source of data from large, clinically representative patient samples, electronic health records (EHRs) provide a platform to develop and validate clinical prediction models, as well as potentially implement them in routine clinical care. The current review describes promising use cases for the application of precision psychiatry to EHR data and considers their performance in terms of discrimination (ability to separate individuals with and without the outcome) and calibration (extent to which predicted risk estimates correspond to observed outcomes), as well as their potential clinical utility (weighing benefits and costs associated with the model compared to different approaches across different assumptions of the number needed to test). We review 4 externally validated clinical prediction models designed to predict psychosis onset, psychotic relapse, cardiometabolic morbidity, and suicide risk. We then discuss the prospects for clinically implementing these models and the potential added value of integrating data from evidence syntheses, standardized psychometric assessments, and biological data into EHRs. Clinical prediction models can utilize routinely collected EHR data in an innovative way, representing a unique opportunity to inform real-world clinical decision making. Combining data from other sources (e.g., meta-analyses) or enhancing EHR data with information from research studies (clinical and biomarker data) may enhance our abilities to improve the performance of clinical prediction models., (Copyright © 2024 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

Objectives: To assess the feasibility of assessing long-term outcomes of peripheral vascular intervention (PVI) by linking data from a clinical registry to electronic health records (EHR) data from a clinical research network., Design: Observational cohort study., Setting: Vascular Quality Initiative registry linked to INSIGHT Clinical Research Network, which aggregated EHR data from multiple institutions in New York City., Participants: Patients receiving PVI during 1 January 2013-30 November 2021 in four centres in New York City., Primary and Secondary Outcome Measures: We examined the proportion of registry patients retained in EHR over time and predictors of EHR retention after year 1. We evaluated the implications of EHR attrition by examining amputation-free survival (AFS) in the observed data and predicted data when patients discontinued in the EHR were hypothesised to have increased risks of events than the observed average., Results: We included 1405 patients receiving PVI (age=70.8±11.2 years, 51.3% male). Among eligible patients, 75.2% were retained in EHR through year 3. Patients who aged 75 years or above (vs <65: OR 0.34, 95% CI 0.18 to 0.62), had Medicaid (vs Medicare: OR 0.41, 95% CI 0.22 to 0.79), congestive heart failure (OR 0.54, 95% CI 0.32 to 0.90), dialysis (OR 0.47, 95% CI 0.24 to 0.91) and reduced ambulation (OR 0.34, 95% CI 0.15 to 0.75) were less likely to be retained in EHR. When discontinued patients were hypothesised to have increased risks of death or amputation than observed, AFS estimates diverged from the observed data around 6-12 months., Conclusions: Studies using registry-EHR data may benefit from the timeliness of the data but may be most appropriate to focus on short-term to intermediate-term outcomes of interventions and devices. Future research is needed to investigate the value of registry-EHR linkage in facilitating short-term to intermediate-term outcome assessment following vascular interventions and advanced statistical approaches to account for non-random missing long-term data., Competing Interests: Competing interests: KGS reports unrestricted research grants from Philips, Merck, Shockwave and Johnson & Johnson; she is a consultant for Optum Labs, Cook, Tegus, Twill and Abbott Vascular. CM-H reports unrestricted research grants from Abbott Vascular, Philips and Shockwave and is a consultant for Cook and Penumbra. The other authors report no competing interests., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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.)

Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ML prediction models includes population-based registers and electronic health records. These can contain rich information on individual and familial medical histories and socio-demographics. This review summarizes studies published between 2010-2022 that used ML algorithms to develop predictive models for NDDs using population-based registers and electronic health records. A literature search identified 1191 articles, of which 32 were retained. Of these, 47% developed ASD prediction models and 25% ADHD models. Classical ML methods were used in 82% of studies and in particular tree-based prediction models performed well. The sensitivity of the models was lower than 75% for most studies, while the area under the curve (AUC) was greater than 75%. The most important predictors were patient and familial medical history and sociodemographic factors. Using private in-house datasets makes comparing and validating model generalizability across studies difficult. The ML model development and reporting guidelines were adopted only in a few recently reported studies. More work is needed to harness the power of data for detecting NDDs early., Competing Interests: Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that there are no competing interests., (© 2024. The Author(s).)

Background: While PTSD is commonly associated with multiple comorbidities, studies have yet to quantify the impact of these comorbidities on key clinical outcomes and HCRU. This study explored risks of emergency room (ER) visits, inpatient admissions (IA), suicidal ideation (SI), and treatment follow-up duration (FU), amongst PTSD patients with comorbid MDD and/or SUD., Methods: Using real-world data (RWD) generated by electronic health records accessed from the NeuroBlu database, a cohort of adolescent patients (12-17 yrs) was examined over a one-year study period following PTSD diagnosis., Results: 5794 patients were included in the cohort. Compared to patients with only PTSD (n = 3061), those with comorbid MDD (n = 1820) had greater odds of ER (4.5 times), IA (1.6 times), and FU (4.3 times). Those with comorbid SUD (n = 653) had greater odds of IA (4.5 times), shorter FU (34 days), and lower odds of ER (0.5 times). Both comorbidities (n = 260) had greater odds of ER (3.8 times), IA (2.6 times), SI (3.6 times), and shorter FU (12 days)., Limitations: These RWD had a high proportion of missingness. Health records of patients who changed service providers could not be accounted for in this study., Conclusions: Both MDD and SUD substantially elevated the risk of HCRU and suicidal ideation for PTSD patients., Competing Interests: Declaration of competing interest A. John Rush has received consulting fees from Compass Inc., Curbstone Consultant LLC, Emmes Corp., Evecxia Therapeutics, Inc., Holmusk Technologies, Inc., ICON, PLC, Johnson and Johnson (Janssen), Liva-Nova, MindStreet, Inc., Neurocrine Biosciences Inc., Otsuka-US;speaking fees from Liva-Nova, Johnson and Johnson (Janssen); and royalties from Wolters Kluwer Health, Guilford Press and the University of Texas Southwestern Medical Center, Dallas, TX (for the Inventory of Depressive Symptoms and its derivatives). He is also named co- inventor on two patents: U.S. Patent No. 7,795,033: Methods to Predict the Outcome of Treatment with Antidepressant Medication, Inventors: McMahon FJ, Laje G, Manji H, Rush AJ, Paddock S, Wilson AS; and U.S. Patent No. 7,906,283: Methods to Identify Patients at Risk of Developing Adverse Events During Treatment with Antidepressant Medication, Inventors:McMahon FJ, Laje G, Manji H, Rush AJ, Paddock S. Kelly Chan, Li Tong Low, Joshua Wong, and Sherwin Kuah are employees of Holmusk Technologies, Inc. when the research was undertaken., (Copyright © 2024. Published by Elsevier B.V.)

Databases that link molecular data to clinical outcomes can inform precision cancer research into novel prognostic and predictive biomarkers. However, outside of clinical trials, cancer outcomes are typically recorded only in text form within electronic health records (EHRs). Artificial intelligence (AI) models have been trained to extract outcomes from individual EHRs. However, patient privacy restrictions have historically precluded dissemination of these models beyond the centers at which they were trained. In this study, the vulnerability of text classification models trained directly on protected health information to membership inference attacks is confirmed. A teacher-student distillation approach is applied to develop shareable models for annotating outcomes from imaging reports and medical oncologist notes. 'Teacher' models trained on EHR data from Dana-Farber Cancer Institute (DFCI) are used to label imaging reports and discharge summaries from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset. 'Student' models are trained to use these MIMIC documents to predict the labels assigned by teacher models and sent to Memorial Sloan Kettering (MSK) for evaluation. The student models exhibit high discrimination across outcomes in both the DFCI and MSK test sets. Leveraging private labeling of public datasets to distill publishable clinical AI models from academic centers could facilitate deployment of machine learning to accelerate precision oncology research., Competing Interests: Competing interests here are no patents related to this research. Dr. Kehl reports funding from the American Association for Cancer Research to his institution related to this research and honoraria from UpToDate and travel sponsored by Meta in the context of a grant submission process unrelated to this research. Dr. Choueiri reports institutional and/or personal, paid and/or unpaid support for research, advisory boards, consultancy, and/or honoraria past 5 years, ongoing or not, from: Alkermes, Arcus Bio, AstraZeneca, Aravive, Aveo, Bayer, Bristol Myers-Squibb, Bicycle Therapeutics, Calithera, Circle Pharma, Deciphera Pharmaceuticals, Eisai, EMD Serono, Exelixis, GlaxoSmithKline, Gilead, HiberCell, IQVA, Infinity, Institut Servier, Ipsen, Jansen, Kanaph, Lilly, Merck, Nikang, Neomorph, Nuscan/PrecedeBio, Novartis, Oncohost, Pfizer, Roche, Sanofi/Aventis, Scholar Rock, Surface Oncology, Takeda, Tempest, Up-To-Date, CME events (Peerview, OncLive, MJH, CCO and others), outside the submitted work. He also reports institutional patents filed on molecular alterations and immunotherapy response/toxicity, and ctDNA. He reports equity in Tempest, Pionyr, Osel, Precede Bio, CureResponse, InnDura Therapeutics, Premium, and Bicycle; committee participation in NCCN, GU Steering Committee, ASCO (BOD 6-2024-, ESMO, ACCRU, KidneyCan). He reports that medical writing and editorial assistance support may have been funded by Communications companies in part. He reports that he has mentored several non-US citizens on research projects with potential funding (in part) from non-US sources/Foreign Components. His institution (Dana-Farber Cancer Institute) may have received additional independent funding of drug companies or/and royalties potentially involved in research around the subject matter. Dr. Bakouny reports Honoraria from UpToDate; serving as Associate Editor at Journal of Clinical Oncology Clinical Cancer Informatics (JCO CCI); serving as co-chair of the American Society of Clinical Oncology’s International Medical Graduate Community of Practice (ASCO IMG CoP); and serving as co-founder of the IMG Oncologists nonprofit non-governmental organization. Dr. Schrag reports funding from AACR to her institution related to this research. Ms. Nichols reports funding from AACR to her institution related to this research. The other authors have no competing interests to disclose., (© 2024. The Author(s).)

Mining of electronic health records (EHR) promises to automate the identification of comprehensive disease phenotypes. However, the realization of this promise is hindered by the unavailability of generalizable ground-truth information, data incompleteness and heterogeneity, and the lack of generalization to multiple cohorts. We present here a data-driven approach to identify clinical states that we implement for 585 critical care patients with suspected pneumonia recruited by the SCRIPT study, which we compare to and integrate with 9,918 pneumonia patients from the MIMIC-IV dataset. We extract and curate from their structured EHRs a primary set of clinical features (53 and 59 features for SCRIPT and MIMIC-IV, respectively), including disease severity scores, vital signs, and so on, at various degrees of completeness. We aggregate irregular time series into daily frequency, resulting in 12,495 and 94,684 patient-day pairs for SCRIPT and MIMIC, respectively. We define a "common-sense" ground truth that we then use in a semisupervised pipeline to optimize choices for data preprocessing, and reduce the feature space to four principal components. We describe and validate an ensemble-based clustering method that enables us to robustly identify five clinical states, and use a Gaussian mixture model to quantify uncertainty in cluster assignment. Demonstrating the clinical relevance of the identified states, we find that three states are strongly associated with disease outcomes (dying vs. recovering), while the other two reflect disease etiology. The outcome associated clinical states provide significantly increased discrimination of mortality rates over standard approaches., Competing Interests: Competing interests statement:The authors declare no competing interest.

Purpose: Previous research and pharmacovigilance monitoring activities have suggested a potential association between indapamide use and rhabdomyolysis. This study aims to investigate the potential causal relationship between the use of indapamide and rhabdomyolysis., Methods: A case-control study conducted using electronic health records data, between July 1, 2016 and December 31, 2022. Patients who have rhabdomyolysis event (cases) were matched to four controls bases on age, gender, and date. We examined the odds for indapamide exposure through three risk periods: current use, recent use, and former. The study outcome was ascertained through the presence of CK level over 1000 U/L (i.e., rhabdomyolysis event). Subsequently, a multivariable conditional logistic regression analysis was utilized to assess the causal association of indapamide exposure on the likelihood of developing rhabdomyolysis, while accounting for potential confounding variables., Results: The study population consisted of 2965 cases and 11 860 controls. The results of the conditional logistic regression analysis indicated a lack of association between exposure to indapamide for the current users with an odds ratio (OR) of 0.6 (95% confidence intervals (CI): 0.39-1.05). The odds of recent indapamide use among cases was lower than controls (OR 0.2; 95% CI: 0.14-0.47). Lastly, the OR for former use of indapamide was 0.1 (95% CI: 0.07-0.23)., Conclusions: In this study, we did not find association between indapamide use and rhabdomyolysis regardless timing of exposure., (© 2024 John Wiley & Sons Ltd.)

Background: Heart failure is common, complex, and often associated with coexisting chronic medical conditions and a high mortality. We aimed to assess the epidemiology of people admitted to hospital with heart failure with reduced ejection fraction (HFrEF) and heart failure with preserved ejection fraction (HFpEF), including the period covering the COVID-19 pandemic, which was previously not well characterised., Methods: In this retrospective, cohort study, we used whole-population electronic health records with 57 million individuals in England to identify patients hospitalised with heart failure as the primary diagnosis in any consultant episode of an in-patient admission to a National Health Service (NHS) hospital. We excluded individuals with less than 1 year of medical history records in primary or secondary care; admissions to NHS hospitals for which less than 10% of heart failure cases were linkable to the National Heart Failure Audit (NHFA); individuals younger than 18 years at the time of the heart failure hospitalisation; and patients who died in hospital during the index heart failure admission. For patients with new onset heart failure, we assessed incidence rates of 30-day and 1-year all-cause and cause-specific (cardiovascular, non-cardiovascular, and heart failure-related) emergency rehospitalisation and mortality after discharge, and dispensed guideline-recommended medical therapy (GRMT). Follow-up occurred from the index admission to the earliest occurrence of the event of interest, death, or end of data coverage. We estimated adjusted hazard ratios (HRs) to compare HFrEF with HFpEF. We computed population-attributable fractions to quantify the percentage of outcomes attributable to coexisting chronic medical conditions., Findings: Among 233 320 patients identified who survived the index heart failure admission across 335 NHS hospitals between Jan 1, 2019, and Dec 31, 2022, 101 320 (43·4%) had HFrEF, 71 910 (30·8%) had HFpEF, and 60 090 (25·8%) had an unknown classification. In patients with new onset heart failure, there were reductions in all-cause 30-day (-5·2% [95% CI -7·7 to -2·6] in 2019-22) and 1-year rehospitalisation rates (-3·9% [-6·6 to -1·2]). Declining 30-day rehospitalisation rates affected patients with HFpEF (-4·8% [-9·2 to -0·2]) and HFrEF (-6·2% [-10·5 to -1·6]), although 1-year rates were not statistically significant for patients with HFpEF (-2·2% [-6·6 to 2·3] vs -5·7% [-10·6 to -0·5] for HFrEF). There were no temporal trends in incidence rates of 30-day or 1-year mortality after discharge. The rates of all-cause (HR 1·20 [1·18-1·22]) and cause-specific rehospitalisation were uniformly higher in those with HFpEF than those with HFrEF. Patients with HFpEF also had higher rates of 1-year all-cause mortality after discharge (HR 1·07 [1·05-1·09]), driven by excess risk of non-cardiovascular death (HR 1·25 [1·21-1·29]). Rates of rehospitalisation and mortality were highest in patients with coexisting chronic kidney disease, chronic obstructive pulmonary disease, dementia, and liver disease. Chronic kidney disease contributed to 6·5% (5·6-7·4) of rehospitalisations within 1 year for HFrEF and 5·0% (4·1-5·9) of rehospitalisations for HFpEF, double that of any other coexisting condition. There was swift implementation of newer GRMT, but markedly lower dispensing of these medications in patients with coexisting chronic kidney disease., Interpretation: Rates of rehospitalisation in patients with heart failure in England have decreased during 2019-22. Further population health improvements could be reached through enhanced implementation of GRMT, particularly in patients with coexisting chronic kidney disease, who, despite being at high risk, remain undertreated., Funding: Wellcome Trust, Health Data Research UK, British Heart Foundation Data Science Centre., Competing Interests: Declaration of interests RAF received studentship awards from the Health Data Research UK-The Alan Turing Institute Wellcome Trust PhD Programme in Health Data Science (grant 218529/Z/19/Z). PR received a grant from the Dr Johannes and Hertha Tuba Foundation. BLN reports fees for travel support, advisory boards, scientific presentations, and steering committee roles from AstraZeneca, Alexion, Bayer, Boehringer Ingelheim, Cambridge Healthcare Research, Cornerstone Medical Education, Janssen, Limbic, Medscape, Novo Nordisk, and Travere Therapeutics with all honoraria paid to the George Institute for Global Health. CT received a studentship from the University College London UK Research and Innovation Centre for doctoral training in AI-enabled healthcare (EP/S021612/1), Medical Research Council Clinical Top-Up, and a studentship from the National Institute for Health and Care Research (NIHR) Biomedical Research Centre at University College London Hospital NHS Trust. NC received a personal fellowship from the Research Foundation Flanders and a research grant from the European Society of Cardiology. ER is funded by Forte Swedish Research Council for Health, Working Life And Welfare (2022–00882) individual postdoctoral fellowship, and Vetenskapsrådet Swedish Research Council (grant 2023–01982). SD received research funding from GlaxoSmithKline, AstraZeneca, Bayer, and BenevolentAI. CS is a director of the British Heart Foundation Data Science Centre and a chief scientist and deputy director at Health Data Research UK; has codeveloped National Health Service (NHS) England Secure Data Environment; and leads the CVD-COVID UK/COVID-IMPACT Consortium. SEP has a leadership role for the European Association of Cardiovascular Imaging, received consulting fees from Circle Cardiovascular Imaging, and holds an advisory role for the PROTEUS trial (NCT05028179). GMC served on the board of directors of Satellite Healthcare; served as chair or cochair of trial steering committees for Akebia, AstraZeneca, CSL Behring, Sanifit, and Vertex; served as an advisor for Applaud, CloudCath, Durect, Eliaz Therapeutics, Miromatrix, Outset, Physiowave, Renibus, and Unicycive; and served on data safety monitoring boards for Bayer, Mineralys, and ReCor. KK acted as a consultant or speaker and receiving grants for investigator-initiated studies from AstraZeneca, Abbott, Amgen, Bayer, Daiichi-Sankyo, Embecta, Nestle Health Science, Novartis, Novo Nordisk, Roche, Servier, Sanofi-Aventis, Lilly, MSD, Boehringer Ingelheim, Oramed Pharmaceuticals, and Applied Therapeutics; and was a chair of the scientific advisory group for Emergencies Ethnicity Subgroup. JS has direct or indirect stock ownership in Anagram Kommunikation, Sence Research, Symptoms Europe, and MinForskning; and professional services to Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Eli Lilly, Gilead, GSK, Göteborg University, Itrim, Ipsen, Janssen, Karolinska Institutet, LIF, Linköping University, Novo Nordisk, Parexel, Pfizer, Region Stockholm, Region Uppsala, Sanofi, STRAMA, Takeda, TLV, Uppsala University, Vifor Pharma, and WeMind. CA received grants from the National Health Medical Research Council (Medical Research Futures Fund) and NSW Health; and honoraria from AstraZeneca, Novo Nordisk, and Amgen. JGFC reports receipt of personal honoraria for lectures and advisory boards from Pharmacosmos, Vifor, AstraZeneca, Amgen, Bayer, Novartis, and Servier; has received research grants through The University of Glasgow from Pharmacosmos and Vifor; and received funding from the British Heart Foundation Center of Research Excellence (RE/18/6134217). JD reports grants, personal fees, and non-financial support from MSD and Novartis; grants from Pfizer and AstraZeneca; and is part of the International Cardiovascular and Metabolic Advisory Board for Novartis, Steering Committee of UK Biobank, MRC International Advisory Group, MRC High Throughput Science Omics Panel, Scientific Advisory Committee for Sanofi, International Cardiovascular and Metabolism Research and Development Portfolio Committee for Novartis, AstraZeneca Genomics Advisory Board, Scientific Advisory Board of Nightingale Health, Access Board of Our Future Health, and Scientific Advisory Committee of Leducq Foundation. JJVM received payments through Glasgow University for work on clinical trials, consulting, and other activities from Amgen, AstraZeneca, Bayer, Cardurion, Cytokinetics, GSK, KBP Biosciences, and Novartis; personal consultancy fees from Alnylam Pharma, Bayer, BMS, George Clinical, Ionis Pharma, Novartis, Regeneron Pharma, and River 2 Renal; personal lecture fees from Abbott, Alkem Metabolics, AstraZeneca, Blue Ocean Scientific Solutions, Boehringer Ingelheim, Canadian Medical and Surgical Knowledge, Emcure Pharma, Eris Lifesciences, European Academy of CME, Hikma Pharmaceuticals, Imagica Health, Intas Pharma, JB Pharma, Lupin Pharma, Medscape (Heart.org), ProAdWise Communications, Radcliffe Cardiology, Sun Pharma, The Corpus, Translation Research Group, and Translational Medicine Academy; and is a director of Global Clinical Trial Partners. MV received research grant support, served on advisory boards, or had speaker engagements with American Regent, Amgen, AstraZeneca, Bayer, Baxter, Boehringer Ingelheim, Chiesi, Cytokinetics, Lexicon Pharmaceuticals, Merck, Novartis, Novo Nordisk, Pharmacosmos, Relypsa, Roche Diagnostics, Sanofi, and Tricog Health; and participates on clinical trial committees for AstraZeneca, Galmed, Novartis, Bayer, Occlutech, and Impulse Dynamics. AMW received funding from the British Heart Foundation Data Science Centre (HDRUK2023.0239) and NIHR (NIHR303137). All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: Clinical diagnoses are typically made by following a series of steps recommended by guidelines that are authored by colleges of experts. Accordingly, guidelines play a crucial role in rationalizing clinical decisions. However, they suffer from limitations, as they are designed to cover the majority of the population and often fail to account for patients with uncommon conditions. Moreover, their updates are long and expensive, making them unsuitable for emerging diseases and new medical practices., Methods: Inspired by guidelines, we formulate the task of diagnosis as a sequential decision-making problem and study the use of Deep Reinforcement Learning (DRL) algorithms to learn the optimal sequence of actions to perform in order to obtain a correct diagnosis from Electronic Health Records (EHRs), which we name a diagnostic decision pathway. We apply DRL to synthetic yet realistic EHRs and develop two clinical use cases: Anemia diagnosis, where the decision pathways follow a decision tree schema, and Systemic Lupus Erythematosus (SLE) diagnosis, which follows a weighted criteria score. We particularly evaluate the robustness of our approaches to noise and missing data, as these frequently occur in EHRs., Results: In both use cases, even with imperfect data, our best DRL algorithms exhibit competitive performance compared to traditional classifiers, with the added advantage of progressively generating a pathway to the suggested diagnosis, which can both guide and explain the decision-making process., Conclusion: DRL offers the opportunity to learn personalized decision pathways for diagnosis. Our two use cases illustrate the advantages of this approach: they generate step-by-step pathways that are explainable, and their performance is competitive when compared to state-of-the-art methods., 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: According to the U.S. Centers for Disease Control and Prevention, approximately 10.2 % of fatal overdoses in 2022 were among people experiencing homelessness or housing instability. In the United States, the majority of all drug overdoses now involve stimulants., Methods: We linked stimulant-involved fatal overdose records occurring between 2017 and 2021 from Kentucky's Drug Overdose Fatality Surveillance System to the electronic health records (EHR) of the state's largest safety-net hospital network. We used State Unintentional Drug Overdose Reporting System (SUDORS) definitions of homelessness or housing instability to establish baseline estimates before linking decedents to medical records. After linkage, we augmented SUDORS data with structured administrative billing codes, semi-structured address data, and unstructured clinical notes identifying homelessness from the EHR., Results: There were 313 individuals with stimulant-involved fatal overdoses linked to at least one medical encounter in the EHR (2017-2021). Thirty-three individuals (10.5 %) were identified as having unstable housing according to SUDORS. After linkage, 130 individuals (41.5 %) had evidence of housing instability. For this period, these 313 individuals represent 8.0 % of stimulant-involved overdoses in KY or 38.5 % of stimulant-involved overdoses from residents of the primary and secondary catchment area of our healthcare network., Conclusions: The single-site increase in observed housing instability in stimulant-involved fatal overdoses suggests that increased data linkage between state medicolegal death investigation system and EHRs would significantly improve the public health surveillance of overdoses., Competing Interests: Declaration of Competing Interest None., (Copyright © 2024. Published by Elsevier B.V.)

Survivors of child sex trafficking (SCST) experience high rates of adverse health outcomes. Amidst the duration of their victimization, survivors regularly seek healthcare yet fail to be identified. This study sought to utilize artificial intelligence (AI) to identify SCST and describe the elements of their healthcare presentation. An AI-supported keyword search was conducted to identify SCST within the electronic medical records (EMR) of ∼1.5 million patients at a large midwestern pediatric hospital. Descriptive analyses were used to evaluate associated diagnoses and clinical presentation. A sex trafficking-related keyword was identified in .18% of patient charts. Among this cohort, the most common associated diagnostic codes were for Confirmed Sexual/Physical Assault; Trauma and Stress-Related Disorders; Depressive Disorders; Anxiety Disorders; and Suicidal Ideation. Our findings are consistent with the myriad of known adverse physical and psychological outcomes among SCST and illuminate the future potential of AI technology to improve screening and research efforts surrounding all aspects of this vulnerable population., 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: More evidence for patterns of healthcare utilisation and associated costs among people receiving opioid agonist therapy (OAT) is needed. We investigated primary and secondary healthcare usage and costs among methadone and buprenorphine recipients in England., Methods: We conducted a cohort study using the Clinical Practice Research Datalink GOLD and Aurum databases of patients who were prescribed OAT between 1 January 2007 and 31 July 2019. The cohort was linked to Hospital Episode Statistics admitted patient care, outpatient and emergency department data, neighbourhood- and practice-level Index of Multiple Deprivation quintiles and mortality records. Negative binomial regression models were applied to estimate weighted rate ratios (wRR) of healthcare utilisation. Total and mean costs were calculated using Unit Costs of Health and Social Care and the National Healthcare Service Payment by Results National Tariffs., Results: Among 12,639 patients observed over 39,016 person-years, we found higher rate of hospital admissions (wRR 1.18; 1.08-1.28) among methadone compared with buprenorphine recipients. The commonest hospital discharge diagnoses among methadone patients were infectious diseases (19.2%), mental and behavioural disorders (17.0%) and drug-related poisoning (16.5%); the three commonest among buprenorphine patients were mental and behavioural diseases (21.5%), endocrine (13.8%) and genitourinary system diseases (13.1%). Methadone patients had similar mean costs compared with buprenorphine patients (cost difference: £539.01; 432.11-1006.69)., Discussion and Conclusions: Differences in healthcare utilisation frequency for methadone versus buprenorphine recipients were observed. The differences in associated costs were mainly driven by hospital admissions. These findings offer valuable insights for optimising care strategies and resource allocation for OAT recipients., (© 2024 The Author(s). Drug and Alcohol Review published by John Wiley & Sons Australia, Ltd on behalf of Australasian Professional Society on Alcohol and other Drugs.)

Our retrospective study (2010-2020) examined treatment patterns, outcomes, and healthcare resource utilization in Finnish acute myeloid leukemia (AML) patients. Data covered 153 patients diagnosed at Hospital District of Southwest Finland (HDSF) and 107 from other districts who underwent allogeneic stem cell transplantation (aSCT) at HDSF. Of the 153 patients, 56.2% received intensive chemotherapy (IC), while 43.8% deemed ineligible for IC received low-intensity therapies or best supportive care (BSC). Median overall survival for IC patients was 31.2 months, compared to 5.3 months for those under azacytidine and 1.2 months on BSC. Majority (57.5%) of patients over 60 with intermediate/high European leukemia network risk had poor outcomes with IC and couldn't proceed to aSCT. These patients carried the highest costs and hospital resource use per patient month. Most common reasons for transplant ineligibility after IC were refractory disease and infection. Our data provides a comprehensive view on AML treatment landscape from a period when the latest treatment advancements were not yet accessible. The data describes patient groups with poor prognosis and increased healthcare burden, emphasizing the need to improve treatment practices and identify better ways to get more patients to transplant, in a rapidly evolving treatment landscape., (© 2024 The Author(s). European Journal of Haematology published by John Wiley & Sons Ltd.)

Objective: To integrate long-term daily continuous glucose monitoring (CGM) device data with electronic health records (EHR) for patients with type 1 and type 2 diabetes (T1D and T2D) in the national Veterans Affairs Healthcare System to assess real-world patterns of CGM use and the reliability of EHR-based CGM information. Research Design and Methods: This observational study used Dexcom CGM device data linked with EHR (from 2015 to 2020) for a large national cohort of patients with diabetes. We tracked the initiation and consistency of CGM use, assessed concordance of CGM use and measures of glucose control between CGM device data and EHR records, and examined results by age, ethnicity, and diabetes type. Results: The time from pharmacy release of CGM to patients to initiation of uploading CGM data to Dexcom servers averaged 3 weeks but demonstrated wide variation among individuals; importantly, this delay decreased markedly over the later years. The average daily wear time of CGM exceeded 22 h over nearly 3 years of follow-up. Patterns of CGM use were generally consistent across age, race/ethnicity groups, and diabetes type. There was strong concordance between EHR-based estimates of CGM use and Dexcom CGM wear time and between estimates of glucose control from both sources. Conclusions: The study demonstrates our ability to reliably integrate CGM devices and EHR data to provide valuable insights into CGM use patterns. The results indicate in the real-world environment that CGM is worn consistently over many years for both patients with T1D and T2D within the Veterans Affairs Healthcare System and is similar across major race/ethnic groups and age-groups.

Background: Orlistat is recommended as an adjunct to diet and exercise for weight loss in the treatment of type 2 diabetes mellitus (T2DM)., Aim: To explore associations between patient characteristics and orlistat prescribing, and to determine associations of orlistat with weight loss in T2DM and prediabetes., Design and Setting: Cohort study using anonymised health records from a UK database of general practice., Method: The UK Clinical Practice Research Datalink (CPRD) Aurum database was searched to compile a cohort of patients aged ≥18 years, first diagnosed with T2DM or prediabetes in 2016 or 2017. Once the data had been collated, multivariable logistic regression models were used to determine associations with starting orlistat and stopping it early (<12 weeks of prescriptions) and orlistat's associations with weight loss in those who had not been prescribed second-line antidiabetic medications., Results: Out of 100 552 patients with incident T2DM or prediabetes, 655 (0.8%) patients with T2DM and 128 (0.7%) patients with prediabetes were prescribed orlistat. Younger people, females, those in areas of deprivation, current smokers, those coprescribed metformin, and those recorded as having hypertension were statistically significantly more likely to be prescribed orlistat; higher baseline glycated haemoglobin levels were associated with early stopping. In comparison with patients not on orlistat, those who continued using it for ≥12 weeks were more likely to lose ≥5% weight (adjusted odds ratio [AOR] 1.69, 95% confidence interval [CI] = 1.07 to 2.67) but those who stopped orlistat early were less likely to lose ≥5% weight (AOR 0.56, 95% CI = 0.29 to 1.09)., Conclusion: Orlistat was significantly associated with weight loss in patients with T2DM and prediabetes when taken for at least 12 weeks; however, it was infrequently prescribed and often taken for <12 weeks. Orlistat may be a useful adjunct to lifestyle modifications for patients with T2DM and prediabetes, but barriers to continued use means it may not be effective for everyone in managing weight loss., (© The Authors.)

Background: Question answering (QA) systems for patient-related data can assist both clinicians and patients. They can, for example, assist clinicians in decision-making and enable patients to have a better understanding of their medical history. Substantial amounts of patient data are stored in electronic health records (EHRs), making EHR QA an important research area. Because of the differences in data format and modality, this differs greatly from other medical QA tasks that use medical websites or scientific papers to retrieve answers, making it critical to research EHR QA., Objective: This study aims to provide a methodological review of existing works on QA for EHRs. The objectives of this study were to identify the existing EHR QA datasets and analyze them, study the state-of-the-art methodologies used in this task, compare the different evaluation metrics used by these state-of-the-art models, and finally elicit the various challenges and the ongoing issues in EHR QA., Methods: We searched for articles from January 1, 2005, to September 30, 2023, in 4 digital sources, including Google Scholar, ACL Anthology, ACM Digital Library, and PubMed, to collect relevant publications on EHR QA. Our systematic screening process followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 4111 papers were identified for our study, and after screening based on our inclusion criteria, we obtained 47 papers for further study. The selected studies were then classified into 2 non-mutually exclusive categories depending on their scope: "EHR QA datasets" and "EHR QA models.", Results: A systematic screening process obtained 47 papers on EHR QA for final review. Out of the 47 papers, 53% (n=25) were about EHR QA datasets, and 79% (n=37) papers were about EHR QA models. It was observed that QA on EHRs is relatively new and unexplored. Most of the works are fairly recent. In addition, it was observed that emrQA is by far the most popular EHR QA dataset, both in terms of citations and usage in other papers. We have classified the EHR QA datasets based on their modality, and we have inferred that Medical Information Mart for Intensive Care (MIMIC-III) and the National Natural Language Processing Clinical Challenges datasets (ie, n2c2 datasets) are the most popular EHR databases and corpuses used in EHR QA. Furthermore, we identified the different models used in EHR QA along with the evaluation metrics used for these models., Conclusions: EHR QA research faces multiple challenges, such as the limited availability of clinical annotations, concept normalization in EHR QA, and challenges faced in generating realistic EHR QA datasets. There are still many gaps in research that motivate further work. This study will assist future researchers in focusing on areas of EHR QA that have possible future research directions., (©Jayetri Bardhan, Kirk Roberts, Daisy Zhe Wang. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 30.10.2024.)

Introduction: Clinical notes, biomarkers, and neuroimaging have proven valuable in dementia prediction models. Whether commonly available structured clinical data can predict dementia is an emerging area of research. We aimed to predict gold-standard, research-based diagnoses of dementia including Alzheimer's disease (AD) and/or Alzheimer's disease related dementias (ADRD), in addition to ICD-based AD and/or ADRD diagnoses, in a well-phenotyped, population-based cohort using a machine learning approach., Methods: Administrative healthcare data (k = 163 diagnostic features), in addition to census/vital record sociodemographic data (k = 6 features), were linked to the Cache County Study (CCS, 1995-2008)., Results: Among successfully linked UPDB-CCS participants (n = 4206), 522 (12.4%) had incident dementia (AD alone, AD comorbid with ADRD, or ADRD alone) as per the CCS "gold standard" assessments. Random Forest models, with a 1-year prediction window, achieved the best performance with an Area Under the Curve (AUC) of 0.67. Accuracy declined for dementia subtypes: AD/ADRD (AUC = 0.65); ADRD (AUC = 0.49). Accuracy improved when using ICD-based dementia diagnoses (AUC = 0.77)., Discussion: Commonly available structured clinical data (without labs, notes, or prescription information) demonstrate modest ability to predict "gold-standard" research-based AD/ADRD diagnoses, corroborated by prior research. Using ICD diagnostic codes to identify dementia as done in the majority of machine learning dementia prediction models, as compared to "gold-standard" dementia diagnoses, can result in higher accuracy, but whether these models are predicting true dementia warrants further research., (© 2024. The Author(s).)

The use of electronic health records (EHRs) holds the potential to enhance clinical trial activities. However, the identification of eligible patients within EHRs presents considerable challenges. We aimed to develop a CriteriaMapper system for phenotyping eligibility criteria, enabling the identification of patients from EHRs with clinical characteristics that match those criteria. We utilized clinical trial eligibility criteria and patient EHRs from the Mount Sinai Database. The CriteriaMapper system was developed to normalize the criteria using national standard terminologies and in-house databases, facilitating computability and queryability to bridge clinical trial criteria and EHRs. The system employed rule-based pattern recognition and manual annotation. Our system normalized 367 out of 640 unique eligibility criteria attributes, covering various medical conditions including non-small cell lung cancer, small cell lung cancer, prostate cancer, breast cancer, multiple myeloma, ulcerative colitis, Crohn's disease, non-alcoholic steatohepatitis, and sickle cell anemia. About 174 criteria were encoded with standard terminologies and 193 were normalized using the in-house reference tables. The agreement between automated and manual normalization was high (Cohen's Kappa = 0.82), and patient matching demonstrated a 0.94 F1 score. Our system has proven effective on EHRs from multiple institutions, showing broad applicability and promising improved clinical trial processes, leading to better patient selection, and enhanced clinical research outcomes., (© 2024. The Author(s).)

Background: Medical errors are becoming a major problem for health care providers and those who design health policies. These errors cause patients' illnesses to worsen over time and can make recovery impossible. For the benefit of patients and the welfare of health care providers, a decrease in these errors is required to maintain safe, high-quality patient care., Objective: This study aimed to improve the ability of health care professionals to diagnose diseases and reduce medical errors., Methods: Data collection was performed at Dr George Mukhari Academic Hospital using convenience sampling. In total, 300 health care professionals were given a self-administered questionnaire, including doctors, dentists, pharmacists, physiologists, and nurses. To test the study hypotheses, multiple linear regression was used to evaluate empirical data., Results: In the sample of 300 health care professionals, no significant correlation was found between medical error reduction (MER) and knowledge quality (KQ) (β=.043, P=.48). A nonsignificant negative relationship existed between MER and information quality (IQ) (β=-.080, P=.19). However, a significant positive relationship was observed between MER and electronic health records (EHR; β=.125, 95% CI 0.005-0.245, P=.042)., Conclusions: Increasing patient access to medical records for health care professionals may significantly improve patient health and well-being. The effectiveness of health care organizations' operations can also be increased through better health information systems. To lower medical errors and enhance patient outcomes, policy makers should provide financing and support for EHR adoption as a top priority. Health care administrators should also concentrate on providing staff with the training they need to operate these systems efficiently. Empirical surveys in other public and private hospitals can be used to further test the validated survey instrument., (©Bester Chimbo, Lovemore Motsi. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 16.10.2024.)

Background: Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment choice have been limited., Method: We aimed to determine if machine learning methods applied to electronic health records could predict differential response to lithium or olanzapine. From electronic United Kingdom primary care records, we extracted a cohort of individuals prescribed either lithium (19,106 individuals) or olanzapine (12,412) monotherapy. Machine learning models were used to predict successful monotherapy maintenance treatment, using 113 clinical and demographic variables, 8,017 (41.96%) lithium responders and 3,831 (30.87%) olanzapine responders., Results: We found a quantitative structural difference in that lithium maintenance responders were weakly predictable in our holdout sample, consisting of the 5% of patients with the most recent exposure. Age at first diagnosis, age at first treatment and the time between these were the most important variables in all models., Discussion: Even if we failed to predict successful monotherapy olanzapine treatment, and so to definitively separate lithium vs . olanzapine responders, the characterization of the two groups may be used for classification by proxy. This can, in turn, be useful for establishing maintenance therapy. The further exploration of machine learning methods on EHR data for drug treatment selection could in the future play a role for clinical decision support. Signals in the data encourage further experiments with larger datasets to definitively separate lithium vs . olanzapine responders., Competing Interests: Joseph F. Hayes has received consultancy fees from juli Health and Wellcome Trust. No other authors declare competing interests., (© 2024 Hayes et al.)

Rare genetic diseases (RGDs) affect a significant number of individuals, particularly in pediatric populations. This study investigates the efficacy of identifying RGD diagnoses through electronic health records (EHRs) and natural language processing (NLP) tools, and analyzes the prevalence of identified RGDs for potential underdiagnosis at Cincinnati Children's Hospital Medical Center (CCHMC). EHR data from 659,139 pediatric patients at CCHMC were utilized. Diagnoses corresponding to RGDs in Orphanet were identified using rule-based and machine learning-based NLP methods. Manual evaluation assessed the precision of the NLP strategies, with 100 diagnosis descriptions reviewed for each method. The rule-based method achieved a precision of 97.5% (95% CI: 91.5%, 99.4%), while the machine-learning-based method had a precision of 73.5% (95% CI: 63.6%, 81.6%). A manual chart review of 70 randomly selected patients with RGD diagnoses confirmed the diagnoses in 90.3% (95% CI: 82.0%, 95.2%) of cases. A total of 37,326 pediatric patients were identified with 977 RGD diagnoses based on the rule-based method, resulting in a prevalence of 5.66% in this population. While a majority of the disorders showed a higher prevalence at CCHMC compared with Orphanet, some diseases, such as 1p36 deletion syndrome, indicated potential underdiagnosis. Analyses further uncovered disparities in RGD prevalence and age of diagnosis across gender and racial groups. This study demonstrates the utility of employing EHR data with NLP tools to systematically investigate RGD diagnoses in large cohorts. The identified disparities underscore the need for enhanced approaches to guarantee timely and accurate diagnosis and management of pediatric RGDs., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)