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The Covid-19 pandemic has placed unprecedented pressure on healthcare systems and workers around the world. Such pressures may impact on working conditions, psychological wellbeing and perception of safety. In spite of this, no study has assessed the relationship between safety attitudes and psychological outcomes. Moreover, only limited studies have examined the relationship between personal characteristics and psychological outcomes during Covid-19. From 22nd March 2020 to 18th June 2020, healthcare workers from the United Kingdom, Poland, and Singapore were invited to participate using a self-administered questionnaire comprising the Safety Attitudes Questionnaire (SAQ), Oldenburg Burnout Inventory (OLBI) and Hospital Anxiety and Depression Scale (HADS) to evaluate safety culture, burnout and anxiety/depression. Multivariate logistic regression was used to determine predictors of burnout, anxiety and depression. Of 3,537 healthcare workers who participated in the study, 2,364 (67%) screened positive for burnout, 701 (20%) for anxiety, and 389 (11%) for depression. Significant predictors of burnout included patient-facing roles: doctor (OR 2.10; 95% CI 1.49–2.95), nurse (OR 1.38; 95% CI 1.04–1.84), and ‘other clinical’ (OR 2.02; 95% CI 1.45–2.82); being redeployed (OR 1.27; 95% CI 1.02–1.58), bottom quartile SAQ score (OR 2.43; 95% CI 1.98–2.99), anxiety (OR 4.87; 95% CI 3.92–6.06) and depression (OR 4.06; 95% CI 3.04–5.42). Significant factors inversely correlated with burnout included being tested for SARS-CoV-2 (OR 0.64; 95% CI 0.51–0.82) and top quartile SAQ score (OR 0.30; 95% CI 0.22–0.40). Significant factors associated with anxiety and depression, included burnout, gender, safety attitudes and job role. Our findings demonstrate a significant burden of burnout, anxiety, and depression amongst healthcare workers. A strong association was seen between SARS-CoV-2 testing, safety attitudes, gender, job role, redeployment and psychological state. These findings highlight the importance of targeted support services for at risk groups and proactive SARS-CoV-2 testing of healthcare workers.

Background A significant proportion of individuals experience lingering and debilitating symptoms following acute COVID-19 infection. The National Institute for Health and Care Excellence (NICE) have coined the persistent cluster of symptoms as post-COVID syndrome. This has been further sub-categorised into acute post-COVID syndrome for symptoms persisting three weeks beyond initial infection and chronic post-COVID syndrome for symptoms persisting beyond twelve weeks. The aim of this review was to detail the prevalence of clinical features and identify potential predictors for acute and chronic post-COVID syndrome. Methods A systematic literature search, with no language restrictions, was performed to identify studies detailing characteristics and outcomes related to survivorship of post-COVID syndrome. The last search was performed on 6 March 2021 and all pre-dating published articles included. A means of proportion meta-analysis was performed to quantify characteristics of acute and chronic post-COVID syndrome. Study quality was assessed with a specific risk of bias tool. PROSPERO Registration: CRD42020222855 Findings A total of 43 studies met the eligibility criteria; of which, 38 allowed for meta-analysis. Fatigue and dyspnoea were the most prevalent symptoms in acute post-COVID (0·37 and 0·35) and fatigue and sleep disturbance in chronic post-COVID syndrome (0·48 and 0·44), respectively. The available evidence is generally of poor quality, with considerable risk of bias, and are of observational design. Interpretation In conclusion, this review highlights that flaws in data capture and interpretation, noted in the uncertainty within our meta-analysis, affect the applicability of current knowledge. Policy makers and researchers must focus on understanding the impact of this condition on individuals and society with appropriate funding initiatives and global collaborative research.

Covid-19 has placed an unprecedented demand on healthcare systems worldwide. A positive safety culture is associated with improved patient safety and, in turn, with patient outcomes. To date, no study has evaluated the impact of Covid-19 on safety culture. The Safety Attitudes Questionnaire (SAQ) was used to investigate safety culture at a large UK healthcare trust during Covid-19. Findings were compared with baseline data from 2017. Incident reporting from the year preceding the pandemic was also examined. SAQ scores of doctors and “other clinical staff”, were relatively higher than the nursing group. During Covid-19, on univariate regression analysis, female gender, age 40–49 years, non-White ethnicity, and nursing job role were all associated with lower SAQ scores. Training and support for redeployment were associated with higher SAQ scores. On multivariate analysis, non-disclosed gender (−0.13), non-disclosed ethnicity (−0.11), nursing role (−0.15), and support (0.29) persisted to a level of significance. A significant decrease (p < 0.003) was seen in error reporting after the onset of the Covid-19 pandemic. This is the first study to investigate SAQ during Covid-19. Differences in SAQ scores were observed during Covid-19 between professional groups when compared to baseline. Reductions in incident reporting were also seen. These changes may reflect perception of risk, changes in volume or nature of work. High-quality support for redeployed staff may be associated with improved safety perception during future pandemics.

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ObjectivesTo determine the frequency of use and spatial distribution of health record systems in the English National Health Service (NHS). To quantify transitions of care between acute hospital trusts and health record systems to guide improvements to data sharing and interoperability.DesignRetrospective observational study using Hospital Episode Statistics.SettingAcute hospital trusts in the NHS in England.ParticipantsAll adult patients resident in England that had one or more inpatient, outpatient or accident and emergency encounters at acute NHS hospital trusts between April 2017 and April 2018.Primary and secondary outcome measuresFrequency of use and spatial distribution of health record systems. Frequency and spatial distribution of transitions of care between hospital trusts and health record systems.Results21 286 873 patients were involved in 121 351 837 encounters at 152 included trusts. 117 (77.0%) hospital trusts were using electronic health records (EHR). There was limited regional alignment of EHR systems. On 11 017 767 (9.1%) occasions, patients attended a hospital using a different health record system to their previous hospital attendance. 15 736 863 (73.9%) patients had two or more encounters with the included trusts and 3 931 255 (25.0%) of those attended two or more trusts. Over half (53.6%) of these patients had encounters shared between just 20 pairs of hospitals. Only two of these pairs of trusts used the same EHR system.ConclusionsEach year, millions of patients in England attend two or more different hospital trusts. Most of the pairs of trusts that commonly share patients do not use the same record systems. This research highlights significant barriers to inter-hospital data sharing and interoperability. Findings from this study can be used to improve electronic health record system coordination and develop targeted approaches to improve interoperability. The methods used in this study could be used in other healthcare systems that face the same interoperability challenges.

Abstract-This paper presents power models for multiplicationThe contributions of the work in this paper can be summaand addition components on FPGAs which can be used at a high-rized as follows: level design description stage to estimate their logic and intracomponent routing power consumption. The models presented * an improved power model which allows for non-zero are parameterized by the word-length of the component and mean Gaussian input signals, allowing a greater range the word-level statistics of its input signals. A key feature of of signals to be modelled accurately, and, these power models is the ability to handle both zero mean * establishing a link between the logic power consumed by II. BACKGROUND flow. The models have a mean relative error of 7.2% compared to bit-level power simulation of the placed-and-routed design.The average power consumed in a particular capacitive element within a device can be calculated using (1), where: I. INTRODUCTION a is the average number of logic transitions that the signal Power consumption of Field-Programmable Gate Arrays on the element makes during one clock cycle, known as the (FPGAs) is rapidly increasing due to more tightly packed switching activity of the signal, C is the capacitance of the logic and higher clock speeds. This is causing costs to rise for element, Vdd is the power supply voltage, and fclk is the clock larger devices as they now require more expensive packaging, frequency heatsinks, or fans, and prevents smaller devices being used 1 in portable applications where short battery life and little p -a C. VC d fclk (1) opportunity for heat dissipation are dominant factors. Digital 2 circuits consume power when transistors within the circuit By using this equation on every capacitive component in switch as computation is performed (dynamic power), and also an FPGA the dynamic power consumed on the device can be when no switching is taking place, due to leakage currents in estimated. However this requires knowledge of the average the device (static power). It is FPGA vendors who choose the activity of each signal in the circuit and the capacitance number and layout of the transistors on a device, and hence which that signal switches. These are only available once a they shoulder the task of optimizing the static power of future design has been synthesized, placed and routed (to obtain the architectures. Hardware designers, however, can influence the capacitance values), and then simulated at a low level (to dynamic power consumed on an FPGA through decisions in obtain the activities of each signal) with test vectors which the design process, hence it is essential to provide tools which are expected to be typical inputs to the design. This process allow the comparison of the effects of different design changes is very computationally intensive and so is unsuitable for on dynamic power consumption at various stages of the design use in power-conscious high-level synthesis tools where many flow. iterations of power estimates followed by design modificati...

Background The distribution, utilisation and accessibility of surgical robotics in England is unknown. Methods A nationwide Freedom of Information (FOI) request was sent to all acute National Health Service (NHS) trusts. Accessibility was assessed for 32 843 Lower Super Output Areas in England. Results All 149 acute NHS trusts responded to the FOI request. Sixty‐one robots are distributed between 48 trusts. The number of robots and robotic procedures has increased annually. Urological procedures comprise 84.2% of robotic procedures. Procedure volume varies between robotic centres ranging from 1 to 683 in 2018. Over 2.4 million people have a travel time of over 1 hour to their nearest robotic centre. Discussion National accessibility to robotic services and case volumes are variable and does not represent good value for the NHS. A national robotic surgery registry could improve the quality of robotic surgery and is needed to dynamically assess national provision of this technology.