Exploratory analysis of novel electronic health record variables for quantification of healthcare delivery strain, prediction of mortality, and prediction of imminent discharge

Abstract

Objective: To explore the relationship between novel, time-varying predictors for healthcare delivery strain (eg, counts of patient orders per hour) and imminent discharge and in-hospital mortality.

Materials and methods: We conducted a retrospective cohort study using data from adults hospitalized at 21 Kaiser Permanente Northern California hospitals between November 1, 2015 and October 31, 2020 and the nurses caring for them. Patient data extracted included demographics, diagnoses, severity measures, occupancy metrics, and process of care metrics (eg, counts of intravenous drip orders per hour). We linked these data to individual registered nurse records and created multiple dynamic, time-varying predictors (eg, mean acute severity of illness for all patients cared for by a nurse during a given hour). All analyses were stratified by patients' initial hospital unit (ward, stepdown unit, or intensive care unit). We used discrete-time hazard regression to assess the association between each novel time-varying predictor and the outcomes of discharge and mortality, separately.

Results: Our dataset consisted of 84 162 161 hourly records from 954 477 hospitalizations. Many novel time-varying predictors had strong associations with the 2 study outcomes. However, most of the predictors did not merely track patients' severity of illness; instead, many of them only had weak correlations with severity, often with complex relationships over time.

Discussion: Increasing availability of process of care data from automated electronic health records will permit better quantification of healthcare delivery strain. This could result in enhanced prediction of adverse outcomes and service delays.

Conclusion: New conceptual models will be needed to use these new data elements.

Keywords: healthcare delivery strain; hospital occupancy; hospital strain; patient-nurse interactions; time-varying predictors.