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MODELING THE US HEALTH WORKFORCE: SUMMARY OF THE RN SUPPLY AND DEMAND FORECASTING
MEETING (MONTANA, 2016) AND IMPLICATIONS FOR MODELING
OCTOBER 24, 2016
Tim Dall Managing Director, Life Sciences
IHS Markit
+1 202 870 9211
IHWC 2016 TECHNICAL SKILLS DAY
RN Supply and Demand Forecasting Meeting
Big Sky, Montana, July 2016
• Meeting hosted by Montana State University, Center for Interdisciplinary
Health Workforce Studies
• Funding from the U.S. Bureau of Health Workforce, Health Resources
and Services Administration
• Meeting brought together ~20 health workforce researchers and nurse
workforce experts
• Goal: improving nurse workforce forecasts to provide information
needed by policy makers, employers, educators, researchers and others
working to assure a strong, appropriately sized, and capable nursing
workforce
• One day focusing on modeling nurse supply
• One day focusing on modeling nurse demand
• Opportunities to share methods and data, and provide constructive criticism
2
Two Demand Modeling Approaches Presented
• Adjusted Risk Choice & Outcomes Legislative Assessment (ARCOLA)
model
• Microsimulation model used to simulate insurance enrollment patterns under
the Affordable Care Act
• Estimated demand for services based on insurance changes → estimate
demand for nurses based on service demand
• Main challenge with this approach is the ARCOLA model is designed to model
changes in insurance coverage; this study was a workforce application
• Health Workforce Simulation Model (HWSM)
• Microsimulation model that simulates health care use for a representative
sample of the population, then simulates demand for health workforce based
on projected demand for services
• Main challenge with this approach is projecting future changes in care use and
delivery patterns under emerging care delivery models
3
Two Supply Modeling Approaches Presented
• Cohort-based model
• Approach models how many nurses from a cohort (specified by birth year) will
remain in the workforce over time
• Approach provides insights to workforce participation rates over time within a
cohort of nurses
• Main challenge of this approach is it does not capture large variation across
cohorts in number of individuals entering nursing as a profession
• Microsimulation-based approach
• Starts with a database of nurses and simulates individual career choices
• Approach to modeling workforce decisions (active in the labor force, hours
worked, retirement) appears to produce aggregate patterns similar to the
cohort-based approach
• Faces many of the same challenges of the cohort-based model: external
“shocks” can cause nurse workforce behavior to deviate from historical
patterns 4
Summary of Goals and Criteria for Building Workforce
Models
• Provide the most accurate projections possible
• Provide flexibility to model wide range of scenarios reflecting new
policies, emerging trends in care delivery, and other (e.g., economic)
factors
• Build on solid theoretical underpinnings
• Build dynamic model: integrate professions and specialties
• Adaptable to different geographic units (national, state, local level)
• Provide platform for continued model improvement; incorporate new
research as it becomes available
• Make model transparent (through reports and presentations)
5
Flow Diagram for the Supply Component of HWSM
6
Starting Year Supply
Demographic and Geographic Characteristics
Data Sources: American Community Survey, association registries, state licensure files
New Entrants
Demographic and Geographic Characteristics
Data Sources: Integrated Postsecondary Education Data System, professional associations
Attrition
Retirement
Career Change
Age/Sex Specific Mortality Data Source: Centers for Disease Control and Prevention
Workforce Participation and Hours Worked
Data Sources: American Community Survey, survey data from state licensure boards, occupation-specific surveys
End Year Supply
By Demographic
and Geographic
Characteristics
Mortality
Data Sources: Starting Supply
• American Community Survey (ACS)
• Active supply defined as nurses working or seeking employment
• Multiple years data used
• Current work using 2014 ACS, with 5-year file (2010-2014) used for some analyses
• Distribution by state, age, sex and education level
• For current work, using licensure data from states that have voluntarily provided
data (GA, OR, SC, TX); ACS data for all other states
• Data strength and weakness
• ACS: Cannot distinguish between nurses working in nursing positions and in positions
that do not require a nursing degree
• ACS: Information on patient care hours not available
• ACS: Small sample size for smaller states
• Licensure files: most states have ‘cleaned’ their data so the data are in good shape;
desire for HRSA supply estimates to use best available source of data and consistent
with numbers published by individual states; shorter time lag between when data are
generated and used
7
7
Trends in Number of US Educated First Time NCLEX-RN
Takers, 2001-2015
8
68,759 70,692
76,688
87,171
99,186
110,703
119,565
129,111
134,727 140,882
144,554 150,266
155,098 157,882
157,957
-
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Firs
t Ti
me
, U.S
. Ed
uca
ted
Can
dia
tes
Taki
ng
NC
LEX
-RN
Year
Data Source: National Council of State Boards of Nursing, Exam Statistics and Publications,
2001 to 2015 data from various reports.
HR
SA
20
04
re
po
rt
HR
SA
20
14
re
po
rt
HR
SA
20
16
re
po
rt
RN Retirement Patterns
Estimated patterns using 2010-2015 licensure data from Oregon, South Carolina, and Texas; and
2008 Sample Survey of RNs (for nurses under age 50).
9
0
10
20
30
40
50
60
70
80
90
100
25 30 35 40 45 50 55 60 65 70 75
Cu
mu
lati
ve P
rob
abili
ty R
etir
ed
fro
m N
urs
ing
Nurse Age
Texas South Carolina Oregon (Intention, adj) HRSA Model
Summary Regression Results for RNs
Parameter
Predicting
Hourly Wage a
Predicting
Hours/Week a
Predicting Labor Force
Participation, age <50 (CI) b
Intercept -2.67 ** 35.15 **
Unemployment rate (state, year) -0.15 ** 0.05 * 1.03 1.01 1.05
State occupation mean hourly wage 0.85 **
Predicted hourly wage 0.01 0.97 0.96 0.99
Age 35 to 44 3.87 ** 0.26 **
Age 45 to 54 5.21 ** 1.20 **
Age 55 to 59 5.79 ** 0.88 **
Age 60 to 64 5.74 ** -0.31 **
Age 65 to 69 4.70 ** -4.54 **
Age 70+ 2.07 ** -8.57 **
Age 30-34 0.69 0.63 0.77
Age 35-39 0.89 0.79 1.00
Age 40 to 44 0.97 0.86 1.08
Age 45 to 49 1.12 0.99 1.27
Male 1.18 ** 2.78 ** 0.71 0.58 0.87
Age 30-34 * male 2.20 1.59 3.06
Age 35-39 * male 2.81 1.96 4.02
Age 40 to 44 * male 2.63 1.87 3.70
Age 45 to 49 * male 1.94 1.38 2.74
Year 2011 -0.38 ** 0.14 0.93 0.84 1.03
Year 2012 0.39 ** 0.21 * 0.92 0.83 1.02
Year 2013 0.14 0.30 ** 0.93 0.84 1.05
Year 2014 -0.29 ** 0.38 ** 0.97 0.85 1.10
Non-Hispanic black -0.15 2.28 ** 1.32 1.17 1.49
Non-Hispanic other -0.66 ** 1.43 ** 1.23 1.10 1.37
Hispanic 1.12 ** 1.43 ** 1.38 1.19 1.60
Have nursing baccalaureate degree 2.55 ** -0.24 ** 0.98 0.91 1.05
Having nursing graduate degree 4.10 ** 1.56 ** 0.91 0.80 1.03
Population % suburban 12.99 ** 0.73 2.27 1.33 3.89
Population % rural 0.56 1.41 ** 0.77 0.52 1.15
Sample size 150,504 150,504 89,370
R-squared 0.12 0.04
10
Notes: Analysis of
the American
Community Survey; a Ordinary least
squares regression
coefficients.
Statistically
significant at the
0.01 (**) or 0.05 (*)
level. b Odds ratios
and 95%
confidence interval
(CI) from logistic
regression.
Comparison groups
are female,
year=2010, non-
Hispanic white, age
<35 (for wages and
hours) or age <30
(for labor force
participation).
Labor force
participation
regression uses
only clinicians
under age 50.
Comparison of Actual to Predicted Hours Worked by RNs:
Example: Data for the State of South Carolina
11
0
5
10
15
20
25
30
35
40
Less than 35years
35 to 44 years 45 to 54 years 55 to 59 years 60 to 64 years 65 years andmore
Ho
urs
Wo
rke
d p
er
We
ek
RN Age
Actual
Predicted
Projected Percentage Growth in RN Supply & Demand:
Example: Data for the State of Georgia
12
32%
28%
0%
27%
25%
23%
18%
-5%
0%
5%
10%
15%
20%
25%
30%
35%
2015 2020 2025 2030Pe
rce
nta
ge G
row
th in
RN
Su
pp
ly &
De
man
d (
rela
tive
to
20
15
)
Year
Supply: 10% incnew grads
Supply: Ret 2yrslater
Baseline Demand
Supply: Status Quo
Supply: Ret 2yrsearlier
Supply: 10% decnew grads
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© 2016 IHS
Published RN Supply and
Demand Projections
Forthcoming
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