Melissa Informatics Combines AI and Traditional Data Quality to Increase Insight into Electronic Medical Records

Melissa Informatics Combines AI and Traditional Data Quality to Increase Insight into Electronic Medical Records

Clinical Research Customer Case Study Featured at Bio-IT World Demo

Rancho Santa Margarita, CALIF – April 2, 2019 – Melissa, a leading provider of global contact data quality and identity verification solutions, will offer a real-world look at how to clean, harmonize, and connect disparate content sources for clinical discovery at the upcoming Bio-IT World Conference. Melissa will share a clinical research customer case study to illustrate how traditional data quality methods effectively blend with ontology-enabled artificial intelligence (AI) for machine reasoning. Together these technologies improve data normalization and integration, powering new levels of insight into electronic medical records (EMR).

The case study features the Melissa Informatics Sentient™ (MIS) platform, an array of data quality solutions, datasets, and knowledge engineering resources applied to help the Parkinsons Institute and Clinical Center (PICC) extract, curate, normalize, integrate, and realize useful outcomes from EMR data.

“By blending AI and sophisticated data quality, PICC transformed data such as unstructured text, XML, tables, tsv and other data formats into a research quality, well-managed data resource,” said Bob Stanley, senior director, customer projects, Melissa Informatics. “The resulting clean, well-integrated data supports both clinical and business goals, including researching and publishing data discoveries, and driving revenue-generating, pharmaceutical research partnerships.”

For case study details, visit Melissa Informatics at booth 523 at the Bio-IT World Conference, April 16-18, 2019, at the Seaport World Trade Center in Boston; click here if you prefer to set a briefing appointment. To connect with members of Melissa’s global intelligence team, visit www.Melissa.com or call 1-800-MELISSA.