Nursing Unit Staffing: An Innovative Model Incorporating Patient Acuity and Patient Turnover: A Dissertation

• Main Author: Tierney, Shirley J.
• Format: Others
• Published: eScholarship@UMMS 2010
• Subjects: Nursing Staff; Hospital; Personnel Staffing and Scheduling; Nursing Administration Research; Workload; Health and Medical Administration; Nursing; Nursing Administration
• Online Access: https://escholarship.umassmed.edu/gsn_diss/18; https://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=1017&context=gsn_diss

Changes in reimbursement make it imperative for nurse managers to develop tools and methods to assist them to stay within budget. Disparity between planned staffing and required staffing often requires supplemental staffing and overtime. In addition, many states are now mandating staffing committees to demonstrate effective staff planning. This retrospective quantitative study developed an empirical method for building nursing unit staffing plans through the incorporation of patient acuity and patient turnover as adjustments towards planning nursing workload. The theoretical framework used to guide this study was structural contingency theory (SCT). Patient turnover was measured by Unit Activity Index (UAI). Patient acuity was measured using case mix index (CMI). Nursing workload was measured as hours per patient day (HPPD). The adjustment to HPPD was made through the derivation of a weight factor based on UAI and CMI. The study consisted of fourteen medical, surgical, and mixed medical-surgical units within a large academic healthcare center. Data from 3 fiscal years were used. This study found that there were significant, but generally weak correlations between UAI and CMI and HPPD. The method of deriving a weight factor for adjusting HPPD was not as important as the decision-making relative to when to adjust planned HPPD. In addition, the measure of unit activity index was simplified which will assist researchers to more easily calculate patient turnover. As a result of this study, nurse managers and will be better able to adjust and predict HPPD in cases where benchmarking has been problematic. Data-driven adjustments to HPPD based on UAI and CMI will assist the nurse manager to plan and budget resources more effectively.