Showing 206-210 of 257 resources:
Applying big data analytics to patient records, OM1 offers insights to hospitals and pharma

Published by TechCrunch, December 17 2019, written by Jonathan Shieber OM1, a big-data analytics company for the healthcare industry, has raised $50 million in its latest round of financing to expand its sales and marketing and product development activities as it brings clinical insights to hospitals and big pharma companies alike. The new financing highlights[…]

OM1 Announces Dr. Gary Curhan as Chief Medical Officer

PR NEWSWIRE – BOSTON, October 4, 2019 —OM1, a leading health outcomes and technology company, today announced it has appointed Dr. Gary Curhan as the company’s Chief Medical Officer. In this role, Dr. Curhan will contribute his clinical and scientific expertise to enhance the development and delivery of OM1’s real-world evidence initiatives and programs. Dr.[…]

Harmonized Outcome Measures For Use In Asthma Patient Registries And Clinical Practice

Journal of Allergy and Clinical Immunology Volume 144, Issue 3, September 2019, Pages 671-681.e1 Harmonized outcome measures for use in asthma patient registries and clinical practice Richard E. Gliklich, MD,a,b Mario Castro, MD, MPH,c Michelle B. Leavy, MPH,a Valerie G. Press, MD, MPH,d Amisha Barochia, MBBS, MHS,e Christopher L. Carroll, MD, MS,f Julie Harris, MBA,g[…]

From Big Data to Rapid Analytics for Rheumatology – Data Quality, Tools, and Usage

Wednesday, August 14, 2019 Being able to quickly and cost-effectively collect, analyze, and share real-world evidence on a treatment is critical to improving outcomes in rheumatology conditions. Fortunately, advances in data linkage and processing capabilities are enabling greater access to patient journeys via deep, linked clinical data. At the same time, new tools are empowering[…]

E-Book: Findings From The OM1 Real-World Data Cloud – RA, NASH, DEPRESSION, SLE

This report pulls together key findings from the OM1™ Real-World Data Cloud. The report covers examples of big data and machine learning applications in evaluating real-world outcomes and is broken into four chapters: Age and Gender Differences in Comorbidities among Patients with Rheumatoid Arthritis Depression and Patient Outcomes among Rheumatoid Arthritis Patients Findings from a[…]