Browse through all publications from the Institute of Global Health Innovation, which our Patient Safety Research Collaboration is part of. This feed includes reports and research papers from our Centre. 

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  • 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
    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.

  • 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 Bet 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
    Archer SA, Pinto A, Vuik S, Bicknell C, Faiz O, Byrne B, Johnston M, Skapinakis P, Athanasiou T, Vincent C, Darzi Aet al., 2019,

    Surgery, complications and quality of life: a longitudinal cohort study exploring the role of psychosocial factors

    , Annals of Surgery, Vol: 270, Pages: 95-101, ISSN: 0003-4932

    Objective:To determine if psychosocial factors moderate the relationship between surgical complications and quality of life (QoL).Summary Background:Patients who experience surgical complications have significantly worse post-operative QoL than patients with an uncomplicated recovery. Psychosocial factors, such as coping style and level of social support influence how people deal with stressful events, but it is unclear if they impact on QoL following a surgical complication. These findings can inform the development of appropriate interventions that support patients post-operatively. Methods:This is a longitudinal cohort study; data were collected at pre-op, 1 month post-op, 4 months post-op and 12 months post-op. A total of 785 patients undergoing major elective gastro-intestinal, vascular or cardio-thoracic surgery were recruited from 28 National Health Service (NHS) sites in England and Scotland took part in the study.Results:Patients who experience major surgical complications report significantly reduced levels of physical and mental QoL (p<0.05) but they make a full recovery over time. Findings indicate that a range of psychosocial factors such as the use of humor as a coping style and the level of health care professional support may moderate the impact of surgical complications on QoL.Conclusion:Surgical complications alongside other socio-demographic and psychosocial factors contribute to changes in QoL; the results from this exploratory study suggest that interventions that increase the availability of healthcare professional support and promote more effective coping strategies prior to surgery may be useful, particularly in the earlier stages of recovery where QoL is most severely compromised. However, these relationships should be further explored in longitudinal studies that include other types of surgery and employ rigorous recruitment and follow up procedures.

  • 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
    Aufegger L, Bicknell C, Soane E, Ashrafian H, Darzi Aet al., 2019,

    Understanding health management and safety decisions using signal processing and machine learning

    , BMC Medical Research Methodology, Vol: 19, ISSN: 1471-2288

    BackgroundSmall group research in healthcare is important because it deals with interaction and decision-making processes that can help to identify and improve safer patient treatment and care. However, the number of studies is limited due to time- and resource-intensive data processing. The aim of this study was to examine the feasibility of using signal processing and machine learning techniques to understand teamwork and behaviour related to healthcare management and patient safety, and to contribute to literature and research of teamwork in healthcare.MethodsClinical and non-clinical healthcare professionals organised into 28 teams took part in a video- and audio-recorded role-play exercise that represented a fictional healthcare system, and included the opportunity to discuss and improve healthcare management and patient safety. Group interactions were analysed using the recurrence quantification analysis (RQA; Knight et al., 2016), a signal processing method that examines stability, determinism, and complexity of group interactions. Data were benchmarked against self-reported quality of team participation and social support. Transcripts of group conversations were explored using the topic modelling approach (Blei et al., 2003), a machine learning method that helps to identify emerging themes within large corpora of qualitative data.ResultsGroups exhibited stable group interactions that were positively correlated with perceived social support, and negatively correlated with predictive behaviour. Data processing of the qualitative data revealed conversations focused on: (1) the management of patient incidents; (2) the responsibilities among team members; (3) the importance of a good internal team environment; and (4) the hospital culture.ConclusionsThis study has shed new light on small group research using signal processing and machine learning methods. Future studies are encouraged to use these methods in the healthcare context, and to conduct further research

  • Journal article
    Dilley J, Camara M, Omar I, Carter A, Pratt P, Vale J, Darzi A, Mayer EKet al., 2019,

    Evaluating the impact of image guidance in the surgical setting: A systematic review

    , Surgical Endoscopy, Vol: 33, Pages: 2785-2793, ISSN: 0930-2794

    BACKGROUND: Image guidance has been clinically available for over a period of 20 years. Although research increasingly has a translational emphasis, overall the clinical uptake of image guidance systems in surgery remains low. The objective of this review was to establish the metrics used to report on the impact of surgical image guidance systems used in a clinical setting. METHODS: A systematic review of the literature was carried out on all relevant publications between January 2000 and April 2016. Ovid MEDLINE and Embase databases were searched using a title strategy. Reported outcome metrics were grouped into clinically relevant domains and subsequent sub-categories for analysis. RESULTS: In total, 232 publications were eligible for inclusion. Analysis showed that clinical outcomes and system interaction were consistently reported. However, metrics focusing on surgeon, patient and economic impact were reported less often. No increase in the quality of reporting was observed during the study time period, associated with study design, or when the clinical setting involved a surgical specialty that had been using image guidance for longer. CONCLUSIONS: Publications reporting on the clinical use of image guidance systems are evaluating traditional surgical outcomes and neglecting important human and economic factors, which are pertinent to the uptake, diffusion and sustainability of image-guided surgery. A framework is proposed to assist researchers in providing comprehensive evaluation metrics, which should also be considered in the design phase. Use of these would help demonstrate the impact in the clinical setting leading to increased clinical integration of image guidance systems.

  • Journal article
    Cohen D, Vlaev I, Heitmueller A, Parston G, Schmidtke KA, Darzi Aet al., 2019,

    Validation of behavioral simulations: a case study on enhancing collaboration between partnership organizations

    , Journal of Public Health, Vol: 27, Pages: 367-378, ISSN: 1741-3842

    AimThe current article provides a detailed account of a behavioral simulation called Lateral Play. Lateral Play aimed to enhance collaborations and optimize shared decision-making across organizations within a newly formed partnership. The current article aims to enhance appreciation of the behavioral simulation methodology and encourage its use.Subjects and MethodsHealth service leaders from different organizations within a newly formed partnership gathered in the simulated community and took up roles similar to their real-life positions. The simulation presented participants with problems and opportunities similar to those that they would experience in real life, such as the need to consolidate services and create new care pathways. To evaluate Lateral Play’s effectiveness, self-reported and observational data were collected. These data include information about participants’ reactions, learning and behavior, and the newly formed partnership’s organizational results.ResultsLateral Play allowed health leaders to better understand how they could enhance collaborations and optimize shared decision-making across their newly formed partnership. The data suggest that simulations can promote effective collaborations.ConclusionsUse of behavioral simulations should be encouraged to promote policy awareness and understanding, refine implementation strategies and improve outcomes in newly formed partnerships.

  • Conference paper
    Patel N, Kogkas A, Ben Glover AD, Mylonas Get al., 2019,


    , Annual Meeting of the British-Society-of-Gastroenterology (BSG), Publisher: BMJ PUBLISHING GROUP, Pages: A38-A39, ISSN: 0017-5749
  • Journal article
    Martin G, Clarke J, Liew F, Arora S, King D, Paul A, Darzi Aet al., 2019,

    Evaluating the impact of organisational digital maturity on clinical outcomes in secondary care in England

    , npj Digital Medicine, Vol: 2, ISSN: 2398-6352

    All healthcare systems are increasingly reliant on health information technology to support the delivery of high-quality, efficient and safe care. Data on its effectiveness are however limited. We therefore sought to examine the impact of organisational digital maturity on clinical outcomes in secondary care within the English National Health Service. We conducted a retrospective analysis of routinely collected administrative data for 13,105,996 admissions across 136 hospitals in England from 2015 to 2016. Data from the 2016 NHS Clinical Digital Maturity Index were used to characterise organisational digital maturity. A multivariable regression model including 12 institutional covariates was utilised to examine the relationship between one measure of organisational digital maturity and five key clinical outcome measures. There was no significant relationship between organisational digital maturity and risk-adjusted 30-day mortality, 28-day readmission rates or complications of care. In multivariable analysis risk-adjusted long length of stay and harm-free care were significantly related to aspects of organisational digital maturity; digitally mature hospitals may not only deliver more harm-free care episodes but also may have a significantly increased risk of patients experiencing a long length of stay. Organisational digital maturity is to some extent related to selected clinical outcomes in secondary care in England. Digital maturity is, however, also strongly linked to other institutional factors that likely play a greater role in influencing clinical outcomes. There is a need to better understand how health IT impacts care delivery and supports other drivers of hospital quality.

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