Automated methods to extract patient new information from clinical notes in electronic health record systems.

摘要

The widespread adoption of Electronic Health Record (EHR) has resulted in rapid text proliferation within clinical care. Clinicians' use of copying and pasting functions in EHR systems further compounds this by creating a large amount of redundant clinical information in clinical documents. A mixture of redundant information (especially outdated and incorrect information) and new information in a single clinical note increases clinicians' cognitive burden and results in decision-making difficulties. Moreover, replicated erroneous information can potentially cause risks to patient safety. However, automated methods to identify redundant or relevant new information in clinical texts have not been extensively investigated.;The overarching goal of this research is to develop and evaluate automated methods to identify new and clinically relevant information in clinical notes using expert-derived reference standards. Modified global alignment methods were adapted to investigate the pattern of redundancy in individual longitudinal clinical notes as well as a larger group of patient clinical notes. Statistical language models were also developed to identify new and clinically relevant information in clinical notes. Relevant new information identified by automated methods will be highlighted in clinical notes to provide visualization cues to clinicians. New information proportion (NIP) was used to indicate the quantity of new information in each note and also navigate clinician notes with more new information. Classifying semantic types of new information further provides clinicians with specific types of new information that they are interested in finding. The techniques developed in this research can be incorporated into production EHR systems and could potentially aid clinicians in finding and synthesizing new information in a note more purposely, and could finally improve the efficiency of healthcare delivery.