Software Engineering Insights

The healthcare industry is on a constant lookout for digital solutions to conduct the financial, administrative, and clinical operations of a hospital. These days, these are largely carried out by means of a set of informatics tools, collectively known as Hospital Information Systems (HIS). In addition to a  patient's health information, inventory and personnel data are also stored, managed, and transmitted across a HIS.

Fast Healthcare Interoperability Resources (FHIR) is a healthcare data exchange standard created by Health Level Seven or HL7. Having developed advanced tools for safe and secure data exchange such as EMR, Diagnostic Data, or PHI – FHIR has given a boost to the healthcare sector.

The evolution of React Native technology is quite fascinating. What started as Facebook’s internal Hackathon project in 2013 is now one of the trending frameworks for Android and iOS mobile app development. In March 2015, Facebook, in its F8 conference announced the availability of React Native on Github. And now, developers are making the most of modern web techniques on mobile.

Artificial Intelligence (AI) and its multiple sub-domains are being increasingly employed in various industries and businesses to aid in repetitive processes. But there has been a burgeoning interest from established tech giants and startups in using AI to make everyday life a walk in the park.

User experience (UX) has always been the element of precedence in a mobile app. A lot of efforts are put in to refine usability of an app and all of it starts with the login screen design. 

Cross-platform development toolkits are winning over the developers’ community. Reusable blocks of UI, reactive performance, code usability on multiple platforms are some of the reasons why these cross-platform app development frameworks are gaining ground. One such sought-after framework for mobile app development is Flutter by Google.

The accuracy of deep learning models depends upon the quality, quantity, and contextual meaning of the training data set. However, the scarcity of this up-to-the-market data set is one of the major challenges in building DL models. 

Over the years, traditional financial systems have grappled with a set of enduring challenges – they've been exclusive, often inefficient, and quite opaque. Geographic constraints and bureaucratic complexities have left a significant portion of the population without access to essential financial services. These hurdles, coupled with the reliance on centralized intermediaries, have introduced risks and bottlenecks, impeding the smooth flow of transactions.

Artificial Intelligence (AI) is everywhere. The possibility is that you are using it in one way or the other and you don't even know about it. One of the popular applications of AI in custom software development is Machine Learning (ML), in which computers, software, and devices perform via cognition (very similar to the human brain). Herein, we share few examples of machine learning that we use every day and perhaps have no idea that they are driven by ML.

With increasing complexity and demand for highly scalable and robust applications, conventional monolithic architecture is no longer the best choice. After a certain threshold, monolithic architecture tends to hinder application performance and scalability. Moreover, with an enormous codebase, making changes to the tightly coupled, dependent processes in the monolithic architecture drastically increases the impact of single process failure.