Implicit Bias in ICU Electronic Health Record Data Measurement Frequencies and Missingness Rates of Clinical Variables

Background: Disparities in data collection within electronic health records (EHRs), especially in Intensive Care Units (ICUs), can reveal underlying biases that may affect patient outcomes. Identifying and mitigating these biases is critical for ensuring equitable healthcare. This study aims to develop an analytical framework for measurement patterns, including missingness rates and measurement frequencies, evaluate the association between them and demographic factors, and assess their impact on in-hospital mortality prediction. Methods: We conducted a retrospective cohort study using the Medical Information Mart for Intensive Care III (MIMIC-III) database, which includes data on over 40,000 ICU patients from Beth Israel Deaconess Medical Center (2001–2012). Adult patients with ICU stays longer than 24 hours were included. Measurement patterns, such as missingnessrates and measurement frequencies, were derived from EHR data and analyzed. Targeted Machine Learning (TML) methods were used to assess potential biases in measurement patterns across demographic factors (age, gender, race/ethnicity) while controlling for confounders such as other demographics and disease severity. The predictive power of measurement patterns on in-hospital mortality was evaluated. Results: Among 23,426 patients, significant demographic disparities were observed in the first 24 hours of ICU stays. Elderly patients (≥ 65 years) had more frequent temperature measurements compared to younger patients, while males had slightly fewer missing temperature measurements than females. Racial disparities were notable: White patients had more frequent blood pressure and oxygen saturation (SpO2) measurements compared to Black and Hispanic patients. Measurement patterns were associated with ICU mortality, with models based solely on these patterns achieving an area under the receiver operating characteristic curve (AUC) of 0.76 (95% CI: 0.74–0.77). Conclusions: This study underscores the significance of measurement patterns in ICU EHR data, which are associated with patient demographics and ICU mortality. Analyzing patterns of missing data and measurement frequencies provides valuable insights into patient monitoring practices and potential biases in healthcare delivery. Understanding these disparities is critical for improving the fairness of healthcare delivery and developing more accurate predictive models in critical care settings.