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National Experiences in Health Workforce Modeling: What’s

New in the United States?

Erin P. Fraher PhD MPP Assistant Professor, Departments of Family Medicine and Surgery

University of North Carolina at Chapel Hill

OECD Expert Meeting on Health Workforce Planning 17 September 2012

This project is funded by a grant from the Physicians Foundation.

Caveat: Health Workforce Modeling in the United States Not Directly Connected to Policy Decisions

Even with workforce pressures of health reform, the United States does not “workforce plan”

Reform legislation created: National Health Workforce

Commission (not funded)

Center for Health Workforce Analysis

Modeling efforts underway by government, academia and private sector


And Even in a Country That Does Workforce Planning, They Say….

Some of the so-called ‘failures’ of workforce planning…have been less about problems with planning and more about

unrealistic expectations on the part of policy-makers, who have not recognised

the limitations... Nevertheless, the system can be improved…

Imison C, Buchan J, Xavier S. “NHS Workforce Planning: Limitations and Possibilities.” The Kings Fund, 2009. http://www.kingsfund.org.uk/publications/nhs_workforce.html


Modeling Efforts Underway by United States Federal Government

Center for Workforce Analysis is a center within the Health Resources and Services Administration

Schedule for Health Workforce Forecasting in the U.S.

2012: Design integrated model for physicians, nurse practitioners, and physician assistants

2013: Redesigning nursing and oral health workforce models

2014: Updating model for physicians, NPs, and PAs

2015: Designing new model for allied health workforce

2016: Update nursing models and projections


Federal Health Workforce Forecasting Priorities in the U.S.

Integrated Forecasting Systems – currently bringing together projections of physicians, nurse practitioners, and physician assistants

Allows simulation of team-based care and substitution

Enhanced Scenario Modeling – future will emphasize need for nimble, flexible models

Allows models to simulate effect of various scenarios

Identify specific metrics to track scenarios


Federal Health Workforce Forecasting Priorities in the U.S. (continued)

Distributional Issues – moving toward modeling at sub-national levels

National figures mask significant variation at the state and local level.

Microsimulation – contracts awarded in September 2012 to build supply and demand models that simulate employment and care-seeking behavior of individual practitioners and patients

Hope to improve accuracy, facilitate local-area modeling, and enhance options for scenario-building


Federal Health Workforce Forecasting Priorities in the U.S. (continued)

Supply/Demand Interactions – quantify ways in which workforce supply and demand interact

Goal: produce more realistic assessments of how labor markets adapt to shortages and build dynamic models accounting for interactions

Economic Impacts – define impact of recession on employment and care-seeking behavior

Goal: understand influence of economic drivers on workforce participation rates for physicians and nurses in 2012


Critiquing Current Models (a partial list of overgeneralizations and limitations)

“Stock and flow” models assume relatively homogenous physician stocks “Can’t be a little bit pregnant”

National models

Silo-based by physician specialty, do not allow for:

Heterogeneity in scope of services within specialties (not all doctors have similar scope of service)

Degree of heterogeneity varies between specialties (some specialties have doctors with similar scopes)

Proprietary (read: black box) & uncustomizable models

Limited ability to evaluate and compare scenarios


UNC’s Physician Projection Model Aims to Build, and Improve, on Past Models

Goal: Create open source physician projection model that incorporates numerous innovations

Conceptual: concept of “plasticity”—multiple potential configurations of specialties can meet community’s health care needs

Methods: agent-based modeling reflects “real world” uncertainty of individual physicians’ decisions about how much, where and in what clinical areas to practice

Process: model NOT based on full data availability; incorporates clinical input to check “face validity” of findings

Usability: open source design facilitates interactions and (debate!) among workforce policy stakeholders


Model Framework in Three Pieces


Forecasting the Utilization of Physician Services

Modeling utilization, not demand and not need

Forecasting use of physician services for population in defined geography:

in four medical settings: physician offices, hospital inpatient settings, hospital outpatient settings and emergency departments

for 18 Clinical Service Areas (e.g. respiratory conditions, circulatory conditions, endocrinology, mental health, etc.)


Aggregating from Individual to Estimates of County Utilization

Using data on patient-level characteristics and health care utilization patterns, we extrapolate from individual- to county-level utilization

Example: If males use 1.5 visits and females use 2.5 visits, then if 40% of the county is male, the mean visits for the area is .4 * 1.5 + .6 * 2.5 = 2.1 visits


And Allows Contextual Factors to Have Differing Effects Across Types of Services

Predicted Mental Health and Respiratory Office-Based Visits (OBV) Per Capita

.3

.35

.4

.45

OB

V p

er

cap

tia

10 15 20 25 30 County poverty rate

Mental Health

Respiratory

North Carolina counties (N=100). Predicted utilization based on MEPS 2009 and county-level data.

OBV per capita


Forecasting the Supply of Physician Services

Retirements Attrition

Training Pipeline Ratio

HC/FTE

Current

workforce

Projected

future

workforce

Diffusion

Re-entry


Forecasting Physician Supply

Uses agent-based modeling to simulate location choices, workforce participation rates and decisions about which clinical services physicians provide

Forecast supply by headcount and full-time equivalents for 35 specialty groupings

Newly trained and existing workforce are diffused out to different geographies according to different “push” and “pull” factors


County A

County B

County C County D

2012

This doc retires


2013 County A

County B

County C County D

Spike in demand for services provided by

Squares


2014

No substantive changes – stay where I am, maybe I reduce my

hours because I had a baby

County A

County B

County C County D


2015 A new square finishes residency and moves to the area

Decrease in relative demand in County B makes County A more appealing

County A

County B

County C County D


2016

Approaching retirement age. County D (a rural area) has more appealing amenities for near-retirement age

County A

County B

County C County D


2017

Retirement

County A

County B

County C County D


Mapping Services to Providers

Key decision: no silo-based modeling

Recognize that services provided vary across, and within, specialties

How to model a specialist’s range of services?

We refer to this concept as “plasticity”


Scopes of Services for 10 GP/FP in NAMCS

Respiratory

Circulatory

Endocrine

Skin

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

Endocrine/immunity

Mental

Nervous system

Circulatory

Respiratory

Skin

Musculoskeletal

Symptoms & signs

Other

A Random Sample of Ten GPs/FPs has Heterogenous Scopes of Services

Individual GP/FP

Services Provided

Pe

rce

nt

of

Vis

its


Infectious

Neoplasms

Skin

Symptoms & Signs

Other

Services Provided

…But Dermatologists Provide Relatively Similar Scopes of Services

Scopes of Services for 10 Dermatologists in NAMCS

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10

Pe

rce

nt

of

Vis

its

Individual Dermatologists

…mostly “skin” and cancer.


Takeaway on “Plasticity”

Heterogeneity in scope of service varies within specialty (not all doctors have similar scope of service)

Degree of heterogeneity varies across specialty (some specialties have more similar doctors)

Key question: What determines the specific scope of services among physicians with similar training?

Are they responsive to relative local demands? How much?


Our Approach to Modeling Plasticity: A Sample Matrix

SPECIALTY Neoplasms Circulatory Respiratory Pregnancy/ch

CARDIOLOGY (HEART) 145,802 23,684,068 593,326 898

DERMATOLOGY (SKIN) 11,913,249 154,326 187,179 16,234

FAMILY PRACTICE 1,772,218 26,485,370 19,943,025 1,264,030

GYNECOLOGY/OBSTETRICS 2,575,715 496,124 17,533 29,821,750

INTERNAL MEDICINE 1,545,030 18,097,752 5,496,049 32,315

Within a CSA, how are visits distributed across specialties

34%

0%

38%

1%

26%4% 54% 40% 3%

Within a specialty, how are visits distributed across CSAs?


How Do We Map Services to Providers? A Five-Step Process

Step 1: Model health care utilization of community as function of individual- and contextual-level factors

Step 2: Collect data on local physician supply

Step 3: Allocate physician visits across specialties according to national averages

Step 4: Allow physicians to adjust their service portfolio to account for variations in local utilization of services and availability of other providers in area

Step 5: Compare capacity for visits for a particular Clinical Service Area to the predicted utilization of that health service


How Does the Model Account for Uncertainty?

Best guess is shown, but can also see range of likely outcomes

http://www.nhc.noaa.gov/gifs/TrackForecastConeDean.gif


Comparing Policies

Comparing status quo with a new policy

Short run versus longer term

Sense of “how sure” policy will make difference

800

09

00

01

00

00

110

00

120

00

Ph

ysic

ian

s

2012 2014 2016 2018 2020 2022Year

Status Quo Policy A


Contact info

Erin Fraher, PhD

Principal Investigator

[email protected]

919-966-5012

http://www.healthworkforce.unc.edu

mailto:[email protected]

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