Identifying patients at risk for chronic obstructive pulmonary disease (COPD) exacerbations is an important component of optimal patient care because exacerbation can significantly impact a patient’s mortality. While predictive algorithms based on claims data are useful, additional precision can be gained by incorporating clinical data from the unstructured text of electronic health records (EHR). Rutgers’ Robert Wood Johnson Barnabas Health System (RWJBH), GSK, and Deep 6 AI conducted a study to develop an algorithm to identify COPD exacerbations via EHR data. The algorithm used artificial intelligence (AI) and natural language processing (NLP) to mine EHR data for clinical characteristics of patients with exacerbation that are not available in traditional claims data or coded EHR data. It leveraged risk factors identified in COPDGene®, one of the largest studies ever to investigate the underlying genetic factors of COPD. The promise of this algorithm is that it can help clinicians better understand COPD disease progression and optimize treatments in care settings. To read the full story.