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Identifying and Analyzing Patient Volume Fluctuations on Labor and Delivery
Abstract Number: S-29
Abstract Type: Original Research
Accurate estimation of patient volume provides hospital leaders with the ability to appropriately match staff resources to patient needs. For this reason, it is important to be able to predict volume fluctuations as accurately as possible. In this study, we analyzed the fluctuation in patient volume as well as the potential causes for the day-to-day variation in the Labor and Delivery (L&D) Unit.
When staffing remains fixed, patient volume fluctuations may cause inefficiencies in staff utilization. Several studies have shown an association between staff overutilization and decreased nursing staff retention rates. A correlation between increased near-miss events and overworked medical personnel has been reported by several organizations.
Registrars utilized an electronic scheduling system (ESS) to schedule cesarean sections (CS) and inductions. Providers utilized a separate electronic medical record (EMR) to document care of patients. We obtained institutional data from both the ESS and EMR for the period May 2012 to May 2013 and separated total deliveries into non-CS, CS from the Triage Unit, CS from labor rooms, and scheduled CS. Scheduled inductions were also analyzed.
We found a significant daily variation in patient volume on L&D, as exemplified by April 2013 (Figure 1). Data for the period May 2012 to May 2013, analyzed by day of week using ANOVA, showed a statistically significant peak in average total number of deliveries Wednesday and Thursday (17.3), compared with the average number on Monday, Tuesday, and Friday (16.1) and on Saturday and Sunday (13.5), p<.05. This included scheduled CS, scheduled inductions, and all unscheduled deliveries. The proportion of scheduled deliveries was 30%.
Our data suggest that predictable patterns in patient volume fluctuations on L&D may present us with opportunities to actively manage patient flow and reduce these fluctuations by redirecting selected patients to low volume days. In conjunction with a dynamic nursing staffing model, this could help us better match patient volume to staff resources.
1. Zwink JE et al. Nurse manager perceptions of role satisfaction and retention at an academic medical center. J Nurs Adm 43, 135–41 (2013).
2. Clark SL et al. A systematic approach to the identification and classification of near-miss events on labor and delivery in a large, national health system. Am J Obstet Gynecol 207, 441–5 (2012).