FinTech and the COVID-19 pandemic: Evidence from electronic payment systems
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PROVIDER: S-EPMC9811919 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
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Project description: Not available
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Project description:Primary outcome(s): The incidence of obstructive colorectal cancer
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Project description:We document the effects of the COVID−19 pandemic on digital finance and fintech adoption. Drawing on mobile application data from a globally representative sample, we find that the spread of COVID− 19 and related government lockdowns led to a sizeable increase in the rate of finance app downloads. We then analyze factors that may have driven this effect on the demand−side and better understand the “winners” from this digital acceleration on the supply−side. Our overall results suggest that traditional incumbents saw the largest growth in their digital offerings during the initial period, but that “BigTech” companies and newer fintech providers ultimately outperformed them over time. Finally, we drill−down further on the adoption of fintech apps pertaining to both the asset and liability side of the traditional bank balance sheet, to explore the implications that the accelerated trends in digitization may have for the future landscape of financial intermediation.
| S-EPMC9759262 | biostudies-literature
Project description:ObjectiveTo evaluate the completeness of diagnosis recording in problem lists in a hospital electronic health record (EHR) system during the COVID-19 pandemic.DesignRetrospective chart review with manual review of free text electronic case notes.SettingMajor teaching hospital trust in London, one year after the launch of a comprehensive EHR system (Epic), during the first peak of the COVID-19 pandemic in the UK.Participants516 patients with suspected or confirmed COVID-19.Main outcome measuresPercentage of diagnoses already included in the structured problem list.ResultsPrior to review, these patients had a combined total of 2841 diagnoses recorded in their EHR problem lists. 1722 additional diagnoses were identified, increasing the mean number of recorded problems per patient from 5.51 to 8.84. The overall percentage of diagnoses originally included in the problem list was 62.3% (2841 / 4563, 95% confidence interval 60.8%, 63.7%).ConclusionsDiagnoses and other clinical information stored in a structured way in electronic health records is extremely useful for supporting clinical decisions, improving patient care and enabling better research. However, recording of medical diagnoses on the structured problem list for inpatients is incomplete, with almost 40% of important diagnoses mentioned only in the free text notes.
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Project description:This study examines the spillover effect between financial technology (Fintech) stocks and other financial assets (gold, Bitcoin, a global equity index, crude oil, and the US Dollar) during the COVID-19 crisis. Employing daily data from June 2019 to August 2020, our empirical analysis shows that the outbreak of COVID-19 exacerbated volatility transmission across asset classes, while subsequent decreases in new confirmed cases globally reduced the intensity of these spillovers. The evidence for the USD and gold supports their safe haven properties during catastrophic events, while innovative technology products as represented by a financial technology index (KFTX) and Bitcoin were highly susceptible to external shocks. These results show that when push comes to shove, the buck stops with the USD and gold and that the exorbitant privilege enjoyed by the USD prevailed during the COVID-19 pandemic.
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2025-03-28 | GSE249139 | GEO
Project description:The coronavirus disease 2019 (COVID-19) pandemic continues its global spread. Coordinated effort on a vast scale is required to halt its progression and to save lives. Electronic health record (EHR) data are a valuable resource to mitigate the COVID-19 pandemic. We review how the EHR could be used for disease surveillance and contact tracing. When linked to "omics" data, the EHR could facilitate identification of genetic susceptibility variants, leading to insights into risk factors, disease complications, and drug repurposing. Real-time monitoring of patients could enable early detection of potential complications, informing appropriate interventions and therapy. We reviewed relevant articles from PubMed, MEDLINE, and Google Scholar searches as well as preprint servers, given the rapidly evolving understanding of the COVID-19 pandemic.
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Project description:Reimbursement of English mental health hospitals is moving away from block contracts and towards activity and outcome-based payments. Under the new model, patients are categorised into 20 groups with similar levels of need, called clusters, to which prices may be assigned prospectively. Clinicians, who make clustering decisions, have substantial discretion and can, in principle, directly influence the level of reimbursement the hospital receives. This may create incentives for upcoding. Clinicians are supported in their allocation decision by a clinical clustering algorithm, the Mental Health Clustering Tool, which provides an external reference against which clustering behaviour can be benchmarked. The aims of this study are to investigate the degree of mismatch between predicted and actual clustering and to test whether there are systematic differences amongst providers in their clustering behaviour. We use administrative data for all mental health patients in England who were clustered for the first time during the financial year 2014/15 and estimate multinomial multilevel models of over, under, or matching clustering. Results suggest that hospitals vary systematically in their probability of mismatch but this variation is not consistently associated with observed hospital characteristics.
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