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.

Figure 1.

Laboratory orders per hour and acute physiology in sepsis patients, first 72 h after hospital admission. The top line (in red) and the left Y-axis are for the mean hourly LAPS2 (Laboratory-based Acute Physiology Score, version 2); the bottom line (in blue) and the right Y-axis are for the mean number of laboratory orders per patient per hour. The upper panels are for ward and intensive care unit (ICU) patients who survived to discharge; the lower panels are for decedents. The number of patient hours for ward survivors and decedents were 3 403 240 and 165 373, respectively; for ICU patients, the corresponding numbers were 683 667 and 134 420, respectively. See text and Table 2 for details on the LAPS2.

Figure 2.

Laboratory orders per hour and acute physiology in acute myocardial infarction patients, first 72 h after hospital admission. Color, axes, and hospital unit schema are the same as in Figure 1. The number of patient hours for ward survivors and decedents were 592 970 and 12 034, respectively; for ICU patients, the corresponding numbers were 238 283 and 13 053, respectively.

Figure 3.

Laboratory orders per hour and acute physiology in sepsis patients in the 72 h prior to discharge. Color, axes, and hospital unit schema are the same as in Figure 1, except that the X-axis looks backward from discharge (which is the time of death for decedents). The number of patient hours for ward survivors and decedents were 3 432 018 and 167 164, respectively; for ICU patients, the corresponding numbers were 691 802 and 135 829, respectively.

Figure 4.

Laboratory orders per hour and acute physiology in acute myocardial infarction patients in the 72 h prior to discharge. Schema are the same as Figure 3. The number of patient hours for ward survivors and decedents were 595 963 and 12 136, respectively; for ICU patients, the corresponding numbers were 239 700 and 13 179, respectively.