A GLOBAL ATM LOCATOR AND A METHOD THEREOF

The present disclosure is related to a global Automated Teller Machine (ATM) locator and a method thereof. The method includes receiving, by a global ATM locator, a request message from an electronic device of a user for details related to a nearest ATM. The global ATM locator initiates an API call to a payment network provider associated with the global network provider to fetch the information related to the nearest ATM for the user, as per the information given in the request message and current location of the user. Thereafter, the global ATM locator receives the requested details related to the nearest ATM such as balance amount, denominations of the balance amount, location of the ATM, wheelchair accessibility at the ATM, depositing capability at the ATM, access fee of the ATM and the like. The received information is provided to the user such that can make an informed decision about which ATM to visit for performing desired financial operations. Further, the present disclosure enables the user to query a global ATM locator to receive information related to not just the nearest ATM, but also other details such as balance amount in the ATM, working status of the ATM, wheelchair accessibility at the ATM, depositing capability at the ATM, Access fee/transaction fee of the ATM, and the like without the need to physically visit the ATM center. Therefore, the present disclosure enhances user experience, eliminates the time and resources involved in the tedious process of searching for an ATM center for performing certain financial operations, and also reduces delays in the financial operations that can be performed through the ATM

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified