An Econometric Analysis of Patient Flows in the Cardiac Intensive Care Unit

Abstract

This paper explores the rationing of bed capacity in a cardiac intensive care unit (ICU). We find that the length of stay for patients admitted to the ICU is influenced by the occupancy level of the ICU. In particular, a patient is likely to be discharged early when the occupancy in the ICU is high. This in turn leads to an increased likelihood of the patient having to be readmitted to the ICU at a later time. Such "bounce-backs" have implications for the overall ICU effective capacity-an early discharge immediately frees up capacity, but at the risk of a (potentially much higher) capacity requirement when the patient needs to be readmitted. We analyze these capacity implications, shedding light on the question of whether an ICU should apply an aggressive discharge strategy or if it should follow the old quality slogan and "do it right the first time. " By comparing the total capacity usage for patients who were discharged early versus those who were not, we show that an aggressive discharge policy applied to patients with lower clinical severity levels frees up capacity in the ICU. However, we find that an increased number of readmissions of patients with high clinical severity levels occur when the ICU is capacity constrained, thereby effectively reducing peak bed capacity. Christian TerwieschThe Wharton School, University of Pennsylvania terwiesch@wharton.upenn.eduThis paper explores the rationing of bed capacity in a cardiac intensive care unit (ICU). We …nd that the length of stay for patients admitted to the ICU is in ‡uenced by the occupancy level of the ICU. In particular, a patient is likely to be discharged early when the occupancy in the ICU is high. This in turn leads to an increased likelihood of the patient having to be readmitted to the ICU at a later time. Such "bounce-backs" have implications for the overall ICU e¤ective capacity -an early discharge immediately frees up capacity, but at the risk of a (potentially much higher) capacity requirement when the patient needs to be readmitted. We analyze these capacity implications, shedding light on the question if an ICU should apply an aggressive discharge strategy or if it should follow the old quality slogan and "do it right the …rst time." By comparing the total capacity usage for patients who were discharged early versus those who were not, we show that an aggressive discharge policy applied to patients with lower clinical severity levels frees up capacity in the ICU. However, we …nd that an increased number of readmissions of patients with high clinical severity levels occur when the ICU is capacity constrained, thereby e¤ectively reducing peak bed capacity.

The Impact of Delays on Service Times in the Intensive Care Unit

“…Moreover, it is possible that the delay effects will be seen prior to 100% occupancy as some beds may be reserved in anticipation of patient arrivals from other hospital units, such as the Operating Room. This characterization of the ICU being busy is similar to the approaches taken in Kc and Terwiesch (2012), Kim et al (2012), Chan et al (2012) and Batt and Terwiesch (2012) among others. Note that we examined other measures of busy, including different thresholds and times at which the occupancy was measured.…”

“…As we will see in our analysis of queueing systems with delay-dependent service times, this impact can be substantial. We note that prior work has demonstrated that when the ICU is busy, patient LOS may decrease (Kc and Terwiesch 2012). In their work, they focus on a single cardiac ICU where patients are cared for following cardiac surgery.…”

“…By ignoring the delay effects, ICUs continue to be highly congested. Such congestion can lead to other reactive actions such as rerouting (Kim et al 2012), patient speedup (Kc and Terwiesch 2012), and ambulance diversion (Allon et al 2013), which can also be detrimental to patient outcomes. From both a patient as well as systems level perspective, it is desirable to reduce delays.…”

“…Hospitals have developed a number of approaches to deal with ICU congestion. For instance, ICU congestion can result in discharging current patients preemptively (Chalfin 2005, Dobson et al 2010, Kc and Terwiesch 2012, blocking new patients via ambulance diversion (Allon et al 2013) or rerouting patients to different units (Thompson et al 2009, Kim et al 2012.…”

The Impact of Size and Occupancy of Hospital on the Extent of Ambulance Diversion: Theory and Evidence

“…The service time of each patient (irrespective of the mode of arrival) in the ED is exponentially distributed with mean m 1 (see Green and Nguyen 2001 for a discussion of this assumption) and that the length of stay of each patient (emergency as well as nonemergency) in the inpatient department is exponentially distributed with mean m 2 . Recent evidence suggests that length of stay might be dependent on the level of congestion in the inpatient department (Kc and Terwiesch 2012). We do not incorporate this effect to maintain analytical tractability.…”