Segmed Blogs

The field of oncology research and innovation is continuously evolving and progressing, leading to new advancements in cancer diagnosis, treatment, and prevention. These advancements include targeted therapies, immunotherapies, and personalized medicine, revolutionizing oncology care and improving patient outcomes. Additionally, emerging technologies such as artificial intelligence, genomics, and big data analytics play a crucial role in oncology research by identifying novel biomarkers, early detection and diagnosis, and improving treatment approaches for individual cancer patients. Furthermore, there is a growing focus on diversity in oncology research and clinical trials, ensuring that the benefits of new treatments approach all populations. However, it is essential to recognize that despite significant advancements in oncology research and innovation, limitations and challenges must be addressed. One primary concern is the high cost of some cutting-edge treatments, which limits acc...

There are many reasons why healthcare organizations are hesitant to sell their data. For one, they may feel like they are giving up too much control over their data. Once sold, how will they know where it may end up? Will the company that purchases the data turn around and resell it? To what types of companies? How many times? In this blog post, we will explore some of the reasons why healthcare organizations are reluctant to sell their data. Let's start with one of the most important topics, the brass tax. Up until 2017/2018, medical data was not an item with much monetary value at all. Before medical records could be tied together, providing longitudinal data, while maintaining patient privacy and artificial intelligence to perform de-identification were both developed; manual intervention was required to anonymize the data. To produce de-identified data in any profitable volume was essentially impossible. Fast forward only a few years and now AI driven de-id is easy and affordab...

Developing an AI algorithm that will make it into the clinical setting is not easy. The whole process is challenging, from gathering and curating medical data, to training, validating, and testing an AI model, and finally to deploying the algorithm in the clinics. The World Health Organization (WHO) recently published a framework that helps developers of AI-based software as a medical device (AI-SaMD) to understand the requirements of evidence generation from development to post-market surveillance [1]. It is expected that AI will significantly improve healthcare delivery and outcomes. The clinical application of AI models is, however, still challenging, not only in low- and middle-income countries, but also in high-income countries. One of the biggest hurdles for clinical application of AI algorithms is access to well-curated and diverse data used for training, validation, and testing. Many AI models are developed in an academic setting, based on homogeneous data from a single or a f...

The healthcare industry is experiencing a data revolution. With the rise of electronic health records, medical imaging, genomics, and wearable devices, the amount of data generated in healthcare is exploding. This data holds invaluable insights that can improve patient outcomes, advance medical research, and speed product development. The major obstacles prevent stakeholders from tapping into these insights. Data is fragmented across countless systems and institutions. Rigid privacy regulations restrict access. Manual processes bog down data pipelines. Segmed is here to help. Our secure, HIPAA-compliant platform seamlessly connects global healthcare data sources into one centralized marketplace. We automate cumbersome tasks like licensing, extraction, de-identification, and delivery, condensing an 18-month process into just 14 days. The result? Faster access to high-quality, real-world data for our customers. But what is real-world data? Real-World Data: The Competitive Advantage Real-...