In the constantly changing healthcare environment, smooth data exchange is not just a luxury; it is a vital necessity. Integrating patient records from Electronic Health Records (EHRs), clinical data repositories, and other healthcare systems unlocks valuable insights, empowering providers to deliver personalized care, fuel groundbreaking research, and streamline operational efficiency.

Terence Bennett - January 16, 2024 REST APIs for the healthcare industry are emerging as a secure simple and modern way of accessing sensitive data. Medical institutions need to modernize IT infrastructure, make services and data more transparent, provide self-­‐service options for patients, and develop mobile applications. In some cases, they are developing REST API services for internal use.

Healthcare is an industry where timely and accurate data can mean the difference between life and death. The significance of healthcare data integration, therefore, cannot be overstated. By seamlessly merging disparate data sources, healthcare data integration ensures that providers have the most accurate and up-to-date patient data at their fingertips. This not only streamlines workflows but also enhances patient care, leading to better health outcomes.

Medical devices have been used for centuries but are only being regulated recently to ensure patient safety. Every medical device must meet the requirements of the regulations in a country or region before entering the market. Very recently, going digital has changed not only our lifestyles but also the operations of medical devices.

Following the popularity of ChatGPT, AI (Artificial Intelligence) chatbots have become a trend in various fields. The application of these chatbots for organizations to provide immediate and accurate solutions is limitless. One such field is healthcare, where these health bots would play a vital role in helping patients by providing real-time virtual assistance whenever and wherever needed.

This tutorial shows how to use ClearML to manage MONAI experiments. Originating from a project co-founded by NVIDIA, MONAI stands for Medical Open Network for AI. It is a domain-specific open-source PyTorch-based framework for deep learning in healthcare imaging. This blog shares how to use the ClearML handlers in conjunction with the MONAI Toolkit. To view our code example, visit our GitHub page.

When considering some of the latest statistics related to Health Insurance Portability and Accountability Act of 1996 (HIPAA) violations and consequences, ensuring compliance is more critical than ever. For example, healthcare data breaches are increasing year-over-year, yet 75% of healthcare organizations say their infrastructure is not prepared to respond to cybersecurity threats effectively. HIPAA is a U.S. federal law.

A patient interaction turned into clinician notes in seconds, increasing patient engagement and clinical efficiency. Novel compounds designed with desired properties, accelerating drug discovery. Realistic synthetic data created at scale, expediting research in rare under-addressed disease areas.

As medical technology becomes more connected, ensuring the security of medical devices is vital. Technology advances, bringing new opportunities for innovation and to improve the lives of patients — but emerging technology also presents opportunities for malicious actors.

The U.S. Food and Drug Administration (FDA) regulates several industries in the United States. Medical devices. Food and beverages. Cosmetics. Each industry has its own regulations that must be met to gain FDA approval. But complying with regulations and getting FDA approval is a time- and resource-intensive process. Follow along or jump to the sections that interest you: See Traceability In Helix ALM.