TY - JOUR
T1 - 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
AU - Rijk, Merijn H.
AU - Platteel, Tamara N.
AU - Mulder, Marissa M.M.
AU - Geersing, Geert Jan
AU - Rutten, Frans H.
AU - van Smeden, Maarten
AU - Venekamp, Roderick P.
AU - Leeuwenberg, Tuur M.
N1 - Publisher Copyright:
© 2023 The Author(s)
PY - 2024/2
Y1 - 2024/2
N2 - 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.
AB - 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.
KW - Electronic health record
KW - Lower respiratory tract infection
KW - Missing data
KW - Natural language processing
KW - Primary care
KW - Routine health care data
UR - http://www.scopus.com/inward/record.url?scp=85181691620&partnerID=8YFLogxK
U2 - 10.1016/j.jclinepi.2023.111240
DO - 10.1016/j.jclinepi.2023.111240
M3 - Article
C2 - 38072176
AN - SCOPUS:85181691620
SN - 0895-4356
VL - 166
SP - 1
EP - 10
JO - Journal of Clinical Epidemiology
JF - Journal of Clinical Epidemiology
M1 - 111240
ER -