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Estimating Nursing Intensity and Direct Cost Using the Nurse-Patient Assignment

Welton, John M. PhD, RN; Zone-Smith, Laurie PhD, RN; Bandyopadhyay, Dipankar PhD

Journal of Nursing Administration: June 2009 - Volume 39 - Issue 6 - pp 276-284
doi: 10.1097/NNA.0b013e3181a72911
Articles

Background: This study examines the feasibility of using the nurse-patient assignment (NPA) to calculate direct nursing hours and costs for each inpatient-day. The NPA data are collected at every hospital and therefore represent a readily available information source that can establish the intensity and economic value of nursing care at US hospitals.

Method: Direct nursing care hours for each patient were collected twice a day using an existing nursing intensity database at a single university hospital between January 2004 and June 2005 for a total of 11,582 patient-days. Nursing intensity was also calculated for each shift using the NPA. Mean unit and hospital nursing hours were calculated and compared with the direct nursing care hours using ordinary least squares regression.

Results: For the day shift, the NPA estimate explained 77.2% (r2 = 0.772) of the variance of patient-level nursing intensity. Unit and hospital mean estimates of nursing intensity had lower r2 of 0.574 and 0.456, respectively. The night-shift NPA, unit, and hospital r2 estimates were 0.824, 0.633, and 0.579, respectively.

Conclusion: The use of the NPA can provide a robust and easy method to calculate nursing intensity for individual patients using assignment data available in nearly all care settings. The NPA estimate can be used to allocate direct nursing time and costs for each patient within the hospital billing system and can also be used in pay-for-performance or for benchmarking nursing intensity within and across hospitals.

Authors' Affiliations: Associate Professor (Dr Welton), College of Nursing, Medical University of South Carolina, Charleston; Manager (Dr Zone-Smith), Clinical Services Special Projects Medical University Hospital Authority, Charleston, South Carolina; Assistant Professor of Biostatistics (Dr Bandyopadhyay), Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston.

Corresponding author: Dr Welton, Medical University of South Carolina, College of Nursing, 99 Jonathan Lucas St, Room 527, Charleston, SC 29425 (weltonj@musc.edu).

© 2009 Lippincott Williams & Wilkins, Inc.