Machine learning experts working at Google Health have published a new study in tandem with the University of California San Francisco's (UCSF) computational health sciences department that describes a machine learning model the researchers built that can anticipate normal physician drug prescribing patterns, using a patient's electronic health records (EHR) as input. That's useful because around 2% of patients who end up hospitalized are affected by preventable mistakes in medication prescriptions, some instances of which can even lead to death. The researchers describe the system as working in a similar manner to automated, machine learning-based fraud detection tools that are commonly used by credit card companies to alert customers of possible fraudulent transactions: They essentially build a baseline of what's normal consumer behavior based on past transactions, and then alert your bank's fraud department or freeze access when they detect a behavior that is not in line with an individual's baseline behavior. Similarly, the model trained by Google and UCSF worked by identifying any prescriptions that "looked abnormal for the patient and their current situation." That's a much more challenging proposition in the case of prescription drugs versus consumer activity -- because courses of medication, their interactions with one another and the specific needs, sensitivities and conditions of any given patient all present an incredibly complex web to untangle.