Time is Money: The Value of Time Spent
Waiting for Health Care in the United
States
Julia Prentice*
Health Care Financing and Economics,
Boston VA Healthcare System
and
Boston University School of Public Health
Steve Pizer
Health Care Financing and Economics,
Boston VA
and
Boston University School of Public Health
*Support was provided under Grant No. IAD 06-112 from the Department of Veteran Affairs, Health Services
Research and Development Service and Grant No: 62967 from the Health Care Financing and Organization
Program under the Robert Wood Johnson Foundation.
Waiting is a Price
• Number of days between appt. request and
day appt. is scheduled
• Reconciles gap between demand and
supply when prices are too low
• Outpatient waiting times have been low
- Postpone Medicare reimbursement rate cuts
• Proposed health reform may increase waits
- Stimulate demand but no supply expansion
Insurance Reform + Cost Control =
Waiting
Research Objective
• Examine the time/cost tradeoff
veterans make when choosing health
care
- How much more will veterans pay to avoid
long waits?
Choose Between Two Systems
• VA
- Low out of pocket cost to patients
- Long waits
• Medicare
- Higher out of pocket cost to patients
- Shorter waits
Study Population
• Veterans eligible for Medicare and VA
- Respondents to the MCBS survey who served in
armed forces
- Non-institutionalized
- MCBS 2001-2003
- n=1513
• Exclude respondents with employersponsored health insurance or Medicaid
Data sources
• MCBS 2001-2003
- Medicare insurance choice
- FFS, HMO or Medigap
- Medicare outpatient utilization
• VA administrative/encounter data
- VA facility level wait times
- VA outpatient utilization
• Medicare Personal Plan Finder/Insurer for
HMO and Medigap plans
- Annual premium
Methodological Approach
1) Predict number of VA and Medicare outpatient
visits
- Accounting for VA wait time
2) Predict Medicare plan choice
- Accounting for VA and Medicare outpatient utilization
3) Policy simulation
- Increase VA wait time- what happens to Medigap market
share?
Methodological Approach
1) Predict number of VA and Medicare outpatient
visits
- Accounting for VA wait time
2) Predict Medicare plan choice
- Accounting for VA and Medicare outpatient utilization
3) Policy simulation
- Increase VA wait time- what happens to Medigap market
share?
First Stage Equation
• Dependent variables
- Number of VA visits in a year
- Number of Medicare visits in a year
• Main independent variable
- VA facility level wait time averaged over the year
- Average wait until next available appt. for new patients
- Wait time at VA facility nearest to respondent
• Zero-inflated negative binomial
- Accounts for heaping at 0
-69% had no VA visits, 11% had no MCR visits
Zero-Inflated Negative Binomials
Predicting # of VA outpatient visits
Independent Variables
Facility average wait
time in days
β
-0.0064
Robust
P-Value
Standard Error
0.0072
0.373
Predicting # of Medicare Outpatient Visits
Facility average wait
0.0103
0.0049
time in days
0.034
*Model also includes non-VA health insurance options in county, VA priority status, distance to
nearest VA facilities, year dummies, demographics (e.g. education, gender), lagged chronic
conditions and hospital referral regions.
First Stage Conclusion
Longer VA wait time
Higher Medicare utilization
Methodological Approach
1) Predict number of VA and Medicare outpatient
visits
- Accounting for VA wait time
2) Predict Medicare plan choice
- Accounting for VA and Medicare outpatient utilization
3) Policy simulation
- Increase VA wait time- what happens to Medigap market
share?
Second Stage Equation
Nested Logit Predicting Medicare Plan Choice
FFS
HMO
1-RX
2-RX 3-No RX
Medigap
1-RX
2-No RX
• Main independent variables
- Annual premium of Medicare option
- VA and Medicare outpatient utilization
Nested Logit*
Predicting Medicare plan chosen
Top level- FFS, HMO or Medigap
β
Standard Error
VA outpatient visits
-0.1746
0.0348
<0.0001
Medicare outpatient visits
-0.0658
0.0117
<0.0001
VA outpatient visits
-0.2058
0.0282
<0.0001
Medicare outpatient visits
0.0414
0.0069
<0.0001
HMO
P-Value
Medigap
Bottom level- Specific Plan Characteristics
Annual premium
-0.0007
0.00006
<0.0001
*Model also includes coverage, VA and Medicare outpatient utilization interacted with coverage and premium
and the residual from the negative binomial equations in stage 1 in the top level and interacted with coverage
and annual premium in the bottom level.
First and Second Stage Conclusion
Longer VA wait time
Higher Medicare utilization
Increased probability of choosing Medigap
Methodological Approach
1) Predict number of VA and Medicare outpatient
visits
- Accounting for VA wait time
2) Predict Medicare plan choice
- Accounting for VA and Medicare outpatient utilization
3) Policy simulation
- Increase VA wait time- what happens to Medigap market
share?
Policy Simulation
• Increase VA wait time by 1 standard deviation
- Add 9.83 days
• Predict expected number of VA and Medicare
outpatient visits
- Use increased wait time
• Use this revised prediction in choice model
-Predict change in probability of choosing Mgap
• Decrease Mgap premium by 1 standard deviation
- Subtract $615
- Predict change in choosing Mgap
VA Wait Time Policy Simulation
Results
Increase VA Facility Wait time
Baseline Simulation % Change
VA wait time
42.27
52
23
Medigap
market share
0.50
0.53
6
(probability)
Elasticity
Medigap market share/Wait: 6/23=0.26
Medigap Premium Policy Simulation
Results
Decrease Medigap premium
Baseline Simulation % Change
Medigap
premium
Medigap
market share
(probability)
2106
1491
29
0.50
0.59
15
Elasticity
Medigap market share/Premium: 15/29=0.52
Conclusions
Evidence of Time/Money Tradeoff
Longer VA wait time
Higher Medicare utilization
Increased probability of choosing Medigap
Conclusions
● Increase VA wait time
Mgap market share
- Elasticity=0.26
● Decrease Mgap prem
Mgap market share
- Elasticity=0.52
● Similar elasticities to previous research on
health care choices
● Wait times influence health care choice
Contact Information
Julia Prentice*
Health Care Financing and Economics
Boston VA Healthcare System
and
Boston University School of Public Health
Julia.Prentice@va.gov; jprentic@bu.edu
Steve Pizer
Health Care Financing and Economics,
Boston VA
and
Boston University School of Public Health
Steven.Pizer@va.gov, pizer@bu.edu