OM1 Blog

In the rapidly evolving landscape of clinical trials, data collection remains one of the most time-consuming and cost-intensive processes. For decades, the manual abstraction of clinical data into electronic case report forms (eCRFs) has been the predominant method for gathering information from trial participants.

Rheumatologic diseases like systemic lupus erythematosus, ankylosing spondylitis, and rheumatoid arthritis can present significant challenges for biopharma. Real-world data (RWD), combined with advanced AI technologies, offers a transformative way to bridge this gap and map patient journeys in rheumatology from pre-diagnosis through disease management and into health outcomes.

One of the key frameworks for understanding data analytics in Real-World Evidence (RWE) is the “Five Vs”: Velocity, Volume, Variety, Veracity, and Value.

Understanding the patient journey has become crucial for improving treatment outcomes and addressing unmet needs. Pharmaceutical companies need deeper insights into the patient experience.

When dealing with depression research, accurate real-world data is crucial to demonstrate real-world effectiveness. But what happens when critical data is buried in unstructured clinical notes? We'll explore how generating estimated PHQ-9 scores from unstructured EHR data with AI can advance depression treatment and research.

As a leading health outcomes organization, our teams work in partnership with clients every day to measure, compare and predict treatment outcomes. Equity is increasingly at the forefront of many of our conversations, in both our client work and within our own workplace.

Real-world data (RWD) can be used to find appropriate patients for observational studies and clinical trials, but for study eligibility, clinicians and researchers need details not found in claims.

Boston is recognized as a hub for medical advancement, with some of the best hospitals and medical professionals in the U.S. and the world. But what about patients who cannot access advanced care given barriers related to where they live, their available resources or health insurance coverage?

Integrated evidence generation (IEG) is a framework for generating evidence to support decision-making in healthcare. To make these data fit for purpose, the data used and analyses generated must meet the evidentiary standard for the particular use case.