Schmid L, Lacey L; Academy for Health Services Research and Health Policy. Meeting.
Abstr Acad Health Serv Res Health Policy Meet. 2000; 17: UNKNOWN.
Presented by: Lorrie Schmid, B.A., Research Associate, North Carolina Center for Nursing, 222 North Person St., Raleigh, NC 27601. Tel: 919-715-3523; Fax: 919-715-3528; E-mail: lschmid@ga.unc.edu.
Research Objective: The ability to accurately forecast the size and characteristics of a state's nursing labor force is an important component of long range planning policy for health care resources. One tool available for this purpose is the Bureau of Health Professions' Nurse Supply Model (NSM) which provides both national and state-level long range estimations. This paper tests the reliability of the NSM's state level projections by comparing estimates developed from different baseline data sources. Study Design: The NSM is a flow model built around age and educational levels and 'flows' of RN's in and out of licensure and the workforce based on 1992 and 1996 data from the National Sample Survey of Registered Nurses, data from the National League for Nursing, the National Council of State Boards of Nursing and the Bureau of the Census. To test the reliability of the model's estimations, national data elements were replaced with state generated data from the NC Board of Nursing's RN license files, and population data from the NC Office of State Planning. Data elements changed were: RN population, RN activity rate (used to compute supply), RN FTE conversion factors, RN migration rates, and general population figures.Population Studied: All licensed RN's living or working in North Carolina.Principal Findings: National data provided by the NSM produced estimates that overstated RN supply in early estimation years but forecast smaller supplies in later years than was true of estimates based on state generated baseline data. NSM estimates from model data forecast 102,127 RNs in the workforce in 2020. Using state generated data for the baseline input to the model resulted in an estimate of 114,296 RNs. This difference of 12,169 could be the difference between a shortage or no shortage when compared to demand estimates. NSM national data-based estimates also forecast graduate-prepared RNs at 17.2% of total supply in 2020 versus 14.1% forecast by state generated baseline data. Conclusions: States interested in forecasting future supply for registered nurses have few tools available. The NSM model is relatively easy to use and provides all necessary data to make state-level estimations. However, our results show that states should test the model findings by incorporating their own data whenever possible. Other issues related to the impact of NSM model assumptions and processes will be discussed.Implications for Policy, Delivery or Practice: As new nursing shortages take hold in the next few years, state level planners will need appropriate forecasting tools to estimate future supply levels. Understanding the pros and cons of the Bureau of Health Professions' Nurse Supply Model will help planners develop reliable forecasts and discover the intricacies of long range forecasting for health professions.Primary Funding Source: North Carolina Center for Nursing
Publication Types:
Keywords:
- Data Collection
- Demography
- Employment
- Evaluation Studies
- Forecasting
- Health Planning
- Health Services Needs and Demand
- Income
- North Carolina
- Nurses
- Population
- Research
- Research Design
- instrumentation
- nursing
- supply & distribution
- hsrmtgs
Other ID:
UI: 102272762
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