Regardless of their level of training and experience, a doctor cannot access or recall all information applicable to making an accurate diagnosis. At the same time, a doctor welcomes the perspective provided from physicians' experience with similar cases. A machine learning solution that has been trained with peer-reviewed research and patient diagnostic history can classify a patient's risk for a disease within seconds. This provides a doctor with additional insight into a possible diagnosis.
Often, doctors must experiment with drugs until the best course is identified. This is an inefficient process that can be costly to the patient in many ways. Using research and patient histories, a machine learning solution can recommend therapies and pharmaceutical options that optimize the treatment of symptoms while mitigating adverse side effects. This is especially useful in cases where there are many treatment options.
Enriching healthcare information with data not commonly associated with diagnosis and treatment can provide atypical insights. Providers committed to improving healthcare in their communities often must look at factors outside the hospital room. Machine learning can be used to identify patients with comparable health conditions who share similar adverse environmental or socioeconomic factors. Providers can use these insights to propose proactive solutions that address the disease at the root cause, rather than simply treating symptoms.