TY - GEN
T1 - Navigating longitudinal clinical notes with an automated method for detecting new information
AU - Zhang, Rui
AU - Pakhomov, Serguei
AU - Lee, Janet T.
AU - Melton, Genevieve B.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Electronic Health Records
KW - Information Storage and Retrieval
KW - Natural Language Processing
KW - Text Mining
UR - http://www.scopus.com/inward/record.url?scp=84894384190&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894384190&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-289-9-754
DO - 10.3233/978-1-61499-289-9-754
M3 - Conference contribution
C2 - 23920658
AN - SCOPUS:84894384190
SN - 9781614992882
T3 - Studies in Health Technology and Informatics
SP - 754
EP - 758
BT - MEDINFO 2013 - Proceedings of the 14th World Congress on Medical and Health Informatics
PB - IOS Press
T2 - 14th World Congress on Medical and Health Informatics, MEDINFO 2013
Y2 - 20 August 2013 through 23 August 2013
ER -