No matter how much the medical industry relies on precision in diagnosis and treatment, the language that’s generated in a clinical setting in the form of medical records is still open to nuance and interpretation.
Add modernization efforts that tap the potential of big data and artificial intelligence, and the proper interpretation of data becomes even more critical. One particularly expansive growth area in medical technology is Natural Language Processing (NLP).
NLP is where linguistics and artificial intelligence collide, and it has increasingly become part of our everyday lives. Your own engagement with NLP probably happens most often when you verbally interact with a smart speaker or over an automated customer support phone call.
NLP is used in the medical industry to process documentation that previously would have required a person sitting at a desk reviewing one page at a time. Now language can be parsed for meaning and intent through sophisticated neural networks and machine learning algorithms.
The implications for this shift are massive—think significant administrative cost savings, faster diagnoses, and ultimately healthier patients. Early signs of diseases are caught faster, subtle shifts in data generate alerts to the appropriate specialists, and patients are empowered with more data-driven treatment plans.
Michael Ames, SADA Managing Director, Vertical Markets, has witnessed the medical industry’s rapid adoption of NLP solutions. “NLP helps medical organizations process so much data so much faster, leading to much faster patient outcomes,” says Ames, “At the same time, trading some of the traditional and laborious human oversight for NLP means we have to be vigilant about making sure our systems interpret data properly.”
Research firm Gigaom recently compared three leading NLP solutions for healthcare: Google Cloud Healthcare API, Azure Cognitive Services for Language–Text Analytics for Health, and Amazon Comprehensive Medical. Researchers manually annotated medical notes to identify terms within the documents from a common set of entities and relationships. Each system was evaluated for its precision and accuracy, identifying false negatives, true positives, and false positives.
These results were then analyzed and ranked, with Google Cloud Healthcare API coming out on top, with the highest precision and lowest false positives of the three solutions. Health industry organizations that are already modernizing their systems with Google Cloud, including telemedicine, scheduling, and other powerful tools have another reason to integrate a now-proven NLP solution into their technology stacks. And organizations that are early in their digital transformation journey can get even more value by migrating to Google Cloud.
“Google Cloud continues to impress with the rigor of their analytics, particularly in medical industry applications,” says Ames, “When you visit your doctor, you expect clinical data to be accurate. Google Cloud Healthcare API sets the bar for healthcare NLP solutions, as this report demonstrates.”
Review the report and learn more about how Google Cloud Healthcare API was ranked highest in comparison to other healthcare NLP solutions.