Yale Study Shows Limitations of Applying Artificial Intelligence to Registry Databases

Artificial intelligence will play a pivotal role in the future of health care, medical experts say, but so far, the industry has been unable to fully leverage this tool. A Yale study has illuminated the limitations of these analytics when applied to traditional medical databases -- suggesting that the key to unlocking their value may be in the way datasets are prepared. Machine learning techniques are well-suited for processing complex, high-dimensional data or identifying nonlinear patterns, which provide researchers and clinicians with a framework to generate new insights. But the study suggests that achieving the potential of artificial intelligence will require improving the data quality of electronic health records (EHR). "Our study found that advanced methods that have revolutionized predictions outside healthcare did not meaningfully improve prediction of mortality in a large national registry. These registries that rely on manually abstracted data within a restricted number of fields may, therefore, not be capturing many patient features that have implications for their outcomes," said Rohan Khera, MD, MS, the first author of the new study published in JAMA Cardiology.