Article

Characterizing the use and contents of free-text family history comments in the electronic health record.

Department of Medicine.
AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 01/2012; 2012:85-92.
Source: PubMed

ABSTRACT The detailed collection of family history information is becoming increasingly important for patient care and biomedical research. Recent reports have highlighted the need for efforts to better understand collection and use of this information in resources such as the Electronic Health Record (EHR). This two-part study involved characterizing the use and contents of free-text comments within the family history section of an EHR. Based on a manual review of a subset of 11,456 cancer-related family history entries, 20 "reasons for use" were identified and the distribution across these reasons determined. A semi-automated analysis of the 3,358 unique comments associated with these entries was then performed to identify and quantify key categories of information. Implications of this study include guiding efforts for the improved use, collection, and subsequent analysis of family history information in the EHR.

0 Followers
 · 
66 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Background: Alcohol use is a significant part of a patient's history, but details about consumption are not always documented. Electronic Health Record (EHR) systems have the potential to improve assessment of alcohol use and misuse; however, a challenge is that critical information may be primarily in free-text rather than in a structured and standardized format, thereby limiting its use. Objective: To characterize the use and contents of free-text documentation for alcohol use in the social history module of an EHR. Methods: This study involved a retrospective analysis of 500 alcohol use entries that include structured fields as well as a free-text comment field. Two coding schemes were developed and used to analyze these entries for: (1) quantifying the reasons for using free-text comments and (2) categorizing information in the free-text into separate elements. In addition, for entries indicating possible alcohol misuse, a preliminary review of other structured parts of the EHR was conducted to determine if this was also documented elsewhere. Results: The top three reasons for using free-text were limited ability to describe alcohol use frequency (75%), amount (22%), and status (18%) with available structured fields. Within the free-text, descriptions of frequency were most common (79%) using words or phrases conveying occasional (61%), daily (13%), or weekly (12%) use. Of the 36 cases suggesting alcohol misuse, 44% had mention of alcohol problems in the problem list or past medical history. Conclusions: Based on the early findings, implications for improving the structured collection and use of alcohol use information in the EHR are provided in four areas: (1) system enhancements, (2) user training, (3) decision support, and (4) standards. Next steps include examining how alcohol use is documented in other parts of the EHR (e. g., clinical notes) and how documentation practices vary based on patient, provider, and clinic characteristics.
    Applied Clinical Informatics 01/2014; 5(2):402-15. DOI:10.4338/ACI-2013-12-RA-0101 · 0.39 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Despite increased functionality for obtaining family history in a structured format within electronic health record systems, clinical notes often still contain this information. We developed and evaluated an Unstructured Information Management Application (UIMA)-based natural language processing (NLP) module for automated extraction of family history information with functionality for identifying statements, observations (e.g., disease or procedure), relative or side of family with attributes (i.e., vital status, age of diagnosis, certainty, and negation), and predication ("indicator phrases"), the latter of which was used to establish relationships between observations and family member. The family history NLP system demonstrated F-scores of 66.9, 92.4, 82.9, 57.3, 97.7, and 61.9 for detection of family history statements, family member identification, observation identification, negation identification, vital status, and overall extraction of the predications between family members and observations, respectively. While the system performed well for detection of family history statements and predication constituents, further work is needed to improve extraction of certainty and temporal modifications.
    AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium 01/2014; 2014:1709-17.