Imperial College London

MrErikMayer

Faculty of MedicineDepartment of Surgery & Cancer

Clinical Reader in Urology
 
 
 
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Contact

 

+44 (0)20 3312 6428e.mayer

 
 
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Location

 

1020Queen Elizabeth the Queen Mother Wing (QEQM)St Mary's Campus

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Summary

 

Publications

Publication Type
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227 results found

Piggin M, Johnson H, Papadimitriou D, Glampson B, Aylin P, Mayer Eet al., 2022, Insight Report: Digital health online public involvement session on using artificial intelligence to improve health and care in North West London, Insight Report: Digital health online public involvement session on using artificial intelligence to improve health and care in North West London

Summary report on the views of members of the public on using Artificial intelligence as part ofbuilding the digital healthcare programme of research in North West London.

Report

Espinosa-Gonzalez A, Prociuk D, Fiorentino F, Ramtale C, Mi E, Mi E, Glampson B, Neves AL, Okusi C, Husain L, Macartney J, Brown M, Browne B, Warren C, Chowla R, Heaversedge J, Greenhalgh T, de Lusignan S, Mayer E, Delaney BCet 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, 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

Journal article

Lear R, Freise L, Kybert M, Darzi A, Neves AL, Mayer Eet al., 2022, Patients’ willingness and ability to identify and respond to errors in their personal health records: a mixed methods analysis of cross-sectional survey data, Journal of Medical Internet Research, Vol: 24, ISSN: 1438-8871

Background:Errors in electronic health records are known to contribute to patient safety incidents, yet systems for checking the accuracy of patient records are almost non-existent. Personal health records, enabling patient access to, and interaction, with the clinical record, offer a valuable opportunity for patients to actively participate in error surveillance.Objective:The aim of this study was to evaluate patients’ willingness and ability to identify and respond to errors in their personal health records.Methods:A cross-sectional survey study was conducted using an online questionnaire. Patient sociodemographic data were collected, including age, gender, ethnicity, educational level, health status, geographical location, motivation to self-manage, and digital health literacy (measured by the eHEALS tool). Patients with experience of using the Care Information Exchange (CIE) portal, who specified both age and gender, were included in these analyses. Patients’ responses to four relevant survey items (closed-ended questions, some with space for free-text comments) were examined to understand their willingness and ability to identify and respond to errors in their personal health records. Multinomial logistic regression was used to identify patient characteristics that predict i) ability to understand information in CIE, and ii) willingness to respond to errors in their records. The Framework Method was used to derive themes from patients’ free-text responses.Results:Of 445 patients, 40.7% (n=181) “definitely” understood CIE information and around half (49.4%, n=220) understood CIE information “to some extent”. Patients with high digital health literacy (eHEALS score ≥30) were more confident in their ability to understand their records compared to patients with low digital health literacy (odds ratio (OR) 7.85, 95% confidence interval (CI) 3.04-20.29, P<.001). Information-related barriers (medical terminology; lack of

Journal article

Rodrigues D, Kreif N, Lawrence-Jones A, Barahona M, Mayer Eet al., 2022, Reflection on modern methods: constructing directed acyclic graphs (DAGs) with domain experts for health services research, International Journal of Epidemiology, Vol: 51, ISSN: 0300-5771

Directed acyclic graphs (DAGs) are a useful tool to represent, in a graphical format, researchers’ assumptions about the causal structure among variables while providing a rationale for the choice of confounding variables to adjust for. With origins in the field of probabilistic graphical modelling, DAGs are yet to be widely adopted in applied health research, where causal assumptions are frequently made for the purpose of evaluating health services initiatives. In this context, there is still limited practical guidance on how to construct and use DAGs. Some progress has recently been made in terms of building DAGs based on studies from the literature, but an area that has received less attention is how to create DAGs from information provided by domain experts, an approach of particular importance when there is limited published information about the intervention under study. This approach offers the opportunity for findings to be more robust and relevant to patients, carers and the public, and more likely to inform policy and clinical practice. This article draws lessons from a stakeholder workshop involving patients, health care professionals, researchers, commissioners and representatives from industry, whose objective was to draw DAGs for a complex intervention—online consultation, i.e. written exchange between the patient and health care professional using an online system—in the context of the English National Health Service. We provide some initial, practical guidance to those interested in engaging with domain experts to develop DAGs.

Journal article

Espinosa-Gonzalez A, Prociuk D, Fiorentino F, Ramtale C, Mi E, Mi E, Glampson B, Neves AL, Okusi C, Hussain L, Macartney J, Brown M, Browne B, Warren C, Chowla R, Heaversedge J, Greenhalgh T, de Lusignan S, Mayer E, Delaney Bet 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

Working paper

van Dael J, Reader TW, Gillespie AT, Freise L, Darzi A, Mayer EKet al., 2022, Do national policies for complaint handling in English hospitals support quality improvement? Lessons from a case study, Journal of the Royal Society of Medicine, Pages: 1-9, ISSN: 0141-0768

ObjectivesA range of public inquiries in the English National Health Service have indicated repeating failings in complaint handling, and patients are often left dissatisfied. The complex, bureaucratic nature of complaints systems is often cited as an obstacle to meaningful investigation and learning, but a detailed examination of how such bureaucratic rules, regulations, and infrastructure shape complaint handling, and where change is most needed, remains relatively unexplored. We sought to examine how national policies structure local practices of complaint handling, how they are understood by those responsible for enacting them, and if there are any discrepancies between policies-as-intended and their reality in local practice.DesignCase study involving staff interviews and documentary analysis.SettingA large acute and multi-site NHS Trust in England.ParticipantsClinical, managerial, complaints, and patient advocacy staff involved in complaint handling at the participating NHS Trust (n=20).Main outcome measuresNot applicable.ResultsFindings illustrate four areas of practice where national policies and regulations can have adverse consequences within local practices, and partly function to undermine an improvement-focused approach to complaints. These include muddled routes for raising formal complaints, investigative procedures structured to scrutinize the ‘validity’ of complaints, futile data collection systems, and adverse incentives and workarounds resulting from bureaucratic performance targets.ConclusionThis study demonstrates how national policies and regulations for complaint handling can impede, rather than promote, quality improvement in local settings. Accordingly, we propose a number of necessary reforms, including patient involvement in complaints investigations, the establishment of independent investigation bodies, and more meaningful data analysis strategies to uncover and address systemic causes behind recurring complaints.

Journal article

Rodrigues D, Kreif N, Saravanakumar K, Delaney B, Barahona M, Mayer Eet 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

Journal article

Kaura A, Trickey A, Shah A, Benedetto U, Glampson B, Mulla A, Mercuri L, Gautama S, Costelloe C, Goodman I, Redhead J, Saravanakumar K, Mayer E, Mayet Jet al., 2022, Comparing the longer-term effectiveness of a single dose of the Pfizer-BioNTech and Oxford-AstraZeneca COVID-19 vaccines across the age spectrum, EClinicalMedicine, Vol: 46, ISSN: 2589-5370

Background:A single dose strategy may be adequate to confer population level immunity and protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, especially in low- and middle-income countries where vaccine supply remains limited. We compared the effectiveness of a single dose strategy of the Oxford-AstraZeneca or Pfizer-BioNTech vaccines against SARS-CoV-2 infection across all age groups and over an extended follow-up period.Methods:Individuals vaccinated in North-West London, UK, with either the first dose of the Oxford-AstraZeneca or Pfizer-BioNTech vaccines between January 12, 2021 to March 09, 2021, were matched to each other by demographic and clinical characteristics. Each vaccinated individual was additionally matched to an unvaccinated control. Study outcomes included SARS-CoV-2 infection of any severity, COVID-19 hospitalisation, COVID-19 death, and all-cause mortality. Findings:Amongst matched individuals, 63,608 were in each of the vaccine groups and 127,216 were unvaccinated. Between 14-84 days of follow-up after matching, there were 534 SARS-CoV-2 infections, 65 COVID-19 hospitalisations, and 190 deaths, of which 29 were categorized as due to COVID-19. The incidence rate ratio (IRR) for SARS-CoV-2 infection was 0.85 (95% confidence interval [CI], 0.69 to 1.05) for Oxford-Astra-Zeneca, and 0.69 (0.55 to 0.86) for Pfizer-BioNTech. The IRR for both vaccines was the same at 0.25 (0.09 to 0.55) and 0.14 (0.02 to 0.58) for reducing COVID-19 hospitalization and COVID-19 mortality, respectively. The IRR for all-cause mortality was 0.25 (0.15 to 0.39) and 0.18 (0.10 to 0.30) for the Oxford-Astra-Zeneca and Pfizer-BioNTech vaccines, respectively. Age was an effect modifier of the association between vaccination and SARS-CoV-2 infection of any severity; lower hazard ratios for increasing age. Interpretation: A single dose strategy, for both vaccines, was effective at reducing COVID-19 mortality and hospitalization rates. The mag

Journal article

Khanbhai M, Symons J, Flott K, Harrison-White S, Spofforth J, Klaber R, Manton D, Darzi A, Mayer Eet al., 2022, Enriching the value of patient experience feedback: interactive dashboard development using co-design and heuristic evaluation, JMIR Human Factors, Vol: 9, Pages: 1-14, ISSN: 2292-9495

Background:There is an abundance of patient experience data held within healthcare organisations but stakeholders and staff are often unable to use the output in a meaningful and timely way to improve care delivery. Dashboards, which use visualised data to summarise key patient experience feedback, have the potential to address these issues.Objective:The aim of this study was to develop a patient experience dashboard with an emphasis on FFT reporting as per the national policy drive. An iterative process involving co-design involving key stakeholders was used to develop the dashboard, followed by heuristic usability testing.Methods:A two staged approach was employed; participatory co-design involving 20 co-designers to develop a dashboard prototype followed by iterative dashboard testing. Language analysis was performed on free-text patient experience data from the Friends and Family Test (FFT) and the themes and sentiment generated was used to populate the dashboard with associated FFT metrics. Heuristic evaluation and usability testing were conducted to refine the dashboard and assess user satisfaction using the system usability score (SUS).Results:Qualitative analysis from the co-design process informed development of the dashboard prototype with key dashboard requirements and a significant preference for bubble chart display. Heuristic evaluation revelated the majority of cumulative scores had no usability problem (n=18), cosmetic problem only (n=7), or minor usability problem (n= 5). Mean SUS was 89.7 (SD 7.9) suggesting an excellent rating.Conclusions:The growing capacity to collect and process patient experience data suggests that data visualisation will be increasingly important in turning the feedback into improvements to care. Through heuristic usability we demonstrated that very large FFT data can be presented into a thematically driven, simple visual display without loss of the nuances and still allow for exploration of the original free-text comments. T

Journal article

Smalley K, Aufegger L, Flott K, Mayer E, Darzi Aet al., 2022, The Self-Management Abilities Test (SMAT): a tool to identify the self-management abilities of adults with bronchiectasis, npj Primary Care Respiratory Medicine, Vol: 32, Pages: 1-7, ISSN: 2055-1010

Bronchiectasis is an increasingly common chronic respiratory disease which requires a high level of patient engagement in self-management. Whilst the need for self-management has been recognised, the knowledge and skills needed to do so – and the extent to which patients possess these – has not been well-specified. On one hand, understanding the gaps in people’s knowledge and skills can enable better targeting of self-management supports. On the other, clarity about what they do know can increase patients’ confidence to self-manage. This study aims to develop an assessment of patients’ ability to self-manage effectively, through a consensus-building process with patients, clinicians, and policymakers. The study employs a modified, online 3-round Delphi to solicit the opinions of patients, clinicians, and policymakers (N=30) with experience of bronchiectasis. The first round seeks consensus on the content domains for an assessment of bronchiectasis self-management ability. Subsequent rounds propose and refine multiple-choice assessment items to address the agreed domains. A group of 10 clinicians, 10 patients, and 10 policymakers provide both qualitative and quantitative feedback. Consensus is determined using content validity ratios. Qualitative feedback is analysed using the summative content analysis method. Overarching domains are: General Health Knowledge, Bronchiectasis-Specific Knowledge, Symptom Management, Communication, and Addressing Deterioration, each with two sub-domains. A final assessment tool of 20 items contains two items addressing each sub-domain. This study establishes that there is broad consensus about the knowledge and skills required to self-manage bronchiectasis effectively, across stakeholder groups. The output of the study is an assessment tool that can be used by patients and their healthcare providers to guide the provision of self-management education, opportunities, and support.

Journal article

van Dael J, Smalley K, Gillespie A, Reader T, Papadimitriou D, Glampson B, Marshall D, Mayer Eet al., 2022, Getting the whole story: integrating patient complaints and staff reports of unsafe care, Journal of Health Services Research and Policy, Vol: 27, Pages: 41-49, ISSN: 1355-8196

Objective: It is increasingly recognized that patient safety requires heterogeneous insights from a range of stakeholders, yet incident reporting systems in health care still primarily rely on staff perspectives. This paper examines the potential of combining insights from patient complaints and staff incident reports for a more comprehensive understanding of the causes and severity of harm. Methods: Using five years of patient complaints and staff incident reporting data at a large multi-site hospital in London (in the United Kingdom), this study conducted retrospective patient-level data linkage to identify overlapping reports. Using a combination of quantitative coding and in-depth qualitative analysis, we then compared level of harm reported, identified descriptions of adjacent events missed by the other party and examined combined narratives of mutually identified events. Results: Incidents where complaints and incident reports overlapped (n=446, 8.5% of all complaints and 0.6% of all incident reports) represented a small but critical area of investigation, with significantly higher rates of Serious Incidents and severe harm. Linked complaints described greater harm from safety incidents in 60% of cases, reported many surrounding safety events missed by staff (n=582), and provided contesting stories of why problems occurred in 46% cases, and complementary accounts in 26% cases.Conclusions: This study demonstrates the value of using patient complaints to supplement, test, and challenge staff reports, including to provide greater insight on the many potential factors that may give rise to unsafe care. Accordingly, we propose that a more holistic analysis of critical safety incidents can be achieved through combining heterogeneous data from different viewpoints, such as better integration of patient complaints and staff incident reporting data.

Journal article

Khanbhai M, Warren L, Symons J, Flott K, Harrison-White S, Manton D, Darzi A, Mayer Eet al., 2022, Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care, International Journal of Medical Informatics, Vol: 157, Pages: 1-7, ISSN: 1386-5056

BackgroundPatient centred care necessitates that healthcare experiences and perceived outcomes be considered across all transitions of care. Information encoded within free-text patient experience comments relating to transitions of care are not captured in a systematic way due to the manual resource required. We demonstrate the use of natural language processing (NLP) to extract meaningful information from the Friends and Family Test (FFT).MethodsFree-text fields identifying favourable service (“What did we do well?”) and areas requiring improvement (“What could we do better?”) were extracted from 69,285 FFT reports across four care settings at a secondary care National Health Service (NHS) hospital. Sentiment and patient experience themes were coded by three independent coders to produce a training dataset. The textual data was standardised with a series of pre-processing techniques and the performance of six machine learning (ML) models was obtained. The best performing ML model was applied to predict the themes and sentiment from the remaining reports. Comments relating to transitions of care were extracted, categorised by sentiment, and care setting to identify the most frequent words/combinations presented as tri-grams and word clouds.ResultsThe support vector machine (SVM) ML model produced the highest accuracy in predicting themes and sentiment. The most frequent single words relating to transition and continuity with a negative sentiment were “discharge” in inpatients and Accident and Emergency, “appointment” in outpatients, and “home’ in maternity. Tri-grams identified from the negative sentiments such as ‘seeing different doctor’, ‘information aftercare lacking’, ‘improve discharge process’ and ‘timing discharge letter’ have highlighted some of the problems with care transitions. None of this information was available from the quantitative data.Conc

Journal article

Neves AL, van Dael J, O'Brien N, Flott K, Ghafur S, Darzi A, Mayer Eet al., 2021, Use and impact of virtual primary care on quality and safety: The public's perspectives during the COVID-19 pandemic, Journal of Telemedicine and Telecare, ISSN: 1357-633X

IntroductionWith the onset of Coronavirus disease (COVID-19), primary care has swiftly transitioned from face-to-face to virtual care, yet it remains largely unknown how this has impacted the quality and safety of care. We aim to evaluate patient use of virtual primary care models during COVID-19, including change in uptake, perceived impact on the quality and safety of care and willingness of future use.MethodologyAn online cross-sectional survey was administered to the public across the United Kingdom, Sweden, Italy and Germany. McNemar tests were conducted to test pre- and post-pandemic differences in uptake for each technology. One-way analysis of variance was conducted to examine patient experience ratings and perceived impacts on healthcare quality and safety across demographic characteristics.ResultsRespondents (n = 6326) reported an increased use of telephone consultations ( + 6.3%, p < .001), patient-initiated services ( + 1.5%, n = 98, p < 0.001), video consultations ( + 1.4%, p < .001), remote triage ( + 1.3, p < 0.001) and secure messaging systems ( + 0.9%, p = .019). Experience rates using virtual care technologies were higher for men (2.4  ±  1.0 vs. 2.3  ±  0.9, p < .001), those with higher literacy (2.8  ±  1.0 vs. 2.3  ±  0.9, p < .001), and participants from Germany (2.5  ±  0.9, p < .001). Healthcare timeliness and efficiency were the dimensions most often reported as being positively impacted by virtual technologies (60.2%, n = 2793 and 55.7%, n = 2,401, respectively), followed by effectiveness (46.5%, n = 

Journal article

Khanbhai M, Flott K, Manton D, Harrison-White S, Klaber R, Darzi A, Mayer Eet al., 2021, Identifying factors that promote and limit the effective use of real-time patient experience feedback: a mixed-methods study in secondary care, BMJ Open, Vol: 11, Pages: 1-7, ISSN: 2044-6055

Objectives:The Friends and Family Test (FFT) is commissioned by the National Health Service (NHS) in England to capture patient experience as a real-time feedback initiative for patient-centred quality improvement (QI). The aim of this study was to create a process map in order to identify the factors that promote and limit the effective use of FFT as a real-time feedback initiative for patient-centred QI. Setting:This study was conducted at a large London NHS Trust. Services include accident and emergency, inpatient, outpatient and maternity, which routinely collect FFT patient experience data. Participants:Healthcare staff and key stakeholders involved in FFT.Interventions:Semi-structured interviews were conducted on fifteen participants from a broad range of professional groups to evaluate their engagement with the FFT. Interview data were recorded, transcribed, and analysed for using deductive thematic analysis.Results:Concerns related to inefficiency in the flow of FFT data, lack of time to analyse FFT reports (with emphasis on high level reporting rather than QI), insufficient access to FFT reports and limited training provided to understand FFT reports for frontline staff. The sheer volume of data received was not amenable to manual thematic analysis resulting in inability to acquire insight from the free-text. This resulted in staff ambivalence towards FFT as a near real-time feedback initiative.Conclusions:The results state that there is too much FFT free text for meaningful analysis, and the output is limited to the provision of sufficient capacity and resource to analyse the data, without consideration of other options, such as text analytics and amending the data collection tool.

Journal article

Neves AL, Smalley K, Freise L, Harrison P, Darzi A, Mayer Eet al., 2021, Sharing electronic health records with patients: Who is using the Care Information Exchange portal? A cross-sectional study, Jornal of Medical Internet Research, Vol: 13, Pages: 1-12, ISSN: 1438-8871

Background: Sharing electronic health records with patients has been shown to improve patient safety and quality of care, and patient portals represent a powerful and convenient tool to enhance patient access to their own healthcare data. However, the success of patient portals will only be possible through sustained adoption by its end-users: the patients. A better understanding of the characteristics of users and non-users is critical to understand which groups remain underserved or excluded from using such tools.Objective: To identify the determinants of usage of the Care Information Exchange (CIE), a shared patient portal program in the United Kingdom.Methods: A cross-sectional study was conducted, using an online questionnaire. Information collected included age, gender, ethnicity, educational level, health status, postcode and digital literacy. Registered individuals were defined as having had an account created in the portal, independent of their actual use of the platform; users were defined as having ever used the portal. Multivariate logistic regression was used to model the probability of being a user. Statistical analysis was performed in R, and Tableau ® was used to create maps of the proportion of CIE users by postcode area.Results: A total of 1,083 subjects replied to the survey (+186% of the estimated minimum target sample). The proportion of users was 61.6% (n=667), and within these, the majority (57.7%, n=385) used the portal at least once a month. To characterise the users and non-users of the system, we performed a sub-analysis of the sample, including only participants that had provided at least information regarding gender and age category. The sub-analysis included 650 individuals (59.8% women, 84.8% over 40 years). The majority of the subjects were white (76.6%, n=498), resident in London (64.7%, n=651), and lived in North West London (55.9%, n=363). Individuals with a higher educational degree (undergraduate/professional or postgraduat

Journal article

Hunter B, Reis S, Campbell D, Matharu S, Ratnakumar P, Mercuri L, Hindocha S, Kalsi H, Mayer E, Glampson B, Robinson E, Al-Lazikani B, Scerri L, Bloch S, Lee Ret al., 2021, Development of a structured query language and natural language processing algorithm to identify lung nodules in a cancer centre, Frontiers in Medicine, Vol: 8, Pages: 1-10, ISSN: 2296-858X

Importance: The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation.Objective: To automate lung nodule identification in a tertiary cancer centre.Methods: This retrospective cohort study used Electronic Healthcare Records to identify CT reports generated between 31st October 2011 and 24th July 2020. A structured query language/natural language processing tool was developed to classify reports according to lung nodule status. Performance was externally validated. Sentences were used to train machine-learning classifiers to predict concerning nodule features in 2,000 patients.Results: 14,586 patients with lung nodules were identified. The cancer types most commonly associated with lung nodules were lung (39%), neuro-endocrine (38%), skin (35%), colorectal (33%) and sarcoma (33%). Lung nodule patients had a greater proportion of metastatic diagnoses (45 vs. 23%, p < 0.001), a higher mean post-baseline scan number (6.56 vs. 1.93, p < 0.001), and a shorter mean scan interval (4.1 vs. 5.9 months, p < 0.001) than those without nodules. Inter-observer agreement for sentence classification was 0.94 internally and 0.98 externally. Sensitivity and specificity for nodule identification were 93 and 99% internally, and 100 and 100% at external validation, respectively. A linear-support vector machine model predicted concerning sentence features with 94% accuracy.Conclusion: We have developed and validated an accurate tool for automated lung nodule identification that is valuable for service evaluation and research data acquisition.

Journal article

Piggin M, Johnson H, Papadimitriou D, Mayet J, Glampson B, Aylin P, Mayer Eet al., 2021, Insight Report: Digital health online public involvement session on building our digital healthcare programme in North West London, Insight Report: Digital health online public involvement session on building our digital healthcare programme in North West London

Summary report on the views of members of the public on building the digital healthcare programme of research in North West London.

Report

Piggin M, Johnson H, Papadimitriou D, Kaura A, Quint J, Glampson B, Mayer E, Aylin Pet al., 2021, Insight Report: Digital health online public involvement session on using real world evidence to improve health and care in North West London, Insight Report: Digital health online public involvement session on using real world evidence to improve health and care in North West London

Summary report on the views of members of the public on real world evidence studies undertaken aspart of building the digital healthcare programme of research in North West London

Report

Fiorentino F, Prociuk D, Espinosa Gonzalez AB, Neves AL, Husain L, Ramtale S, Mi E, Mi E, Macartney J, Anand S, Sherlock J, Saravanakumar K, Mayer E, de Lusignan S, Greenhalgh T, Delaney Bet 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

Journal article

Fiorentino F, Prociuk D, Espinosa Gonzalez AB, Neves AL, Husain L, Ramtale SC, Mi E, Mi E, Macartney J, Anand SN, Sherlock J, Saravanakumar K, Mayer E, de Lusignan S, Greenhalgh T, Delaney BCet 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

Journal article

Glampson B, Brittain J, Kaura A, Mulla A, Mercuri L, Brett S, Aylin P, tessa S, goodman I, Redhead J, kavitha S, Mayer Eet al., 2021, North West London Covid-19 Vaccination Programme: Real-world evidence for Vaccine uptake and effectiveness: Retrospective Cohort Study, JMIR Public Health and Surveillance, Vol: 7, Pages: 1-17, ISSN: 2369-2960

Background:On March 11, 2020 the World Health Organisation declared the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) causing Coronavirus Disease 2019 (COVID-19) syndrome, as a pandemic. The UK mass vaccination programme commenced on December 08, 2020 vaccinating groups of the population deemed to be most vulnerable to severe COVID-19 infection.Objective:To assess the early vaccine administration coverage and outcome data across an integrated care system in North West London (NWL), leveraging a unique population-level care dataset. Vaccine effectiveness of a single dose of the Oxford/Astrazeneca and Pfizer/BioNtech vaccines were compared.Methods:A retrospective cohort study identified 2,183,939 individuals eligible for COVID-19 vaccination between December 08, 2020 and February 24, 2021 within a primary, secondary and community care integrated care dataset. These data were used to assess vaccination hesitancy across ethnicity, gender and socio-economic deprivation measures (Pearson Product-Moment Correlations); investigated COVID-19 transmission related to vaccination hubs; and assessed the early effectiveness of COVID-19 vaccination (after a single dose) using time to event analyses with multivariable Cox regression analysis to investigate if vaccination independently predicted positive SARS-CoV-2 in those vaccinated compared to those unvaccinated.Results: In the study 5.88% (24,332/413,919) of individuals declined and did not receive a vaccination. Black or Black British individuals had the highest rate of declining a vaccine at 16.14% (4,337/26,870). There was a strong negative association between socio-economic deprivation and rate of declining vaccination (r=-0.94, P=.002) with 13.5% (1980/14571) of individuals declining vaccination in the most deprived areas compared to 0.98% (869/9609) in the least. In the first six days after vaccination 344 of 389587 individuals tested positive for SARS-CoV-2 (0.09%). The rate increased to 0.13% (525/389,243)

Journal article

Neves AL, Pereira Rodrigues P, Mulla A, Glampson B, Willis T, Mayer Eet al., 2021, Using electronic health records to develop and validate a machine learning tool to predict type 2 diabetes outcomes: a study protocol, BMJ Open, Vol: 11, Pages: 1-5, ISSN: 2044-6055

Introduction: Type 2 diabetes (T2DM) is a major cause of blindness, kidney failure, myocardial infarction, stroke and lower limb amputation. We are still unable, however, to accurately predict or identify which patients are at a higher risk of deterioration. Most risk stratification tools do not account for novel factors such as socio-demographic determinants, self-management ability, or access to healthcare. Additionally, most tools are based in clinical trials, with limited external generalisability.Objective: The aim of this work is to design and validate a machine learning-based tool to identify patients with T2DM at high risk of clinical deterioration, based on a comprehensive set of patient level characteristics retrieved from a population health linked dataset.Sample and design: Retrospective cohort study of patients with diagnosis of T2DM on Jan 1st, 2015, with a 5-year follow-up. Anonymised electronic health care records from the Whole System Integrated Care (WSIC) database will be used. Preliminary outcomes: Outcome variables of clinical deterioration will include retinopathy, chronic renal disease, myocardial infarction, stroke, peripheral arterial disease, or death. Predictor variables will include sociodemographic and geographic data, patients’ ability to self-manage disease, clinical and metabolic parameters and healthcare service usage. Prognostic models will be defined using multi-dependence Bayesian networks (BN). The derivation cohort, comprising 80% of the patients, will be used to define the prognostic models. Model parameters will be internally validated by comparing the area under the receiver operating characteristic (ROC) curve (AUC) in the derivation cohort with those calculated from a leave-one-out and a 10 times 2-fold cross-validation. Ethics and dissemination: The study has received approvals from the Information Governance Committee at the Whole Systems Integrated Care. Results will be made available to people with type 2 diabetes

Journal article

Glampson B, Brittain J, Kaura A, Mulla A, Mercuri L, Brett SJ, Aylin P, Sandall T, Goodman I, Redhead J, Saravanakumar K, Mayer EKet al., 2021, Assessing COVID-19 vaccine uptake and effectiveness through the north west London vaccination program: retrospective cohort study, Publisher: JMIR Publications

Background:Real world data supporting the effectiveness of the COVID-19 vaccination strategy in the UK population is needed to guide health policy. This real-word data-driven evidence study of the UK COVID-19 Vaccination Programme in the Northwest London (NWL) population used a unique dataset established as part of the Gold Command Covid-19 response in NWL (iCARE https://imperialbrc.nihr.ac.uk/facilities/icare/), which included the pre-established Whole System Integrated Care (WSIC) data collated for the purposes of population health in the sector.Objective:To assess the early vaccine administration coverage and vaccine effectiveness and outcome data across an integrated care system of eight CCGs leveraging a unique population-level care datasetMethods:Design - Retrospective cohort study. Setting - Individuals eligible for COVID 19 vaccination in North West London based on linked primary and secondary care data. Participants - 2,183,939 individuals eligible for COVID 19 vaccinationResults:During the NWL vaccine programme study time period 5.88% of individuals declined and did not receive a vaccination. Black or black British individuals had the highest rate of declining a vaccine at 16.14% (4,337). There was a strong negative association between deprivation and rate of declining vaccination (r=-0.94, p<0.01) with 13.5% of individuals declining vaccination in the most deprived postcodes compared to 0.98% in the least deprived postcodes. In the first six days after vaccination 344 of 389587 individuals tested positive for COVID-19 (0.09%). The rate increased to 0.13% (525/389,243) between days 7 and 13, before then gradually falling week on week. At 28 days post vaccination there was a 74% (HR 0.26 (0.19-0.35)) and 78% (HR 0.22 (0.18-0.27)) reduction in risk of testing positive for COVID -19 for individuals that received the Oxford/Astrazeneca and Pfizer/BioNTech vaccines respectively, when compared with unvaccinated individuals. After vaccination very low rates of

Working paper

Jay APM, Aldiwani M, O'Callaghan ME, Pearce AK, Huddart RA, Mayer E, Reid AH, Nicol DLet al., 2021, Features and Management of Late Relapse of Nonseminomatous Germ Cell Tumour, EUROPEAN UROLOGY OPEN SCIENCE, Vol: 29, Pages: 82-88, ISSN: 2666-1691

Journal article

Fankhauser CD, Afferi L, Stroup SP, Rocco NR, Olson K, Bagrodia A, Cazzaniga W, Mayer E, Nicol D, Islamoglu E, De Vergie S, Ragheed S, Eggener SE, Nazzani S, Nicolai N, Hugar L, Sexton WJ, Matei D-V, Hermanns T, Hamilton RJ, Hiester A, Albers P, Clarke N, Mattei Aet al., 2021, Perioperative safety and short-term oncological outcomes of minimally invasive retroperitoneal lymph node dissection, Publisher: ELSEVIER, Pages: S911-S912, ISSN: 0302-2838

Conference paper

Espinosa-Gonzalez AB, Neves AL, Fiorentino F, Prociuk D, Husain L, Ramtale SC, Mi E, Mi E, Macartney J, Anand SN, Sherlock J, Saravanakumar K, Mayer E, de Lusignan S, Greenhalgh T, Delaney BCet 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

Journal article

Glampson B, Brittain J, Kaura A, Mulla A, Mercuri L, Brett S, Aylin P, Sandall T, Goodman I, Redhead J, Saravanakumar K, Mayer EKet al., 2021, North West London Covid-19 vaccination programme: real-world evidence for vaccine uptake and effectiveness, Publisher: Cold Spring Harbor Laboratory

Objective To assess the early vaccine administration coverage and vaccine effectiveness and outcome data across an integrated care system of eight CCGs leveraging a unique population-level care datasetDesign Retrospective cohort study.Setting Individuals eligible for COVID 19 vaccination in North West London based on linked primary and secondary care data.Participants 2,183,939 individuals eligible for COVID 19 vaccinationResults During the NWL vaccine programme study time period 5.88% of individuals declined and did not receive a vaccination. Black or black British individuals had the highest rate of declining a vaccine at 16.14% (4,337). There was a strong negative association between deprivation and rate of declining vaccination (r=-0.94, p<0.01) with 13.5% of individuals declining vaccination in the most deprived postcodes compared to 0.98% in the least deprived postcodes.In the first six days after vaccination 344 of 389587 individuals tested positive for COVID-19 (0.09%). The rate increased to 0.13% (525/389,243) between days 7 and 13, before then gradually falling week on week.At 28 days post vaccination there was a 74% (HR 0.26 (0.19-0.35)) and 78% (HR 0.22 (0.18-0.27)) reduction in risk of testing positive for COVID-19 for individuals that received the Oxford/Astrazeneca and Pfizer/BioNTech vaccines respectively, when compared with unvaccinated individuals.After vaccination very low rates of hospital admission were seen in individuals testing positive for COVID-19 (0.01% of all patients vaccinated).Conclusions This study provides further evidence that a single dose of either the Pfizer/BioNTech vaccine or the Oxford/Astrazeneca vaccine is effective at reducing the risk of testing positive for COVID-19 up to 60 days across all adult age groups, ethnic groups, and risk categories in an urban UK population. There was no difference in effectiveness up to 28 days between the Oxford/Astrazeneca and Pfizer/BioNtech vaccines.In those declining vaccination higher

Working paper

Khanbhai M, Anyadi P, Symons J, Flott K, Darzi A, Mayer Eet al., 2021, Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review, BMJ Health & Care Informatics, Vol: 28, ISSN: 2632-1009

Objectives Unstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data.Methods Databases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded.Results Nineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers.Conclusion NLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.

Journal article

Freise L, Neves AL, Flott K, Harrison P, Kelly J, Darzi A, Mayer EKet al., 2021, Assessment of patients' ability to review electronic health record information to identify potential errors: cross-sectional web-based survey, JMIR Formative Research, Vol: 5, ISSN: 2561-326X

Background: Sharing personal health information positively impacts quality of care across several domains, and particularly, safety and patient-centeredness. Patients may identify and flag up inconsistencies in their electronic health records (EHRs), leading to improved information quality and patient safety. However, in order to identify potential errors, patients need to be able to understand the information contained in their EHRs.Objective: The aim of this study was to assess patients’ perceptions of their ability to understand the information contained in their EHRs and to analyze the main barriers to their understanding. Additionally, the main types of patient-reported errors were characterized.Methods: A cross-sectional web-based survey was undertaken between March 2017 and September 2017. A total of 682 registered users of the Care Information Exchange, a patient portal, with at least one access during the time of the study were invited to complete the survey containing both structured (multiple choice) and unstructured (free text) questions. The survey contained questions on patients’ perceived ability to understand their EHR information and therefore, to identify errors. Free-text questions allowed respondents to expand on the reasoning for their structured responses and provide more detail about their perceptions of EHRs and identifying errors within them. Qualitative data were systematically reviewed by 2 independent researchers using the framework analysis method in order to identify emerging themes.Results: A total of 210 responses were obtained. The majority of the responses (123/210, 58.6%) reported understanding of the information. The main barriers identified were information-related (medical terminology and knowledge and interpretation of test results) and technology-related (user-friendliness of the portal, information display). Inconsistencies relating to incomplete and incorrect information were reported in 12.4% (26/210) of the res

Journal article

Kowa J-Y, Soneji N, Sohaib SA, Mayer E, Hazell S, Butterfield N, Shur J, Ap Dafydd Det al., 2021, Detection and staging of radio-recurrent prostate cancer using multiparametric MRI., British Journal of Radiology, Vol: 94, Pages: 1-10, ISSN: 0007-1285

OBJECTIVE: We determined the sensitivity and specificity of multiparametric magnetic resonance imaging (MP-MRI) in detection of locally recurrent prostate cancer and extra prostatic extension in the post-radical radiotherapy setting. Histopathological reference standard was whole-mount prostatectomy specimens. We also assessed for any added value of the dynamic contrast enhancement (DCE) sequence in detection and staging of local recurrence. METHODS: This was a single centre retrospective study. Participants were selected from a database of males treated with salvage prostatectomy for locally recurrent prostate cancer following radiotherapy. All underwent pre-operative prostate-specific antigen assay, positron emission tomography CT, MP-MRI and transperineal template prostate mapping biopsy prior to salvage prostatectomy. MP-MRI performance was assessed using both Prostate Imaging-Reporting and Data System v. 2 and a modified scoring system for the post-treatment setting. RESULTS: 24 patients were enrolled. Using Prostate Imaging-Reporting and Data System v. 2, sensitivity, specificity, positive predictive value and negative predictive value was 64%, 94%, 98% and 36%. MP-MRI under staged recurrent cancer in 63%. A modified scoring system in which DCE was used as a co-dominant sequence resulted in improved diagnostic sensitivity (61%-76%) following subgroup analysis. CONCLUSION: Our results show MP-MRI has moderate sensitivity (64%) and high specificity (94%) in detecting radio-recurrent intraprostatic disease, though disease tends to be under quantified and under staged. Greater emphasis on dynamic contrast images in overall scoring can improve diagnostic sensitivity. ADVANCES IN KNOWLEDGE: MP-MRI tends to under quantify and under stage radio-recurrent prostate cancer. DCE has a potentially augmented role in detecting recurrent tumour compared with the de novo setting. This has relevance in the event of any future modified MP-MRI scoring system for the irradiated gl

Journal article

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