In this study, we found that children's hospitals were frequently crowded, but rarely acutely responded in a meaningful way to high midnight occupancy, even when it was as high as 95%. We found considerable variability both within and across hospitals.
As a whole, PHIS hospitals were often at high occupancy, with 70% of all midnights above 85% occupancy, including 42% of midnights above 95%. Prior studies show that patient safety, quality, and efficiency can be adversely impacted by occupancy above 85–90%.7, 8, 10
While there may be debate about this threshold, occupancy levels >85–95% at midnight suggest an even higher level in daytime and raise concerns about quality, safety, and access. To ensure high-quality care – as well as optimal patient/family experiences, avoidance of rejected transports and referrals, lessened ED crowding, decreased staff stress, and ability to deliver on educational missions – hospitals can consider multiple strategies, like those described below, to better analyze and manage patient flow.
During times of high occupancy, hospitals are not decreasing the number of medical or surgical scheduled admissions. This finding is not surprising given that canceling scheduled admissions at the last minute would likely upset patients, families, and providers. As an alternative strategy, flow experts joined by the American Hospital Association, the Institute of Medicine, and Institute for Healthcare Improvement argue hospitals should smooth scheduled admissions to accommodate known increases in unscheduled admissions by day-of-week and season.6, 20–23
Short-stay and low-severity patients
At times of high-occupancy, hospitals had both increases and decreases of short-stay admissions (≤1 day). More than half of hospitals had increased admissions of low-severity patients during high-occupancy periods. This mixed response likely reflects the differing pressures experienced by inpatient and ED providers during high-occupancy periods. For inpatient providers, there may be pressure to discharge patients quickly, whereas ED providers faced with ED crowding (correlated with times of high hospital occupancy) experience pressure to admit patients quickly to improve ED bed availability.24
Taken together, our findings and others suggest that increasing ED admissions (especially for low-severity patients) may increase inpatient staff workload and ultimately reduce overall efficiency as staff appropriately divert attention away from discharging to caring for sicker and newer patients.3
Because a substantial proportion of pediatric hospitalizations are brief and in one national study, one-third of admissions stayed 0 or 1 night25
– hospitals should devote resources to optimizing the efficiency of care for these patients in order to decrease their contribution to high hospital occupancy. To achieve this goal, potential strategies include extended-care ED units, short-stay inpatient units, streamlined paperwork and protocols, partnerships with community hospitals, as well as off-site urgent care facilities and extended hours for primary care practices.
Children's hospitals, particularly when serving as the sole pediatric facility in a region, may face difficulty in delaying or rejecting patients. In this study, several hospitals increased outgoing transfers, but incoming transfers did not vary. The relative stability of incoming transfers may reflect a lower rate
of accepting transfers, as the number of other institutions seeking to transfer patients would be expected to rise in high-volume winter months. Though PHIS data did not include data on number of rejected and delayed transfers, it is likely that rejections occur at a higher rate during high-occupancy periods. To better handle transfer requests during high occupancy periods, tertiary-care children's hospitals may create formal relationships with community hospitals and transfer agreements in order to offload lower-severity patients while maintaining the supply of higher-acuity beds. Though two studies have demonstrated that transfers are safe, hospitals must weigh this against other studies showing families' preference not to be transferred.26, 27
In this study, as many as one-third of hospitals showed a decrease in LOS (measured by SLOSR) during times of high occupancy and the effect was seen more in non-ACSC than in ACSC diagnoses. This observation is consistent with hospital strategies to attempt to lessen crowding by being more efficient, such as by discharging prior to rounds (which can be modulated as needed). The many hospitals in PHIS with SLOSR at or near 1 and short LOS, however, would have limited success in substantially increasing functional capacity in this manner. This also assumes reductions in LOS could be achieved without harming quality, safety, or patient satisfaction.
This study has several limitations. First, using administrative data precluded us from modeling the full complexity of possible hospital responses. Hospitals have different staffing models and those with high baseline provider-to-patient ratios may be better able to accommodate fluctuations in census without altering the dependent variables considered in our analysis. For this reason, our approach may have underestimated both the frequency and magnitude of hospitals' response to high census. Additionally, the smallest LOS increment was one day, which limited finer variation in SLOSR and precluded measure of LOS less than one day.
Second, we assumed a fixed number of staffed beds for the whole year, which may not accurately reflect actual available bed count on specific days. This may fluctuate due to the periodic closing and opening of beds or units related to construction or flexing of staff. Furthermore, some hospitals may deliberately use non-traditional bed spaces during periods of high census. These strategies were out of scope of the paper and do not reduce overall burden on hospitals or their staffs. Further, in some cases they require that patients stay in non-traditional areas of the hospital, which may not necessarily be aligned with delivering the highest quality care. The direction of systematic bias could be in either direction: overestimate days the hospital is at high occupancy if the denominator is too low if there are more staff beds or underestimate if beds are not staffed and not used, but appear in the denominator.
Third, the use of midnight census (instead of daily peak) would bias us away from overestimation. Midnight census is the only universally-available and reliable census data that is available within PHIS (and retrospectively at most hospitals), and as such it provides a standard snapshot of daily census overall. Even though it is known to be lower than peak census, due to continued evening and overnight admissions during the time when discharges slow and then cease to occur,28
it is an accurate look at census at one point in time and can be used in the appropriate context for throughput decision making.
Fourth, we standardized admissions in PHIS as scheduled or unscheduled using KID proportions of admission type. To determine the extent and direction of misclassification, we compared the proportion of admissions designated as scheduled with actual data from a subset of 5 hospitals. The total proportion of admissions (surgical + medical) derived to be scheduled in the PHIS database was less than those coded as such by the hospitals; however, direct standardization overestimated the number of scheduled medical admissions, particularly for patients admitted on weekends. This would likely cause us to underestimate day-of-the-week fluctuations in scheduled medical admissions, thereby biasing to the null hypothesis. Conversely, KID had high proportions of surgical admissions being “elective”, which lends confidence to the estimates in our study.
Fifth, our model assumed hospitals responded to excess capacity by altering dependent variables 1–2 days later. It is possible that the hospital response occurred more than 2 days after a high-occupancy trigger. This would also result in underestimation of hospital response. However, even if a hospital responded several days later, such an approach would not address the immediate patient flow and safety issues associated with high occupancy.
Sixth, the terms “response” or “reaction” implies a degree of causality that can only be imputed from the temporal relationships. For example, it was impossible to know whether the rise in scheduled admissions was a response to or a cause of the corresponding midnight occupancy level. It is also possible that hospitals did not actively try to manage or respond to high occupancy. While out of scope from this study, surveying hospitals about what census-management strategies they attempt and which are successful would also be informative.
Finally, only free-standing children's hospitals were included in this study. It is uncertain whether children's hospitals located within tertiary care medical centers and non-children's hospitals that care for children would respond differently.
In summary, we found that among 39 children's hospitals, meaningful responses to high occupancy were rare, in that they would not substantially reduce inpatient crowding. Given that studies in adult hospitals have found increases in sentinel events and medical errors associated with crowding, our findings raise concerns about adverse effects on patient safety and quality of care in the pediatric inpatient population. Hospitals and clinicians need to better understand the exact thresholds for defining high occupancy and its effects on patient outcomes, quality of care, safety, staff stress and satisfaction, education and training, as well as short- and long-term referral patterns and finance. Beyond occupancy, dynamic measures like throughput or throughput-to-staff ratios may be more important to safety and quality and a better standard for hospitals to use for activating responses. There may be good clinical and business cases for targeting high occupancy or throughput – or their ratio to staff and other resources – as a “never event” like other patient safety goals, which necessitates further research on the processes and outcomes involving patient flow at children's hospitals.