healthcare technology

Use of eHealth technologies is common in the multiple sclerosis (MS) population and facilitates the exchange of health care information with providers, according to a recent study.

However, use of eHealth and mHealth technologies varies substantially with sociodemographic factors, and health care providers need to be aware of these disparities as these technologies are increasingly leveraged in health care settings.

Researchers surveyed participants in the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry in 2017 about their use of eHealth technologies using questions adapted from the Health Information National Trends (HINTS) 4 Cycle 4 survey. They found:

• Of 6,423 participants included in the analysis, most participants were female, and white, with a mean (SD) age of 59.7 (10.1) years.
• Overall, 5,408 (84.2%) had exchanged medical information with a health professional most often using a secure online portal (1,839, 28.6%), followed by email (1,327, 20.7%).
• Of the 5,529 smartphone and tablet users, 2,556 (46.2%) used an mHealth app.

read original at https://www.mdedge.com/clinicalneurologynews/clinical-edge/summary/multiple-sclerosis/use-ehealth-technologies-common-ms

Researchers at Vanderbilt University Medical Center have used natural language processing technology in an electronic medical records system to identify patients with multiple sclerosis and collect data on traits of their disease course.

The work is significant, researchers say, because much remains unknown about the course of the disease, which varies widely among patients. “Most research studies have focused on the origin of the disease, partly because of the difficulty in ascertaining sufficient longitudinal clinical data to study the disease course,” according to the study published in the Journal of the American Medical Informatics Association.

“Electronic medical records may provide such a tool. We have previously shown that genomic signals of MS risk may be replicated using EMR-derived cohorts. In this paper, we evaluated algorithms to extract detailed clinical information for the disease course of MS.”

The study used algorithms based on ICD-9 codes, text keywords and medications to identify 5,789 patients with MS, and collected detailed data on the clinical course of the patients’ disease to measure progression of disability. “For all clinical traits extracted, precision was at least 87 percent and specificity was greater than 80 percent.”