Mining the Hidden Gems in Unstructured EMR Data
Pharma companies are increasingly turning to real-world data to answer their commercial business questions, but not all realize that unstructured EMR data is the unsung hero of most queries. Whether a manufacturer is struggling to find a niche patient population, conduct unbiased outcomes research, or generate persuasive proof points, unstructured data can fill in the gaps left by other real-world data sources.
Until recently, this valuable information has been virtually impossible to analyze at scale. Much of the patient data contained in EMR systems—like a patient’s demographic information, vitals, and procedural history—adheres to a defined format, which makes analysis feasible. But the qualitative information recorded by a patient’s care team, such as clinical notes, radiology reports, and discharge summaries, is stored in free-text fields.
For years, the complexity of turning this data into insights meant manufacturers were unable to see the complete patient journey. But why is this data so pivotal in the first place?
Consider Sarah, a grandmother recently diagnosed with stage 3 breast cancer. While medical claims and lab results reveal glimpses of Sarah’s treatment journey, the richest details about her care—her tumor size, biomarker levels, diagnostic notes, symptoms, and physician sentiment—are buried in her electronic medical records.
For the pharma company whose therapy is designed to treat Sarah’s tumor, this data is essential for finding Sarah and others like her: a highly specific subset of post-lumpectomy, stage 3 patients with both ER positive and HER2 negative tumors less than 3 cm in size. Without the ability to parse this unstructured EMR data, the manufacturer will never be able to find Sarah in time to impact her treatment—or improve her outcomes.
Read the full article in BioPharma Dive. Learn how MMIT’s longitudinal, analytic-ready unstructured data can help your team identify patients and track disease progression.
