Intel puts the global predictive analytics market in healthcare at US$18.49 billion in 2024, with projected growth to US$67.26 billion by 2030 (Intel's healthcare predictive analytics overview). That number matters less as market hype and more as an operational signal. …
Imagine trying to understand the health of an entire city by looking at one person’s medical chart. It’s an impossible task. This is the fundamental problem that Population Health Analytics Platforms were designed to solve. They act as the central …
Healthcare app projects rarely fail because a team cannot ship code. They fail because regulatory scope, data handling, clinical workflow, and release controls were defined too late. Typical delivery timelines still run from several months to well over a year, …
Software quality isn't a secondary concern in healthcare. It's a patient safety control. A recent FDA recall analysis cited by Coforge found that software failures were the root cause in 24% of all medical device recalls, and more than 90% …
Healthcare data security failures are expensive. IBM's annual breach research has repeatedly placed healthcare among the costliest sectors for a data breach, which is one reason security decisions now shape product architecture, vendor selection, and operating models, not just audit …
A lot of teams hit the same moment. The product roadmap is moving, a pilot customer wants security answers, engineering wants to choose the stack, and someone asks, “How do we make this HIPAA compliant without slowing everything down?” That …
Your hospital is probably in a familiar bind. Core systems still run. Clinicians know their workarounds. Finance knows which batch jobs must never fail. But every change takes too long, every integration feels brittle, and every security review exposes another …
Most hospital leaders don't start thinking about hospital management system modernization because they want a new architecture diagram. They start because the current environment has become exhausting to run. Admissions rekey data into multiple systems. Finance depends on brittle interfaces. …
Most healthcare leaders are in the same spot right now. Teams are trying to reduce admin burden, improve patient access, protect margins, and keep clinicians focused on care instead of paperwork. The problem isn’t a lack of software. There are …
The current corporate narrative often suggests that generative AI is ready to replace human designers. However, actual implementation shows that AI is primarily an efficiency tool that shifts where design effort lives: away from pixel-pushing and toward strategic orchestration. While …
Clinical leaders don’t need another abstract explainer on AI in care delivery. They need a reliable way to decide whether an AI-enabled clinical decision support system will improve decisions at the bedside, fit the workflow, and survive compliance review once …
AI is already strongest where diagnostic work is visual, repetitive, and high volume. In reviewed studies, AI systems in medical imaging showed sensitivity ranges of 56.4% to 95.7%, compared with 23.2% to 76% for radiologists, while maintaining comparable specificity. That …
