Projected Registered Nurse Workforce
in Hawai’i 2005 - 2020January 2007 (download pdf file)

Summary & Introduction | I. Nursing Supply Model | II. Nursing Demand Model

III. Projecting RN Shortage | IV. Limitations | V. Recommendations

VI. Conclusions | VII. References

II. The Nursing Demand Model (NDM)

Factors considered in the Nursing Demand Model

A. Factors Affecting the Number of People Requiring Care

The NDM ² default uses U.S. Census Bureau population projections to estimate the number of people requiring care in each healthcare setting. These projections are by age group, sex and rural/urban setting. The NDM ² then converts population projections into numbers of people needing care in each of the twelve care settings. Historic healthcare experience is used to determine the usage of healthcare facilities by people of different ages, sexes and urban/rural settings. These figures are then adjusted for trends in the healthcare market environment, economic conditions, demographics and geographic location.

The factors affecting the number of patients in different types of healthcare settings are shown below in Table 2.1.

• A negative sign (−) indicates that an increase in that factor will decrease the number of people using that health care setting. For example, an increase in the percentage of the population in HMOs will decrease the number of people making in-patient visits to hospitals.

• A positive sign (+) indicates that an increase in that factor will increase the number of people using that health care environment. For example, an increase in the percentage of hospital surgeries that are performed as outpatient surgeries will increase the number of people making out-patient visits to hospitals.

Table 2.1 Factors Affecting Patient Numbers in Healthcare Settings

Table 2.1 highlights the variables that can be influenced to affect nursing demand. For example, increasing Medicaid eligibility increases the population that uses a variety of healthcare facilities and, therefore, the demand for nursing in a variety of settings. Increases in HMO usage rates conversely decrease the number of inpatient days. The number of emergency department visits and nursing facility residents also decline as HMO enrollment rates increase and thus, decrease nursing demand.

B. FTE RNs per Capita

The next step in estimating the demand for FTE RNs is to calculate the required FTE RNs per capita in each healthcare setting, also referred to as staffing intensity. The nurse staffing intensity measures used by NDM are shown in Table 2.2, below. Typically staffing intensity is measured either as FTE RNs required per 1,000 patient units or as FTE RNs per 10,000 in population. For nurse educators, staffing intensity is measured as a constant number of educators per RN.

In Table 2.2 nurse staffing intensity in inpatient, outpatient and emergency care in short-term hospital; long-term hospital; nursing facility; home health; and physicians’ offices were determined by regressing historic staffing intensities on factors reflecting the healthcare environment, economic conditions, health and acuity, and geographic location.

In occupational health, schools, public health, other settings, and nurse education; the ratio of FTE RNs per unit of population is assumed to remain constant over time, based on 1996 usage patterns.

Table 2.2 RN Staffing Intensity by Healthcare Setting

For the first seven health care settings in Table 2.2, the factors affecting staffing intensity are shown in Table 2.3, below.

• A negative sign (−) indicates that an increase in that factor will decrease staffing intensity. For example, an increase in the ratio of RN to LPN wages will decrease the number of FTE RNs per 1,000 in-patient visits to hospitals.

• A positive sign (+) indicates that an increase in that factor will increase the staffing intensity. For example, an increase in the Medicare payment per home health visit will increase the number of FTE RNs per 1,000 home health visits.

Table 2.3 identifies the coefficient variables that can be influenced to affect staffing intensity and, consequently, nursing demand. Increasing the percentage of hospital surgeries that are performed on an outpatient basis, for example, increases staffing intensity and, therefore, the demand for nursing in outpatient settings. An increase in RN wages relative to LPN wages, on the other hand, decreases nursing demand.

The NDM uses “relative wages” to determine nursing demand. For example, if wages for registered nurses, LPNs and nursing assistants all rise by ten percent, there would be no impact on nursing demand. However, if registered nurse wages rose by a greater percentage than LPN or aide wages, nursing demand would decline in some healthcare settings as relatively less expensive LPNs and nursing aides are substituted for registered nurses.

Table 2.3 Factors Affecting RN Staffing Intensity in Health Care Settings

C. FTE Registered Nurse Demand

The final step estimates FTE RN demand. The demand is calculated as the units of healthcare usage in each setting multiplied by FTE RNs per unit of health care usage. For example, if there are 2.6 million outpatients hospital visits estimated for a year and there is one FTE RN per 1,000 visits, expected demand in that healthcare setting would be 2,600 FTE RNs.

D. Nursing Demand Model & Hawai’i

At the national level the variables in the model are assumed to reflect the nursing demand experience. However for smaller states, like Hawai’i, some adjustments are required to reduce error and better reflect the healthcare environment.

i. Hawai’i Population Projections

The default NDM uses U.S. Census Bureau population projections by year, age group and sex to the year 2020. The age groups are 0-4, 5-17, 18-24, 35-44, 45- 64, 65-74, 75-84, and 85+ years old. The Census projections were made in 1996 and again in 2003. The NDM creators adjusted the 1996 Census projections so they pass through actual census population counts for the year 2000. However, recent population and economic projections for the State of Hawai’i indicate the U.S. Census Bureau figures to be high.

The Census Bureau also publishes population estimates for the years 2000 through 2004. The NDM projections are compared to Census population estimates for those four years.

Table 2.4 shows a comparison between US Census and CDC population projections for the State of Hawai’i. The comparison shows a small 1.3% difference at 2005, 3.4% difference at 2010, 7.4% difference at 2015, and 12.7% difference by 2020.

Table 2.4 Comparison of US Census and CDC population projections for the State of Hawai’i 2000 - 2020

ii. Registered Nurse Usage

The NDM predicts FTE RN usage from 1996 through 2020. In its default setting, the model calibrates its predictions to nursing demand in the base year 1996. The default NDM identifies Hawai’i’s base year FTE RNs as 8,228, an over estimate of 23.7%. The NDM is adjusted to account for this over estimate by decreasing FTE RN population to 6,275 as shown in Table 2.5.

Table 2.5 Adjusting the NDM Base Year to Reflect Hawai’i Licensed RNs in 1996