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  • Journal article
    Modi HN, Singh H, Fiorentino F, Orihuela-Espina F, Athanasiou T, Yang G-Z, Darzi A, Leff DRet al., 2019,

    Association of residents' neural signatures with stress resilience during surgery

    , JAMA Surgery, Vol: 154, ISSN: 2168-6254

    Importance: Intraoperative stressors may compound cognitive load, prompting performance decline and threatening patient safety. However, not all surgeons cope equally well with stress, and the disparity between performance stability and decline under high cognitive demand may be characterized by differences in activation within brain areas associated with attention and concentration such as the prefrontal cortex (PFC). Objective: To compare PFC activation between surgeons demonstrating stable performance under temporal stress with those exhibiting stress-related performance decline. Design, Setting, and Participants: Cohort study conducted from July 2015 to September 2016 at the Imperial College Healthcare National Health Service Trust, England. One hundred two surgical residents (postgraduate year 1 and greater) were invited to participate, of which 33 agreed to partake. Exposures: Participants performed a laparoscopic suturing task under 2 conditions: self-paced (SP; without time-per-knot restrictions), and time pressure (TP; 2-minute per knot time restriction). Main Outcomes and Measures: A composite deterioration score was computed based on between-condition differences in task performance metrics (task progression score [arbitrary units], error score [millimeters], leak volume [milliliters], and knot tensile strength [newtons]). Based on the composite score, quartiles were computed reflecting performance stability (quartile 1 [Q1]) and decline (quartile 4 [Q4]). Changes in PFC oxygenated hemoglobin concentration (HbO2) measured at 24 different locations using functional near-infrared spectroscopy were compared between Q1 and Q4. Secondary outcomes included subjective workload (Surgical Task Load Index) and heart rate. Results: Of the 33 participants, the median age was 33 years, the range was 29 to 56 years, and 27 were men (82%). The Q1 residents demonstrated task-induced increases in HbO2 across the bilateral ventrolateral PFC (VLPFC) and right dorsolateral P

  • Journal article
    Vlaev I, King D, Darzi A, Dolan Pet al., 2019,

    Changing health behaviors using financial incentives: a review from behavioral economics

    , BMC Public Health, Vol: 19, ISSN: 1471-2458

    BackgroundIncentives are central to economics and are used across the public and private sectors to influence behavior. Recent interest has been shown in using financial incentives to promote desirable health behaviors and discourage unhealthy ones.Main textIf we are going to use incentive schemes to influence health behaviors, then it is important that we give them the best chance of working. Behavioral economics integrates insights from psychology with the laws of economics and provides a number of robust psychological phenomena that help to better explain human behavior. Individuals’ decisions in relation to incentives may be shaped by more subtle features – such as loss aversion, overweighting of small probabilities, hyperbolic discounting, increasing payoffs, reference points – many of which have been identified through research in behavioral economics. If incentives are shown to be a useful strategy to influence health behavior, a wider discussion will need to be had about the ethical dimensions of incentives before their wider implementation in different health programmes.ConclusionsPolicy makers across the world are increasingly taking note of lessons from behavioral economics and this paper explores how key principles could help public health practitioners design effective interventions both in relation to incentive designs and more widely.

  • Journal article
    Runciman M, Darzi A, Mylonas G, 2019,

    Soft robotics in minimally invasive surgery

    , Soft Robotics, Vol: 6, Pages: 423-443, ISSN: 2169-5172

    Soft robotic devices have desirable traits for applications in minimally invasive surgery (MIS) but many interdisciplinary challenges remain unsolved. To understand current technologies, we carried out a keyword search using the Web of Science and Scopus databases, applied inclusion and exclusion criteria, and compared several characteristics of the soft robotic devices for MIS in the resulting articles. There was low diversity in the device designs and a wide-ranging level of detail regarding their capabilities. We propose a standardised comparison methodology to characterise soft robotics for various MIS applications, which will aid designers producing the next generation of devices.

  • Conference paper
    Fathi J, Vrielink TJCO, Runciman MS, Mylonas GPet al., 2019,

    A Deployable Soft Robotic Arm with Stiffness Modulation for Assistive Living Applications

    , International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 1479-1485, ISSN: 1050-4729
  • Journal article
    Geeson C, Wei L, Franklin BD, 2019,

    Development and performance evaluation of the Medicines Optimisation Assessment Tool (MOAT): a prognostic model to target hospital pharmacists' input to prevent medication-related problems

    , BMJ QUALITY & SAFETY, Vol: 28, Pages: 645-656, ISSN: 2044-5415
  • Journal article
    Espinosa-González AB, Delaney BC, Marti J, Darzi Aet al., 2019,

    The impact of governance in primary health care delivery: a systems thinking approach with a European panel

    , Health Research Policy and Systems, Vol: 17, Pages: 1-16, ISSN: 1478-4505

    Enhancing primary health care (PHC) is considered a policy priority for health systems strengthening due to PHC’s ability to provide accessible and continuous care and manage multimorbidity. Research in PHC often focuses on the effects of specific interventions (e.g. physicians’ contracts) in health care outcomes. This informs narrowly designed policies that disregard the interactions between the health functions (e.g. financing and regulation) and actors involved (i.e. public, professional, private), and their impact in care delivery and outcomes. The purpose of this study is to analyse the interactions between PHC functions and their impact in PHC delivery, particularly in providers’ behaviour and practice organisation.

  • Journal article
    Martin G, Ghafur S, Cingolani I, Symons J, King D, Arora S, Darzi Aet al., 2019,

    The effects and preventability of 2627 patient safety incidents related to health information technology failures: a retrospective analysis of 10 years of incident reporting in England and Wales

    , The Lancet Digital Health, Vol: 1, Pages: e127-e135, ISSN: 2589-7500

    BackgroundThe use of health information technology (IT) is rapidly increasing to support improvements in the delivery of care. Although health IT is delivering huge benefits, new technology can also introduce unique risks. Despite these risks, evidence on the preventability and effects of health IT failures on patients is scarce. In our study we therefore sought to evaluate the preventability and effects of health IT failures by examining patient safety incidents in England and Wales.MethodsWe designed our study as a retrospective analysis of 10 years of incident reporting in England and Wales. We used text mining with the words “computer”, “system”, “workstation”, and “network” to explore free-text incident descriptors to identify incidents related to health IT failures following a previously described approach. We then applied an n-gram model of searching to identify contiguous sequences of words and provide spatial context. We examined incident details, recorded harm, and preventability. Standard descriptive statistics were applied. Degree of harm was identified according to standardised definitions and preventability was assessed by two independent reviewers.FindingsWe identified 2627 incidents related to health IT failures. 2557 (97%) of 2627 incidents were assessed for harm (70 incidents were excluded). 2106 (82%) of 2557 health IT failures caused no harm to patients, 331 (13%) caused low harm, 102 (4%) caused moderate harm, 14 (1%) caused severe harm, and four (<1%) contributed to the death of a patient. 1964 (75%) of 2627 incidents were deemed to be preventable.InterpretationHealth IT is fundamental to the delivery of high-quality care, yet there is a poor understanding of the effects of IT failures on patient safety and whether they can be prevented. Failures are complex and involve interlinked aspects of technology, people, and the environment. Health IT failures are undoubtedly a potential source of subst

  • Journal article
    Goiana-da-Silva F, Cruz-E-Silva D, Allen L, Gregório MJ, Severo M, Nogueira PJ, Nunes AM, Graça P, Lopes C, Miraldo M, Breda J, Wickramasinghe K, Darzi A, Araújo F, Mikkelsen B, Miraldo M, Silva F, Gregorio M, Cruz-e-Silva D, Severo M, Nogueira P, Nunes A, Graça P, Lopes C, Breda J, Allen L, Wickramasinghe K, Darzi A, Mikkelsen B, Araújo Fet al., 2019,

    Modelling impacts of food industry co-regulation on noncommunicable disease mortality, Portugal

    , Bulletin of the World Health Organization, Vol: 97, Pages: 450-459, ISSN: 0042-9686

    Objective: To model the reduction in premature deaths attributed to noncommunicable diseases if targets for reformulation of processed food agreed between the Portuguese health ministry and the food industry were met. Methods: The 2015 co-regulation agreement sets voluntary targets for reducing sugar, salt and trans-fatty acids in a range of products by 2021. We obtained government data on dietary intake in 2015-2016 and on population structure and deaths from four major noncommunicable diseases over 1990-2016. We used the Preventable Risk Integrated ModEl tool to estimate the deaths averted if reformulation targets were met in full. We projected future trends in noncommunicable disease deaths using regression modelling and assessed whether Portugal was on track to reduce baseline premature deaths from noncommunicable diseases in the year 2010 by 25% by 2025, and by 30% before 2030. Findings: If reformulation targets were met, we projected reductions in intake in 2015-2016 for salt from 7.6 g/day to 7.1 g/day; in total energy from 1911 kcal/day to 1897 kcal/day due to reduced sugar intake; and in total fat (% total energy) from 30.4% to 30.3% due to reduced trans-fat intake. This consumption profile would result in 248 fewer premature noncommunicable disease deaths (95% CI: 178 to 318) in 2016. We projected that full implementation of the industry agreement would reduce the risk of premature death from 11.0% in 2016 to 10.7% by 2021. Conclusion: The co-regulation agreement could save lives and reduce the risk of premature death in Portugal. Nevertheless, the projected impact on mortality was insufficient to meet international targets.

  • Journal article
    Harkanen M, Vehvilainen-Julkunen K, Murrells T, Rafferty AM, Franklin BDet al., 2019,

    Medication administration errors and mortality: Incidents reported in England and Wales between 2007-2016

    , RESEARCH IN SOCIAL & ADMINISTRATIVE PHARMACY, Vol: 15, Pages: 858-863, ISSN: 1551-7411
  • Journal article
    Sun Y, Lo FPW, Lo B, 2019,

    EEG-based user identification system using 1D-convolutional long short-term memory neural networks

    , Expert Systems with Applications, Vol: 125, Pages: 259-267, ISSN: 0957-4174

    Electroencephalographic (EEG) signals have been widely used in medical applications, yet the use of EEG signals as user identification systems for healthcare and Internet of Things (IoT) systems has only gained interests in the last few years. The advantages of EEG-based user identification systems lie in its dynamic property and uniqueness among different individuals. However, it is for this reason that manually designed features are not always adapted to the needs. Therefore, a novel approach based on 1D Convolutional Long Short-term Memory Neural Network (1D-Convolutional LSTM) for EEG-based user identification system is proposed in this paper. The performance of the proposed approach was validated with a public database consists of EEG data of 109 subjects. The experimental results showed that the proposed network has a very high averaged accuracy of 99.58%, when using only 16 channels of EEG signals, which outperforms the state-of-the-art EEG-based user identification methods. The combined use of CNNs and LSTMs in the proposed 1D-Convolutional LSTM can greatly improve the accuracy of user identification systems by utilizing the spatiotemporal features of the EEG signals with LSTM, and lowering cost of the systems by reducing the number of EEG electrodes used in the systems.

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