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  • Journal article
    Garfield S, Furniss D, Husson F, Etkind M, Williams M, Norton J, Ogunleye D, Jubraj B, Lakhdari H, Franklin BDet al., 2020,

    How can patient-held lists of medication enhance patient safety? A mixed-methods study with a focus on user experience

    , BMJ Quality & Safety, Vol: 29, Pages: 764-773, ISSN: 2044-5415

    Background Patients often carry medication lists to mitigate information loss across healthcare settings. We aimed to identify mechanisms by which these lists could be used to support safety, key supporting features, and barriers and facilitators to their use.Methods We used a mixed-methods design comprising two focus groups with patients and carers, 16 semistructured interviews with healthcare professionals, 60 semistructured interviews with people carrying medication lists, a quantitative features analysis of tools available for patients to record their medicines and usability testing of four tools. Findings were triangulated using thematic analysis. Distributed cognition for teamwork models were used as sensitising concepts.Results We identified a wide range of mechanisms through which carrying medication lists can improve medication safety. These included improving the accuracy of medicines reconciliation, allowing identification of potential drug interactions, facilitating communication about medicines, acting as an aide-mémoire to patients during appointments, allowing patients to check their medicines for errors and reminding patients to take and reorder their medicines. Different tools for recording medicines met different needs. Of 103 tools examined, none met the core needs of all users. A key barrier to use was lack of awareness by patients and carers that healthcare information systems can be fragmented, a key facilitator was encouragement from healthcare professionals.Conclusion Our findings suggest that patients and healthcare professionals perceive patient-held medication lists to have a wide variety of benefits. Interventions are needed to raise awareness of the potential role of these lists in enhancing patient safety. Such interventions should empower patients and carers to identify a method that suits them best from a range of options and avoid a ‘one size fits all’ approach.

  • Journal article
    Ghafur S, Van Dael J, Leis M, Darzi A, Sheikh Aet al., 2020,

    Public perceptions on data sharing: key insights from the UK and the USA

    , The Lancet Digital Health, Vol: 2, Pages: E444-E446, ISSN: 2589-7500
  • Journal article
    Barakat S, Franklin BD, 2020,

    An Evaluation of the Impact of Barcode Patient and Medication Scanning on Nursing Workflow at a UK Teaching Hospital

    , PHARMACY, Vol: 8
  • Journal article
    Cursi F, Mylonas GP, Kormushev P, 2020,

    Adaptive kinematic modelling for multiobjective control of a redundant surgical robotic tool

    , Robotics, Vol: 9, Pages: 68-68, ISSN: 2218-6581

    Accurate kinematic models are essential for effective control of surgical robots. For tendon driven robots, which are common for minimally invasive surgery, the high nonlinearities in the transmission make modelling complex. Machine learning techniques are a preferred approach to tackle this problem. However, surgical environments are rarely structured, due to organs being very soft and deformable, and unpredictable, for instance, because of fluids in the system, wear and break of the tendons that lead to changes of the system’s behaviour. Therefore, the model needs to quickly adapt. In this work, we propose a method to learn the kinematic model of a redundant surgical robot and control it to perform surgical tasks both autonomously and in teleoperation. The approach employs Feedforward Artificial Neural Networks (ANN) for building the kinematic model of the robot offline, and an online adaptive strategy in order to allow the system to conform to the changing environment. To prove the capabilities of the method, a comparison with a simple feedback controller for autonomous tracking is carried out. Simulation results show that the proposed method is capable of achieving very small tracking errors, even when unpredicted changes in the system occur, such as broken joints. The method proved effective also in guaranteeing accurate tracking in teleoperation.

  • Journal article
    Krasuska M, Williams R, Sheikh A, Franklin BD, Heeney C, Lane W, Mozaffar H, Mason K, Eason S, Hinder S, Dunscombe R, Potts HWW, Cresswell Ket al., 2020,

    Technological Capabilities to Assess Digital Excellence in Hospitals in High Performing Health Care Systems: International eDelphi Exercise

    , JOURNAL OF MEDICAL INTERNET RESEARCH, Vol: 22, ISSN: 1438-8871
  • Journal article
    Lichtner V, Franklin BD, Dalla-Pozza L, Westbrook Jet al., 2020,

    Electronic ordering and the management of treatment interdependencies: a qualitative study of paediatric chemotherapy

    , BMC MEDICAL INFORMATICS AND DECISION MAKING, Vol: 20
  • Journal article
    Atchison C, PristerĂ  P, Cooper E, Papageorgiou V, Redd R, Piggin M, Flower B, Fontana G, Satkunarajah S, Ashrafian H, Lawrence-Jones A, Naar L, Chigwende J, Gibbard S, Riley S, Darzi A, Elliott P, Ashby D, Barclay W, Cooke GS, Ward Het al., 2020,

    Usability and acceptability of home-based self-testing for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) antibodies for population surveillance

    , Clinical Infectious Diseases, Vol: 2020, Pages: 1-10, ISSN: 1058-4838

    BACKGROUND: This study assesses acceptability and usability of home-based self-testing for SARS-CoV-2 antibodies using lateral flow immunoassays (LFIA). METHODS: We carried out public involvement and pilot testing in 315 volunteers to improve usability. Feedback was obtained through online discussions, questionnaires, observations and interviews of people who tried the test at home. This informed the design of a nationally representative survey of adults in England using two LFIAs (LFIA1 and LFIA2) which were sent to 10,600 and 3,800 participants, respectively, who provided further feedback. RESULTS: Public involvement and pilot testing showed high levels of acceptability, but limitations with the usability of kits. Most people reported completing the test; however, they identified difficulties with practical aspects of the kit, particularly the lancet and pipette, a need for clearer instructions and more guidance on interpretation of results. In the national study, 99.3% (8,693/8,754) of LFIA1 and 98.4% (2,911/2,957) of LFIA2 respondents attempted the test and 97.5% and 97.8% of respondents completed it, respectively. Most found the instructions easy to understand, but some reported difficulties using the pipette (LFIA1: 17.7%) and applying the blood drop to the cassette (LFIA2: 31.3%). Most respondents obtained a valid result (LFIA1: 91.5%; LFIA2: 94.4%). Overall there was substantial concordance between participant and clinician interpreted results (kappa: LFIA1 0.72; LFIA2 0.89). CONCLUSION: Impactful public involvement is feasible in a rapid response setting. Home self-testing with LFIAs can be used with a high degree of acceptability and usability by adults, making them a good option for use in seroprevalence surveys.

  • Journal article
    Runciman M, Darzi A, Mylonas GP, 2020,

    Soft robotics in minimally invasive surgery (Part 2)

    , Galvanotechnik, Vol: 111, Pages: 1236-1237, ISSN: 0016-4232
  • Journal article
    Clarke J, Beaney T, Majeed A, Darzi A, Barahona Met al., 2020,

    Identifying naturally occurring communities of primary care providers in the English National Health Service in London

    , BMJ Open, Vol: 10, Pages: 1-7, ISSN: 2044-6055

    Objectives - Primary Care Networks (PCNs) are a new organisational hierarchy with wide-ranging responsibilities introduced in the National Health Service (NHS) Long Term Plan. The vision is that they represent ‘natural’ communities of general practices (GP practices) working together at scale and covering a geography that make sense to practices, other healthcare providers and local communities. Our study aims to identify natural communities of GP practices based on patient registration patterns using Markov Multiscale Community Detection, an unsupervised network-based clustering technique to create catchments for these communities.Design - Retrospective observational study using Hospital Episode Statistics – patient-level administrative records of inpatient, outpatient and emergency department attendances to hospital.Setting – General practices in the 32 Clinical Commissioning Groups of Greater London Participants - All adult patients resident in and registered to a GP practices in Greater London that had one or more outpatient encounters at NHS hospital trusts between 1st April 2017 and 31st March 2018.Main outcome measures The allocation of GP practices in Greater London to PCNs based on the registrations of patients resident in each Lower Super Output Area (LSOA) of Greater London. The population size and coverage of each proposed PCN. Results - 3,428,322 unique patients attended 1,334 GPs in 4,835 LSOAs in Greater London. Our model grouped 1,291 GPs (96.8%) and 4,721 LSOAs (97.6%), into 165 mutually exclusive PCNs. The median PCN list size was 53,490, with a lower quartile of 38,079 patients and an upper quartile of 72,982 patients. A median of 70.1% of patients attended a GP within their allocated PCN, ranging from 44.6% to 91.4%.Conclusions - With PCNs expected to take a role in population health management and with community providers expected to reconfigure around them, it is vital we recognise how PCNs represent their communities. O

  • Journal article
    van Dael J, Reader T, Gillespie A, Neves A, Darzi A, Mayer Eet al., 2020,

    Learning from complaints in healthcare: a realist review of academic literature, policy evidence, and frontline insights

    , BMJ Quality and Safety, Vol: 29, Pages: 684-695, ISSN: 2044-5415

    Introduction A global rise in patient complaints has been accompanied by growing research to effectively analyse complaints for safer, more patient-centric care. Most patients and families complain to improve the quality of healthcare, yet progress has been complicated by a system primarily designed for case-by-case complaint handling.Aim To understand how to effectively integrate patient-centric complaint handling with quality monitoring and improvement.Method Literature screening and patient codesign shaped the review’s aim in the first stage of this three-stage review. Ten sources were searched including academic databases and policy archives. In the second stage, 13 front-line experts were interviewed to develop initial practice-based programme theory. In the third stage, evidence identified in the first stage was appraised based on rigour and relevance, and selected to refine programme theory focusing on what works, why and under what circumstances.Results A total of 74 academic and 10 policy sources were included. The review identified 12 mechanisms to achieve: patient-centric complaint handling and system-wide quality improvement. The complaint handling pathway includes (1) access of information; (2) collaboration with support and advocacy services; (3) staff attitude and signposting; (4) bespoke responding; and (5) public accountability. The improvement pathway includes (6) a reliable coding taxonomy; (7) standardised training and guidelines; (8) a centralised informatics system; (9) appropriate data sampling; (10) mixed-methods spotlight analysis; (11) board priorities and leadership; and (12) just culture.Discussion If healthcare settings are better supported to report, analyse and use complaints data in a standardised manner, complaints could impact on care quality in important ways. This review has established a range of evidence-based, short-term recommendations to achieve this.

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