160 results found
Wang W, Snell LB, Ferrari D, et al., 2022, Real-world effectiveness of steroids in severe COVID-19: a retrospective cohort study, BMC INFECTIOUS DISEASES, Vol: 22
Wang W, Rudd AG, Wang Y, et al., 2022, Risk prediction of 30-day mortality after stroke using machine learning: a nationwide registry-based cohort study (vol 22, 195, 2022), BMC NEUROLOGY, Vol: 22
Meza-Torres B, Delanerolle G, Okusi C, et al., 2022, Differences in Clinical Presentation With Long COVID After Community and Hospital Infection and Associations With All-Cause Mortality: English Sentinel Network Database Study, JMIR PUBLIC HEALTH AND SURVEILLANCE, Vol: 8, ISSN: 2369-2960
Mayor N, Meza-Torres B, Okusi C, et al., 2022, Developing a Long COVID Phenotype for Postacute COVID-19 in a National Primary Care Sentinel Cohort: Observational Retrospective Database Analysis, JMIR PUBLIC HEALTH AND SURVEILLANCE, Vol: 8, ISSN: 2369-2960
de Jong VMT, Rousset RZ, Antonio-Villa NE, et al., 2022, Clinical prediction models for mortality in patients with covid-19: external validation and individual participant data meta-analysis, BMJ-BRITISH MEDICAL JOURNAL, Vol: 378, ISSN: 0959-535X
- Author Web Link
- Citations: 6
Thygesen JH, Tomlinson C, Hollings S, et al., 2022, COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records, LANCET DIGITAL HEALTH, Vol: 4, Pages: E542-E557
- Author Web Link
- Citations: 8
Molokhia M, Ayis S, Karamanos A, et al., 2022, What factors influence differential uptake of NHS Health Checks, diabetes and hypertension reviews among women in ethnically diverse South London? Cross-sectional analysis of 63,000 primary care records, ECLINICALMEDICINE, Vol: 49
Wang W, Rudd AG, Wang Y, et al., 2022, Risk prediction of 30-day mortality after stroke using machine learning: a nationwide registry-based cohort study, BMC NEUROLOGY, Vol: 22
- Author Web Link
- Citations: 3
Sivan M, Greenhalgh T, Darbyshire JL, et al., 2022, LOng COvid Multidisciplinary consortium Optimising Treatments and services acrOss the NHS (LOCOMOTION): protocol for a mixed-methods study in the UK, BMJ OPEN, Vol: 12, ISSN: 2044-6055
- Author Web Link
- Citations: 6
Drake A, Sassoon I, Balatsoukas P, et al., 2022, The relationship of socio-demographic factors and patient attitudes to connected health technologies: A survey of stroke survivors, HEALTH INFORMATICS JOURNAL, Vol: 28, ISSN: 1460-4582
Wang W, Snell LB, Ferrari D, et al., 2022, Real-world effectiveness of steroids in severe COVID-19: longer courses associated with lower risk of death or ICU admission
<jats:title>Abstract</jats:title> <jats:p>Purpose We aim to investigate the associations of steroid and length of steroid use with outcomes in severe COVID-19.Methods Severe cases of COVID-19, defined by hypoxia at presentation, and admitted to a multi-site healthcare institution in London were analysed between 02-Sep-2020 and 27-May-2021. The associations between duration of steroid treatment (prescription-days) and outcomes were explored using Cox proportional-hazards models adjusting for confounders. Length of steroid treatment was analysed as both a continuous variable and categorised into < 3, 3–10, and > 10 days. The primary outcome was in-hospital mortality and secondary outcome was in-hospital mortality or intensive care unit (ICU) level-3 admission.Results 734 severe COVID-19 cases were included, with 137/734 (18.7%) treated with steroids for < 3 days, 497/734 (67.7%) for 3–10 days, and 100/734 (13.6%) for > 10 days. Cox modelling with continuous days showed increasing length of steroids decreased the hazard of in-hospital mortality by a factor of 0.98 [95% CI: 0.96-1.0] per additional day and in-hospital mortality or ICU admission by a factor of 0.91 [95% CI: 0.87–0.95] per additional day. Further, when taking 3–10 days steroid treatment group as the reference group, > 10 days steroid showed trends towards decreased hazards for death (HR 0.59 [95%CI: 0.30–1.14]) and was significantly protective for death/ICU outcome (HR 0.28 [95%CI: 0.11–0.68]).Conclusion The protective effect of steroid for severe COVID-19 reported in randomised clinical trials was replicated in this large real-world cohort. We found an association between longer steroid courses and lower risk of death or ICU admission that warrants further investigation.</jats:p>
Snell LB, Wang W, Alcolea-Medina A, et al., 2022, Descriptive comparison of admission characteristics between pandemic waves and multivariable analysis of the association of the Alpha variant (B.1.1.7 lineage) of SARS-CoV-2 with disease severity in inner London, BMJ OPEN, Vol: 12, ISSN: 2044-6055
- Author Web Link
- Citations: 3
Chapman M, G-Medhin A, Sassoon I, et al., 2022, Using Microservices to Design Patient-facing Research Software, Pages: 44-54
With a significant amount of software now being developed for use in patient-facing studies, there is a pressing need to consider how to design this software effectively in order to support the needs of both researchers and patients. We posit that a microservice architecture-which offers a large amount of flexibility for development and deployment, while at the same time ensuring certain quality attributes, such as scalability, are present-provides an effective mechanism for designing such software. To explore this proposition, in this work we show how the paradigm has been applied to the design of Consult, a decision support system that provides autonomous support to stroke patients and is characterised by its use of a data-backed AI reasoner. We discuss the impact that the use of this software architecture has had on the teams developing Consult and measure the performance of the system produced. We show that the use of microservices can deliver software that is able to facilitate both research and effective patient interactions. However, we also conclude that the impact of the approach only goes so far, with additional techniques needed to address its limitations.
Tapuria A, Kordowicz M, Ashworth M, et al., 2021, IT Evaluation of Foundation Healthcare Group NHS Vanguard programme: IT simultaneously an enabler and a rate limiting factor, INFORMATICS FOR HEALTH & SOCIAL CARE, Vol: 47, Pages: 317-325, ISSN: 1753-8157
Hay AD, Moore M, Taylor J, et al., 2021, Immediate oral versus immediate topical versus delayed oral antibiotics for children with acute otitis media with discharge: the REST three-arm non-inferiority electronic platform-supported RCT Introduction, HEALTH TECHNOLOGY ASSESSMENT, Vol: 25, Pages: 1-+, ISSN: 1366-5278
Chapman M, Mumtaz S, Rasmussen L, et al., 2021, Desiderata for the development of next-generation electronic health record phenotype libraries, GIGASCIENCE, Vol: 10, ISSN: 2047-217X
- Author Web Link
- Citations: 6
Wongkoblap A, Vadillo MA, Curcin V, 2021, Deep Learning With Anaphora Resolution for the Detection of Tweeters With Depression: Algorithm Development and Validation Study, JMIR MENTAL HEALTH, Vol: 8, ISSN: 2368-7959
- Author Web Link
- Citations: 3
Cabral C, Curtis K, Curcin V, et al., 2021, Challenges to implementing electronic trial data collection in primary care: a qualitative study, BMC Family Practice, Vol: 22, ISSN: 1471-2296
BackgroundWithin-consultation recruitment to primary care trials is challenging. Ensuring procedures are efficient and self-explanatory is the key to optimising recruitment. Trial recruitment software that integrates with the electronic health record to support and partially automate procedures is becoming more common. If it works well, such software can support greater participation and more efficient trial designs. An innovative electronic trial recruitment and outcomes software was designed to support recruitment to the Runny Ear randomised controlled trial, comparing topical, oral and delayed antibiotic treatment for acute otitis media with discharge in children. A qualitative evaluation investigated the views and experiences of primary care staff using this trial software.MethodsStaff were purposively sampled in relation to site, role and whether the practice successfully recruited patients. In-depth interviews were conducted using a flexible topic guide, audio recorded and transcribed. Data were analysed thematically.ResultsSixteen staff were interviewed, including GPs, practice managers, information technology (IT) leads and research staff. GPs wanted trial software that automatically captures patient data. However, the experience of getting the software to work within the limited and complex IT infrastructure of primary care was frustrating and time consuming. Installation was reliant on practice level IT expertise, which varied between practices. Although most had external IT support, this rarely included supported for research IT. Arrangements for approving new software varied across practices and often, but not always, required authorisation from Clinical Commissioning Groups.ConclusionsPrimary care IT systems are not solely under the control of individual practices or CCGs or the National Health Service. Rather they are part of a complex system that spans all three and is influenced by semi-autonomous stakeholders operating at different levels. This led
Ford E, Edelman N, Somers L, et al., 2021, Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners, BMC Medical Informatics and Decision Making, Vol: 21, ISSN: 1472-6947
BACKGROUND: Well-established electronic data capture in UK general practice means that algorithms, developed on patient data, can be used for automated clinical decision support systems (CDSSs). These can predict patient risk, help with prescribing safety, improve diagnosis and prompt clinicians to record extra data. However, there is persistent evidence of low uptake of CDSSs in the clinic. We interviewed UK General Practitioners (GPs) to understand what features of CDSSs, and the contexts of their use, facilitate or present barriers to their use. METHODS: We interviewed 11 practicing GPs in London and South England using a semi-structured interview schedule and discussed a hypothetical CDSS that could detect early signs of dementia. We applied thematic analysis to the anonymised interview transcripts. RESULTS: We identified three overarching themes: trust in individual CDSSs; usability of individual CDSSs; and usability of CDSSs in the broader practice context, to which nine subthemes contributed. Trust was affected by CDSS provenance, perceived threat to autonomy and clear management guidance. Usability was influenced by sensitivity to the patient context, CDSS flexibility, ease of control, and non-intrusiveness. CDSSs were more likely to be used by GPs if they did not contribute to alert proliferation and subsequent fatigue, or if GPs were provided with training in their use. CONCLUSIONS: Building on these findings we make a number of recommendations for CDSS developers to consider when bringing a new CDSS into GP patient records systems. These include co-producing CDSS with GPs to improve fit within clinic workflow and wider practice systems, ensuring a high level of accuracy and a clear clinical pathway, and providing CDSS training for practice staff. These recommendations may reduce the proliferation of unhelpful alerts that can result in important decision-support being ignored.
Tapuria A, Kordowicz M, Ashworth M, et al., 2021, IT Evaluation of Foundation Healthcare Group Vanguard Project., Stud Health Technol Inform, Vol: 281, Pages: 625-629
The aim of the Foundation Healthcare Group (FHG) Vanguard model was to develop a sustainable local hospital model between two National Health Service (NHS) Trusts (a London Teaching Hospital Trust and a District General Hospital Trust) that makes best use of scarce resources and can be replicated across the NHS, UK. The aim of this study was to evaluate the provision, use and implementation of the IT infrastructure; based on qualitative interviews and focused mainly on the perspectives of the IT staff and the clinicians' perspectives. In total 24 interview transcripts, along with 'Acute Care Collaboration' questionnaire responses, were analysed using a thematic framework for IT infrastructure, sharing themes across the vascular, paediatric and cardiovascular strands of the FHG programme. Findings indicated that Skype for Business had been an innovative and helpful development widely available to be used between the two Trusts. Clinicians initially reported lack of IT support and infrastructure expected at the outset for a national Vanguard project, but later appreciated that remote access to most clinical applications between the two Trusts became operational. The Local Care Record (LCR), an IT project was perceived to have been delivered successfully in South London. Shared technology reduced patient travelling time by providing locally based shared care. Spreading and scaling-up innovations from the Vanguard sites was the aspiration and challenge for system leaders.
Chapman M, Domínguez J, Fairweather E, et al., 2021, Using Computable Phenotypes in Point-of-Care Clinical Trial Recruitment., Stud Health Technol Inform, Vol: 281, Pages: 560-564
A key challenge in point-of-care clinical trial recruitment is to autonomously identify eligible patients on presentation. Similarly, the aim of computable phenotyping is to identify those individuals within a population that exhibit a certain condition. This synergy creates an opportunity to leverage phenotypes in identifying eligible patients for clinical trials. To investigate the feasibility of this approach, we use the Transform clinical trial platform and replace its archetype-based eligibility criteria mechanism with a computable phenotype execution microservice. Utilising a phenotype for acute otitis media with discharge (AOMd) created with the Phenoflow platform, we compare the performance of Transform with and without the use of phenotype-based eligibility criteria when recruiting AOMd patients. The parameters of the trial simulated are based on those of the REST clinical trial, conducted in UK primary care.
Tapuria A, Porat T, Kalra D, et al., 2021, Impact of patient access to their electronic health record: systematic review., Informatics for Health and Social Care, Vol: 46, Pages: 194-204, ISSN: 0959-8316
Patient access to their own electronic health records (EHRs) is likely to become an integral part of healthcare systems worldwide. It has the potential to decrease the healthcare provision costs, improve access to healthcare data, self-care, quality of care, and health and patient-centered outcomes. This systematic literature review is aimed at identifying the impact in terms of benefits and issues that have so far been demonstrated by providing patients access to their own EHRs, via providers' secure patient portals from primary healthcare centers and hospitals. Searches were conducted in PubMed, MEDLINE, CINHAL, and Google scholar. Over 2000 papers were screened and were filtered based on duplicates, then by reading the titles and finally based on their abstracts or full text. In total, 74 papers were retained, analyzed, and summarized. Papers were included if providing patient access to their own EHRs was the primary intervention used in the study and its impact or outcome was evaluated. The search technique used to identify relevant literature for this paper involved input from five experts. While findings from 54 of the 74 papers showed positive outcome or benefits of patient access to their EHRs via patient portals, 10 papers have highlighted concerns, 8 papers have highlighted both and 2 have highlighted absence of negative outcomes. The benefits range from re-assurance, reduced anxiety, positive impact on consultations, better doctor-patient relationship, increased awareness and adherence to medication, and improved patient outcomes (e.g., improving blood pressure and glycemic control in a range of study populations). In addition, patient access to their health information was found to improve self-reported levels of engagement or activation related to self-management, enhanced knowledge, and improve recovery scores, and organizational efficiencies in a tertiary level mental health care facility. However, three studies did not find any statistically signific
Snell LB, Wang W, Alcolea-Medina A, et al., 2021, First and second SARS-CoV-2 waves in inner London: A comparison of admission characteristics and the impact of the B.1.1.7 variant
<jats:title>Abstract</jats:title><jats:sec><jats:title>Introduction</jats:title><jats:p>A second wave of SARS-CoV-2 infection spread across the UK in 2020 linked with emergence of the more transmissible B.1.1.7 variant. The emergence of new variants, particularly during relaxation of social distancing policies and implementation of mass vaccination, highlights the need for real-time integration of detailed patient clinical data alongside pathogen genomic data. We linked clinical data with viral genome sequence data to compare cases admitted during the first and second waves of SARS-CoV-2 infection.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Clinical, laboratory and demographic data from five electronic health record (EHR) systems was collected for all cases with a positive SARS-CoV-2 RNA test between March 13th 2020 and February 17th 2021. SARS-CoV-2 viral sequencing was performed using Oxford Nanopore Technology. Descriptive data are presented comparing cases between waves, and between cases of B.1.1.7 and non-B.1.1.7 variants.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>There were 5810 SARS-CoV-2 RNA positive cases comprising inpatients (n=2341), healthcare workers (n=1549), outpatients (n=874), emergency department (ED) attenders not subsequently admitted (n=532), inter-hospital transfers (n=281) and nosocomial cases (n=233). There were two dominant waves of hospital admissions, with wave one starting from March 13<jats:sup>th</jats:sup>(n=838) and wave two from October 20<jats:sup>th</jats:sup>(n=1503), both with a temporally aligned rise in nosocomial cases (n=96 in wave one, n=137 in wave two). 1470 SARS-CoV-2 isolates were successfully sequenced, including 216/838 (26%) admitted cases from wave one, 472/1503 (31%) admitted cases in wave two and 121/233 (52%) nosocomial cases. The
Carr E, Bendayan R, Bean D, et al., 2021, Evaluation and improvement of the National Early Warning Score (NEWS2) for COVID-19: a multi-hospital study, BMC MEDICINE, Vol: 19, ISSN: 1741-7015
- Author Web Link
- Citations: 46
Fairweather E, Chapman M, Curcin V, 2021, A Delayed Instantiation Approach to Template-Driven Provenance for Electronic Health Record Phenotyping, Pages: 3-19, ISSN: 0302-9743
Provenance templates are an established methodology for the capture of provenance data. Each template defines the provenance of a domain-specific action in abstract form, which may then be instantiated as required by a single call to a given service interface. This approach, whilst simplifying the process of recording provenance for the user, introduces computational and storage demands on the capture process, particularly when used by clients with write-intensive provenance requirements such as other service-based software. To address these issues, we adopt a new approach based upon delayed instantiation and present a revised, two-part paradigm for template-driven provenance, in which we separate capture and query functionality to improve the overall efficiency of the model. A dedicated capture service is first employed to record template service requests in a relational database in the form of a meta-level description detailing the construction of each document. These low-overhead records are then accessed by an independent query service to construct views of concrete provenance documents for specific time frames as and when required by the user. These views may subsequently be analysed using query templates, a new technique defined here whereby templates can also be used to search for any matching subgraphs within a document and return the respective instantiating substitutions. We evaluate the performance gains of our new system in the context of Phenoflow, an electronic health record (EHR) phenotyping platform.
Chapman M, Fairweather E, Khan A, et al., 2021, COVID-19 Analytics in Jupyter: Intuitive Provenance Integration Using ProvIt, Pages: 256-262, ISSN: 0302-9743
Whilst the need to record and understand the evolution of data, together with the processes and users associated with those changes, is now widely appreciated, the uptake of solutions to these issues remains slow. Data provenance techniques have the potential to provide such an understanding, but their use is often considered a specialist activity, requiring detailed knowledge of standards such as W3C PROV. In this work, we introduce ProvIt, a suite of tools designed to lower the barriers to entry for the use of provenance technology. We demonstrate the utility of ProvIt by using it to add provenance capabilities to the Jupyter IDE, in order to provide insight into the tools used by a group of researchers analysing a COVID-19 dataset.
Fairweather E, Wittner R, Chapman M, et al., 2021, Non-repudiable Provenance for Clinical Decision Support Systems, Pages: 165-182, ISSN: 0302-9743
Provenance templates are now a recognised methodology for the construction of data provenance records. Each template defines the provenance of a domain-specific action in abstract form, which may then be instantiated as required by a single call to the provenance template service. As data reliability and trustworthiness becomes a critical issue in an increasing number of domains, there is a corresponding need to ensure that the provenance of that data is non-repudiable. In this paper we contribute two new, complementary modules to our template model and implementation to produce non-repudiable data provenance. The first, a module that traces the operation of the provenance template service itself, and records a provenance trace of the construction of an object-level document, at the level of individual service calls. The second, a non-repudiation module that generates evidence for the data recorded about each call, annotates the service trace accordingly, and submits a representation of that evidence to a provider-agnostic notary service. We evaluate the applicability of our approach in the context of a clinical decision support system. We first define a policy to ensure the non-repudiation of evidence with respect to a security threat analysis in order to demonstrate the suitability of our solution. We then select three use cases from within a particular system, Consult, with contrasting data provenance recording requirements and analyse the subsequent performance of our prototype implementation against three different notary providers.
Wongkoblap A, Vadillo MA, Curcin V, 2021, Social media big data analysis for mental health research, Mental Health in a Digital World, Pages: 109-143, ISBN: 9780128222027
Mental health problems are widely recognized as a major public health challenge worldwide. This highlights the need for effective tools for detecting mental health disorders in the population. Social media data is a promising source of information where people publish rich personal information that can be mined to extract valuable psychological information. However, social media data poses its own set of challenges, such as the specific terms and expressions used on different platforms, interactions between different users through likes and shares, and the need to disambiguate between statements about oneself and about third parties. Traditionally, social media natural language processing (NLP) techniques have looked at text classifiers and user classification models separately, which presents a challenge for researchers wanting not only to combine text sentiment and user sentiment analysis but also to extract user’s narratives from the textual content.
Gulliford MC, Charlton J, Boiko O, et al., 2021, Safety of reducing antibiotic prescribing in primary care: a mixed-methods study
<h4>Background</h4>The threat of antimicrobial resistance has led to intensified efforts to reduce antibiotic utilisation, but serious bacterial infections are increasing in frequency.<h4>Objectives</h4>To estimate the risks of serious bacterial infections in association with lower antibiotic prescribing and understand stakeholder views with respect to safe antibiotic reduction.<h4>Design</h4>Mixed-methods research was undertaken, including a qualitative interview study of patient and prescriber views that informed a cohort study and a decision-analytic model, using primary care electronic health records. These three work packages were used to design an application (app) for primary care prescribers.<h4>Data sources</h4>The Clinical Practice Research Datalink.<h4>Setting</h4>This took place in UK general practices.<h4>Participants</h4>A total of 706 general practices with 66.2 million person-years of follow-up from 2002 to 2017 and antibiotic utilisation evaluated for 671,830 registered patients. The qualitative study included 31 patients and 30 health-care professionals from primary care.<h4>Main outcome measures</h4>Sepsis and localised bacterial infections.<h4>Results</h4>Patients were concerned about antimicrobial resistance and the side effects, as well as the benefits, of antibiotic treatment. Prescribers viewed the onset of sepsis as the most concerning potential outcome of reduced antibiotic prescribing. More than 40% of antibiotic prescriptions in primary care had no coded indication recorded across both Vision® and EMIS® practice systems. Antibiotic prescribing rates varied widely between general practices, but there was no evidence that serious bacterial infections were less frequent at higher prescribing practices (adjusted rate ratio for 20% increase in prescribing 1.03, 95% confidence interval 1.00 to 1.06; p = 0.074). The probability o
Chapman M, Rasmussen LV, Pacheco JA, et al., 2021, Phenoflow: A Microservice Architecture for Portable Workflow-based Phenotype Definitions., AMIA Jt Summits Transl Sci Proc, Vol: 2021, Pages: 142-151
Phenotyping is an effective way to identify cohorts of patients with particular characteristics within a population. In order to enhance the portability of a phenotype definition across institutions, it is often defined abstractly, with implementers expected to realise the phenotype computationally before executing it against a dataset. However, un-clear definitions, with little information about how best to implement the definition in practice, hinder this process. To address this issue, we propose a new multi-layer, workflow-based model for defining phenotypes, and a novel authoring architecture, Phenoflow, that supports the development of these structured definitions and their realisation as computable phenotypes. To evaluate our model, we determine its impact on the portability of both code-based (COVID-19) and logic-based (diabetes) definitions, in the context of key datasets, including 26,406 patients at North-western University. Our approach is shown to ensure the portability of phenotype definitions and thus contributes to the transparency of resulting studies.
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