Systems | Development | Analytics | API | Testing

How enterprise-grade data management creates better and more efficient care. In the last few years, the acceptance of telehealth has become more widespread as patients and providers found they could maintain continuity through phone and video collaboration, instead of in-person visits. In many cases, a level of care that once required a drive to the clinic or hospital could be delivered over a mobile phone or laptop, with no travel and no waiting room.

This year may be the most innovative on record. Recent advances in AI are beginning to transform how we live and work. And the potential impacts of artificial intelligence (AI) on the healthcare and life sciences industries are expected to be far-reaching. It’s essential for organizations to leverage vast amounts of structured and unstructured data for effective generative AI (gen AI) solutions that deliver a clear return on investment.

With advancements occurring at a rapid rate, it becomes extremely crucial to ensure the overall safety & quality of healthcare solutions in this MedTech's dynamic landscape. Just as technology has taken the entire world by storm, it is playing an extensive role in the advancement of the healthcare industry, which further enhances the significance of Quality Assurance (QA) and software testing services.

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.

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.