Automatic Detection of Inconsistencies between Free Text and Coded Data in Sarcoma Discharge Letters
Ruty Rinott, Michele Torresani, Rossella Bertulli, Abigail Goldsteen, Paolo Giovanni Casali, Boaz Carmeli, Noam Slonim · Studies in health technology and informatics · 2012
Discordance between data stored in Electronic Health Records (EHR) may have a harmful effect on patient care. Automatic identification of such situations is an important yet challenging task, especially when the discordance involves information stored in free text fields. Here we present a method to automatically detect inconsistencies between data stored in free text and related coded fields. Using EHR data we train an ensemble of classifiers to predict the value of coded fields from the free text fields. Cases in which the classifiers predict with high confidence a code different from the clinicians' choice are marked as potential inconsistencies. Experimental results over discharge letters of sarcoma patients, verified by a domain expert, demonstrate the validity of our method.