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ABSTRCT Objective Using sickle cell disease (SCD) as a model, the objective of this study was to create a comprehensive learning healthcare system to support disease management and research. A multidisciplinary team developed a SCD clinical data dictionary to standardize bedside data entry and inform a scalable environment capable of converting complex electronic healthcare records (EHRs) into knowledge accessible in real time. Materials and Methods Clinicians expert in SCD care developed a data dictionary to describe important SCD-associated health maintenance and adverse events. The SCD data dictionary was deployed in the EHR using EPIC SmartForms, an efficient bedside data entry tool. Additional data elements were extracted from the EHR database (Clarity) using Pentaho Data Integration and stored in a data analytics database (SQL). A custom application, the Sickle Cell Knowledgebase, was developed to improve data analysis and visualization. Utilization, accuracy, and completeness of data entry were assessed. Results The SCD Knowledgebase facilitates generation of patient-level and aggregate data visualization, driving the translation of data into knowledge that can impact care. A single patient can be selected to monitor health maintenance, comorbidities, adverse event frequency and severity, and medication dosing/adherence. Conclusions Disease-specific data dictionaries used at the bedside will ultimately increase the meaningful use of EHR datasets to drive consistent clinical data entry, improve data accuracy, and support analytics that will facilitate quality improvement and research.

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.

Hannah R Whittaker,1 Steven J Kiddle,2 Jennifer K Quint1 1National Heart and Lung Institute, Imperial College London, London, UK; 2MRC Biostatistics Unit, University of Cambridge, Cambridge, UKCorrespondence: Hannah R Whittaker Email h.whittaker@imperial.ac.ukBackground: 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.Keywords: electronic healthcare records, spirometry, COPD, lung function

BackgroundInvestigating obstetric near misses (life-threatening obstetric complications) provides crucial information to prevent maternal mortality and morbidity.AimsTo investigate the rate and type of obstetric near misses among women with serious mental illness (SMI).MethodWe 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).ResultsThe 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).ConclusionsFindings emphasise the importance of integrated physical and mental healthcare before and during pregnancy for women with SMI.

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.

Patients’ online access to their EHR together with the rapid proliferation of medical information on the Internet has changed how patients use information to learn about their health. Patients’ tendency to turn to the Internet to find information about their health and care is well-documented. However, little is known about patients’ information seeking behavior when using online EHRs. By using information horizons as an analytical tool this paper aims to investigate the information behavior of cancer patients who have chosen to view their EHRs (readers) and to those who have not made that option (non-readers). Thirty interviews were conducted with patients. Based on information horizons, it seems that non-reading is associated with living in a narrower information world in comparison to readers. The findings do not suggest that the smallness would be a result of active avoidance of information, or that it would be counterproductive for the patients. The findings suggest, however, that EHRs would benefit from comprehensive linking to authoritative health information sources to help users to understand their contents. In parallel, healthcare professionals should be more aware of their personal role as a key source of health information to those who choose not to read their EHRs. CC BY 4.0© The Author(s) 2021.Corresponding author: Isto Huvila, Department of ALM, Uppsala University, Thunbergsvägen 3H, Uppsala 75105, Sweden. Email: isto.huvila@abm.uu.seThe author(s) disclosed of the following financial support for the research, authorship, and/or publication of this article. This work was partly supported by NordForsk through the funding to Nordic eHealth for Patients: Benchmarking and Developing for the Future (NORDeHEALTH), project number 100477.

Objective: To improve asthma morbidity and mortality in the UK, national asthma guidelines recommend referral to specialist care for the following high risk groups, after a hospital admission for asthma, ≥3 courses of oral corticosteroids (OCS) in 12-months, an incident high-dose inhaled corticosteroid (ICS) prescription or addition of a fourth asthma drug to a patient’s maintenance regimen. We sought to assess the prevalence and temporal change of referrals to identify unmet needs. Methods: We used UK electronic healthcare records, 2006-2017, to identify high risk asthma patients managed within primary care. Referrals to respiratory clinics in secondary care were measured, within 3 months before or 6 months after, an incident ICS, third OCS in a year, or fourth asthma drug; or 12 months after a hospital admission for asthma. A nested case-control and conditional logistic regression was used to evaluate factors associated with receiving a referral. Results: 246,116 asthma patients were eligible. There was a slight increase in secondary care referrals from 2014 onwards but the percentage remained low with

Background Knowledge about the nature of long-term changes in kidney function in the general population is sparse. We aim to identify whether primary care electronic healthcare records capture sufficient information to study the natural history of kidney disease. Methods The National Chronic Kidney Disease Audit database covers ∼14% of the population of England and Wales. Availability of repeat serum creatinine tests was evaluated by risk factors for chronic kidney disease (CKD) and individual changes over time in estimated glomerular filtration rate (eGFR) were estimated using linear regression. Sensitivity of estimation to method of evaluation of eGFR compared laboratory-reported eGFR and recalculated eGFR (using laboratory-reported creatinine), to uncover any impact of historical creatinine calibration issues on slope estimation. Results Twenty-five per cent of all adults, 92% of diabetics and 96% of those with confirmed CKD had at least three creatinine tests, spanning a median of 5.7 years, 6.2 years and 6.1 years, respectively. Median changes in laboratory-reported eGFR (mL/min/1.73 m2/year) were −1.32 (CKD) and −0.60 (diabetes). Median changes in recalculated eGFR were −0.98 (CKD) and −0.11 (diabetes), underestimating decline. Magnitude of underestimation (and between-patient variation in magnitude) decreased with deteriorating eGFR. For CKD Stages 3, 4 and 5 (at latest eGFR), median slopes were −1.27, −2.49 and -3.87 for laboratory-reported eGFR and −0.89, −2.26 and −3.75 for recalculated eGFR. Conclusions Evaluation of long-term changes in renal function will be possible in those at greatest risk if methods are identified to overcome creatinine calibration problems. Bias will be reduced by focussing on patients with confirmed CKD.

Background Healthcare work is, to a considerable extent, cognitive. Subsequently, the analysis and the design of supporting technology must be sensitive to the cognitive and adaptive demands of the work and to the cognitive strategies employed by healthcare practitioners. Despite the vital role that cognition plays in healthcare work, current technocentric design approaches for healthcare technology do not account for it, failing to observe it during analysis and failing to develop support for it during design. Main body By review and analysis of case studies, we show that healthcare systems developed without input from cognitive analysis and cognitive design fail to take account of important healthcare work processes and workflows. In contrast, systems developed with a cognitively-focused design strategy demonstrate how it is possible to introduce technology that supports and enhances the work strategies of those engaged in patient care. Conclusion Significant problems emerge when technological support systems are developed without any serious and comprehensive attempt to understand the cognitive capabilities and skills deployed by those involved in patient care. In contrast, significant benefits accrue from taking full account of those cognitive capabilities and skills. Subsequently, the design and development of supporting technology must be sensitive to the cognitive demands of the work and the cognitive strategies employed by healthcare practitioners.

The healthcare industry deals with highly sensitive data which must be managed in a secure way. Electronic Health Records (EHRs) hold various kinds of personal and sensitive data which contain names, addresses, social security numbers, insurance numbers, and medical history. Such personal data is valuable to the patients, healthcare service providers, medical insurance companies, and research institutions. However, the public release of this highly sensitive personal data poses serious privacy and security threats to patients and healthcare service providers. Hence, we foresee the requirement of new technologies to address the privacy and security challenges for personal data in healthcare applications. Blockchain is one of the promising solutions, aimed to provide transparency, security, and privacy using consensus-driven decentralised data management on top of peer-to-peer distributed computing systems. Therefore, to solve the mentioned problems in healthcare applications, in this paper, we investigate the use of private blockchain technologies to assess their feasibility for healthcare applications. We create testing scenarios using HyperLedger Fabric to explore different criteria and use-cases for healthcare applications. Additionally, we thoroughly evaluate the representative test case scenarios to assess the blockchain-enabled security criteria in terms of data confidentiality, privacy and access control. The experimental evaluation reveals the promising benefits of private blockchain technologies in terms of security, regulation compliance, compatibility, flexibility, and scalability.

ObjectiveThe aim of this study is to guide healthcare instances in applying process analytics on healthcare processes. Process analytics techniques can offer new insights in patient pathways, workflow processes, adherence to medical guidelines and compliance with clinical pathways, but also bring along specific challenges which will be examined and addressed in this paper. MethodsThe following methodology is proposed: log preparation, log inspection, abstraction and selection, clustering, process mining, and validation. It was applied on a case study in the type 2 diabetes mellitus domain. ResultsSeveral data pre-processing steps are applied and clarify the usefulness of process analytics in a healthcare setting. Healthcare utilization, such as diabetes education, is analyzed and compared with diabetes guidelines. Furthermore, we take a look at the organizational perspective and the central role of the GP. This research addresses four challenges: healthcare processes are often patient and hospital specific which leads to unique traces and unstructured processes; data is not recorded in the right format, with the right level of abstraction and time granularity; an overflow of medical activities may cloud the analysis; and analysts need to deal with data not recorded for this purpose. These challenges complicate the application of process analytics. It is explained how our methodology takes them into account. ConclusionProcess analytics offers new insights into the medical services patients follow, how medical resources relate to each other and whether patients and healthcare processes comply with guidelines and regulations. HighlightsProcess analytics offers new insights into healthcare processes.The usefulness of process analytics is shown in a type 2 diabetes mellitus setting.A methodology is proposed to deal with specific challenges.The applicability of the methodology for healthcare challenges is illustrated.

Highlights • This study of 3.7 million people was prompted by concerns that lung cancer in non-smokers (LCINS) is increasing in the UK. • Using a cohort approach, we could account for the increase in never smokers over time as well as the aging population. • Our results suggest that the incidence of LCINS over the past twenty years has reduced dramatically in men and remained fairly stable in women., Introduction : There are reports that lung cancer in non-smokers (LCINS) is increasing in the United Kingdom (UK) and other high-income countries but evidence from large-scale cohort studies to support this claim is limited. Material and methods : Using The Health Improvement Network (THIN) IQVIA™ Medical Research Data, we identified a cohort of 3,679,831 people from the UK self-reporting to their primary care physician as never or non-smokers. We estimated age-adjusted incidence rates for recorded lung cancer before (1998−2007) and after (2008−2018) the introduction of smoke-free legislation using multivariable Poisson regression. We also explored the impact of geographic location, social deprivation and urbanicity. Results : The analysis included 3,212 lung cancer events and 28 million person-years (PYs). Between 1998 and 2007, the age-adjusted rates in men declined by 9% per year (95 %CI: 7–11%) from an estimated 5.6 to 1.5 per 10,000 PYs and by 3% per year (95 %CI: 1–5%) between 2008 and 2018. These trends for men were similar across sociodemographic strata. Between 1998 and 2007, age-adjusted rates were stable for women at 1.5 per 10,000 PYs. However, there was evidence that time trends for women differed depending on levels of social deprivation with rates increasing by 5% per year (95 %CI: 2–9%) from an estimated 1.3–2.1 per 10,000 PYs for women living in the least socially deprived areas. Sex-specific time trends from 2008 to 2016 were broadly similar in a separate cohort of self-reported never smokers from UK Biobank with cancer events linked to national registries. Conclusion In summary, the incidence of LCINS has reduced or remained stable for most of the UK with the possible exception of women living in the least socially deprived areas.

BackgroundConcerns regarding adverse events following vaccination (AEFIs) are a key challenge for public confidence in vaccination. Robust postlicensure vaccine safety monitoring remains critical to detect adverse events, including those not identified in prelicensure studies, and to ensure public safety and public confidence in vaccination. We summarise the literature examined AEFI signal detection using electronic healthcare data, regarding data sources, methodological approach and statistical analysis techniques used.MethodsWe performed a systematic review using the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Five databases (PubMed/Medline, EMBASE, CINAHL, the Cochrane Library and Web of Science) were searched for studies on AEFIs monitoring published up to 25 September 2017. Studies were appraised for methodological quality, and results were synthesised narratively.ResultWe included 47 articles describing AEFI signal detection using electronic healthcare data. All studies involved linked diagnostic healthcare data, from the emergency department, inpatient and outpatient setting and immunisation records. Statistical analysis methodologies used included non-sequential analysis in 33 studies, group sequential analysis in two studies and 12 studies used continuous sequential analysis. Partially elapsed risk window and data accrual lags were the most cited barriers to monitor AEFIs in near real-time.ConclusionRoutinely collected electronic healthcare data are increasingly used to detect AEFI signals in near real-time. Further research is required to check the utility of non-coded complaints and encounters, such as telephone medical helpline calls, to enhance AEFI signal detection.Trial registration numberCRD42017072741

Sepsis affects millions of people worldwide each year. It occurs when a normal human immune response to a bacterial, viral or fungal infection becomes dysfunctional and triggers widespread inflammation that results in severe tissue damage that leads to organ failure, shock, and death. Sepsis, requires immediate treatment and has a high readmission rate for survivors. It is also one of the most expensive conditions to treat. In 2013, there were more than 1.6 million cases of sepsis in the United States with a financial cost of more than $23 billion. Sepsis was first described in antiquity, and given its current name, by the ancient Greek physician Hippocrates. Despite its long medical history, severity, and financial burden, the causes of sepsis are not well understood, and there is no standard approach to diagnosis and treatment. The definition of sepsis, the characterization of its clinical stages, and sepsis monitoring tools have changed three times in the past 25 years, most recently in March 2016. The universal adoption of this latest definition, sepsis-3, and a screening tool, qSOFA, are currently under debate in the medical community. A means to rapidly identify and treat sepsis could reduce the five million deaths due to sepsis each year worldwide. This paper reviews the evolution of the definition of sepsis and the controversy surrounding the sepsis-3 definition and the sepsis screening tool, qSOFA., Comment: 16 pages, 6 figures

The semantic interoperability between health information systems is a major challenge to improve the quality of clinical practice and patient safety. In recent years many projects have faced this problem and provided solutions based on specific standards and technologies in order to satisfy the needs of a particular scenario. Most of such solutions cannot be easily adapted to new scenarios, thus more global solutions are needed.In this work, we have focused on the semantic interoperability of electronic healthcare records standards based on the dual model architecture and we have developed a solution that has been applied to ISO 13606 and openEHR. The technological infrastructure combines reference models, archetypes and ontologies, with the support of Model-driven Engineering techniques. For this purpose, the interoperability infrastructure developed in previous work by our group has been reused and extended to cover the requirements of data transformation.

The life-long clinical information of any person supported by electronic means configures his Electronic Health Record (EHR). This information is usually distributed among several independent and heterogeneous systems that may be syntactically or semantically incompatible. There are currently different standards for representing and exchanging EHR information among different systems. In advanced EHR approaches, clinical information is represented by means of archetypes. Most of these approaches use the Archetype Definition Language (ADL) to specify archetypes. However, ADL has some drawbacks when attempting to perform semantic activities in Semantic Web environments. In this work, Semantic Web technologies are used to specify clinical archetypes for advanced EHR architectures. The advantages of using the Ontology Web Language (OWL) instead of ADL are described and discussed in this work. Moreover, a solution combining Semantic Web and Model-driven Engineering technologies is proposed to transform ADL into OWL for the CEN EN13606 EHR architecture.

Possibly the most important requirement to support co-operative work among health professionals and institutions is the ability of sharing EHRs in a meaningful way, and it is widely acknowledged that standardization of data and concepts is a prerequisite to achieve semantic interoperability in any domain. Different international organizations are working on the definition of EHR architectures but the lack of tools that implement them hinders their broad adoption. In this paper we present ResearchEHR, a software platform whose objective is to facilitate the practical application of EHR standards as a way of reaching the desired semantic interoperability. This platform is not only suitable for developing new systems but also for increasing the standardization of existing ones. The work reported here describes how the platform allows for the edition, validation, and search of archetypes, converts legacy data into normalized, archetypes extracts, is able to generate applications from archetypes and finally, transforms archetypes and data extracts into other EHR standards. We also include in this paper how ResearchEHR has made possible the application of the CEN/ISO 13606 standard in a real environment and the lessons learnt with this experience. © 2011 Elsevier Inc.., This work has been partially supported by the Spanish Ministry of Science and Innovation under Grants TIN2010-21388-C02-01 and TIN2010-21388-C02-02, and by the Health Institute Carlos in through the RETICS Combiomed, RD07/0067/2001. Our most sincere thanks to the Hospital of Fuenlabrada in Madrid, including its Medical Director Pablo Serrano together with Marta Terron and Luis Lechuga for their support and work during the development of the medications reconciliation project.