The widespread use of electronic health records (EHRs) has created unprecedented opportunities to apply meaningful clinical data for quality improvement. Through machine learning, large data sets of patient clinical information gathered through routine clinical encounters can be analyzed for risk prediction and treatment algorithms. Although randomized clinical trials remain the criterion standard study type to show causality, the use of big data is showing promise as an alternative for discovery.1 This alternative is of particular significance in surgery, where randomized clinical trials are not feasible or likely.
Ishii LE, Rhee JS. Harnessing the Power of Data in Facial Plastic and Reconstructive Surgery—From Refuse to Riches. JAMA Facial Plast Surg. 2017;19(6):532–533. doi:10.1001/jamafacial.2017.1484
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