Navigating longitudinal clinical notes with an automated method for detecting new information.
Automated methods to detect new information in clinical notes may be valuable for navigating and using information in these documents for patient care. Statistical language models were evaluated as a means to quantify new information over longitudinal clinical notes for a given patient. The new information proportion (NIP) in target notes decreased logarithmically with increasing numbers of previous notes to create the language model. For a given patient, the amount of new information had cyclic patterns. Higher NIP scores correlated with notes having more new information often with clinically significant events, and lower NIP scores indicated notes with less new information. Our analysis also revealed "copying and pasting" to be widely used in generating clinical notes by copying information from the most recent historical clinical notes forward. These methods can potentially aid clinicians in finding notes with more clinically relevant new information and in reviewing notes more purposefully which may increase the efficiency of clinicians in delivering patient care.
Institute for Health Informatics, University of Minnesota, Minneapolis, MN, USA.
Rui
R
Zhang
Serguei
S
Pakhomov
Janet T
JT
Lee
Genevieve B
GB
Melton
D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D055991:Health Records, Personal; D008137:Longitudinal Studies; D016347:Medical Records Systems, Computerized; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D012984:Software; D014584:User-Computer Interface