Healthcare industry leverages data and artificial intelligence to prevent risk

By February 18, 2012
Keywords : Smart city, America
doctor with digital screens

Many business categories are faced with more data than they can handle. The healthcare industry, for one, is deploying specific tech to best use this data to its advantage.


Intelligence tools can provide industry with the ability to limit risk and reduce costs. This can be best illustrated by examining the roles such tools play in a category as healthcare. Within the healthcare system, huge amounts of data have not yet been integrated into the system, individuals with widely varying needs and situations often are miscategorized, and vast sums of money are wasted in both these cases and others. Data-driven “health care intelligence” is the industry’s hope for improving “efficiency, quality, and safety of care delivery.” The right tools can save millions of dollars, help patients avoid unnecessary care, and stretch resources farther.

High volume capacity tools to analyze patient and hospital data

Predictive modeling tools analyze pharmacy and medical claims data to create a risk score with between 20 to 30 percent accuracy, limited due to data that can be over 30 days old. Newer systems will be able to handle, deliver and analyze “Electronic Medical Records (EMR), Hospital Information Systems (HIS) and Health Information Exchanges (HIE)” immediately, drastically improving this risk assessment. The current system cannot predict cost or risk with enough precision to identify at-risk patients, or target the most effective drugs and therapies. New technology must handle new clinical data effectively and efficiently.

Cutting edge Artificial Intelligence technology to predict risks

Taking direction from the financial and defense industries, complex data can be analyzed with the help of more sophisticated artificial intelligence. Artificial Intelligence technology form companies such as UHealthSolutions uses an approach involving several techniques. Neural networks are trainable software that can recognize patterns or groups such as high risk patients. Genetic algorithms mimic bacteria colonies that change and grow to identify key factors such as health care risk drivers. Fuzzy logic analyzes complex data flexibly to increase accuracy, and rule systems add data and gather useful statistics. Effective data processing and AI integration will benefit enterprises as new ways to predict and respond to risk, and save money.

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