Author: Manmath Mohanty

In a fast-paced world of research, laboratories face increasing pressure to innovate rapidly. Amidst this, data emerges as the ultimate tool, with big pharma companies leading the charge in utilizing AI-driven analytics for lab efficiency improvement.

Clinical trials are at the forefront of medical advancement, playing a pivotal role in bringing new treatments and therapies to patients. However, despite their importance, patient recruitment remains a significant bottleneck in the clinical trial process.

Patient recruitment in clinical trials has long been a challenging and time-consuming process, causing delays and increasing costs. Clinical trials come with stringent eligibility criteria, and potential participants often have reservations about safety, the time commitment required, or a simple lack of awareness about available trials.

In the world of clinical trials, achieving real-time end-to-end visibility has become more than just a trend; it’s a critical necessity.

In the world of clinical trials, data is at the heart of the quest for safer and more effective treatments. However, as trials grow in scale and complexity, the data they generate from various sources has surged to unprecedented levels.

Airlines will continue to adapt to the evolving new normal, operate under strict budget constraints, and prioritize cash preservation to tackle the uncertainties and challenges.

In a world of rapidly changing technology, many organizations still rely on legacy mainframes to keep their most critical operations running. These age-old systems have been tuned and customized to meet the functional requirements of the business, and as a result, have become locked-in to vendors over the years