Incomplete and possibly selective recording of signs, symptoms, and measurements in free text fields of primary care electronic health records of adults with lower respiratory tract infections

Abstract

Objectives: To assess the completeness of recording of relevant signs, symptoms, and measurements in Dutch free text fields of primary care electronic health records (EHR) of adults with lower respiratory tract infections (LRTI).

Study design and setting: Retrospective cohort study embedded in a prediction modeling project using routine health care data of the Julius General Practitioners' Network of adult patients with LRTI. Free text fields of 1,000 primary care consultations of LRTI episodes between 2016 and 2019 were manually annotated to retrieve data on the recording of sixteen relevant signs, symptoms, and measurements.

Results: For 12/16 (75%) of the relevant signs, symptoms, and measurements, more than 50% of the values was not recorded. The patterns of recorded values indicated selective recording of positive or abnormal values. Recording rates varied across consultation type (physical consultation vs. home visit), diagnosis (acute bronchitis vs. pneumonia), antibiotic prescription issued (yes vs. no), and between practices.

Conclusion: In EHR of primary care LRTI patients, recording of signs, symptoms, and measurements in free text fields is incomplete and possibly selective. When using free text data in EHR-based research, careful consideration of its recording patterns and appropriate missing data handling techniques is therefore required.

Keywords: Electronic health record; Lower respiratory tract infection; Missing data; Natural language processing; Primary care; Routine health care data.