<p><strong>OBJECTIVE: </strong>Describe variability in admission, discharge, and occupancy patterns for surgical patients at a large children's hospital and assess the relationship between scheduled admissions and occupancy.</p>

<p><strong>BACKGROUND: </strong>High hospital occupancy degrades quality of care and access, whereas low levels of occupancy use hospital resources inefficiently. Variability in scheduling patients for surgical procedures may affect occupancy and be amenable to alteration.</p>

<p><strong>METHODS: </strong>This is a retrospective administrative data analysis that took place at 1 urban, tertiary-care children's hospital. A total of 8552 surgical patients hospitalized from July 1, 2009, to June 30, 2010, were included in the analysis, and admission-discharge-transfer data for 1 fiscal year were abstracted for analysis of admission and occupancy patterns.</p>

<p><strong>RESULTS: </strong>Among 6257 surgical admissions for non-intensive care unit (ICU) patients, 49% were emergent and 51% were scheduled. Variation in admission volume by day of week was more than 3 times higher for scheduled admissions than for emergent admissions. For non-ICU surgical patients with length of stay 7 days or less (97%), Mondays and Tuesdays generated 42% of scheduled patient occupancy time. Thursdays and Fridays often had high occupancy of surgical patients (&gt;90% of designated beds filled), whereas Saturdays, Sundays, and Mondays were often at low occupancy for those beds (&lt;80% filled). Only 20% of all days in the year had designated non-ICU surgery beds with occupancy between 80% and 95%.</p>

<p><strong>CONCLUSIONS: </strong>Scheduled admissions contribute significantly to variability in occupancy. Predictable patterns of admissions lead to high occupancy on some days and unused capacity on others, with few days being at an optimal level of occupancy. These predictable patterns suggest opportunities to improve hospital operations with changes in scheduled admission patterns, which present a different problem than random demand.</p>

<p><strong>OBJECTIVES: </strong>Hospital crowding adversely affects access to emergent and elective care, quality and safety, patient and staff satisfaction, and trainee education. Reliable and valid measurements are crucial to operational planning and improvement, but traditional measures of hospital utilization may not accurately describe dynamic demand at hospitals. The goal of this study was to determine how effectively traditional measures express demand for hospital services and to describe the dynamic nature of patient flow in children's hospitals compared with alternative measures.</p>

<p><strong>METHODS: </strong>This was a retrospective administrative data review conducted at an urban, tertiary care children's hospital. A total of 28045 inpatients (22310 from fiscal year 2008 and 5735 from 2 months in the fall of 2009 during the H1N1 influenza surge [October 16, 2009-November 18, 2009]) were reviewed. Admission-discharge-transfer data were abstracted from a total of 14 months (1 fiscal year and a separate 2 months including an influenza surge). Hourly hospital census and occupancy were determined. Measures of mean and median length of stay (LOS) were compared. Turnover was calculated by summing admissions, discharges, and interunit transfers. Coefficient of variation (SD/mean) was used to describe variation.</p>

<p><strong>RESULTS: </strong>Midnight occupancy was generally closer to minimum occupancy than peak. Peak was usually between 10 AM and 12 PM and was a mean of 40/% points higher than midnight (but as large as 13% points). Median LOS was 58% shorter than average LOS. There was a high level of turnover, and patient-days did not express the wide variation in how demand was generated by day of week.</p>

<p><strong>CONCLUSIONS: </strong>Traditional metrics for hospital utilization do not accurately express the true extent and dynamic nature of patient flow and inpatient demand at a children's hospital. Hospitals and government regulatory agencies should consider additional means of measuring utilization to accurately determine demand, including its variation, and thus assist in budgeting, staffing, and capacity planning.</p>