Data Stream and Hospital-Level Variation in Intensive Care Unit Vital-Sign Record Density Across Medical Information Mart for Intensive Care IV and eICU Collaborative Research Database: Retrospective Data Quality Study
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1Saïd Business School, University of Oxford, Oxford, England, United Kingdom.
Background:
Electronic health record-derived process measures such as vital-sign record density are used for quality monitoring, risk prediction, and cross-system benchmarking, but whether they retain consistent meaning across sites and data streams is rarely tested.
Objective:
This study aimed to determine whether vital-sign record density varies primarily by data stream, database, contributing site, or care unit, and what follows for comparative use.
Methods:
We retrospectively analyzed the Medical Information Mart for Intensive Care IV (MIMIC-IV) version 3.1 and the eICU Collaborative Research Database (eICU-CRD) version 2.0. Record density and timing metrics were computed for the first 24 intensive care unit hours from nurse-charted streams and, in eICU-CRD, from the monitor stream. Heart rate was primary; oxygen saturation and respiratory rate were replication variables. Variance was decomposed by database, hospital, and unit using η2 with hospital-clustered bootstrap inference and mixed-effects sensitivity analyses. A hospital-level plausibility floor of 12 nurse-charted heart rate observations per 24 hours was applied for the principal analysis; unrestricted and alternative-floor analyses are reported as sensitivities.
Results:
A pooled-item MIMIC-IV heart rate count included alarm-limit documentation events, so label-based pooling mixes the two. In 117,572 eICU-CRD stays present in both streams, nurse- and monitor-derived counts were almost unrelated (Spearman ρ=0.024), with bottom-decile overlap at chance. Between-database η2 for record count was 0.960 against the eICU monitor stream and 0.026 against the nurse stream. Eight of 68 eICU-CRD hospitals fell below the plausibility floor and were excluded from principal estimates, a restriction introduced during review. Among the remaining 60 hospitals (88,560 stays), hospital η2 for record count was 0.354 (95% CI 0.259-0.450) against unit-within-hospital η2 of 0.043; for the interval-structure metrics, the hospital and unit components were not separable. For record count, the hospital component fell from 0.441 unrestricted to 0.321 at an hourly floor. A negative-binomial mixed model gave latent-scale hospital and unit variance-partition coefficients of 0.348 (95% CI 0.252-0.457) and 0.112 (0.078-0.163), equivalent to 0.318 and 0.110 observed. A pooled bottom-decile rule agreed with hospital-specific rules on 52.5% (4726/9006) of flagged stays, with 6 hospitals supplying half the pooled decile. The hospital component for record count replicated at 0.322 for oxygen saturation and 0.333 for respiratory rate.
Conclusions:
Within a multisite nurse-charted stream, vital-sign record volume varied more across contributing sites than across care units within hospitals; for the interval-structure metrics, the site and unit components were not separable at this cohort size. Apparent cross-database differences were reproduced by source-table choice, and site-level variation remained after excluding the most evident contribution-completeness outliers. Comparative use therefore requires specifying the generating stream, enforcing the analytic window, and explicitly modeling site-level variation; the residual site component should not be interpreted as documentation practice alone.
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