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Supply, Demand, and Waiti

for VA Outpatient Ca

Steven D. Pizer, Julia C. Prentic

May 20, 2009
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Acknowledgements & Dis

• Acknowledge funding from VA 

and the Robert Wood Johnson Fo

• Thanks to Graeme Fincke and D

Miller for helpful comments.

• Not official position of Departme

Veterans Affairs or Boston Univ
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Motivation

• We can’t always get an appointm

the doctor when we want to.

• More of a problem outside of U.• Waiting times reduce demand wh

are held down– like in VHA.

• VA tried to reduce waits without 

prices; what happened?
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Summary of Stud

• Jointly model supply and deman

of waiting times and other variab

• Use monthly data on appointmen

requested, appointments schedulwaiting times, all VAMCs, 2002

• Estimate waiting time elasticities 

and demand.
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Research Question

• Theory says waiting times should 

supply and reduce demand.

• How does VA supply respond to • How does VA demand respond t• If VA spends money to increase 

Do waits fall? Does demand inc
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Key Concept: Elasti

• Definition of Elasticity:• Elasticity is a unitless measure o

responsiveness.

• In this case, responsiveness of su

demand to changes in waiting tim

% change in Y% change in X Elasticity =
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Preview of Result

• Supply elasticity is positive, but • Demand elasticity is negative an• If budget expands to reduce wait

20% of new money will serve neinduced by lower wait.
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Outline of Talk

• Background• Conceptual Model• Statistical Model• Data and Variables• Results• Discussion and Policy Implicatio
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Background: VHA 

• 23 million veterans in US; 9 million o• 25% of elderly Medicare beneficiarie

veterans.

• About 8 million VHA enrollees.• 5.5 million unique patients treated in • 43% of VA patients also use non-VA 

12 months (Shen et al., 2003).

• Current and potential overlap between 

other health plans/programs.
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VA Waiting Time

• Waiting times have been an issue 

managers and Congress at least sexpansion of eligibility (1998).
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August 2001
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March 2007
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September 2007
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VA Policy Responses (sinc

• Vets with 50%+ service-connected di

w/in 30 days.

• Vets seeking care for service-connect

conditions: 30 days.

• All others: w/in 120 days.• Requests acted upon w/in 7 days.• Advanced Clinic Access; System Red• Closed eligibility for higher income v
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VA Waiting Time D

• VA implemented electronic sche

automatic calculation of wait tim

• Clinic-month average wait times 

in data available to researchers fr

• VA wait times increase risk of m

preventable hospitalization for gclinic patients (Prentice and Pize2008).
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Wait Time Calculat

• Wait times defined as facility av

number of days between request appointment for NEW patient.

– Avoids confusion with follow-ups

• Focus on primary care because s

care patients often travel longer dbetween facilities.

• Primary care accounts for about 

clinic visits.
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Waiting Time Tren

• Wait times declined from 2002 t• Unique VA patients increased sh

(12%) over same years.

• Cost per unique patient increased

more than National Health Expen
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New Patient Waits, 2

Wait <30 days Wait 30-39 days

Wait 40-49 days Wait >=50 days
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New Patient Waits, 2

Wait <30 days Wait 30-39 days

Wait 40-49 days Wait >=50 days
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Unique VA Patien

4,000,0004,200,0004,400,0004,600,0004,800,0005,000,0005,200,0005,400,000

FY2002 FY2003 FY2004 FY2005 FY2006 FY2007 F
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Per Capita National He

Expenditures and VA Cos

$0

$1,000$2,000$3,000$4,000$5,000$6,000$7,000$8,000

2001 2002 2003 2004 2005 2006 2007 

NHE VA

NHE in calendar ye
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Research Questions, A

• Looks like VA found resources i

reduce waits; demand responded

• Can we see demand response at l• How much of new resources wen

patients?

• Did local supply respond to wait
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Conceptual Model: De

• Following Martin et al., JHE 200• Patients choose providers based 

and “quality” (wait time, etc.).

• Utility of treatment decays if trea

delayed from point of diagnosis and Feigenbaum, 1984).

• Derived demand for provider i:

– Q: quality; c: cost; z: demand shift

D
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Conceptual Model: Su

• Managers care about volume and 

(motivated by satisfaction and in

• Managers dislike effort, so • Production function:

– z: budget, labor market . . .

• Choose effort to max U, s.t. f(.), 

EQ supply:

U 

i 

=

(, ,, ) 

s i iii fSQez =

* ( ,, , ) s d i iii i S sQ c z z =
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Implications of Mo

• Behavioral equations:• If D=S*, then dQ=0, equilibrium• If D>S*, then dQ<0, waits increa• If D<S*, then dQ>0, waits decre

( ,

Di i

= dQ 

* ( ,

i i

S sQ 

=
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Statistical Model: Assum

• Quality = waiting time; Cost = d• Supply and Demand simultaneou

determined.

• Supply responds to last month’s • Demand responds to this month’• Joint determination of demand an

means we need instrument for w2-month lag of wait time.
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Statistical Model: Speci



1 23 4 ln ln d 

D W z Year M

it it it 

=+ + + + 

α β ββ β 

*

1 12 3

4 56

ln ln s d it it it it

s

i it

S W zz

VISN Year Month

αβ β β

β ββ ε

=+ + + +

−

++ +

1 22 3 

ln ln d 

W W z Year 

it it it 

α β ββ β 

=+ + + + 

−

Estimate 3 equations, clustering on VAMC.Log-log specification gives elasticity estimates.S & D residuals are correlated; estimate by 2-stag
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Variables

• z(s): Preliminary budget allocatio

of OP clinics.

• z(d): Distance, poverty, unemplo

uninsurance, hospital beds, physurban, home values, % college, % construction, % health care, % w
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Data Sources

• Distance: PSSG distance by ZIP

by share of medical center or clinvisits.

• Budget: Allocation Resource Ce• Unemployment: Bureau of Labo• Uninsurance: March Current Pop

Survey (state-year).

• Other z(d): Area Resource File.
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Construction of Supply & 

• Supply: Total number of primary 

appointments scheduled to occur 

• Demand: Total number of prima

appointments requested during m

regardless of when scheduled to 

– D(t) = Average appts/day*Days re– Days requested in t = Number of b

in t + Wait(t) – Wait(t-1).
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Sample

# of VAMCs* X 

2002 120 

2003 120 

2004 120 

2005 118 

Total 478 

Missing/zero waits Sample for 1st stage 

*28 VAMCs were reorganizing/merging during these years.
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Results: Stage 1; Curren

Variable Coeff Std Error 

Ln(wait) t-2 0.72 0.02 

Unemployment 0.015 0.008 

Ln(hospbed/pop) -0.077 0.037 

Ln(area) 0.048 0.025 

% construction 0.039 0.015 

N=5682; R-sq=0.72 Models include ye
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Results: Stage 2; Sup

Ln(wait) t-1 0.06*** % urban 

Ln(budget) 0.54*** Ln(home valOP clinic count 0.02*** % college Unemployment 0.03*** % constr Ln(hosp/pop) -0.19*** % fish & ag #Mcare HMOs 0.01*** % health car

N=5,427; R-sq=0.85217 obs dropped due to demand <=0.

***
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Results: Stage 2; Dem

Pred. Ln(wait) -0.19*** Ln(GP/pop) 

Ln(MC dist) 0.30*** % urban Ln(OP dist) -0.07*** Ln(home val% poverty -0.03*** % college % uninsured 0.03*** % healthcare Ln(hosp/pop) -0.21*** % wh collar 

N=5,427; R-sq=0.35 S&D r
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Discussion

• VA supply responds to waiting times

elasticity is low. 

– Waits have to grow by 17% to get 1% in

supply.

• Demand response to waiting times is 

– 5% increase in waits causes 1% decline 

• Therefore, if VA finds resources to re

20% will go to new patients. 
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Limitations

• Don’t have data on requests for 

appointments that were never sch

• Don’t have good data on waits fo

patients.

• No data on demand for non-VA 

demand shocks could be correlat

– Would bias results toward zero.
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Policy Implication

• Will National Healthcare Reform 

longer (non-VA) wait times?
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Insurance Reform + Cost C

Waiting
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Now a National Con
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Blogs Too!
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Policy Implication

• Expansion of non-VA coverage c

reduce VA demand, causing wai

• Falling waits will reinforce dema

10% reduction in wait induces onincrease in demand.

• On the other hand, if non-VA wa

increase, demand will shift to VA unless VA counters with restricteeligibility.
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Supply, Demand, and Waiti

for VA Outpatient Ca

Steven D. Pizer, Julia C. Prentic

May 20, 2009