Characterising the health and social care segment of the BCS (The Chartered Institute for IT) membership and their continuing professional development needs.

OBJECTIVES: The aim of this study was to identify and characterise the health and social care membership of the British Computer Society (BCS), an international informatics professional organisation, and to determine their ongoing development needs. METHODS: A prepiloted online survey included items on professional regulatory body, job role, work sector, qualifications, career stage, BCS membership (type, specialist group/branch activity (committees, event attendance)), use of BCS.org career planning/continuing professional development (CPD) tools, self-reported digital literacy and other professional registrations. The quantitative data were analysed using descriptive statistics in JASP V.0.9.2 to report frequencies and correlations. RESULTS: Responses were received from 152 participants. Most were male (n=103; 68%), aged 50-59 years (n=41; 28%), working in England (n=107; 71%) with master's or honours degrees (n=80; 53%). Most were either new (5 years or less; n=61; 40%) or long-term members (21 years or more; n=43; 28%) of BCS. Most were not interested in health specialist groups (n=57; 38%) preferring non-health specialist groups such as information management (n=54; 37%) and project management (n=52; 34%). DISCUSSION: This is the first paper to characterise the health and social care membership of an IT-focused professional body and to start to determine their CPD needs. There are further challenges ahead in curating the content and delivery. CONCLUSION: This study is the starting point from which members' CPD needs, and ongoing interest, in being recognised as health and social care professional members, can be acknowledged and explored. Further research is planned with the participants who volunteered to be part of designing future CPD content and delivery.

Codeveloping an effective EMPA to maturity in an acute NHS Trust: an implementer report.

INTRODUCTION: University Hospitals Leicester has codeveloped, with Nervecentre, an Electronic Prescribing and Medicines Administration System that meets specific clinical and interoperability demands of the National Health Service (NHS). METHODS: The system was developed through a frontline-led and agile approach with a project team consisting of clinicians, Information Technology (IT) specialists and the vendor's representatives over an 18-month period. RESULTS: The system was deployed successfully with more than a thousand transcriptions during roll-out. Despite the high caseload and novelty of the system, there was no increase in error rates within the first 3 months of roll-out. Healthcare professionals perceived the new system as efficient with improved clinical workflow, and safe through an integrated medication alert system. DISCUSSION: This case study demonstrates how NHS trusts can successfully co-develop, with vendors, new IT systems which meet interoperability standards such as Fast Healthcare Interoperability Resources, while improving front line clinical experience. CONCLUSION: Alternative methods to the 'big bang' deployment of IT projects, such as 'gradual implementation', must be demonstrated and evaluated for their ability to deliver digital transformation projects in the NHS successfully.

Validation of parsimonious prognostic models for patients infected with COVID-19.

OBJECTIVES: Predictive studies play important roles in the development of models informing care for patients with COVID-19. Our concern is that studies producing ill-performing models may lead to inappropriate clinical decision-making. Thus, our objective is to summarise and characterise performance of prognostic models for COVID-19 on external data. METHODS: We performed a validation of parsimonious prognostic models for patients with COVID-19 from a literature search for published and preprint articles. Ten models meeting inclusion criteria were either (a) externally validated with our data against the model variables and weights or (b) rebuilt using original features if no weights were provided. Nine studies had internally or externally validated models on cohorts of between 18 and 320 inpatients with COVID-19. One model used cross-validation. Our external validation cohort consisted of 4444 patients with COVID-19 hospitalised between 1 March and 27 May 2020. RESULTS: Most models failed validation when applied to our institution's data. Included studies reported an average validation area under the receiver-operator curve (AUROC) of 0.828. Models applied with reported features averaged an AUROC of 0.66 when validated on our data. Models rebuilt with the same features averaged an AUROC of 0.755 when validated on our data. In both cases, models did not validate against their studies' reported AUROC values. DISCUSSION: Published and preprint prognostic models for patients infected with COVID-19 performed substantially worse when applied to external data. Further inquiry is required to elucidate mechanisms underlying performance deviations. CONCLUSIONS: Clinicians should employ caution when applying models for clinical prediction without careful validation on local data.

Predictive modeling of COVID-19 case growth highlights evolving racial and ethnic risk factors in Tennessee and Georgia.

INTRODUCTION: The SARS-CoV-2 (COVID-19) pandemic has exposed the need to understand the risk drivers that contribute to uneven morbidity and mortality in US communities. Addressing the community-specific social determinants of health (SDOH) that correlate with spread of SARS-CoV-2 provides an opportunity for targeted public health intervention to promote greater resilience to viral respiratory infections. METHODS: Our work combined publicly available COVID-19 statistics with county-level SDOH information. Machine learning models were trained to predict COVID-19 case growth and understand the social, physical and environmental risk factors associated with higher rates of SARS-CoV-2 infection in Tennessee and Georgia counties. Model accuracy was assessed comparing predicted case counts to actual positive case counts in each county. RESULTS: The predictive models achieved a mean R2 of 0.998 in both states with accuracy above 90% for all time points examined. Using these models, we tracked the importance of SDOH data features over time to uncover the specific racial demographic characteristics strongly associated with COVID-19 incidence in Tennessee and Georgia counties. Our results point to dynamic racial trends in both states over time and varying, localized patterns of risk among counties within the same state. For example, we find that African American and Asian racial demographics present comparable, and contrasting, patterns of risk depending on locality. CONCLUSION: The dichotomy of demographic trends presented here emphasizes the importance of understanding the unique factors that influence COVID-19 incidence. Identifying these specific risk factors tied to COVID-19 case growth can help stakeholders target regional interventions to mitigate the burden of future outbreaks.

Telemammography for breast cancer screening: a cost-effective approach in Argentina.

OBJECTIVES: Argentina is a low and middle-income country (LMIC) with a highly fragmented healthcare system that conflicts with access to healthcare stated by the country's Universal Health Coverage plan. A tele-mammography network could improve access to breast cancer screening decreasing its mortality. This research aims to conduct an economic evaluation of the implementation of a tele-mammography program to improve access to healthcare. METHODS: A cost-utility analysis was performed to explore the incremental benefit of annual tele-mammography screening for at-risk Argentinian women over 40 years old. A Markov model was developed to simulate annual mammography or tele-mammography screening in two hypothetical population-based cohorts of asymptomatic women. Parameter uncertainty was evaluated through deterministic and probabilistic sensitivity analysis. Model structure uncertainty was also explored to test the robustness of the results. RESULTS: It was estimated that 31 out of 100 new cases of breast cancer would be detected by mammography and 39/100 by tele-mammography. The model returned an incremental cost-effectiveness ratio (ICER) of £26 051/quality-adjusted life-year (QALY) which is lower than the WHO-recommended threshold of £26 288/QALY for Argentina. Deterministic sensitivity analysis showed the ICER is most sensitive to the uptake and sensitivity of the screening tests. Probabilistic sensitivity analysis showed tele-mammography is cost-effective in 59% of simulations. DISCUSSION: Tele-mammography should be considered for adoption as it could improve access to expertise in underserved areas where adherence to screening protocols is poor. Disaggregated data by province is needed for a better- informed policy decision. Telemedicine could also be beneficial in ensuring the continuity of care when health systems are under stress like in the current COVID-19 pandemic. CONCLUSION: There is a 59% chance that tele-mammography is cost-effective compared to mammography for at-risk Argentinian women over 40- years old, and should be adopted to improve access to healthcare in underserved areas of the country.

Comparison and impact of COVID-19 for patients with cancer: a survival analysis of fatality rate controlling for age, sex and cancer type.

OBJECTIVES: Prior research has reported an increased risk of fatality for patients with cancer, but most studies investigated the risk by comparing cancer to non-cancer patients among COVID-19 infections, where cancer might have contributed to the increased risk. This study is to understand COVID-19's imposed HR of fatality while controlling for covariates, such as age, sex, metastasis status and cancer type. METHODS: We conducted survival analyses of 4606 cancer patients with COVID-19 test results from 16 March to 11 October 2020 in UK Biobank and estimated the overall HR of fatality with and without COVID-19 infection. We also examined the HRs of 13 specific cancer types with at least 100 patients using a stratified analysis. RESULTS: COVID-19 resulted in an overall HR of 7.76 (95% CI 5.78 to 10.40, p<10-10) by following 4606 patients with cancer for 21 days after the tests. The HR varied among cancer type, with over a 10-fold increase in fatality rate (false discovery rate ≤0.02) for melanoma, haematological malignancies, uterine cancer and kidney cancer. Although COVID-19 imposed a higher risk for localised versus distant metastasis cancers, those of distant metastases yielded higher overall fatality rates due to their multiplicative effects. DISCUSSION: The results confirmed prior reports for the increased risk of fatality for patients with COVID-19 plus hematological malignancies and demonstrated similar findings of COVID-19 on melanoma, uterine, and kidney cancers. CONCLUSION: The results highlight the heightened risk that COVID-19 imposes on localised and haematological cancer patients and the necessity to vaccinate uninfected patients with cancer promptly, particularly for the cancer types most influenced by COVID-19. Results also suggest the importance of timely care for patients with localised cancer, whether they are infected by COVID-19 or not.

Linking prediction models to government ordinances to support hospital operations during the COVID-19 pandemic.

OBJECTIVES: We describe a hospital's implementation of predictive models to optimise emergency response to the COVID-19 pandemic. METHODS: We were tasked to construct and evaluate COVID-19 driven predictive models to identify possible planning and resource utilisation scenarios. We used system dynamics to derive a series of chain susceptible, infected and recovered (SIR) models. We then built a discrete event simulation using the system dynamics output and bootstrapped electronic medical record data to approximate the weekly effect of tuning surgical volume on hospital census. We evaluated performance via a model fit assessment and cross-model comparison. RESULTS: We outlined the design and implementation of predictive models to support management decision making around areas impacted by COVID-19. The fit assessments indicated the models were most useful after 30 days from onset of local cases. We found our subreports were most accurate up to 7 days after model run.DiscusssionOur model allowed us to shape our health system's executive policy response to implement a 'hospital within a hospital'-one for patients with COVID-19 within a hospital able to care for the regular non-COVID-19 population. The surgical scheduleis modified according to models that predict the number of new patients withCovid-19 who require admission. This enabled our hospital to coordinateresources to continue to support the community at large. Challenges includedthe need to frequently adjust or create new models to meet rapidly evolvingrequirements, communication, and adoption, and to coordinate the needs ofmultiple stakeholders. The model we created can be adapted to other health systems,provide a mechanism to predict local peaks in cases and inform hospitalleadership regarding bed allocation, surgical volumes, staffing, and suppliesone for COVID-19 patients within a hospital able to care for the regularnon-COVID-19 population. CONCLUSION: Predictive models are essential tools in supporting decision making when coordinating clinical operations during a pandemic.

Survey protocol for exploring video and phone use in Aotearoa New Zealand general practice: considerations for future telehealth.

INTRODUCTION: Telehealth became the most practical option for general practice consultations in Aotearoa New Zealand (NZ) as a result of the national lockdowns in response to the COVID-19 pandemic. What is the consumer experience of access to telehealth and how do consumers and providers perceive this mode of care delivery going forward? METHODS AND ANALYSIS: A national survey of general practice consumers and providers who used telehealth services since the national lockdowns in 2020 will be distributed. It is based on the Unified Theory of Acceptance and Use of Technology framework of technology acceptance and the access to care framework. The data will be statistically analysed to create a foundation for in-depth research on the use of telehealth services in NZ general practice services, with a specific focus on consumer experiences and health outcomes. ETHICS AND DISSEMINATION: Ethics approval was granted by the Auckland Health Research Ethics Committee on 13/11/2020, reference AH2539. The survey will be disseminated online.

Performance of national COVID-19 'symptom checkers': a comparative case simulation study.

OBJECTIVES: Identifying those individuals requiring medical care is a basic tenet of the pandemic response. Here, we examine the COVID-19 community triage pathways employed by four nations, specifically comparing the safety and efficacy of national online 'symptom checkers' used within the triage pathway. METHODS: A simulation study was conducted on current, nationwide, patient-led symptom checkers from four countries (Singapore, Japan, USA and UK). 52 cases were simulated to approximate typical COVID-19 presentations (mild, moderate, severe and critical) and COVID-19 mimickers (eg, sepsis and bacterial pneumonia). The same simulations were applied to each of the four country's symptom checkers, and the recommendations to refer on for medical care or to stay home were recorded and compared. RESULTS: The symptom checkers from Singapore and Japan advised onward healthcare contact for the majority of simulations (88% and 77%, respectively). The USA and UK symptom checkers triaged 38% and 44% of cases to healthcare contact, respectively. Both the US and UK symptom checkers consistently failed to identify severe COVID-19, bacterial pneumonia and sepsis, triaging such cases to stay home. CONCLUSION: Our results suggest that whilst 'symptom checkers' may be of use to the healthcare COVID-19 response, there is the potential for such patient-led assessment tools to worsen outcomes by delaying appropriate clinical assessment. The key features of the well-performing symptom checkers are discussed.

Network graph representation of COVID-19 scientific publications to aid knowledge discovery.

INTRODUCTION: Numerous scientific journal articles related to COVID-19 have been rapidly published, making navigation and understanding of relationships difficult. METHODS: A graph network was constructed from the publicly available COVID-19 Open Research Dataset (CORD-19) of COVID-19-related publications using an engine leveraging medical knowledge bases to identify discrete medical concepts and an open-source tool (Gephi) to visualise the network. RESULTS: The network shows connections between diseases, medications and procedures identified from the title and abstract of 195 958 COVID-19-related publications (CORD-19 Dataset). Connections between terms with few publications, those unconnected to the main network and those irrelevant were not displayed. Nodes were coloured by knowledge base and the size of the node related to the number of publications containing the term. The data set and visualisations were made publicly accessible via a webtool. CONCLUSION: Knowledge management approaches (text mining and graph networks) can effectively allow rapid navigation and exploration of entity inter-relationships to improve understanding of diseases such as COVID-19.

What people share about the COVID-19 outbreak on Twitter? An exploratory analysis.

BACKGROUND: The recent outbreak of respiratory illness caused by COVID-19 in Wuhan, China, has received global attention as it has infected thousands of individuals there, and later it has also been reported from other countries internationally. This study aims at performing an exploratory study on Twitter to understand the information shared among the community regarding the COVID-19 outbreak. METHODS: COVID-19 related tweets were collected from Twitter using keywords from 18 January to 25 January 2020. Top-ranking tweets were taken as samples and then categorised based on the content. Expressions or opinion tweets were analysed qualitatively to understand the mindset of the people regarding the outbreak. Theme wise reachability evaluation of the messages was also performed. RESULTS: Based on the content of the tweets, five themes were evolved: (1) general information; (2) health information; (3) expressions; (4) humour and (5) others. 57.42% of messages are general information followed by expressive tweets (24.12%). Humorous messages were liked the most, whereas health information tweets were retweeted the maximum. Fear was the predominant emotion expressed in the messages. CONCLUSION: The results of the study would be useful to focus on the dissemination of the right information and effective communication on Twitter related to health and outbreak management.

Rapid implementation of Microsoft Teams in response to COVID-19: one acute healthcare organisation's experience.

Background COVID-19 presented significant challenges to healthcare organisations, which needed to rapidly remodel their services but were unable to allow staff to meet face to face to minimise infection risk. During this communication predicament, National Health Service (NHS) Digital announced the provision of Microsoft Teams, a digital communication and collaboration tool, which was implemented at Royal Free London NHS Foundation Trust within 2 weeks.Method Given the need to deploy at scale, rapidly and with minimal resource, an agile decentralised innovation management approach was used, empowering staff to be local implementors.Results Resulting use cases were highly original and varied, ranging from a COVID-19 Education Programme to coordination of oxygen demand. Analytics showed rapid and persistent adoption, surpassing 500 daily active users within 11 days. Usage continues to increase, consistent with a direct network effect.Conclusion These findings suggest a high demand for this format of communication and high willingness to adopt it. Further qualitative research into staff perceptions would be valuable to confirm this, and to assess the user experience.Overall, this has been a radical approach to digital implementation in healthcare, and has so far proved effective in delivering a cost minimal, rapid communication tool at scale in the midst of a global pandemic.

How telemedicine integrated into China's anti-COVID-19 strategies: case from a National Referral Center.

INTRODUCTION: We present the integration of telemedicine into the healthcare system of West China Hospital of Sichuan University (WCH), one of the largest hospitals in the world with 4300 inpatient beds, as a means for maximising the efficiency of healthcare delivery during the COVID-19 pandemic. METHODS: Implemented on 22 January 2020, the telemedicine technology allowed WCH providers to conduct teleconsultations, telerounds, teleradiology and tele-intensive care unit, which in culmination provided screening, triage and treatment for COVID-19 and other illnesses. To encourage its adoption, the government and the hospital publicised the platform on social media and waived fees. DISCUSSION: From 1 February to 1 April 2020, 10557 online COVID-19 consultations were conducted for 6662 individuals; meanwhile, 32676 patients without COVID completed virtual follow-ups. We discuss that high-quality, secure, affordable and user-friendly telemedical platforms should be integrated into global healthcare systems to help decrease the transmission of the virus and protect healthcare providers from infection.