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To quantify the difference in weekday versus weekend occupancy and the opportunity to smooth inpatient occupancy to reduce crowding at children’s hospitals.
Daily inpatient census data for 39 free-standing, tertiary-care children’s hospitals were used to calculate occupancy and to model the impact of reducing variation in occupancy and the change in the number of patients, patient days, and hospitals exposed to high occupancy pre- and post-smoothing. We also calculated the proportion of weekly admissions that would require different scheduling to achieve within-week smoothing.
Overall, hospitals’ mean occupancy ranged from 70.9%–108.1% on weekdays and 65.7%–94.9% on weekends. Weekday occupancy exceeded weekend occupancy with a median difference of 8.2%-points. The mean post-smoothing reduction in weekly maximum occupancy across all hospitals was 6.6%-points. Through smoothing, 39,607 patients from the 39 hospitals were removed from exposure to occupancy levels >95%. To achieve within-week smoothing, a median 2.6% of admissions would have to be scheduled on a different day of the week; this equates to a median of 7.4 patients per week (range 2.3–14.4).
Hospitals do have substantial unused capacity and smoothing occupancy over the course of a week could be a useful strategy that hospitals can use to reduce crowding and protect patients from crowded conditions.
High levels of hospital occupancy are associated with compromises to quality of care and access (often referred to as “crowding”)1–8, while low occupancy may be inefficient and also impact quality.9,10 Despite this, hospitals typically have uneven occupancy. Although some demand for services is driven by factors beyond the control of a hospital (e.g., seasonal variation in viral illness), approximately 15–30% of admissions to children’s hospitals are scheduled from days to months in advance and usually arrive on weekdays.11–14 For example, of the 3.4 million “elective” admissions in the 2006 Healthcare Cost and Utilization Project Kids Inpatient Database (HCUP KID), only 13% were admitted on weekends.14 Combined with short length of stay (LOS) for such patients, this leads to higher mid-week and lower weekend occupancy.12
Hospitals respond to crowding in a number of ways, but often focus on reducing LOS to make room for new patients.11,15,16 For hospitals that are relatively efficient in terms of LOS, efforts to reduce it may not increase functional capacity adequately. In children’s hospitals, median lengths of stay are 2–3 days and one-third of hospitalizations are one day or less.17 Thus, even 10–20% reductions in LOS trims hours, not days, from typical stays. Practical barriers (e.g., reluctance to discharge in the middle of the night or family preferences and work schedules) and undesired outcomes (e.g., increased hospital re-visits) are additional pitfalls encountered by relying on throughput enhancement alone.
Managing scheduled admissions through “smoothing” is an alternative strategy to reduce variability and high occupancy.6,12,18–20 The concept is to proactively control the entry of patients, when possible, to achieve more even levels of occupancy, instead of the peaks and troughs commonly encountered. Nonetheless, it is not a widely-used approach.18,20,21 We hypothesized that children’s hospitals had substantial unused capacity that could be used to smooth occupancy, which would reduce weekday crowding. While it is obvious that smoothing will reduce peaks to average levels (and also raise troughs), we sought to quantify just how large this difference was – and thereby quantify the potential of smoothing to reduce inpatient crowding (or, conversely, expose more patients to high levels of occupancy). Is there enough variation to justify smoothing, and, if a hospital does smooth, what is the expected result? If the number of patients removed from exposure to high occupancy is not substantial, other means to address inpatient crowding might be of more value. Our aims were to quantify the difference in weekday versus weekend occupancy, report on mathematical feasibility of such an approach, and determine the difference in number of patients exposed to various levels of high-occupancy.
This retrospective study was conducted with resource-utilization data from 39 freestanding, tertiary-care children’s hospitals in the Pediatric Health Information System (PHIS). Participating hospitals are located in non-competing markets of 23 states plus the District of Columbia and affiliated with the Child Health Corporation of America (CHCA, Shawnee Mission, KS). They account for 80% of free-standing and 20% of all general, tertiary-care children’s hospitals. Data quality and reliability are assured through joint ongoing, systematic monitoring. The Children’s Hospital of Philadelphia Committees for the Protection of Human Subjects approved the protocol with a waiver of informed consent.
Patients admitted January 1 - December 31, 2007 were eligible for inclusion. Due to variation in the presence of birthing, neonatal intensive care, and behavioral health units across hospitals, these beds and associated patients were excluded. Inpatients enter hospitals either as scheduled (often referred to as “elective”) or unscheduled (emergent or urgent) admissions. Because PHIS does not include these data, KID was used to standardize the PHIS data for proportion of scheduled admissions.22 (KID is a healthcare database of 2–3 million pediatric inpatient discharges developed through federal-state-industry partnership and sponsored by the Agency for Healthcare Research and Quality (AHRQ).) Each encounter in KID includes a principal International Classification of Diseases, 9th revision, (ICD-9) discharge diagnosis code and is designated by the hospital as “elective” (ranging from chemotherapy to tonsillectomy) or not. Because admissions, rather than diagnoses, are scheduled, a proportion of patients with each primary diagnosis in KID are scheduled (e.g., 28% of patients with a primary diagnosis of esophageal reflux). Proportions in KID were matched to principal diagnoses in PHIS.
The census was the number of patients registered as inpatients (including those physically in the ED from time of ED arrival) – whether observation or inpatient status – at midnight, the conclusion of the day. Hospital capacity was set using CHCA data (and confirmed by each hospital’s administrative personnel) as the number of licensed in-service beds available for patients in 2007; we assumed beds were staffed and capacity fixed for the year. Occupancy was calculated by dividing census by capacity. Maximum occupancy in a week referred to the highest occupancy level achieved in a 7-day period (Monday–Sunday). We analyzed a set of thresholds for high-occupancy (85%, 90%, 95%, and 100%), because there is no consistent definition for when hospitals are at high occupancy or when crowding occurs, though crowding has been described as starting at 85% occupancy.23–25
The hospital was the unit of analysis. We report hospital characteristics, including capacity, number of discharges, and census region, and annual standardized length of stay ratio (SLOSR) as observed-to-expected LOS.
A retrospective smoothing algorithm set each hospital’s daily occupancy during a week to that hospital’s mean occupancy for the week; effectively spreading the week’s volume of patients evenly across the days of the week. While inter-week and inter-month smoothing were considered, intra-week smoothing was deemed more practical for the largest number of patients, as it would not mean delaying care by more than one week. In the case of a planned treatment course (e.g. chemotherapy), only intra-week smoothing would maintain the necessary scheduled intervals of treatment.
To approximate the number of patient admissions that would require different scheduling during a particular week to achieve smoothed weekly occupancy, we determined the total number of patient-days in the week that required different scheduling and divided by the average LOS for the week. We then divided the number of admissions-to-move by total weekly admissions to compute the percentage at each hospital across 52 weeks of the year.
We focused on the frequency and severity of high occupancy and the number of patients exposed to it. This framework led to four measures that assess the opportunity and effect of smoothing:
All analyses were performed separately for each hospital for the entire year and then for winter (December–March), the period during which most crowding occurred. Analyses were performed using SAS (version 9.2, SAS Institute, Inc, Cary, NC); p-values <0.05 were considered statistically significant.
The characteristics of the 39 hospitals are provided in Table 1. Based on standardization with KID, 23.6% of PHIS admissions were scheduled (range 18.1–35.8%) or a median of 81.5 scheduled admissions per week per hospital; 26.6% of weekday admissions were scheduled versus 16.1% for weekends. Overall, 12.6% of scheduled admissions entered on weekends. For all patients, median LOS was 3 days (interquartile range [IQR]: 2–5 days), but median LOS for scheduled admissions was 2 days (IQR 1–4 days). The median LOS and IQR were the same by day of admission for all days of the week. Most hospitals had an overall SLOSR close to 1 (median: 0.9, IQR: 0.9–1.1). Overall, hospital mean midnight occupancy ranged from 70.9%–108.1% on weekdays and 65.7%–94.9% on weekends. Uniformly, weekday occupancy exceeded weekend occupancy, with a median difference of 8.2%-points (IQR: 7.2%–9.5%-points). There was a wide range of median hospital weekday-weekend occupancy differences across hospitals (Figure 1). The overall difference was less in winter (median difference, 7.7%-points; IQR: 6.3%–8.8%-points) than in summer (median difference, 8.6%-points; IQR: 7.4%–9.8%-points (Wilcoxon Sign Rank test, p<0.001). Thirty-five hospitals (89.7%) exceeded the 85%-occupancy threshold and 29 (74.4%) exceeded the 95%-occupancy threshold on at least 20% of weekdays (Table 2). Across all the hospitals, the median difference in weekly maximum and weekly mean occupancy was 6.6%-points (IQR: 6.2%–7.4%-points) (Figure 2).
Smoothing reduced the number of hospitals at each occupancy threshold, except 85% (Table 2, rows C and D). As a linear relationship, the reduction in weekday peak occupancy (y) based on a hospital’s median difference in weekly maximum and weekly mean occupancy (x) was y = 2.69 + 0.48x. Thus, a hospital with a 10%-point difference between weekday and weekend occupancy could reduce weekday peak by 7.5%-points.
Smoothing increased the number of patients exposed to the lower thresholds (85% and 90%), but decreased the number of patients exposed to >95% occupancy (Table 2). For example, smoothing at the 95% threshold resulted in 630 fewer patients per hospital exposed to that threshold. If all 39 hospitals had within-week smoothing, a net of 39,607 patients would have been protected from exposure to >95% occupancy and a net of 50,079 patients from 100% occupancy.
To demonstrate the varied effects of smoothing, Table 3 and Figure 3 present representative categories of response to smoothing depending on pre-smoothing patterns. While not all hospitals decreased occupancy to below thresholds after smoothing (Types B and D), the overall occupancy was reduced and fewer patients were exposed to extreme levels of high occupancy (e.g. >100%).
To achieve within-week smoothing, a median of 7.4 patient-admissions per week (range: 2.3–14.4) would have to be scheduled on a different day of the week. This equates to a median of 2.6% (IQR: 2.25%, 2.99%; range: 0.02%–9.2%) of all admissions – or 9% of a typical hospital-week’s scheduled admissions.
This analysis of 39 children’s hospitals found high levels of occupancy and weekend occupancy lower than weekday occupancy (median difference 8.2%-points). Only 12.6% of scheduled admissions entered on weekends. Thus, weekend capacity is available to offset high weekday occupancy. Hospitals at the higher end of the occupancy thresholds (95%, 100%) would reduce the number of days operating at very high occupancy and the number of patients exposed to such levels by smoothing. This change is mathematically feasible, as a median of 7.4 patients would have to be proactively scheduled differently each week, just under one-tenth of scheduled admissions. Since LOS by day of admission was the same (median 2 days), the opportunity to affect occupancy by shifting patients should be relatively similar for all days of the week. In addition, these admissions were short, conferring greater flexibility. Implementing smoothing over the course of the week does not necessarily require admitting patients on weekends. For example, Monday admissions with an anticipated 3-day LOS could enter on Friday with anticipated discharge on Monday to alleviate mid-week crowding and take advantage of unoccupied weekend beds.26
At the highest levels of occupancy, smoothing reduces the frequency of reaching these maximum levels, but can have the effect of actually exposing more patient-days to a higher occupancy. For example, for 9 hospitals in our analysis with >20% of days over 100%, smoothing decreased days over 100%, but exposed weekend patients to higher levels of occupancy (Figure 3). Since most admissions are short and most scheduled admissions currently occur on weekdays, the number of individual patients (not patient-days) newly exposed to such high occupancy may not increase much after smoothing at these facilities. Regardless, hospitals with such a pattern may not be able to rely solely on smoothing to avoid weekday crowding, and, if they are operating efficiently in terms of SLOSR, might be justified in building more capacity.
Consistent with our findings, the Institute for Healthcare Improvement, the Institute for Healthcare Optimization, and the American Hospital Association Quality Center stress that addressing “artificial variability” of scheduled admissions is a “critical first step” to improving patient flow and quality of care while reducing costs.18,21,27 Our study suggests that small numbers of patients need to be proactively scheduled differently to decrease mid-week peak occupancy, so only a small proportion of families would need to find this desirable to make it attractive for hospitals and patients. This type of proactive “smoothing” decreases peak occupancy on weekdays, reducing the safety risks associated with high occupancy, improving acute access for emergent patients, shortening wait-times and loss of scheduled patients to another facility, and increasing procedure volume (3–74% in one study).28 Smoothing may also increase quality and safety on weekends, as emergent patients admitted on weekends experience more delays in necessary treatment and have worse outcomes.29–32 In addition, increasing scheduled admissions to span weekends may appeal to some families wishing to avoid absence from work to be with their hospitalized child, to parents concerned about school performance – and may also appeal to staff members seeking flexible schedules. Increasing weekend hospital capacity is safe, feasible and economical, even when considering the increased wages for weekend work.33,34 Finally, smoothing over the whole week allows fixed costs (e.g., surgical suites, imaging equipment) to be allocated over 7 days rather than 5 and allows for better matching of revenue to the fixed expenses.
Rather than a prescriptive approach, our work suggests hospitals need identify only a small number of patients to proactively shift, providing them opportunities to adapt the approach to local circumstances. The particular patients to move around may also depend on the costs and benefits of services (e.g. radiologic, laboratory, operative) and the hospital’s existing patterns of staffing. A number of hospitals that have engaged in similar work have achieved sustainable results, such as Seattle Children’s Hospital, Boston Medical Center, St. John’s Regional Health Center, and New York University Langone Medical Center.19,26,35–37 In these cases, proactive smoothing took advantage of unused capacity and decreased crowding on days that had been traditionally very full. Hospitals that rarely or never have high-occupancy days and that do not expect growth in volume may not need to employ smoothing, whereas others that have crowding issues primarily in the winter may wish to implement smoothing techniques seasonally.
Aside from attempting to reduce high-occupancy through modification of admission patterns, other proactive approaches include optimizing staffing and processes around care, improving efficiency of care, and building additional beds.16,25,38,39 However, the expense of construction and the scarcity of capital often preclude this last option. Among children’s hospitals, with SLOSR close to 1, implementing strategies to reduce the LOS during periods of high occupancy may not result in meaningful reductions in LOS as such approaches would only decrease the typical child’s hospitalization by hours, not days. In addition to proactive strategies, hospitals also rely on reactive approaches, such as ED boarding, placing patients in hallways on units, diverting ambulances or transfers, or canceling scheduled admissions at the last moment, to decrease crowding.16,39,40
This study has several limitations. First, use of administrative data precluded modeling all responses. For example, some hospitals may be better able to accommodate fluctuations in census or high occupancy without compromising quality or access. Second, we only considered intra-week smoothing, but hospitals may benefit from smoothing over longer periods of time, especially since children’s hospitals are busier in winter months, but incoming scheduled volume is often not reduced.11 Hospitals with large occupancy variations across months may want to consider broadening the time horizon for smoothing and weigh the costs and benefits over that period of time, including parental and clinician concerns and preferences for not delaying treatment. At the individual hospital level, discrete-event simulation would likely be useful to consider the trade-offs of smoothing to different levels and over different periods of time. Third, we assumed a fixed number of beds for the year, an approach that may not accurately reflect actual available beds on specific days. This limitation was minimized by counting all beds for each hospital as available for all the days of the year, so that hospitals with a high census when all available beds are included would have an even higher percent occupancy if some of those beds were not actually open. In a related way, then, we also do not consider how staffing may need to be altered or augmented to care for additional patients on certain days. Fourth, midnight census, the only universally available measure, was used to determine occupancy rather than peak census. Midnight census provides a standard snapshot, but is lower than mid-day peak census.41 In order to account for these limitations, we considered several different thresholds of high occupancy. Fifth, we smoothed at the hospital level, but differential effects may exist at the unit level. Sixth, to determine proportion of scheduled admissions, we used HCUP KID proportions on PHIS admissions. Overall, this approach likely overestimated scheduled medical admissions on weekends thus biasing our result towards the null hypothesis. Finally, only freestanding children’s hospitals were included in this study. While this may limit generalizability, the general concept of smoothing occupancy should apply in any setting with substantial and consistent variation.
In summary, our study revealed that children’s hospitals often face high mid-week occupancy, but also have substantial unused weekend capacity. Hospitals facing challenges with high weekday occupancy could proactively use a smoothing approach to decrease the frequency and severity of high occupancy. Further qualitative evaluation is also warranted around child, family, and staff preferences concerning scheduled admissions, school, and work.
Conflict of Interest: The authors have no potential conflicts of interest, including specific financial interests and relationships and affiliations relevant to the subject matter or materials discussed in the manuscript.
Financial Disclosure: Dr. Fieldston received support (salary) from the Robert Wood Johnson Foundation Clinical Scholars program from July 2007 to June 2010. Dr. Sills received support from the Agency for Healthcare Research and Quality (5R03HS016418). Dr. Shah received support from the National Institute of Allergy and Infectious Diseases (K01 AI73729) and the Robert Wood Johnson Foundation under its Physician Faculty Scholar Program. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health, the Agency for Healthcare Research and Quality, or the Robert Wood Johnson Foundation.