Imperial College London

Erik Mayer

Faculty of MedicineDepartment of Surgery & Cancer

Clinical Reader in Urology
 
 
 
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Contact

 

e.mayer Website

 
 
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Location

 

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

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Summary

 

Publications

Publication Type
Year
to

238 results found

Ruan Y, Mercuri L, Papadimitriou D, Galdikas A, Roadknight G, Davies J, Glampson B, Mayer E, Hill NE, Rea Ret al., 2023, Increase in hypoglycaemia and hyperglycaemia in people with diabetes admitted to hospital during COVID-19 pandemic, Journal of Diabetes and Its Complications, Vol: 37, ISSN: 1056-8727

BACKGROUND: We used detailed information on patients with diabetes admitted to hospital to determine differences in clinical outcomes before and during the COVID-19 pandemic in the UK. METHODS: The study used electronic patient record data from Imperial College Healthcare NHS Trust. Hospital admission data for patients coded for diabetes was analysed over three time periods: pre-pandemic (31st January 2019-31st January 2020), Wave 1 (1st February 2020-30th June 2020), and Wave 2 (1st September 2020-30th April 2021). We compared clinical outcomes including glycaemia and length of stay. RESULTS: We analysed data obtained from 12,878, 4008 and 7189 hospital admissions during the three pre-specified time periods. The incidence of Level 1 and Level 2 hypoglycaemia was significantly higher during Waves 1 and 2 compared to the pre-pandemic period (25 % and 25.1 % vs. 22.9 % for Level 1 and 11.7 % and 11.5 % vs. 10.3 % for Level 2). The incidence of hyperglycaemia was also significantly higher during the two waves. The median hospital length of stay increased significantly (4.1[1.6, 9.8] and 4.0[1.4, 9.4] vs. 3.5[1.2, 9.2] days). CONCLUSIONS: During the COVID-19 pandemic in the UK, hospital in-patients with diabetes had a greater number of hypoglycaemic/hyperglycaemic episodes and an increased length of stay when compared to the pre-pandemic period. This highlights the necessity for a focus on improved diabetes care during further significant disruptions to healthcare systems and ensuring minimisation of the impact on in-patient diabetes services. SUMMARY: Diabetes is associated with poorer outcomes from COVID-19. However the glycaemic control of inpatients before and during the COVID-19 pandemic is unknown. We found the incidence of hypoglycaemia and hyperglycaemia was significantly higher during the pandemic highlighting the necessity for a focus on improved diabetes care during further pandemics.

Journal article

ICGCTCGA Pan-Cancer Analysis of Whole Genomes Consortium, 2023, Author Correction: Pan-cancer analysis of whole genomes., Nature, Vol: 614

Journal article

Fadel MG, Ahmed M, Pellino G, Rasheed S, Tekkis P, Nicol D, Kontovounisios C, Mayer Eet al., 2023, Retroperitoneal Lymph Node Dissection in Colorectal Cancer with Lymph Node Metastasis: A Systematic Review, CANCERS, Vol: 15

Journal article

Sieverling L, Hong C, Koser SD, Ginsbach P, Kleinheinz K, Hutter B, Braun DM, Cort├ęs-Ciriano I, Xi R, Kabbe R, Park PJ, Eils R, Schlesner M, PCAWG-Structural Variation Working Group, Brors B, Rippe K, Jones DTW, Feuerbach L, PCAWG Consortiumet al., 2022, Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer., Nat Commun, Vol: 13

Journal article

Rubanova Y, Shi R, Harrigan CF, Li R, Wintersinger J, Sahin N, Deshwar AG, PCAWG Evolution and Heterogeneity Working Group, Morris QD, PCAWG Consortiumet al., 2022, Author Correction: Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig., Nat Commun, Vol: 13

Journal article

Bhandari V, Li CH, Bristow RG, Boutros PC, PCAWG Consortiumet al., 2022, Author Correction: Divergent mutational processes distinguish hypoxic and normoxic tumours., Nat Commun, Vol: 13

Journal article

Zhang Y, Chen F, Fonseca NA, He Y, Fujita M, Nakagawa H, Zhang Z, Brazma A, PCAWG Transcriptome Working Group, PCAWG Structural Variation Working Group, Creighton CJ, PCAWG Consortiumet al., 2022, Author Correction: High-coverage whole-genome analysis of 1220 cancers reveals hundreds of genes deregulated by rearrangement-mediated cis-regulatory alterations., Nat Commun, Vol: 13

Journal article

Reyna MA, Haan D, Paczkowska M, Verbeke LPC, Vazquez M, Kahraman A, Pulido-Tamayo S, Barenboim J, Wadi L, Dhingra P, Shrestha R, Getz G, Lawrence MS, Pedersen JS, Rubin MA, Wheeler DA, Brunak S, Izarzugaza JMG, Khurana E, Marchal K, von Mering C, Sahinalp SC, Valencia A, PCAWG Drivers and Functional Interpretation Working Group, Reimand J, Stuart JM, Raphael BJ, PCAWG Consortiumet al., 2022, Author Correction: Pathway and network analysis of more than 2500 whole cancer genomes., Nat Commun, Vol: 13

Journal article

Lear R, Freise L, Kybert M, Darzi A, Neves AL, Mayer EKet al., 2022, Perceptions of Quality of Care Among Users of a Web-Based Patient Portal: Cross-sectional Survey Analysis, JOURNAL OF MEDICAL INTERNET RESEARCH, Vol: 24, ISSN: 1438-8871

Journal article

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

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, Vol: 115, Pages: 390-398, 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

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, 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

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

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

Lear R, Freise L, Kybert M, Darzi A, Neves AL, Mayer EKet al., 2022, Patients’ Willingness and Ability to Identify and Respond to Errors in Their Personal Health Records: Mixed Methods Analysis of Cross-sectional Survey Data (Preprint)

<sec> <title>BACKGROUND</title> <p>Errors in electronic health records are known to contribute to patient safety incidents; however, systems for checking the accuracy of patient records are almost nonexistent. Personal health records (PHRs) enabling patient access to and interaction with the clinical records offer a valuable opportunity for patients to actively participate in error surveillance.</p> </sec> <sec> <title>OBJECTIVE</title> <p>This study aims to evaluate patients’ willingness and ability to identify and respond to errors in their PHRs.</p> </sec> <sec> <title>METHODS</title> <p>A cross-sectional survey was conducted using a web-based questionnaire. Patient sociodemographic data were collected, including age, sex, ethnicity, educational level, health status, geographical location, motivation to self-manage, and digital health literacy (measured using the eHealth Literacy Scale tool). Patients with experience of using the Care Information Exchange (CIE) portal, who specified both age and sex, were included in these analyses. The patients’ responses to 4 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 PHRs. Multinomial logistic regression was used to identify patients’ characteristics that predict the ability to understand information in the CIE and willingness to respond to errors in their records. The framework method was used to derive themes from patients’ free-text responses.</p> </sec> <sec> <titl

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

Jha S, Mayer E, Barahona M, 2022, Improving information fusion on multimodal clinical data in classification settings, Pages: 154-159

Clinical data often exists in different forms across the lifetime of a patient's interaction with the healthcare system-structured, unstructured or semi-structured data in the form of laboratory readings, clinical notes, diagnostic codes, imaging and audio data of various kinds, and other observational data. Formulating a representation model that aggregates information from these heterogeneous sources may allow us to jointly model on data with more predictive signal than noise and help inform our model with useful constraints learned from better data. Multimodal fusion approaches help produce representations combined from heterogeneous modalities, which can be used for clinical prediction tasks. Representations produced through different fusion techniques require different training strategies. We investigate the advantage of adding narrative clinical text to structured modalities to classification tasks in the clinical domain. We show that while there is a competitive advantage in combined representations of clinical data, the approach can be helped by training guidance customized to each modality. We show empirical results across binary/multiclass settings, single/multitask settings and unified/multimodal learning rate settings for early and late information fusion of clinical data.

Conference paper

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, 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

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

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

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