Real-world evidence has been increasingly recognized as an important complement to clinical research. In clinical research, randomized controlled trials often have stringent inclusion criteria which may exclude certain patient groups, such as those with comorbidities. When clinical trial populations diverge from patient populations seen in a real-world care setting, insights gained might not be widely applicable to all people. Analyzing real-world data, such as clinical information recorded in electronic health records (EHR) as part of routine clinical care, can provide insights that are generalizable to real-world populations.
The Hol Picture
Our Insights on Real-World Evidence and Behavioral Health
natural language processing
Capitalizing on untapped value: Extracting environmental stressors from clinical notes via natural language processing
Consider an emergency room doctor who is caring for a patient who has been hospitalized after a suicide attempt. When she consults his chart, she can see that he has been diagnosed with depression, but the structured data that is immediately available does not provide much additional context.
This blog post is a continuation of our Data Differentiators series, in which we’re sharing how the NeuroBlu Database stands out from other EHR-derived real-world data sources. For previous installments of the series, see here and here.