EFFICIENT CLIENT-SIDE DATA PROCESSING FOR HEALTH APPLICATIONS

The quick move to digital technology in healthcare has made web-based apps popular for using remote monitoring, teleconsultations and helping patients manage their conditions on their own.Even though these systems are good for online business, they depend on server-side processing, resulting in longer wait times, a lower-quality user experience and added expenses whenever traffic is high or the network is slow.This analysis examines approaches to handling and optimizing data at the client side of applications using the latest JavaScript techniques for digital health.We have designed a framework that makes use of asynchronous running, splits the code according to need, loads pages only when needed, uses WebAssembly, provides storage through IndexedDB, implements Service Workers and Web Workers to support moving parts of the function from servers to users' devices.We built and tested three simulated health applications-a live health monitoring dashboard, a symptom checker and an online telehealth service-to examine what the client-side changes did.To measure performance, FCP (First Contentful Paint), TTI (Time to Interactive), backend request volume and server CPU load under simulated user activity were used.The findings show better performance such as page load times that are reduced by up to 64%, calls to the API reduced by 67% and over 50% lower work on the backend.The survey results show that people perceive the site as more responsive and less abandon it during the early part of their visit.Besides, the architecture greatly reduced infrastructure costs, making the model for delivering digital health services both scalable and economically sound.The research shows that efficient client-side processing helps healthcare applications run better, be more accessible and lower their costs.The results can be used as guidance by health IT developers, digital platform architects and healthcare organizations to make their web health solutions robust and responsive in any setting.