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Housing instability is considered a significant life stressor and preemptive screening should be applied to identify those at risk for homelessness as early as possible so that they can be targeted for specialized care. We developed models to classify patient outcomes for an established VA Homelessness Screening Clinical Reminder (HSCR), which identifies housing instability, in the two months prior to its administration. Logistic Regression and Random Forest models were fit to classify responses using the last 18 months of document activity. We measure concentration of risk across stratifications of predicted probability and observe an enriched likelihood of finding confirmed false negative responses from veterans with diagnosed housing instability. Positive responses were 34 times more likely to be detected within the top 1 % of patients predicted at risk than from those randomly selected. There is a 1 in 4 chance of detecting false negatives within the top 1 % of predicted risk. Machine learning methods can classify between episodes of housing instability using a data-driven approach that does not rely on variables curated from domain experts. This method has the potential to improve clinicians ability to identify veterans who are experiencing housing instability but are not captured by HSCR.

The development of electronic health (e-health) systems in the last decades led to the emergence of effective infrastructures for managing health records. Due to the vitality and massive amount of healthcare records, blockchain is considered one of the most important infrastructures to store and exchange health records. Access to patient's medical histories and health record management are essential to healthcare systems. Patients' records must be exchanged among various parts of the healthcare system and users should have access to manage their profiles and use online facilities in a secure manner. In this paper, for the first time, the non-fungible token (NFT), which is a nascent technology, is used to certify the information immutability of healthcare records in the blockchain infrastructure. The ownership of property and prevention of information disclosure is guaranteed using the encryption strategy is used in the proposed NFT technology. Furthermore, the proposed model satisfies all security criteria such as data integrity, non-repudiation, anonymity, confidentially and privacy. As a result, the proposed model outperforms the related research in the healthcare domain. The proposed model brings many facilities for both patients and physicians such as secure accessibility, data quality, patient consent, efficient record management and better availability and reliability. [ABSTRACT FROM AUTHOR]

• SeLECTs is likely under-recorded in Wales. Epilepsy incidence has decreased over time. • Children with SeLECTS have a higher proportion of neurodevelopmental comorbidities than the general populaton. • Unlike other epilepsies as a whole, there was no association of social deprivation and incidence of SeLECTs. Information on self-limited epilepsy with centrotemporal spikes (SeLECTS) epidemiology is limited. We aimed to determine the incidence of SeLECTS in children, its association with socioeconomic deprivation and the prevalence of neurodevelopmental comorbidities. We performed a retrospective cohort study (2004–2017) using anonymised, linked, routinely collected, primary care and demographic data for children in Wales. We used primary care diagnosis codes to identify children (aged 0–16 years) with SeLECTS and other epilepsies and to record antiseizure medication (ASM) prescriptions and neurodevelopmental comorbidities. We used a mixed effects Poisson regression model to determine temporal trends of SeLECTS incidence and its association with socioeconomic deprivation. We identified 6,732 children with epilepsy, 186 (3%) with SeLECTS. In 2017, epilepsy and SeLECTS prevalence was 0.55% and 0.02% respectively with corresponding crude incidence of 51.2/100,000/year and 1.1/100,000/year. The incidence of epilepsy in children decreased with decreasing deprivation with an adjusted incidence rate ratio (AIRR) of 0.72 (95% CI 0.64–0.82) in the least deprived compared with the most deprived quintile. The corresponding AIRR for children with SeLECTS was 1.35 (95% CI 0.46–1.99). 34% of children with epilepsy, 18% of children with SeLECTS and 3% of all children in Wales had a neurodevelopmental disorder and or school problems. Half of children with SeLECTS were treated with ASM. We identified a lower than previously reported incidence of SeLECTS, which may be due to under-recording of SeLECTS. There was no change in the incidence of SeLECTS over time, whilst the incidence of childhood epilepsy overall was decreasing. There was no significant association between incidence of SeLECTS and deprivation but the modest sample size needs to be considered. Children with SeLECTS should be screened for neurodevelopmental and or learning comorbidities. Treatment for SeLECTS remains debatable. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract 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.

Context X-linked hypophosphatemia (XLH) is a rare genetic disorder causing renal phosphate wasting, which predicates musculoskeletal manifestations such as rickets. Diagnosis is often delayed. Objective To explore the recording of clinical features, and the diagnostic odyssey of children and adolescents with XLH in primary care electronic healthcare records (EHRs) in the United Kingdom. Methods Using the Optimum Patient Care Research Database, individuals aged 20 years or younger after January 1, 2000, at date of recorded XLH diagnosis were identified using Systematized Nomenclature of Medicine Clinical Terms (SNOMED)/Read codes and age-matched to 100 controls. Recording of XLH-related clinical features was summarized then compared between cases and controls using chi-squared or Fisher's exact test. Results In total, 261 XLH cases were identified; 99 met the inclusion criteria. Of these, 84/99 had at least 1 XLH-related clinical feature recorded in their primary care EHR. Clinical codes for rickets, genu varum, and low phosphate were recorded prior to XLH diagnosis in under 20% of cases (median of 1, 1, and 3 years prior, respectively). Rickets, genu varum, low phosphate, nephrocalcinosis, and growth delay were significantly more likely to be recorded in cases. Conclusion This characterization of the EHR phenotypes of children and adolescents with XLH may inform future case-finding approaches to expedite diagnosis in primary care. [ABSTRACT FROM AUTHOR]

Background Recording and coding of ageing syndromes in hospital records is known to be suboptimal. Natural Language Processing algorithms may be useful to identify diagnoses in electronic healthcare records to improve the recording and coding of these ageing syndromes, but the feasibility and diagnostic accuracy of such algorithms are unclear. Methods We conducted a systematic review according to a predefined protocol and in line with Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Searches were run from the inception of each database to the end of September 2023 in PubMed, Medline, Embase, CINAHL, ACM digital library, IEEE Xplore and Scopus. Eligible studies were identified via independent review of search results by two coauthors and data extracted from each study to identify the computational method, source of text, testing strategy and performance metrics. Data were synthesised narratively by ageing syndrome and computational method in line with the Studies Without Meta-analysis guidelines. Results From 1030 titles screened, 22 studies were eligible for inclusion. One study focussed on identifying sarcopenia, one frailty, twelve falls, five delirium, five dementia and four incontinence. Sensitivity (57.1%–100%) of algorithms compared with a reference standard was reported in 20 studies, and specificity (84.0%–100%) was reported in only 12 studies. Study design quality was variable with results relevant to diagnostic accuracy not always reported, and few studies undertaking external validation of algorithms. Conclusions Current evidence suggests that Natural Language Processing algorithms can identify ageing syndromes in electronic health records. However, algorithms require testing in rigorously designed diagnostic accuracy studies with appropriate metrics reported. [ABSTRACT FROM AUTHOR]

Abstract Background Chronic kidney disease (CKD) affects more than 38 million people in the United States, predominantly those over 65 years of age. While CKD etiology is complex, recent research suggests associations with environmental exposures. Methods Our primary objective is to examine creatinine-based estimated glomerular filtration rate (eGFRcr) and diagnosis of CKD and potential associations with fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) using a random sample of North Carolina electronic healthcare records (EHRs) from 2004 to 2016. We estimated eGFRcr using the serum creatinine-based 2021 CKD-EPI equation. PM2.5 and NO2 data come from a hybrid model using 1 km2 grids and O3 data from 12 km2 CMAQ grids. Exposure concentrations were 1-year averages. We used linear mixed models to estimate eGFRcr per IQR increase of pollutants. We used multiple logistic regression to estimate associations between pollutants and first appearance of CKD. We adjusted for patient sex, race, age, comorbidities, temporality, and 2010 census block group variables. Results We found 44,872 serum creatinine measurements among 7,722 patients. An IQR increase in PM2.5 was associated with a 1.63 mL/min/1.73m2 (95% CI: -1.96, -1.31) reduction in eGFRcr, with O3 and NO2 showing positive associations. There were 1,015 patients identified with CKD through e-phenotyping and ICD codes. None of the environmental exposures were positively associated with a first-time measure of eGFRcr

The widespread adoption of Electronic Healthcare Records has resulted in an abundance of healthcare data. This data holds significant potential for improving healthcare services by providing valuable clinical insights and enhancing clinical decision-making. This paper presents a patient classification methodology that utilizes a multiclass and multilabel diagnostic approach to predict the patient's clinical class. The proposed model effectively handles comorbidities while maintaining a high level of accuracy. The implementation leverages the MIMIC III database as a data source to create a phenotyping dataset and train the models. Various machine learning models are employed in this study. Notably, the natural language processing-based One-Vs-Rest classifier achieves the best classification results, maintaining accuracy and F1 scores even with a large number of classes. The patient diagnostic class prediction model, based on the International Classification of Diseases 9, showcased in this paper, has broad applications in diagnostic support, treatment prediction, clinical assistance, recommender systems, clinical decision support systems, and clinical knowledge discovery engines. [ABSTRACT FROM AUTHOR]

A blockchain is a decentralized digital network system consisting of multiple blocks connected in a chain. Each block comprises multiple transactions and is linked to the previous block through its hash value. The hash values are calculated using the active transactions within each block and the hash value of the preceding block. A positive impact can be made by combining blockchain technology with the healthcare sector. The consensus mechanism is the key protocol for implementing agreements in the blockchain. This paper proposes a combined RSA (Rivest–Shamir–Adleman) cryptography algorithm with a novel consensus protocol IPBFT (Improved Practical Byzantine Fault Tolerance) in the Hyperledger Fabric platform, for a patient-centered approach using blockchain technology in the healthcare sector. It ensures the reliability and integrity of transactions and prevents malicious nodes from disrupting the network. The Hyperledger Fabric platform is used to provide transparency, security, and trust in the blockchain. Performance metrics such as latency, throughput, scalability, and comparative analysis for the proposed algorithm Improved Practical Byzantine Fault Tolerance (IPBFT) and existing Practical Byzantine Fault Tolerance (PBFT) algorithms have been evaluated to achieve enhanced results. The proposed algorithm solves many problems associated with existing Practical Byzantine Fault Tolerance solutions and improves efficiency and security in the healthcare sector. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) affects more than 38 million people in the United States, predominantly those over 65 years of age. While CKD etiology is complex, recent research suggests associations with environmental exposures. Methods: Our primary objective is to examine creatinine-based estimated glomerular filtration rate (eGFRcr) and diagnosis of CKD and potential associations with fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) using a random sample of North Carolina electronic healthcare records (EHRs) from 2004 to 2016. We estimated eGFRcr using the serum creatinine-based 2021 CKD-EPI equation. PM2.5 and NO2 data come from a hybrid model using 1 km2 grids and O3 data from 12 km2 CMAQ grids. Exposure concentrations were 1-year averages. We used linear mixed models to estimate eGFRcr per IQR increase of pollutants. We used multiple logistic regression to estimate associations between pollutants and first appearance of CKD. We adjusted for patient sex, race, age, comorbidities, temporality, and 2010 census block group variables. Results: We found 44,872 serum creatinine measurements among 7,722 patients. An IQR increase in PM2.5 was associated with a 1.63 mL/min/1.73m2 (95% CI: -1.96, -1.31) reduction in eGFRcr, with O3 and NO2 showing positive associations. There were 1,015 patients identified with CKD through e-phenotyping and ICD codes. None of the environmental exposures were positively associated with a first-time measure of eGFRcr < 60 mL/min/1.73m2. NO2 was inversely associated with a first-time diagnosis of CKD with aOR of 0.77 (95% CI: 0.66, 0.90). Conclusions: One-year average PM2.5 was associated with reduced eGFRcr, while O3 and NO2 were inversely associated. Neither PM2.5 or O3 were associated with a first-time identification of CKD, NO2 was inversely associated. We recommend future research examining the relationship between air pollution and impaired renal function. [ABSTRACT FROM AUTHOR]

Introduction: Documentation as well as IT-based management of medical data is of ever-increasing relevance in modern medicine. As radiation oncology is a rather technical, data-driven discipline, standardization, and data exchange are in principle possible. We examined electronic healthcare documents to extract structured information. Planning CT order entry documents were chosen for the analysis, as this covers a common and structured step in radiation oncology, for which standardized documentation may be achieved. The aim was to examine the extent to which relevant information may be exchanged among different institutions. Materials and Methods: We contacted representatives of nine radiation oncology departments. Departments using standardized electronic documentation for planning CT were asked to provide templates of their records, which were analyzed in terms of form and content. Structured information was extracted by identifying definite common data elements, containing explicit information. Relevant common data elements were identified and classified. A quantitative analysis was performed to evaluate the possibility of data exchange. Results: We received data of seven documents that were heterogeneous regarding form and content. 181 definite common data elements considered relevant for the planning CT were identified and assorted into five semantic groups. 139 data elements (76.8%) were present in only one document. The other 42 data elements were present in two to six documents, while none was shared among all seven documents. Conclusion: Structured and interoperable documentation of medical information can be achieved using common data elements. Our analysis showed that a lot of information recorded with healthcare documents can be presented with this approach. Yet, in the analyzed cohort of planning CT order entries, only a few common data elements were shared among the majority of documents. A common vocabulary and consensus upon relevant information is required to promote interoperability and standardization. [ABSTRACT FROM AUTHOR]

The Internet of Things transmits data collected from remote sensors and shares public and private data through the Internet or other communication networks. The IoT healthcare data uses decentralized cloud security for data transmission and storage of healthcare data. Privacy and security are critical components for managing private data and personalization. Due to the Big Data environment in healthcare data processing the sensitive data collection from Electronic Personal Healthcare Records poses challenges and difficulties. The unauthorized policies allow adversaries to obtain data and keys for sharing healthcare records. So Sensitive information in personal healthcare records needs to be highly protected in the medical field. The excessive insensitivity of the current privacy regulations causes delays in complexity and poor performance. Exposure-risk response interactions are necessary to calculate the burden of environmental ailments. To tackle this issue, this paper introduces Blockchain-based Decentralized Secure Sharing using the Shuffled Random Starvation Link Encryption algorithm to protect the sensitive PHR records in the smart IoT. Initially, the proposed quasi-sensitive attribute Identification method is used to analyze and categorize the sensitive cases of PHR. Then encrypt the sensitive attributes using the novel Shuffled Random Starvation Link Encryption algorithm to protect the user’s private data. The Key Authentication Policy is used to verify the key node aggregation and peer-end verification to share information between users. As a result, the proposed system outperforms previous approaches in reliability and security.

Abstract Background Lung cancer screening with low-dose computed tomography for high-risk populations is being implemented in the UK. However, inclusive identification and invitation of the high-risk population is a major challenge for equitable lung screening implementation. Primary care electronic health records (EHRs) can be used to identify lung screening-eligible individuals based on age and smoking history, but the quality of EHR smoking data is limited. This study piloted a novel strategy for ascertaining smoking status in primary care and tested EHR search combinations to identify those potentially eligible for lung cancer screening. Methods Seven primary care General Practices in South Wales, UK were included. Practice-level data on missing tobacco codes in EHRs were obtained. To update patient EHRs with no tobacco code, we developed and tested an algorithm that sent a text message request to patients via their GP practice to update their smoking status. The patient’s response automatically updated their EHR with the relevant tobacco code. Four search strategies using different combinations of tobacco codes for the age range 55-74+ 364 were tested to estimate the likely impact on the potential lung screening-eligible population in Wales. Search strategies included: BROAD (wide range of ever smoking codes); VOLUME (wide range of ever-smoking codes excluding “trivial” former smoking); FOCUSED (cigarette-related tobacco codes only), and RECENT (current smoking within the last 20 years). Results Tobacco codes were not recorded for 3.3% of patients (n = 724/21,956). Of those with no tobacco code and a validated mobile telephone number (n = 333), 55% (n = 183) responded via text message with their smoking status. Of the 183 patients who responded, 43.2% (n = 79) had a history of smoking and were potentially eligible for lung cancer screening. Applying the BROAD search strategy was projected to result in an additional 148,522 patients eligible to receive an invitation for lung cancer screening when compared to the RECENT strategy. Conclusion An automated text message system could be used to improve the completeness of primary care EHR smoking data in preparation for rolling out a national lung cancer screening programme. Varying the search strategy for tobacco codes may have profound implications for the size of the population eligible for lung-screening invitation.

Constantinos Kallis,1 Rafael A Calvo,2 Bjorn Schuller,3 Jennifer K Quint1 1National Heart and Lung Institute, and School of Public Health, Imperial College London, London, UK; 2Dyson School of Engineering, Faculty of Engineering, Imperial College London, London, UK; 3Department of Computing, Faculty of Engineering, Imperial College London, London, UKCorrespondence: Jennifer K Quint, National Heart and Lung Institute and School of Public Health, Floor 9, Sir Michael Uren Building, 86 Wood Ln, London, W12 0BZ, United Kingdom, Email j.quint@imperial.ac.ukBackground: Improving accurate risk assessment of asthma exacerbations, and reduction via relevant behaviour change among people with asthma could save lives and reduce health care costs. We developed a simple personalised risk prediction model for asthma exacerbations using factors collected in routine healthcare data for use in a risk modelling feature for automated conversational systems.Methods: We used pseudonymised primary care electronic healthcare records from the Clinical Practice Research Datalink (CPRD) Aurum database in England. We combined variables for prediction of asthma exacerbations using logistic regression including age, gender, ethnicity, Index of Multiple Deprivation, geographical region and clinical variables related to asthma events.Results: We included 1,203,741 patients divided into three cohorts to implement temporal validation: 898,763 (74.7%) in the training sample, 226,754 (18.8%) in the testing sample and 78,224 (6.5%) in the validation sample. The Area under the ROC curve (AUC) for the full model was 0.72 and for the restricted model was 0.71. Using a cut-off point of 0.1, approximately 27 asthma reviews by clinicians per 100 patients would be prevented compared with a strategy that all patients are regarded as high risk. Compared with patients without an exacerbation, patients who exacerbated were older, more likely to be female, prescribed more SABA and ICS in the preceding 12 months, have history of GORD, COPD, anxiety, depression, live in very deprived areas and have more severe disease.Conclusion: Using information available from routinely collected electronic healthcare record data, we developed a model that has moderate ability to separate patients who had an asthma exacerbation within 3 months from their index date from patients who did not. When comparing this model with a simplified model with variables that can easily be self-reported through a WhatsApp chatbot, we have shown that the predictive performance of the model is not substantially different.Keywords: asthma, exacerbation, risk prediction, electronic healthcare records

BackgroundAcute myocardial infarction (AMI) is an uncommon but fatal complication among patients undergoing elective spinal fusion surgery (SF), total hip arthroplasty (THA), and total knee arthroplasty (TKA). Our objective was to estimate the incidence of AMI among adults undergoing elective SF, THA, and TKA in different post-operative risk windows and characterize high-risk sub-populations in the United States.MethodsA retrospective cohort study was conducted using data from a longitudinal electronic healthcare record (EHR) database from January 1, 2007 to June 30, 2018. ICD codes were used to identify SF, THA, TKA, AMI, and selected clinical characteristics. Incidence proportions (IPs) and 95% confidence intervals were estimated in the following risk windows: index hospitalization, ≤ 30, ≤ 90, ≤ 180, and ≤ 365 days post-operation.ResultsA total of 67,533 SF patients, 87,572 THA patients, and 167,480 TKA patients were eligible for the study. The IP of AMI after SF, THA, and TKA ranged from 0.36, 0.28, and 0.25% during index hospitalization to 1.05, 0.93, and 0.85% ≤ 365 days post-operation, respectively. The IP of AMI was higher among patients who were older, male, with longer hospital stays, had a history of AMI, and had a history of diabetes.ConclusionThe IP of post-operative AMI was generally highest among the SF cohort compared to the THA and TKA cohorts. Additionally, potential high-risk populations were identified. Future studies in this area are warranted to confirm these findings via improved confounder control and to identify effect measure modifiers.

Housing instability is considered a significant life stressor and preemptive screening should be applied to identify those at risk for homelessness as early as possible so that they can be targeted for specialized care. We developed models to classify patient outcomes for an established VA Homelessness Screening Clinical Reminder (HSCR), which identifies housing instability, in the two months prior to its administration. Logistic Regression and Random Forest models were fit to classify responses using the last 18 months of document activity. We measure concentration of risk across stratifications of predicted probability and observe an enriched likelihood of finding confirmed false negative responses from veterans with diagnosed housing instability. Positive responses were 34 times more likely to be detected within the top 1 % of patients predicted at risk than from those randomly selected. There is a 1 in 4 chance of detecting false negatives within the top 1 % of predicted risk. Machine learning methods can classify between episodes of housing instability using a data-driven approach that does not rely on variables curated from domain experts. This method has the potential to improve clinicians’ ability to identify veterans who are experiencing housing instability but are not captured by HSCR.

Background: Lung cancer screening with low-dose computed tomography for high-risk populations is being implemented in the UK. However, inclusive identification and invitation of the high-risk population is a major challenge for equitable lung screening implementation. Primary care electronic health records (EHRs) can be used to identify lung screening-eligible individuals based on age and smoking history, but the quality of EHR smoking data is limited. This study piloted a novel strategy for ascertaining smoking status in primary care and tested EHR search combinations to identify those potentially eligible for lung cancer screening. Methods: Seven primary care General Practices in South Wales, UK were included. Practice-level data on missing tobacco codes in EHRs were obtained. To update patient EHRs with no tobacco code, we developed and tested an algorithm that sent a text message request to patients via their GP practice to update their smoking status. The patient's response automatically updated their EHR with the relevant tobacco code. Four search strategies using different combinations of tobacco codes for the age range 55-74+ 364 were tested to estimate the likely impact on the potential lung screening-eligible population in Wales. Search strategies included: BROAD (wide range of ever smoking codes); VOLUME (wide range of ever-smoking codes excluding "trivial" former smoking); FOCUSED (cigarette-related tobacco codes only), and RECENT (current smoking within the last 20 years). Results: Tobacco codes were not recorded for 3.3% of patients (n = 724/21,956). Of those with no tobacco code and a validated mobile telephone number (n = 333), 55% (n = 183) responded via text message with their smoking status. Of the 183 patients who responded, 43.2% (n = 79) had a history of smoking and were potentially eligible for lung cancer screening. Applying the BROAD search strategy was projected to result in an additional 148,522 patients eligible to receive an invitation for lung cancer screening when compared to the RECENT strategy. Conclusion: An automated text message system could be used to improve the completeness of primary care EHR smoking data in preparation for rolling out a national lung cancer screening programme. Varying the search strategy for tobacco codes may have profound implications for the size of the population eligible for lung-screening invitation. [ABSTRACT FROM AUTHOR]

Deep Learning (DL) has already shown tremendous potential in designing intelligent clinical support systems in biomedicine. Data privacy plays a significant role while training and testing DL models, especially for sensitive data. Privacy-Preserving Deep Learning (PPDL) applications in Healthcare are rapidly growing as medical informatics deals with sensitive data. This work reviews the recent advances in PPDL techniques in Healthcare. It first analyzes the need of PPDL in healthcare informatics using a threat model and then discusses privacy-preserving computation techniques for secure data processing and evaluation. Next, it focuses on DL applications over Healthcare in three categories: (i) PPDL in the private cloud, (ii) PPDL in the public cloud, and (iii) privacy based on modifications in DL architectures. Next, we examine data privacy at different stages of DL deployment in Healthcare, including input, model, training, and output. We also provide a summary of the evaluation outcomes of the solutions reviewed. Additionally, we highlight the unique challenges in PPDL for Healthcare and offer suggestions for future research directions. [ABSTRACT FROM AUTHOR]

Blockchain is regarded as a significant innovation and shows a set of promising features that can certainly address existing issues in real time applications. Decentralization, greater transparency, improved traceability and secure architecture can revolutionize healthcare systems. With the help of advancement in computer technologies, most healthcare institutions try to store patient data digitally rather than on paper. Electronic health records are regarded as some of the most important assets in healthcare system and are required to be shared among different hospitals and other organizations to improve diagnosis efficiency. While sharing patients' details, certain basic standards such as integrity and confidentiality of the information need to be considered. Blockchain technology provides the above standards with features of immutability and granting access to stored information only to authorized users. The examination approach depends on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (or PRISMA) rules and an efficient planned search convention is utilized to look through multiple scientific databases to recognize, investigate and separate every important publication. In this paper, we present a solid systematic review on the blockchain and healthcare domain to identify the existing challenges and benefits of applying blockchain technology in healthcare systems. More than 150 scientific papers published in the last ten years are surveyed, resulting in the identifications and summarization of observations made on the different privacy-preserving approaches and also assessment of their performances. We also present a significant architectural solutions of blockchain to achieve interoperability. Thereby, we attempt to analyse the ideas of blockchain in the medical domain, by assessing the advantages and limitations, subsequently giving guidance to other researchers in the area.

The importance of shared decision making (SDM) for informed consent has been emphasised in the updated regulatory guidelines. Errors of completion, legibility and omission have been associated with paper-based consent forms. We introduced a digital consent process and compared it against a paper-based process for quality and patient reported involvement in shared decision making. 223 patients were included in this multi-site, single centre study. Patient consent documentation was by either a paper consent form or the Concentric digital consent platform. Consent forms were assessed for errors of legibility, completion and accuracy of content. Core risks for 20 orthopaedic operations were pre-defined by a Delphi round of experts and forms analysed for omission of these risks. SDM was determined via the 'collaboRATE Top Score', a validated measure for gold-standard SDM. 72% (n = 78/109) of paper consent forms contained ≥1 error compared to 0% (n = 0/114) of digital forms (P < 0.0001). Core risks were unintentionally omitted in 63% (n = 68/109) of paper-forms compared to less than 2% (n = 2/114) of digital consent forms (P < 0.0001). 72% (n = 82/114) of patients giving consent digitally reported gold-standard SDM compared to 28% (n = 31/109) with paper consent (P < 0.001). Implementation of a digital consent process has been shown to reduce both error rate and the omission of core risks on consent forms whilst increasing the quality of SDM. This novel finding suggests that using digital consent can improve both the quality of informed consent and the patient experience of SDM. • The paper consent process is associated with errors and omissions of core risks. • A digital consent process improved error rate and omission rate of core risks. • Patients reported a higher level of shared decision making with a digital process. • Documentation of consent can be reliably standardised with an electronic record. [ABSTRACT FROM AUTHOR]

Abstract Background This descriptive study assessed the completeness, agreement, and representativeness of ethnicity recording in the United Kingdom (UK) Clinical Practice Research Datalink (CPRD) primary care databases alone and, for those patients registered with a GP in England, when linked to secondary care data from Hospital Episode Statistics (HES). Methods Ethnicity records were assessed for all patients in the May 2021 builds of the CPRD GOLD and CPRD Aurum databases for all UK patients. In analyses of the UK, English data was from combined CPRD-HES, whereas data from Northern Ireland, Scotland, and Wales drew from CPRD only. The agreement of ethnicity records per patient was assessed within each dataset (CPRD GOLD, CPRD Aurum, and HES datasets) and between datasets at the highest level ethnicity categorisation (‘Asian’, ‘black’, ‘mixed’, ‘white’, ‘other’). Representativeness was assessed by comparing the ethnic distributions at the highest-level categorisation of CPRD-HES to those from the Census 2011 across the UK’s devolved administrations. Additionally, CPRD-HES was compared to the experimental ethnic distributions for England and Wales from the Office for National Statistics in 2019 (ONS2019) and the English ethnic distribution from May 2021 from NHS Digital’s General Practice Extraction Service Data for Pandemic Planning and Research with HES data linkage (GDPPR-HES). Results In CPRD-HES, 81.7% of currently registered patients in the UK had ethnicity recorded in primary care. For patients with multiple ethnicity records, mismatched ethnicity within individual primary and secondary care datasets was

Abstract Models that can effectively represent structured Electronic Healthcare Records (EHR) are central to an increasing range of applications in healthcare. Due to the sequential nature of health data, Recurrent Neural Networks have emerged as the dominant component within state-of-the-art architectures. The signature transform represents an alternative modelling paradigm for sequential data. This transform provides a non-learnt approach to creating a fixed vector representation of temporal features and has shown strong performances across an increasing number of domains, including medical data. However, the signature method has not yet been applied to structured EHR data. To this end, we follow recent work that enables the signature to be used as a differentiable layer within a neural architecture enabling application in high dimensional domains where calculation would have previously been intractable. Using a heart failure prediction task as an exemplar, we provide an empirical evaluation of different variations of the signature method and compare against state-of-the-art baselines. This first application of neural-signature methods in real-world healthcare data shows a competitive performance when compared to strong baselines and thus warrants further investigation within the health domain.

Aims People with depression are up to 72% more at risk to develop cardiovascular disease (CVD) in their lifetime. Evidence-based psychotherapies are first-line interventions for the treatment of depression and are delivered nationally in England through the National Health Service via the Improving Access to Psychological Therapy (IAPT) primary care programme. It is currently unknown whether positive therapy outcomes may be associated with cardiovascular risk reduction. This study aimed to examine the association between psychotherapy outcomes for depression and incident CVD. Methods and results A cohort of 636 955 individuals who have completed a course of psychotherapy was built from linked electronic healthcare record databases of national coverage in England: the national IAPT database, the Hospital Episode Statistics (HES) database, and the HES–ONS (Office of National Statistics) mortality database. Multivariable Cox models adjusting for clinical and demographic covariates were run to estimate the association between reliable improvement from depression and the risk of subsequent incidence of cardiovascular events. After a median follow-up of 3.1 years, reliable improvement from depression symptoms was associated with a lower risk of new onset of any CVD [hazard ratio (HR): 0.88, 95% confidence interval (CI): 0.86, 0.89], coronary heart disease (HR: 0.89, 95% CI: 0.86, 0.92), stroke (HR: 0.88, 95% CI: 0.83, 0.94), and all-cause mortality (HR: 0.81, 95% CI: 0.78, 0.84). This association was stronger in the under 60 compared with the over 60 for all outcomes. Results were confirmed in sensitivity analyses. Conclusion Management of depression through psychological interventions may be associated with reduced risk of CVD. More research is needed to understand the causality of these associations. [ABSTRACT FROM AUTHOR]

Background: This descriptive study assessed the completeness, agreement, and representativeness of ethnicity recording in the United Kingdom (UK) Clinical Practice Research Datalink (CPRD) primary care databases alone and, for those patients registered with a GP in England, when linked to secondary care data from Hospital Episode Statistics (HES). Methods: Ethnicity records were assessed for all patients in the May 2021 builds of the CPRD GOLD and CPRD Aurum databases for all UK patients. In analyses of the UK, English data was from combined CPRD-HES, whereas data from Northern Ireland, Scotland, and Wales drew from CPRD only. The agreement of ethnicity records per patient was assessed within each dataset (CPRD GOLD, CPRD Aurum, and HES datasets) and between datasets at the highest level ethnicity categorisation ('Asian', 'black', 'mixed', 'white', 'other'). Representativeness was assessed by comparing the ethnic distributions at the highest-level categorisation of CPRD-HES to those from the Census 2011 across the UK's devolved administrations. Additionally, CPRD-HES was compared to the experimental ethnic distributions for England and Wales from the Office for National Statistics in 2019 (ONS2019) and the English ethnic distribution from May 2021 from NHS Digital's General Practice Extraction Service Data for Pandemic Planning and Research with HES data linkage (GDPPR-HES). Results: In CPRD-HES, 81.7% of currently registered patients in the UK had ethnicity recorded in primary care. For patients with multiple ethnicity records, mismatched ethnicity within individual primary and secondary care datasets was < 10%. Of English patients with ethnicity recorded in both CPRD and HES, 93.3% of records matched at the highest-level categorisation; however, the level of agreement was markedly lower in the 'mixed' and 'other' ethnic groups. CPRD-HES was less proportionately 'white' compared to the UK Census 2011 (80.3% vs. 87.2%) and experimental ONS2019 data (80.4% vs. 84.3%). CPRD-HES was aligned with the ethnic distribution from GDPPR-HES ('white' 80.4% vs. 80.7%); however, with a smaller proportion classified as 'other' (1.1% vs. 2.8%). Conclusions: CPRD-HES has suitable representation of all ethnic categories with some overrepresentation of minority ethnic groups and a smaller proportion classified as 'other' compared to the UK general population from other data sources. CPRD-HES data is useful for studying health risks and outcomes in typically underrepresented groups. [ABSTRACT FROM AUTHOR]

In most healthcare facilities, Electronic Health Records (EHRs) have changed paper-based medical records in the healthcare industry. Nevertheless, there are issues with credibility, management, and secure data storage with the existing EHR frameworks. In order to fill this, this work establishes a cross-chain approach for EHR record sharing that is based on the interoperability of EHRs. Different entities, like patients, healthcare providers, Government Agencies (GA), blockchain, and ledger are included in the proposed system. Several processes including initialization, registration process, agreement and verification process, bank fund transfer and certificate generation, health record exchange among two hospitals, and secret key generation, are used to implement the newly developed authentication approach. In addition, the key is obtained from the interior layers of Deep Residual SpinalNet (Deep RSNet), which is utilized to encrypt the EHR records. The Deep RSNet is the integration of Deep Residual Network (DRN) and SpinalNet. The investigation of the developed technique exposes the supremacy of the Deep RSNet model by achieving minimal values of response time, memory usage, and normalized variance of 53.677 sec, 2.682 MB, and 1.346 respectively. [ABSTRACT FROM AUTHOR]

In the UK, guidelines recommend pancreatic enzyme replacement therapy (PERT) to all people with unresectable pancreatic cancer. In 2023, we published a national audit of PERT which showed suboptimal prescribing and wide regional variation in England. The aim of this manuscript was to describe how we used the PERT audit to drive improvements in healthcare. Building on the PERT audit, we deployed an online dashboard which will deliver ongoing updates of the PERT audit. We developed a collaborative intervention with cancer nurse specialists (CNS) to improve care delivered to people with pancreatic cancer. The intervention called Creating a natiOnAL CNS pancrEatic cancer network to Standardise and improve CarE (COALESCE) will use the dashboard to evaluate improvements in prescribing of PERT. We demonstrated how large databases of electronic healthcare records (EHRs) can be used to improve cancer care. The PERT audit was implemented into a dashboard for tracking the progress of COALESCE. We will measure improvements in PERT prescribing as the intervention with CNS progresses. Improving healthcare is an ongoing and iterative process. By implementing the PERT dashboard, we created a resource-efficient, automated evaluation method enabling COALESCE to deliver a sustainable change. National-scale databases of EHRs enable rapid cycles of audits, providing regular feedback to interventions, working systematically to deliver change. Here, the focus is on pancreatic cancer. However, this methodology is transferable to other areas of healthcare. Nurses play a key role in collecting good quality data which are needed in clinical audits to identify shortcomings in healthcare. Nurse-driven interventions can be designed to improve healthcare. In this study, we capitalize on the unique role of CNS coordinating care for every patient with cancer. COALESCE is the first national collaborative study which uses CNS as researchers and change agents. [ABSTRACT FROM AUTHOR]

Certification mechanisms are often employed to assess and signal difficult-to-observe management practices and foster improvement. In the U.S. healthcare sector, a certification mechanism called meaningful-use attestation was recently adopted as part of an effort to encourage electronic health record (EHR) adoption while also focusing healthcare providers on protecting sensitive healthcare data. This new regime motivated us to examine how meaningful-use attestation influences the occurrence of data breaches. Using a propensity score matching technique combined with a difference-in-differences (DID) approach, our study shows that the impact of meaningful-use attestation is contingent on the nature of data breaches and the time frame. Hospitals that attest to having reached Stage 1 meaningful-use standards observe fewer external breaches in the short term, but do not see continued improvement in the following year. On the other hand, attesting hospitals observe short-term increases in accidental internal breaches but eventually see long-term reductions. We do not find any link between malicious internal breaches and attestation. Our findings offer theoretical and practical insights into the effective design of certification mechanisms. [ABSTRACT FROM AUTHOR]

Models that can effectively represent structured Electronic Healthcare Records (EHR) are central to an increasing range of applications in healthcare. Due to the sequential nature of health data, Recurrent Neural Networks have emerged as the dominant component within state-of-the-art architectures. The signature transform represents an alternative modelling paradigm for sequential data. This transform provides a non-learnt approach to creating a fixed vector representation of temporal features and has shown strong performances across an increasing number of domains, including medical data. However, the signature method has not yet been applied to structured EHR data. To this end, we follow recent work that enables the signature to be used as a differentiable layer within a neural architecture enabling application in high dimensional domains where calculation would have previously been intractable. Using a heart failure prediction task as an exemplar, we provide an empirical evaluation of different variations of the signature method and compare against state-of-the-art baselines. This first application of neural-signature methods in real-world healthcare data shows a competitive performance when compared to strong baselines and thus warrants further investigation within the health domain. [ABSTRACT FROM AUTHOR]

Background: Clinical coding allows for structured and standardised recording of patients' electronic healthcare records. How clinical and non-clinical staff in general practice approach clinical coding is poorly understood., Aim: To explore primary care staff's experiences and views on clinical coding., Design and Setting: Qualitative, semi-structured interview study among primary care staff across Wales., Method: All general practices within Wales were invited to participate via NHS health boards. Semi-structured questions guided interviews, conducted between February 2023 and June 2023. Audio-recorded data were transcribed and analysed using reflexive thematic analysis., Results: A total of 19 participants were interviewed and six themes were identified: coding challenges, motivation to code, making coding easier, daily task of coding, what and when to code, and coding through COVID., Conclusion: This study demonstrates the complexity of clinical coding in primary care. Clinical and non-clinical staff spoke of systems that lacked intuitiveness, and the challenges of multimorbidity and time pressures when coding in clinical situations. These challenges are likely to be exacerbated in socioeconomically deprived areas, leading to underreporting of disease in these areas. Challenges of clinical coding may lead to implications for data quality, particularly the validity of research findings generated from studies reliant on clinical coding from primary care. There are also consequences for patient care. Participants cared about coding quality and wanted a better way of using coding. There is a need to explore technological and non-technological solutions, such as artificial intelligence, training, and education to unburden people using clinical coding in primary care., (© The Authors.)

Abstract Background Acute myocardial infarction (AMI) is an uncommon but fatal complication among patients undergoing elective spinal fusion surgery (SF), total hip arthroplasty (THA), and total knee arthroplasty (TKA). Our objective was to estimate the incidence of AMI among adults undergoing elective SF, THA, and TKA in different post-operative risk windows and characterize high-risk sub-populations in the United States. Methods A retrospective cohort study was conducted using data from a longitudinal electronic healthcare record (EHR) database from January 1, 2007 to June 30, 2018. ICD codes were used to identify SF, THA, TKA, AMI, and selected clinical characteristics. Incidence proportions (IPs) and 95% confidence intervals were estimated in the following risk windows: index hospitalization, ≤ 30, ≤ 90, ≤ 180, and ≤ 365 days post-operation. Results A total of 67,533 SF patients, 87,572 THA patients, and 167,480 TKA patients were eligible for the study. The IP of AMI after SF, THA, and TKA ranged from 0.36, 0.28, and 0.25% during index hospitalization to 1.05, 0.93, and 0.85% ≤ 365 days post-operation, respectively. The IP of AMI was higher among patients who were older, male, with longer hospital stays, had a history of AMI, and had a history of diabetes. Conclusion The IP of post-operative AMI was generally highest among the SF cohort compared to the THA and TKA cohorts. Additionally, potential high-risk populations were identified. Future studies in this area are warranted to confirm these findings via improved confounder control and to identify effect measure modifiers.

Healthcare information is essential for both service providers and patients. Further secure sharing and maintenance of Electronic Healthcare Records (EHR) are imperative. EHR systems in healthcare have traditionally relied on a centralized system (e.g., cloud) to exchange health data across healthcare stakeholders, which may expose private and sensitive patient information. EHR has struggled to meet the demands of several stakeholders and systems in terms of safety, isolation, and other regulatory constraints. Blockchain is a distributed, decentralized ledger technology that can provide secured, validated, and immutable data sharing facilities. Blockchain creates a distributed ledger system using techniques of cryptography (hashes) that are consistent and permit actions to be carried out in a distributed manner without needing a centralized authority. Data exploitation is difficult and evident in a blockchain network due to its immutability. We propose an architecture based on blockchain technology that authenticates the user identity using a Proof of Stake (POS) cryptography consensus mechanism and Secure Hash Algorithm (SHA256) to secure EHR sharing among different electronic healthcare systems. An Elliptic Curve Digital Signature Algorithm (ECDSA) is used to verify EHR sensors to assemble and transmit data to cloud infrastructure. Results indicate that the proposed solution performs exceptionally well when compared with existing solutions, which include Proof-Of-Work (POW), Secure Hash Algorithm (SHA-1), and Message Digest (MD5) in terms of power consumption, authenticity, and security of healthcare records.

The implementation of Electronic Healthcare Records (EHR) is yet to achieve its full extent of utilization. The implemented EHRs are still, mainly used for the administrative purpose rather than clinical use. To shift the EHR use from administrative to clinical we need to improve the way EHRs are interpreted and used. The records inside an EHR should be allied in such a manner that they can provide a multifaceted clinical conceptual view. However, the extraction of information and knowledge from an EHR becomes very critical due to the grouping of data under different headers and the way in which they are associated with the preceding healthcare events. In this paper, we have presented an Electronic Healthcare Record Systems architecture with associated context parameters to improve the performance and utilization of clinical EHR systems. The associated context parameters can act as a key to interpret the patients' health condition and extract desired knowledge from the system. This paper also provides a comparison of different EHR systems on various parameters along with a case study of Mimic III database. [ABSTRACT FROM AUTHOR]

Abstract: A significant proportion of children/adolescents report chronic widespread pain (CWP), but little is known about clinically relevant CWP or what factors lead to onset in this population. Objectives were to report the primary care consultation prevalence of CWP and investigate risk factors associated with onset. A validated algorithm for identifying CWP status from primary care electronic healthcare records was applied to a child or adolescent population (aged 8-18 years). The algorithm records patients who have recurrent pain consultations (axial skeleton and upper or lower limbs) or those with a nonspecific generalised pain disorder (eg, fibromyalgia). Prevalence was described, and a nested case-control study was established to identify risk factors associated with CWP onset using logistic regression producing odds ratios (ORs) and 95% confidence intervals (95% CIs). Two hundred seventy-one children or adolescents were identified with CWP, resulting in a 5-year consultation prevalence of 3.19%. Risk factors significantly associated with CWP onset were as follows: mental health (eg, anxiety/neurosis consultations), neurological (eg, headaches), genitourinary (eg, cystitis), gastrointestinal (eg, abdominal pain), and throat problems (eg, sore throats). Children or adolescents with 1 or 2 risk factors (OR 2.15, 95% CI 1.6-2.9) or 3 or more risk factors (OR 9.17, 95% CI 5.9-14.3) were at significantly increased odds of CWP onset compared with those with none. Findings show a significant proportion of the child or adolescent primary care population has CWP. Most risk factors involved pain-related conditions, suggesting potential pathways of pain development. Further work is now needed to better understand the development of CWP in children and adolescents. [ABSTRACT FROM AUTHOR]

Background: Electronic health record (EHR) databases provide rich, longitudinal data on interactions with healthcare providers and can be used to advance research into respiratory conditions. However, since these data are primarily collected to support health care delivery, clinical coding can be inconsistent, resulting in inherent challenges in using these data for research purposes. Methods: We systematically searched existing international literature and UK code repositories to find respiratory disease codelists for asthma from January 2018, and chronic obstructive pulmonary disease and respiratory tract infections from January 2020, based on prior searches. Medline searches using key terms provided in article lists. Full-text articles, supplementary files, and reference lists were examined for codelists, and codelists repositories were searched. A reproducible methodology for codelists creation was developed with recommended lists for each disease created based on multidisciplinary expert opinion and previously published literature. Results: Medline searches returned 1126 asthma articles, 70 COPD articles, and 90 respiratory infection articles, with 3%, 22% and 5% including codelists, respectively. Repository searching returned 12 asthma, 23 COPD, and 64 respiratory infection codelists. We have systematically compiled respiratory disease codelists and from these derived recommended lists for use by researchers to find the most up-to-date and relevant respiratory disease codelists that can be tailored to individual research questions. Conclusion: Few published papers include codelists, and where published diverse codelists were used, even when answering similar research questions. Whilst some advances have been made, greater consistency and transparency across studies using routine data to study respiratory diseases are needed. [ABSTRACT FROM AUTHOR]

Hospital emergency department (ED) visits by asthmatics differ based on race and season. The objectives of this study were to investigate season- and race-specific disparities for asthma risk, and to identify environmental exposure variables associated with ED visits among more than 42,000 individuals of African American (AA) and European American (EA) descent identified through electronic health records (EHRs). We examined data from 42,375 individuals (AAs = 14,491, EAs = 27,884) identified in EHRs. We considered associated demographic (race, age, gender, insurance), clinical (smoking status, ED visits, FEV1%), and environmental exposures data (mold, pollen, and pollutants). Machine learning techniques, including random forest (RF), extreme gradient boosting (XGB), and decision tree (DT) were used to build and identify race- and -season-specific predictive models for asthma ED visits. Significant differences in ED visits and FEV1% among AAs and EAs were identified. ED visits by AAs was 32.0% higher than EAs and AAs had 6.4% lower FEV1% value than EAs. XGB model was used to accurately classify asthma patients visiting ED into AAs and EAs. Pollen factor and pollution (PM2.5, PM10) were the key variables for asthma in AAs and EAs, respectively. Age and cigarette smoking increase asthma risk independent of seasons. In this study, we observed racial and season-specific disparities between AAs and EAs asthmatics for ED visit and FEV1% severity, suggesting the need to address asthma disparities through key predictors including socio-economic status, particulate matter, and mold. [ABSTRACT FROM AUTHOR]

Automated medical claims processing and billing is a popular application domain of information technology. Managing medical related data is a tedious job for healthcare professionals, which distracts them from their main job of healthcare. The technology used in data management has a sound impact on the quality of healthcare data. Most of Information Technology (IT) organizations use conventional software development technology for the implementation of healthcare systems. The objective of this experimental study is to devise a mechanism for use of rule-based expert systems in medical related edits and compare it with the conventional software development technology. A sample of 100 medical edits is selected as a dataset to be tested for implementation using both technologies. Besides empirical analysis, paired t-test is also used to validate the statistical significance of the difference between the two techniques. The conventional software development technology took 254.5 working hours, while rule-based technology took 81 hours to process these edits. Rule-based technology outperformed the conventional systems by increasing the confidence value to 95% and reliability measure to 0.462 (which is < 0.5) which is three times more efficient than conventional software development technology. [ABSTRACT FROM AUTHOR]

Abstract Background Since the recent conflicts in Iraq and Afghanistan, the short-term focus of military healthcare research has been on the consequences of deployment for mental health and on those wounded or injured in combat. Now that these conflicts have ended for the UK Armed Forces, it is important to consider the longer term physical and mental health consequences, and just as importantly, the links between these. The aims of this study were to determine the most common physical conditions requiring a hospital admission in UK military personnel and whether they were more common in personnel with a mental health condition, smokers, and/or those misusing alcohol compared to those without. Methods Data linkage of a prospective UK military cohort study to electronic admitted patient care records for England, Wales and Scotland. Nine thousand nine hundred ninety military personnel completed phase 2 of a military cohort study (56% response rate, data collected from 2007 to 2009), with analyses restricted to 86% of whom provided consent for linkage to healthcare records (n = 8602). Ninety percent were male and the mean age at phase 2 was 36 years. The outcome was physical non communicable diseases (NCDs) requiring a hospital admission which occurred after phase 2 of the cohort when the mental health, smoking and alcohol use exposure variables had been assessed until the end of March 2014. Results The most common NCDs requiring a hospital admission were gastrointestinal disorders 5.62% (95% Confidence Intervals (CI) 5.04, 6.19) and joint disorders 5.60% (95% CI 5.02, 6.18). Number of NCDs requiring a hospital admission was significantly higher in those with a common mental disorder (Hazard ratio (HR) 1.40 (95% CI 1.16–1.68), post-traumatic stress disorder (HR 1.78 (95% CI 1.32–2.40)) and in current smokers (HR 1.35 (95% CI 1.12–1.64) compared to those without the disorder, and non-smokers, respectively. Conclusions Military personnel with a mental health problem are more likely to have an inpatient hospital admission for NCDs compared to those without, evidencing the clear links between physical and mental health in this population.

Abstract Background Although stroke is a rare complication among spinal surgery patients, the recognition of this adverse event is critical given the aging population undergoing surgical procedures. The objective of this study was to estimate the incidence of stroke among selected adults undergoing elective posterior lumbar fusion (PLF) during various post-operative risk windows and among different subgroups. Methods A retrospective cohort study using a longitudinal electronic healthcare record (EHR) database was conducted from January 1, 2007 to June 30, 2018. Elective PLF, stroke, and select clinical characteristics were defined based on International Classification of Disease codes. Patients aged 18 to 85 years with ≥183 days of enrollment in the database prior to undergoing elective PLF were followed from the index date until the occurrence of stroke, death, loss to follow-up, or end of study period, whichever occurred first. The incidence of stroke was estimated in the following risk windows: index hospitalization, ≤ 30 days, ≤ 90 days, ≤ 180 days, and ≤ 365 days post-operation. Results A total of 43,063 patients were eligible for the study. The incidence of stroke following elective PLF was 0.29% (95% confidence interval [CI]: 0.25, 0.35%) during index hospitalization, 0.44% (95% CI: 0.38, 0.50%) ≤ 30 days, 0.59% (95% CI: 0.52, 0.67%) ≤ 90 days, 0.76% (95% CI: 0.68, 0.85%) ≤ 180 days, and 1.12% (95% CI: 1.03, 1.23%) ≤ 365 days post-operation. Stratified analyses revealed that older patients had a higher incidence of stroke. Additionally, black patients had higher stroke incidences. Post-operative stroke incidence was higher among patients with a history of type 2 diabetes than among patients without such history; similarly, stroke incidence was higher among patients with a history of stroke compared to patients without such history. Conclusions The incidence of stroke following elective PLF using an EHR database in this study is slightly higher than that reported in the literature. Our results suggest that stroke risk modification prior to PLF may be important for patients who are older, black, type 2 diabetic, and/or have a history of stroke.

We evaluated the effect of sodium-glucose cotransporter 2 (SGLT2) inhibition on prostate cancer by evidence triangulation. Using Mendelian randomization, we found that genetically proxied SGLT2 inhibition reduced the risk of overall (odds ratio = 0.56, 95% confidence interval [CI] = 0.38 to 0.82; 79,148 prostate cancer cases and 61,106 controls), advanced, and early-onset prostate cancer. Using electronic healthcare data (n SGLT2i  = 24,155; n DPP4i  = 24,155), we found that the use of SGLT2 inhibitors was associated with a 23% reduced risk of prostate cancer (hazard ratio = 0.77, 95% CI = 0.61 to 0.99) in men with diabetes. Using data from two prospective cohorts (n 4C  = 57,779; n UK&#95;Biobank  = 165,430), we found little evidence to support the association of HbA 1c with prostate cancer, implying a non-glycemic effect of SGLT2 inhibition on prostate cancer. In summary, this study provides multiple layers of evidence to support the beneficial effect of SGLT2 inhibition on reducing prostate cancer risk. Future trials are warranted to investigate whether SGLT2 inhibitors can be recommended for prostate cancer prevention., Competing Interests: Declaration of interests G.D.S. reports scientific advisory board membership for Relation Therapeutics and Insitro. UK Biobank has received ethical approval from the UK National Health Service’s National Research Ethics Service (ref. 11/NW/0382). All other studies contributing data to this analysis had the relevant institutional review board approval from each country and all participants provided informed consent., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Electronic healthcare records (EHR) are increasingly used in epidemiological studies but are often viewed as lacking quality compared to randomised control trials and prospective cohorts. Studies of patients with chronic obstructive pulmonary disease (COPD) often use the rate of forced expiratory volume in 1 second (FEV1) decline as an outcome; however, its definition and robustness in EHR have not been investigated. We aimed to investigate how the rate of FEV1 decline differs by the criteria used in an EHR database. Methods: Clinical Practice Research Datalink and Hospital Episode Statistics were used. Patient populations were defined using 8 sets of criteria around repeated FEV1 measurements. At a minimum, patients had a diagnosis of COPD, were ≥ 35 years old, were current or ex-smokers, and had data recorded from 2004. FEV1 measurements recorded during follow-up were identified. Thereafter, eight populations were defined based on criteria around: i) the exclusion of patients or individual measurements with potential measurement error; ii) minimum number of FEV1 measurements; iii) minimum time interval between measurements; iv) specific timing of measurements; v) minimum follow-up time; and vi) the use of linked data. For each population, the rate of FEV1 decline was estimated using mixed linear regression. Results: For 7/8 patient populations, rates of FEV1 decline (age and sex adjusted) were similar and ranged from − 18.7mL/year (95% CI − 19.2 to − 18.2) to − 16.5mL/year (95% CI − 17.3 to − 15.7). Rates of FEV1 decline in populations that excluded patients with potential measurement error ranged from − 79.4mL/year (95% CI − 80.7 to − 78.2) to − 46.8mL/year (95% CI − 47.6 to − 46.0). Conclusion: FEV1 decline remained similar in a COPD population regardless of number of FEV1 measurements, time intervals between measurements, follow-up period, exclusion of specific FEV1 measurements, and linkage to HES. However, exclusion of individuals with questionable data led to selection bias and faster rates of decline. [ABSTRACT FROM AUTHOR]

Background: Investigating obstetric near misses (life-threatening obstetric complications) provides crucial information to prevent maternal mortality and morbidity.Aims: To investigate the rate and type of obstetric near misses among women with serious mental illness (SMI).Method: We conducted a historical cohort study, using de-identified electronic mental health records linked with maternity data from Hospital Episode Statistics. The English Maternal Morbidity Outcome Indicator was used to identify obstetric near misses at the time of delivery in two cohorts: (1) exposed cohort - all women with a live or still birth in 2007-2016, and a history of secondary mental healthcare before delivery in south-east London (n = 13 570); (2) unexposed cohort - all women with a live or still birth in 2007-2016, resident within south-east London, with no history of mental healthcare before delivery (n = 223 274).Results: The rate of obstetric near misses was 884.3/100 000 (95% CI 733.2-1057.4) maternities in the exposed group compared with 575.1/100 000 (95% CI 544.0-607.4) maternities in the unexposed group (adjusted odds ratio 1.6, 95% CI 1.3-2.0, P < 0.001). Highest risks were for acute renal failure (adjusted odds ratio 2.1, 95% CI 1.1-3.8, P = 0.022); cardiac arrest, failure or infarction (adjusted odds ratio 2.3, 95% CI 1.1-4.8, P = 0.028); and obstetric embolism (adjusted odds ratio 3.1, 95% CI 1.6-5.8, P < 0.001).Conclusions: Findings emphasise the importance of integrated physical and mental healthcare before and during pregnancy for women with SMI. [ABSTRACT FROM AUTHOR]

Abstract Background With the improvements to text mining technology and the availability of large unstructured Electronic Healthcare Records (EHR) datasets, it is now possible to extract structured information from raw text contained within EHR at reasonably high accuracy. We describe a text mining system for classifying radiologists’ reports of CT and MRI brain scans, assigning labels indicating occurrence and type of stroke, as well as other observations. Our system, the Edinburgh Information Extraction for Radiology reports (EdIE-R) system, which we describe here, was developed and tested on a collection of radiology reports.The work reported in this paper is based on 1168 radiology reports from the Edinburgh Stroke Study (ESS), a hospital-based register of stroke and transient ischaemic attack patients. We manually created annotations for this data in parallel with developing the rule-based EdIE-R system to identify phenotype information related to stroke in radiology reports. This process was iterative and domain expert feedback was considered at each iteration to adapt and tune the EdIE-R text mining system which identifies entities, negation and relations between entities in each report and determines report-level labels (phenotypes). Results The inter-annotator agreement (IAA) for all types of annotations is high at 96.96 for entities, 96.46 for negation, 95.84 for relations and 94.02 for labels. The equivalent system scores on the blind test set are equally high at 95.49 for entities, 94.41 for negation, 98.27 for relations and 96.39 for labels for the first annotator and 96.86, 96.01, 96.53 and 92.61, respectively for the second annotator. Conclusion Automated reading of such EHR data at such high levels of accuracies opens up avenues for population health monitoring and audit, and can provide a resource for epidemiological studies. We are in the process of validating EdIE-R in separate larger cohorts in NHS England and Scotland. The manually annotated ESS corpus will be available for research purposes on application.