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.

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.

Don't be misled: 3 misconceptions about external validation of clinical prediction models.

Clinical prediction models provide risks of health outcomes that can inform patients and support medical decisions. However, most models never make it to actual implementation in practice. A commonly heard reason for this lack of implementation is that prediction models are often not externally validated. While we generally encourage external validation, we argue that an external validation is often neither sufficient nor required as an essential step before implementation. As such, any available external validation should not be perceived as a license for model implementation. We clarify this argument by discussing 3 common misconceptions about external validation. We argue that there is not one type of recommended validation design, not always a necessity for external validation, and sometimes a need for multiple external validations. The insights from this paper can help readers to consider, design, interpret, and appreciate external validation studies.