Publications
312 results found
Kwon J, Milne R, Rayner C, et al., 2023, Impact of Long COVID on productivity and informal caregiving., Eur J Health Econ
BACKGROUND: Around 2 million people in the UK suffer from Long COVID (LC). Of concern is the disease impact on productivity and informal care burden. This study aimed to quantify and value productivity losses and informal care receipt in a sample of LC patients in the UK. METHODS: The target population comprised LC patients referred to LC specialist clinics. The questionnaires included a health economics questionnaire (HEQ) measuring productivity impacts, informal care receipt and service utilisation, EQ-5D-5L, C19-YRS LC condition-specific measure, and sociodemographic and COVID-19 history variables. Outcomes were changes from the incident infection resulting in LC to the month preceding the survey in paid work status/h, work income, work performance and informal care receipt. The human capital approach valued productivity losses; the proxy goods method valued caregiving hours. The values were extrapolated nationally using published prevalence data. Multilevel regressions, nested by region, estimated associations between the outcomes and patient characteristics. RESULTS: 366 patients responded to HEQ (mean LC duration 449.9 days). 51.7% reduced paid work hours relative to the pre-infection period. Mean monthly work income declined by 24.5%. The average aggregate value of productivity loss since incident infection was £10,929 (95% bootstrap confidence interval £8,844-£13,014) and £5.7 billion (£3.8-£7.6 billion) extrapolated nationally. The corresponding values for informal caregiving were £8,726 (£6,247-£11,204) and £4.8 billion (£2.6-£7.0 billion). Multivariate analyses found significant associations between each outcome and health utility and C19-YRS subscale scores. CONCLUSION: LC significantly impacts productivity losses and provision of informal care, exacerbated by high national prevalence of LC.
Zhang J, Morley J, Gallifant J, et al., 2023, Mapping and evaluating whole nation data flows: transparency, privacy, and guiding infrastructural transformation, The Lancet: Digital Health, Vol: 5, Pages: e737-e748, ISSN: 2589-7500
The importance of big health data is recognised worldwide. Most UK National Health Service (NHS) care interactions are recorded in electronic health records, resulting in an unmatched potential for population-level datasets. However, policy reviews have highlighted challenges from a complex data-sharing landscape relating to transparency, privacy, and analysis capabilities. In response, we used public information sources to map all electronic patient data flows across England, from providers to more than 460 subsequent academic, commercial, and public data consumers. Although NHS data support a global research ecosystem, we found that multistage data flow chains limit transparency and risk public trust, most data interactions do not fulfil recommended best practices for safe data access, and existing infrastructure produces aggregation of duplicate data assets, thus limiting diversity of data and added value to end users. We provide recommendations to support data infrastructure transformation and have produced a website (https://DataInsights.uk) to promote transparency and showcase NHS data assets.
Delaney B, Dominguez J, Prociuk D, et al., 2023, ROAD2H: development and evaluation of an open-sourceexplainable artificial intelligence approach for managingco-morbidity and clinical guidelines, Learning Health Systems, ISSN: 2379-6146
IntroductionClinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. In this work, we develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle-income country in West Balkans.MethodsWe used an ontological framework, the Transition-based Medical Recommendation (TMR) model, to represent, and reason about, guideline concepts, and chose the 2017 International global initiative for chronic obstructive lung disease (GOLD) guideline and a Serbian hospital as the deployment and evaluation site, respectively. To mitigate potential guideline conflicts, we used a TMR-based implementation of the Assumptions-Based Argumentation framework extended with preferences and Goals (ABA+G). Remote EHR integration of computable guidelines was via a microservice architecture based on HL7 FHIR and CDS Hooks. A prototype integration was developed to manage chronic obstructive pulmonary disease (COPD) with comorbid cardiovascular or chronic kidney diseases, and a mixed-methods evaluation was conducted with 20 simulated cases and five pulmonologists.ResultsPulmonologists agreed 97% of the time with the GOLD-based COPD symptom severity assessment assigned to each patient by the CDSS, and 98% of the time with one of the proposed COPD care plans. Comments were favourable on the principles of explainable argumentation; inclusion of additional co-morbidities was suggested in the future along with customisation of the level of explanation with expertise.ConclusionAn ontological model provided a flexible means of providing argumentation and explainable artificial intelligence for a long-term condition. Exte
Zhang J, Ashrafian H, Delaney B, et al., 2023, Impact of primary to secondary care data sharing on care quality in NHS England hospitals, npj Digital Medicine, Vol: 6, Pages: 1-10, ISSN: 2398-6352
Health information exchange (HIE) is seen as a key component of effective care but remains poorly evidenced at a health system level. In the UK National Health Service (NHS), the ability to share primary care data with secondary care clinicians is a focus of continued digital investment. In this study, we report the evolution of interoperable technology across a period of rapid digital transformation in NHS England from 2015 to 2019, and test association of primary to secondary care data-sharing capabilities with clinical care quality indicators across all acute secondary care providers (n=135 NHS Trusts). In multivariable analyses, data-sharing capabilities are associated with reduction in patients breaching an Accident & Emergency (A&E) 4-hour decision time threshold, and better patient-reported experience of acute hospital care quality. Using synthetic control analyses, we estimate mean 2.271% (STD+/-3.371) absolute reduction in A&E 4-hour decision time breach, 12 months following introduction of data-sharing capabilities. Our findings support current digital transformation programs for developing regional HIE networks but highlight the need to focus on implementation factors in addition to technological procurement.
Mansoubi M, Dawes J, Bhatia A, et al., 2023, Digital home monitoring for capturing daily fluctuation of symptoms; a longitudinal repeated measures study: Long Covid Multi-disciplinary Consortium to Optimise Treatments and Services across the NHS (a LOCOMOTION study), BMJ OPEN, Vol: 13, ISSN: 2044-6055
Ghimire B, Landy R, Maroni R, et al., 2023, Predictors of the experience of a Cytosponge test: analysis of patient survey data from the BEST3 trial., BMC Gastroenterol, Vol: 23
BACKGROUND: The Cytosponge is a cell-collection device, which, coupled with a test for trefoil factor 3 (TFF3), can be used to diagnose Barrett's oesophagus, a precursor condition to oesophageal adenocarcinoma. BEST3, a large pragmatic, randomised, controlled trial, investigated whether offering the Cytosponge-TFF3 test would increase detection of Barrett's. Overall, participants reported mostly positive experiences. This study reports the factors associated with the least positive experience. METHODS: Patient experience was assessed using the Inventory to Assess Patient Satisfaction (IAPS), a 22-item questionnaire, completed 7-14 days after the Cytosponge test. STUDY COHORT: All BEST3 participants who answered ≥ 15 items of the IAPS (N = 1458). STATISTICAL ANALYSIS: A mean IAPS score between 1 and 5 (5 indicates most negative experience) was calculated for each individual. 'Least positive' experience was defined according to the 90th percentile. 167 (11.4%) individuals with a mean IAPS score of ≥ 2.32 were included in the 'least positive' category and compared with the rest of the cohort. Eleven patient characteristics and one procedure-specific factor were assessed as potential predictors of the least positive experience. Multivariable logistic regression analysis using backwards selection was conducted to identify factors independently associated with the least positive experience and with failed swallow at first attempt, one of the strongest predictors of least positive experience. RESULTS: The majority of responders had a positive experience, with an overall median IAPS score of 1.7 (IQR 1.5-2.1). High (OR = 3.01, 95% CI 2.03-4.46, p < 0.001) or very high (OR = 4.56, 95% CI 2.71-7.66, p < 0.001) anxiety (relative to low/normal anxiety) and a failed swallow at the first attempt (OR = 3.37, 95% CI 2.14-5.30, p < 0.001) were highly si
Mekhtieva RL, Forbes B, Alrajeh D, et al., 2023, RECAP-KG: Mining Knowledge Graphs from Raw Primary Care Physician Notes for Remote COVID-19 Assessment in Primary Care., AMIA Annu Symp Proc, Vol: 2023, Pages: 1145-1154
Building Clinical Decision Support Systems, whether from regression models or machine learning requires clinical data either in standard terminology or as text for Natural Language Processing (NLP). Unfortunately, many clinical notes are written quickly during the consultation and contain many abbreviations, typographical errors, and a lack of grammar and punctuation Processing these highly unstructured clinical notes is an open challenge for NLP that we address in this paper. We present RECAP-KG - a knowledge graph construction frame workfrom primary care clinical notes. Our framework extracts structured knowledge graphs from the clinical record by utilising the SNOMED-CT ontology both the entire finding hierarchy and a COVID-relevant curated subset. We apply our framework to consultation notes in the UK COVID-19 Clinical Assessment Service (CCAS) dataset and provide a quantitative evaluation of our framework demonstrating that our approach has better accuracy than traditional NLP methods when answering questions about patients.
Kourtidis P, Nurek M, Delaney B, et al., 2022, Influences of early diagnostic suggestions on clinical reasoning, Cognitive Research: Principles and Implications, Vol: 7, ISSN: 2365-7464
Previous research has highlighted the importance of physicians’ early hypotheses for their subsequent diagnostic decisions. It has also been shown that diagnostic accuracy improves when physicians are presented with a list of diagnostic suggestions to consider at the start of the clinical encounter. The psychological mechanisms underlying this improvement in accuracy are hypothesised. It is possible that the provision of diagnostic suggestions disrupts physicians’ intuitive thinking and reduces their certainty in their initial diagnostic hypotheses. This may encourage them to seek more information before reaching a diagnostic conclusion, evaluate this information more objectively, and be more open to changing their initial hypotheses. Three online experiments explored the effects of early diagnostic suggestions, provided by a hypothetical decision aid, on different aspects of the diagnostic reasoning process. Family physicians assessed up to two patient scenarios with and without suggestions. We measured effects on certainty about the initial diagnosis, information search and evaluation, and frequency of diagnostic changes. We did not find a clear and consistent effect of suggestions and detected mainly non-significant trends, some in the expected direction. We also detected a potential biasing effect: when the most likely diagnosis was included in the list of suggestions (vs. not included), physicians who gave that diagnosis initially, tended to request less information, evaluate it as more supportive of their diagnosis, become more certain about it, and change it less frequently when encountering new but ambiguous information; in other words, they seemed to validate rather than question their initial hypothesis. We conclude that further research using different methodologies and more realistic experimental situations is required to uncover both the beneficial and biasing effects of early diagnostic suggestions.
Tuller D, Blitshteyn S, Davies-Payne D, et al., 2022, 'Psychogenic' POTS: the NYU team misinterprets association as causation, BRAIN, Vol: 145, Pages: E111-E112, ISSN: 0006-8950
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Greenhalgh T, Sivan M, Delaney B, et al., 2022, Authors' reply to Ward., BMJ: British Medical Journal, Vol: 379, Pages: 1-1, ISSN: 0959-535X
Greenhalgh T, Sivan M, Delaney B, et al., 2022, Long covid-an update for primary care., BMJ, Vol: 378, Pages: 1-8, ISSN: 1759-2151
Espinosa-Gonzalez A, Prociuk D, Fiorentino F, et al., 2022, Remote COVID-19 assessment in primary care (RECAP) risk prediction tool: derivation and real-world validation studies, The Lancet Digital Health, Vol: 4, Pages: e646-e656, ISSN: 2589-7500
BACKGROUND: Accurate assessment of COVID-19 severity in the community is essential for patient care and requires COVID-19-specific risk prediction scores adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms, and risk factors, we aimed to develop and validate two COVID-19-specific risk prediction scores. Remote COVID-19 Assessment in Primary Care-General Practice score (RECAP-GP; without peripheral oxygen saturation [SpO2]) and RECAP-oxygen saturation score (RECAP-O2; with SpO2). METHODS: RECAP was a prospective cohort study that used multivariable logistic regression. Data on signs and symptoms (predictors) of disease were collected from community-based patients with suspected COVID-19 via primary care electronic health records and linked with secondary data on hospital admission (outcome) within 28 days of symptom onset. Data sources for RECAP-GP were Oxford-Royal College of General Practitioners Research and Surveillance Centre (RCGP-RSC) primary care practices (development set), northwest London primary care practices (validation set), and the NHS COVID-19 Clinical Assessment Service (CCAS; validation set). The data source for RECAP-O2 was the Doctaly Assist platform (development set and validation set in subsequent sample). The two probabilistic risk prediction models were built by backwards elimination using the development sets and validated by application to the validation datasets. Estimated sample size per model, including the development and validation sets was 2880 people. FINDINGS: Data were available from 8311 individuals. Observations, such as SpO2, were mostly missing in the northwest London, RCGP-RSC, and CCAS data; however, SpO2 was available for 1364 (70·0%) of 1948 patients who used Doctaly. In the final predictive models, RECAP-GP (n=1863) included sex (male and female), age (years), degree of breathlessness (three point scale), temperature symptoms (two point scale), and presence of hypert
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
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- Citations: 8
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
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- Citations: 2
Espinosa-Gonzalez A, Prociuk D, Fiorentino F, et al., 2022, Remote covid assessment in primary care (RECAP) risk prediction tool: derivation and real-world validation studies, Publisher: MedRxiv
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Accurate assessment of COVID-19 severity in the community is essential for best patient care and efficient use of services and requires a risk prediction score that is COVID-19 specific and adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms and risk factors, we sought to develop and validate two COVID-19-specific risk prediction scores RECAP-GP (without peripheral oxygen saturation (SpO2)) and RECAP-O2 (with SpO2).</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Prospective cohort study using multivariable logistic regression for model development. Data on signs and symptoms (model predictors) were collected on community-based patients with suspected COVID-19 via primary care electronic health records systems and linked with secondary data on hospital admission (primary outcome) within 28 days of symptom onset. Data sources: RECAP-GP: Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC) primary care practices (development), Northwest London (NWL) primary care practices, NHS COVID-19 Clinical Assessment Service (CCAS) (validation). RECAP-O2: Doctaly Assist platform (development, and validation in subsequent sample). Estimated sample size was 2,880 per model.</jats:p></jats:sec><jats:sec><jats:title>Findings</jats:title><jats:p>Data were available from 8,311 individuals. Observations, such SpO2, were mostly missing in NWL, RSC, and CCAS data; however, SpO2 was available for around 70% of Doctaly patients. In the final predictive models, RECAP-GP included sex, age, degree of breathlessness, temperature symptoms, and presence of hypertension (Area Under the Curve (AUC): 0.802, Validation Negative Predictive Value (NPV) of ‘low risk’ 98.8%. RECAP-O2 included age, de
Delaney BC, Rayner C, Freyer A, et al., 2022, Recommendations for the recognition, diagnosis, and management of long COVID response, British Journal of General Practice, Vol: 72, Pages: 259-260, ISSN: 0960-1643
Rodrigues D, Kreif N, Saravanakumar K, et al., 2022, Formalising triage in general practice towards a more equitable, safe, and efficient allocation of resources, BMJ: British Medical Journal, Vol: 377, ISSN: 0959-535X
Sivan M, Greenhalgh T, Milne R, et al., 2022, Are vaccines a potential treatment for long covid? Benefits are possible, but we need more evidence and a mechanism of action, BMJ-BRITISH MEDICAL JOURNAL, Vol: 377, ISSN: 0959-535X
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- Citations: 8
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
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- Citations: 14
Greenhalgh T, Griffin S, Gurdasani D, et al., 2022, Covid-19: An urgent call for global "vaccines-plus" action, BMJ-BRITISH MEDICAL JOURNAL, Vol: 376, ISSN: 0959-535X
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- Citations: 15
Ward H, Flower B, Garcia PJ, et al., 2021, Global surveillance, research, and collaboration needed to improve understanding and management of long COVID, The Lancet, Vol: 398, Pages: 2057-2059, ISSN: 0140-6736
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
Fiorentino F, Prociuk D, Espinosa Gonzalez AB, et al., 2021, An Early Warning Risk Prediction Tool (RECAP-V1) for Patients Diagnosed With COVID-19: Protocol for a Statistical Analysis Plan, JMIR Research Protocols, Vol: 10, Pages: e30083-e30083
<jats:sec> <jats:title>Background</jats:title> <jats:p>Since the start of the COVID-19 pandemic, efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalization. The RECAP (Remote COVID-19 Assessment in Primary Care) study investigates the predictive risk of hospitalization, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process performed by clinicians. We aim to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of several general practices across the United Kingdom to construct accurate predictive models. The models will be based on preexisting conditions and monitoring data of a patient’s clinical parameters (eg, blood oxygen saturation) to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death.</jats:p> </jats:sec> <jats:sec> <jats:title>Objective</jats:title> <jats:p>This statistical analysis plan outlines the statistical methods to build the prediction model to be used in the prioritization of patients in the primary care setting. The statistical analysis plan for the RECAP study includes the development and validation of the RECAP-V1 prediction model as a primary outcome. This prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected COVID-19. The model will predict the risk of deterioration and hospitalization.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods</jats:title> <jats:p>After the data have been collected, we will assess the degree of missingness and use a combination
Fiorentino F, Prociuk D, Espinosa Gonzalez AB, et al., 2021, An early warning risk prediction tool (RECAP-V1) for patients diagnosed with COVID-19: the protocol for a statistical analysis plan, JMIR Research Protocols, Vol: 10, ISSN: 1929-0748
Background:Since the start of the Covid-19 pandemic efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalisation. The RECAP (Remote COVID Assessment in Primary Care) study investigates the predictive risk of hospitalisation, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process done by clinicians. The study aims to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of a number of general practices across the UK to construct accurate predictive models that will use pre-existing conditions and monitoring data of a patient’s clinical parameters such as blood oxygen saturation to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death.Objective:We outline the statistical methods to build the prediction model to be used in the prioritisation of patients in the primary care setting. The statistical analysis plan for the RECAP study includes as primary outcome the development and validation of the RECAP-V1 prediction model. Such prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected covid-19. The model will predict risk of deterioration, hospitalisation, and death.Methods:After the data has been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine learning approaches to impute the missing data for the final analysis. For predictive model development we will use multiple logistic regressions to construct the model on a training dataset, as well as validating the model on an independent dataset. The model will also be applied for multiple different datasets
Nurek M, Rayner C, Freyer A, et al., 2021, Recommendations for the recognition, diagnosis, and management of long COVID: a Delphi study, British Journal of General Practice, Vol: 71, Pages: E815-E825, ISSN: 0960-1643
Background In the absence of research into therapies and care pathways for long COVID, guidance based on ‘emerging experience’ is needed.Aim To provide a rapid expert guide for GPs and long COVID clinical services.Design and setting A Delphi study was conducted with a panel of primary and secondary care doctors.Method Recommendations were generated relating to the investigation and management of long COVID. These were distributed online to a panel of UK doctors (any specialty) with an interest in, lived experience of, and/or experience treating long COVID. Over two rounds of Delphi testing, panellists indicated their agreement with each recommendation (using a five-point Likert scale) and provided comments. Recommendations eliciting a response of ‘strongly agree’, ‘agree’, or ‘neither agree nor disagree’ from 90% or more of responders were taken as showing consensus.Results Thirty-three clinicians representing 14 specialties reached consensus on 35 recommendations. Chiefly, GPs should consider long COVID in the presence of a wide range of presenting features (not limited to fatigue and breathlessness) and exclude differential diagnoses where appropriate. Detailed history and examination with baseline investigations should be conducted in primary care. Indications for further investigation and specific therapies (for myocarditis, postural tachycardia syndrome, mast cell disorder) include hypoxia/desaturation, chest pain, palpitations, and histamine-related symptoms. Rehabilitation should be individualised, with careful activity pacing (to avoid relapse) and multidisciplinary support.Conclusion Long COVID clinics should operate as part of an integrated care system, with GPs playing a key role in the multidisciplinary team. Holistic care pathways, investigation of specific complications, management of potential symptom clusters, and tailored rehabilitation are needed.
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
Chapman M, DomÃnguez 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.
Nurek M, Delaney B, Kostopoulou O, 2021, GENERAL PRACTITIONERS' RISK ASSESSMENTS AND ANTIBIOTIC PRESCRIBING DECISIONS IN CHILDREN WITH COUGH: A VIGNETTE STUDY, Publisher: SAGE PUBLICATIONS INC, Pages: E51-E52, ISSN: 0272-989X
Espinosa-Gonzalez AB, Neves AL, Fiorentino F, et al., 2021, Predicting Risk of Hospital Admission in Patients With Suspected COVID-19 in a Community Setting: Protocol for Development and Validation of a Multivariate Risk Prediction Tool, JMIR RESEARCH PROTOCOLS, Vol: 10, ISSN: 1929-0748
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Kourtidis P, Nurek M, Delaney B, et al., 2021, INFLUENCES OF DIAGNOSTIC SUGGESTIONS ON CLINICAL REASONING, Publisher: SAGE PUBLICATIONS INC, Pages: E262-E264, ISSN: 0272-989X
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