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  • Journal article
    Bai W, Cursi F, Guo X, Huang B, Lo B, Yang GZ, Yeatman EMet al., 2022,

    Task-Based LSTM Kinematic Modeling for a Tendon-Driven Flexible Surgical Robot

    , IEEE Transactions on Medical Robotics and Bionics, Vol: 4, Pages: 339-342

    Tendon-driven flexible surgical robots are normally suffering from the inaccurate modeling and imprecise motion control problems due to the nonlinearities of tendon transmission. Learning-based approaches are experimental data-driven with uncertainties modeled empirically, which can be adopted to improve the inevitable issues. This work proposes a LSTM-based kinematic modeling approach with task-based data for a flexible tendon-driven surgical robot to improve the control accuracy. Real experiments demonstrated the effectiveness and superiority of the proposed learned model when completing path following tasks, especially compared to the traditional modeling.

  • Journal article
    Lam K, Chen J, Wang Z, Iqbal F, Darzi A, Lo B, Purkayastha S, Kinross Jet al., 2022,

    Machine learning for technical skill assessment in surgery: a systematic review

    , npj Digital Medicine, Vol: 5, ISSN: 2398-6352

    Accurate and objective performance assessment is essential for both trainees and certified surgeons. However, existing methods can be time consuming, labor intensive and subject to bias. Machine learning (ML) has the potential to provide rapid, automated and reproducible feedback without the need for expert reviewers. We aimed to systematically review the literature and determine the ML techniques used for technical surgical skill assessment and identify challenges and barriers in the field. A systematic literature search, in accordance with the PRISMA statement, was performed to identify studies detailing the use of ML for technical skill assessment in surgery. Of the 1896 studies that were retrieved, 66 studies were included. The most common ML methods used were Hidden Markov Models (HMM, 14/66), Support Vector Machines (SVM, 17/66) and Artificial Neural Networks (ANN, 17/66). 40/66 studies used kinematic data, 19/66 used video or image data, and 7/66 used both. Studies assessed performance of benchtop tasks (48/66), simulator tasks (10/66), and real-life surgery (8/66). Accuracy rates of over 80% were achieved, although tasks and participants varied between studies. Barriers to progress in the field included a focus on basic tasks, lack of standardization between studies, and lack of datasets. ML has the potential to produce accurate and objective surgical skill assessment through the use of methods including HMM, SVM, and ANN. Future ML-based assessment tools should move beyond the assessment ofbasic tasks and towards real-life surgery and provide interpretable feedback with clinical value for the surgeon.

  • Journal article
    Danielli S, Donnelly P, Coffey T, Horn S, Ashrafian H, Darzi A, Danielli S, Donnelly P, Coffey T, Horn S, Ashrafian H, Darzi Aet al., 2022,

    Measuring more than just economic growth to improve well-being

    , Journal of Public Health, Vol: 44, Pages: e76-e78, ISSN: 1741-3842

    It's official: The UK is in a recession. The economy has suffered its biggest slump on record with a drop in gross domestic product (GDP) of 20.4%. 1 This is going to have a significant impact on our health and well-being. It risks creating a spiralling decay as we know good health is not only a consequence, but also a condition for sustained and sustainable economic development. 2 In this way, the health of a nation creates a virtuous circle of improved health and improved economic prosperity. How we measure prosperity is therefore important and needs to be considered.

  • Journal article
    O'Brien N, Flott K, Bray O, Shaw A, Durkin Met al., 2022,

    Implementation of initiatives designed to improve healthcare worker health and wellbeing during the COVID-19 pandemic: comparative case studies from 13 healthcare provider organisations globally

    , Globalization and Health, Vol: 18, ISSN: 1744-8603

    Background: Healthcare workers are at a disproportionate risk of contracting COVID-19. The physical and mental repercussions of such risk have an impact on the wellbeing of healthcare workers around the world. Healthcare workers are the foundation of all well-functioning health systems capable of responding to the ongoing pandemic; initiatives to address and reduce such risk are critical. Since the onset of the pandemic healthcare organizations have embarked on the implementation of a range of initiatives designed to improve healthcare worker health and wellbeing. Methods: Through a qualitative collective case study approach where participants responded to a longform survey, the facilitators, and barriers to implementing such initiatives were explored, offering global insights into the challenges faced at the organizational level. 13 healthcare organizations were surveyed across 13 countries. Of these 13 participants, 5 subsequently provided missing information through longform interviews or written clarifications.Results: 13 case studies were received from healthcare provider organizations. Mental health initiatives were the most commonly described health and wellbeing initiatives among respondents. Physical health and health and safety focused initiatives, such as the adaption of workspaces, were also described. Strong institutional level direction, including engaged leadership, and the input, feedback, and engagement of frontline staff were the two main facilitators in implementing initiatives. The most common barrier to implementation, noted largely by organizations who discussed infection prevention and control initiatives, was inadequate personal protective equipment and supply chain disruption. Conclusions: Common themes emerge globally in exploring the enablers and barriers to implementing initiatives to improve healthcare workers health and wellbeing through the COVID-19 pandemic. Consideration of the themes outlined in the paper by healthcare organizations

  • Journal article
    Elliott P, Bodinier B, Eales O, Wang H, Haw D, Elliott J, Whitaker M, Jonnerby J, Tang D, Walters CE, Atchison C, Diggle PJ, Page AJ, Trotter AJ, Ashby D, Barclay W, Taylor G, Ward H, Darzi A, Cooke GS, Chadeau-Hyam M, Donnelly CAet al., 2022,

    Rapid increase in Omicron infections in England during December 2021: REACT-1 study.

    , Science, Vol: 375, Pages: eabn8347-eabn8347, ISSN: 0036-8075

    The unprecedented rise in SARS-CoV-2 infections during December 2021 was concurrent with rapid spread of the Omicron variant in England and globally. We analyzed prevalence of SARS-CoV-2 and its dynamics in England from end November to mid-December 2021 among almost 100,000 participants from the REACT-1 study. Prevalence was high with rapid growth nationally and particularly in London during December 2021, and an increasing proportion of infections due to Omicron. We observed large falls in swab positivity among mostly vaccinated older children (12-17 years) compared with unvaccinated younger children (5-11 years), and in adults who received a third (booster) vaccine dose vs. two doses. Our results reinforce the importance of vaccination and booster campaigns, although additional measures have been needed to control the rapid growth of the Omicron variant.

  • 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
    Sounderajah V, 2022,

    Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study

    , npj Digital Medicine, Vol: 5, Pages: 1-13, ISSN: 2398-6352

    Artificial intelligence (AI) centred diagnostic systems are increasingly recognized as robust solutions in healthcare delivery pathways. In turn, there has been a concurrent rise in secondary research studies regarding these technologies in order to influence key clinical and policymaking decisions. It is therefore essential that these studies accurately appraise methodological quality and risk of bias within shortlisted trials and reports. In order to assess whether this critical step is performed, we undertook a meta-research study evaluating adherence to the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool within AI diagnostic accuracy systematic reviews. A literature search was conducted on all studies published from 2000 to December 2020. Of 50 included reviews, 36 performed quality assessment, of which 27 utilised the QUADAS-2 tool. Bias was reported across all four domains of QUADAS-2. 243 of 423 studies (57.5%) across all systematic reviews utilising QUADAS-2 reported a high or unclear risk of bias in the patient selection domain, 110 (26%) reported a high or unclear risk of bias in the index test domain, 121 (28.6%) in the reference standard domain and 157 (37.1%) in the flow and timing domain. This study demonstrates incomplete uptake of quality assessment tools in reviews of AI-based diagnostic accuracy studies and highlights inconsistent reporting across all domains of quality assessment. Poor standards of reporting act as barriers to clinical implementation. The creation of an AI specific extension for quality assessment tools of diagnostic accuracy AI studies may facilitate the safe translation of AI tools into clinical practice.

  • 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 = 

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