Abstract Background The detrimental repercussions of the COVID-19 pandemic on the quality of care and clinical outcomes for patients with acute coronary syndrome (ACS) necessitate a rigorous re-evaluation of prognostic prediction models in the context of the pandemic environment. This study aimed to elucidate the adaptability of prediction models for 30-day mortality in patients with ACS during the pandemic periods.

In the ever-evolving landscape of healthcare, the convergence of artificial intelligence (AI) within breast cancer screening and the transformative potential of natural language processing (NLP) in ensuring patient safety stands as a testament to ground-breaking progress.1 2 The seamless integration of AI technologies in radiology is reshaping diagnostic precision while NLP’s capacity to decipher and enhance safety protocols heralds a

Results Between October 2008 and July 2019, a total of 8 035 132 orders were requested in PICS. The majority of the requests were related to laboratory requests after being introduced in January 2016, representing 49% of requests. Prior to this, the most common request type was imaging, representing almost 90% of requests made. Other request types were grouped into requests for

Discussion We conducted a bibliometric analysis of the intersection of ophthalmology and AI between January 2018 and August 2021. Many aspects of the dataset were analysed in order to gain both quantitative and qualitative insights. In particular, investigation into countries of publication and their correlation (or lack thereof) with literature quality was performed, and it was found that smaller countries

Introduction Breast cancer is the first and most common type of cancer in women.1 2 Anatomically, the breast consists of healthy blood vessels, connective tissue, ductal lobules and lymph nodes.3 Breast cancer is a problem with abnormal growth of the breast cells. By 2040, the burden of breast cancer is predicted to increase to over three million new cases and

Abstract Objective Data-driven innovations are essential in strengthening disease control. We developed a low-cost, open-source system for robust epidemiological intelligence in response to the COVID-19 crisis, prioritising scalability, reproducibility and dynamic reporting. Methods A five-tiered workflow of data acquisition; processing; databasing, sharing, version control; visualisation; and monitoring was used. COVID-19 data were initially collated from press releases and then transitioned

Abstract In 2020, we published an editorial about the massive disruption of health and care services caused by the COVID-19 pandemic and the rapid changes in digital service delivery, artificial intelligence and data sharing that were taking place at the time. Now, 3 years later, we describe how these developments have progressed since, reflect on lessons learnt and consider key challenges

Abstract Objective The study aimed to measure the validity of International Classification of Diseases, 10th Edition (ICD-10) code F44.5 for functional seizure disorder (FSD) in the Veterans Affairs Connecticut Healthcare System electronic health record (VA EHR). Methods The study used an informatics search tool, a natural language processing algorithm and a chart review to validate FSD coding. Results The positive

To organise a successful event, several important points should be well thought through before the event (figure 1). The checklist provided below should help any organiser in this process. We further elaborate on some of these key points reflecting on our more mature experience. Datathon check list Venue: Physical/virtual/hybrid, dates, location. Logistics: catering, strong WIFI, rooms and amphitheatre. Partners: Industrial,

Abstract Objectives Digital adaptation kits (DAKs) distill WHO guidelines for digital use by representing them as workflows, data dictionaries and decision support tables. This paper aims to highlight key lessons learnt in coding data elements of the antenatal care (ANC) and family planning DAKs to standardised classifications and terminologies (CATs). Methods We encoded data elements within the ANC and family

Introduction COVID-19, caused by the SARS-CoV-2 virus, was first identified in China in December 2019 and later became a pandemic.1 2 When this manuscript was finalised (12 June 2022), globally, the total number of infected cases had reached 540 318 million and over 6.331 million people had died.3 Telehealth refers to the delivery of healthcare particularly preventive and primary healthcare over a

Results Table 1 provides sample characteristics of the 16 SVIs and COVID-19 cases and COVID-19 rates per 100 000 population after 30 days of the first COVID-19-positive cases in all counties in the 11 states of the US South (1086 counties). On average, 85.3 COVID-19 cases were reported after 30 days of the first reported case in a county, and

AI arrogance and ignorance The current system means that the risk for sharing one’s data is high, with little personal gain. Despite the fact these risks are real to the institution, the failure to disclose data does not eliminate the risk; it merely transfers the risk from the institution to the patients being treated based on the research. Thus, those

Discussion The purpose of this study was to assess willingness of patients with DM to use mobile health applications and associated factors for self-care management. The result showed that willingness to use mHealth applications among patients with DM in the Oromia region was high (71.4%, 95% CI (66.8% to 75.9%)). This result was in line with the willingness to use mHealth

Introduction Over the past decades, a variety of eHealth technologies have been accessible as nations have implemented eHealth efforts to support the objectives for health education and person-centred care.1 Adoption of personal health records (PHRs) has been linked to numerous advantages, including improved patient–provider relationships, patient engagement improvements, better medication adherence, good health outcomes (such as blood pressure and glycaemic