Nurse Critical Shortage Facility Study

• Background
• Study approach
• Study progress
• Preliminary update

A two-year study was conducted at the Center for Health Workforce Studies at the State University of New York (SUNY), Albany, to identify the essential components of a comprehensive, national methodology for identifying facilities and communities with critical shortages of registered nurses (RNs). This study is ongoing.  At the meeting, Ms. Jean Moore, Project Director, discussed the following aspects of the study:

• Background;
• Study approach;
• Study progress; and
• Preliminary update.

Background

There are continuing general shortages of RNs across the country.  These shortages are attributed to declining enrollments in RN education programs and to RNs—young RNs, as well as retirees—leaving the workforce.  Since 1996, the number of RNs per capita in the U.S. has decreased as the supply of RNs has grown more slowly than the U.S. population.  In part, as a result of this shortage, the number of projected RN job openings from the present until 2010 is more than 1 million.  The National Center for Health Workforce Analysis predicts that the RN shortage will grow from an estimated 6 percent in 2000 to an estimated shortage of 20 percent in 2020.

Another issue is the lack of ethnic diversity in the nursing workforce.  While foreign-trained nurses appear to contribute substantially, in many cases these nurses are not culturally competent for the populations they serve.  For example, most foreign-born nurses in New York City are from the Philippines, but a plurality of Asians in New York City are Chinese.

Initiatives administered by the Federal government to address the shortage include programs and policies to increase the pipeline to produce more nurses, improve retention, use RNs more efficiently, optimize immigration of nurses, and provide data about the workforce to inform policy decisions.

As part of this effort, in 2004, the Health Resources and Services Administration (HRSA) issued a Request for Proposals for a two-year research project to gather information and insights in support of the development of a new methodology for identifying health care facilities and communities with critical shortages of RNs.  HRSA’s decision to support this research was based in large part on their concern that its current method for identifying facilities and communities with shortages of RNs was too narrow in scope and that RN shortages were likely to worsen over the next 20 years.  The New York Center for Health Workforce Studies at SUNY Albany was selected to conduct this study.

Study Approach

The primary goal of this study was to conduct research on the necessary components of a comprehensive, nationwide methodology to identify facilities and communities with critical shortages of RNs across the U.S. and its territories in order to target the placement of Federally obligated RN scholars and loan repayers.  Key objectives of the study include:

• Identify and define indicators and measures that reflect critical RN shortages for the various types of facilities;
• Assess the availability of data sets that can be used to determine RN staffing needs nationally;
• Develop quantifiable key measures of nursing shortages based on key indicators described above as well as the available data sets that include the necessary data to calculate the key measures;
• Determine whether these key measures of shortage can be incorporated into a comprehensive national methodology to identify facilities and communities with critical nursing shortages based on the following criteria:
  • The measure accurately quantifies nursing shortages in a specific health care setting; and
  • The measure either can be calculated using an available national data set or the data can be collected and validated at the facility level;
• Establish an analytic framework that can be used for a comprehensive methodology to determine critical nursing shortages across a variety of health care settings.

The study was conducted under the guidance of four expert advisory panels, one for each of four types of health care organizations: hospitals, home health agencies, nursing homes, and public health agencies.  Ultimately, this research will support the development of a comprehensive method for identifying the health care facilities and communities with critical shortages of RNs.  This will permit more effective targeting of Federal and other resources to encourage service-obligated RNs to work in the facilities and communities with the greatest needs.

Study Progress

The panels’ first meetings were in February 2005.  Outcomes from the initial meetings included a set of guiding principles.  A range of theoretical principles and ideals were developed.  These are listed below:

• Context: facility within community. Both facility and community characteristics must be considered, but community characteristics are more important than facility characteristics.
• Demand over need. Analyses should primarily focus on employer demand for RNs (e.g., what the local labor market will actually support) rather than the health needs of the population. High-need areas that have no resources or infrastructure to employ additional RNs would find little benefit in the Nursing Education Loan Repayment Program (NELRP).
• Identify standards for data. Ultimately, it will be important to upgrade Federal, state, and local data systems to support better planning for the nursing workforce including the designation of facilities and communities with shortages of RNs.
• Consider facility culture. Some facilities may experience high RN vacancies not because of difficulties recruiting RNs, but because of persistent RN turnover due to problems of organizational culture within the facility (e.g., poor management).  This is not a “shortage” issue, and the NELRP program is not intended to address such problems.
• Define shortage based on outcomes. Theoretically, a facility can be said to have “too few” RNs when there are not enough RNs for the facility to function effectively. This will be observed in certain outcome measures relating to quality of care and facility functioning.

The principles and ideals relating to practical concerns included:

• Low administrative burden on facilities and HRSA. Data used in the final methodology should not require a large-scale data collection or manipulation.
• Applicable to all facility types. The final shortage methodology should be applicable to and appropriate for all facility types.
• Readily available data over time. Ideally, the final methodology should be supported by existing data that are easy to access and available over time for updating.
• Commonly accepted data elements and indicators. Using established indicators of supply, demand, and shortage is preferable to developing new ones.
• Easy to update to reflect changing environment. Data used for identifying shortages should be easy to update so that designations can be periodically reexamined.  

The principles and ideals relating to fairness included:

• Attention to rural and urban differences. The shortage designation method should not systematically disadvantage either rural or urban facilities.
• Special needs of some facilities. The shortage designation method should recognize extenuating circumstances (e.g., facing critical problems, serving special populations).
• Case mix of patients. The method should recognize that some facilities have higher patient acuity than others which may signify that some facilities require more intensive staffing.
• Accommodate data manipulation. The method should minimize opportunities for facilities and communities to “game” the system to achieve a shortage designation.

These guiding principles will influence the development of the methodology, but data availability will create some constraints.  While finding the right indicators will be a key objective of the study, there are other important issues that will be more difficult to resolve.  These issues include determining how shortage-facility designations will occur, how often designations will be updated, and how resources will be allocated by setting type.

Preliminary Update

While working on different options, staff considered the possibility of incorporating the HRSA Nurse Supply Model (NSM) and Nurse Demand Model (NDM) into the RN shortage designation process. Although the exact analyses included in the NDM could not be replicated at the county level due to data constraints, the basic logic employed in the NDM was very useful in thinking about demand for RNs.

The decision was made to apply a simplified version of the NDM logic to: 1) estimate health care utilization in different settings for counties (e.g., inpatient days); 2) estimate current national RN staffing by setting (e.g., RNs working in inpatient units); 3) calculate national RN staffing intensity for each setting (e.g., RNs per inpatient day); 4) apply national RN staffing intensity ratios to measures of utilization for each county; and 5) sum estimate demand for each setting to produce overall RN demand for individual counties. Each step is summarized briefly below.

1. Estimate Health Care Utilization

The data on county-level health care utilization came primarily from the Area Resource File (ARF). The ARF included data on short-term inpatient days (non-psychiatric hospitals); long-term inpatient days (non-psychiatric hospitals); psychiatric hospital inpatient days; nursing home unit inpatient days (hospitals); outpatient visits (non-emergency); and emergency department visits.

The number of (non-hospital) nursing home residents in a county was obtained from the 2000 Census. This was based on the Census short-form data which is theoretically obtained from 100 percent of the U.S. population.

The number of home health patients per county was estimated using the age and gender distribution of the population, based upon national age-specific and gender-specific utilization rates from the Centers for Disease Control and Prevention (CDC).

Although this estimate was based upon population characteristics rather than actual use of services, home health patients by definition were receiving services where they live, so this was somewhat less problematic than estimating other types of utilization based upon population characteristics.

2. Estimate Current National RN Staffing

Data for current levels of RN staffing by setting were taken from the 2000 NSSRN, which included data on the number of RNs employed in the following types of care: short-term inpatient (non-psychiatric hospitals); long-term inpatient (non-psychiatric hospitals); psychiatric inpatient (non-Federal); nursing home unit (hospital); outpatient (non-emergency); emergency outpatient; non-hospital nursing home; home health; nurse education; public/community health; school health; occupational health; non-hospital ambulatory care; and other nursing care.  These numbers were combined with the national utilization data described above to compute national levels of RN staffing intensity for the various types of care.

3. Estimating RN Demand by County

These national staffing ratios were then applied to the utilization rates for each county. For example, the national ratio was 4.97 RNs working in hospital inpatient units per inpatient day.  If County A has 12,000 inpatient days per year, their demand for RNs in inpatient units is estimated at 59.6 (4.97 x [12,000/1,000]).

Overall RN demand for the county was obtained by summing RN demand in the county across all settings.  (This procedure also opens the possibility of comparing setting-specific demand to setting-specific supply if data on RN supply by setting are available at the county level).

4. Use Supply of RNs to Estimate RN Shortages

RN shortages in each county were estimated as follows:

RN shortage = [Estimated Demand] – [Estimated Supply (adjusted for commuting)]

These raw shortage estimates were then standardized as a percent of demand.

This method has advantages over any of the other methods examined in this study especially in relation to the guiding principles initially proposed for the study.  It uses nationally available data that is periodically updated, it uses actual health care utilization patterns by county, it accounts for multiple types of nursing care (including non-clinical services), and it accounts for differences in RN staffing intensity across settings.

The NDM uses factors such as HMO penetration and LPN staffing in regressions to adjust estimated staffing intensity and make it specific to each county rather than applying national ratios.  A similar procedure might eventually be used to do the same thing here.