Predictive Model to Determine Need for Nursing Workforce

Mary E. Cramer, Li Wu Chen, Keith J. Mueller, Michael Shambaugh-Miller, Sangeeta Agrawal

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


This article describes a statistical modeling study designed to improve targets of need for registered nurse (RN) workforce. The model is place-based and incorporates the concepts of clinical need and regional service utilization. A cross-sectional study was conducted in Nebraska (1993-1999), and the unit of study was the county (N = 66). A mixed-model approach was used, and five predictor variables (% age 20-44,% age 45-64,% age 65+,% White non-Hispanic, and area) were significantly (p <.001) associated with service demand. Coefficient estimates were applied to various population projection scenarios, and the model’s algorithm converted service demand into number of RNs needed to compare numbers of RNs employed with projected need. The implications for RN workforce policy and funding decisions—at both federal and state levels—are significant. Further research with a larger, multistate database will be conducted to refine the model and demonstrate generalizability.

Original languageEnglish (US)
Pages (from-to)174-190
Number of pages17
JournalPolicy, Politics, & Nursing Practice
Issue number3
StatePublished - Aug 2004


  • health service demand
  • nurses
  • shortage
  • utilization
  • workforce

ASJC Scopus subject areas

  • Leadership and Management
  • Issues, ethics and legal aspects


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