The Effect of a Hospital Safety Incentive in
an Employed Population
Dennis P. Scanlon*
Colleen Lucas
Pennsylvania State University
Jon B. Christianson
University of Minnesota
Principal funding from the Agency for Healthcare Research & Quality – ‘Partnership for
Quality’ grant program, Grant #: 2U18 HS13680
* Dr.
Scanlon’s research is supported by an Investigator Award in Health Policy Research
from the Robert Wood Johnson Foundation
Boeing’s ‘Hospital Safety Incentive’
and Health Consumerism Campaign
• The ‘Hospital Safety Incentive’ was one piece of the ‘consumerism’
component of Boeing’s overall health care strategy
– Standard hospital benefit (and out-patient surgery care) changed
from 100% to 95% coverage for union employees on July 1, 2004
– The safety incentive gave union employees the option to return to
a 100% benefit for hospital care (and out-patient surgery care) if
services were received at ‘safe hospitals’ as measured by
compliance with The Leapfrog Group’s patient safety leaps
– Boeing also engaged in a ‘consumerism campaign’ to educate
employees and beneficiaries about issues of health care cost,
quality and safety, and to encourage employees and their
dependents to be ‘partners with Boeing’
Traditional Medical Plan (TMP) Benefits
Before 7/1/2004
After 7/1/2004
Non-Union
Salaried
Employees
Union Hourly
Employees
Non-Union
Salaried
Employees
Union Hourly
Employees
Deductible
(Individual Family)
$200 / $600
$200 / $600
$200 / $600
$200 / $600
Hospital
Coinsurance
0%
0%
0%
5% with 0%
HSI
option
Annual Out-ofPocket
Maximum
(Individual
- Family)
$5,000 /
$15,000
$2,000 /
$4,000
$5,000 /
$15,000
$2,000 /
$4,000
Research Questions
• Were union employees and beneficiaries aware
of the HSI? What characteristics predict
awareness?
• How do commercially insured beneficiaries
weight attributes believed to be important in
the hospital selection decision?
• What (if any) impact has the HSI had on
patients’ assessments regarding the need for
hospital care?
Conceptual Framework for Studying
Tiered Hospital Benefit Programs
Factors Influencing Consumer’s Health Care Choices
Factors Influencing Hospital Choice
Quality
Rating
Prior
Experience
Out of
Pocket
Expenses
Physician
Privileges/
Recommendation
Amenities
Hospital
Choice
Reputation/
Recommend
-ation
Travel Time/
Distance
In Health
Plan
Network
Specialty
Services
Factors Influencing Physician Choice
Hospital
Choice
In Health
Plan
Network
Range of
Services
(lab, x-ray)
Reputation/
Recommendation
Physician
Choice
Credentials/
Board
Certification
Quality
Rating
Factors Influencing Health Plan Choice
Travel Time/
Distance
Prior
Experience
Physician Health Plan
Choice
Choice
Hospital
Network
Out of
Pocket
Expenses
Physician
Network
Health
Plan
Choice
Prior
Experience
Quality
Rating
Reputation/
Recommend
-ation
Covered
Benefits/
Services
Our Approach
•
We collected detailed survey data about the process by which patients ended
up at particular hospitals, and the degree to which patients were involved in
the decision, as well as the relative importance of the various attributes in the
decision
•
We assume some attributes are known with certainty while others are
uncertain, and thus information about the HSI allows individuals to update
their priors about uncertain attributes.
– Cost is an example where a certain attribute became uncertain as a result
of the HSI
•
We hypothesize that the HSI was meant to directly affect cost, quality and
safety, while not directly affecting other attributes such as distance, amenities,
etc.
– If weights for cost, quality and safety change, then the weights of other attributes
will also change if they must sum to one.
•
We estimate regressions for each attribute individually, using interaction terms
to test whether the attribute weights were different for the union beneficiaries
(relative to the non-union beneficiaries) in the post period.
Survey Design
• 20 minute phone interviews, pre/post with a random sample of
beneficiaries (employees or spouses)
– 4 groups pre and post July 1, 2004
• The survey focused on the following areas:
– Awareness of enrollment materials and online decision support tools
– Opinions regarding the quality and safety of health care
– Factors influencing hospital choice (for respondents with a recent
hospitalization)
– Factors important for future choice of hospital if inpatient care is needed
– Factors related to health plan choice
– Demographic characteristics
• Cooperation rate was 69.1% and 70.1% in pre/post periods
Sampling Distribution
(Pre/Post)
Received from
Regence
(Pre/Post)
Sample Drawn
(Pre/Post)
Completed
Interviews
(Pre/Post)
1. Nonhospitalized,
Union
35,490 / 29,883
749 / 829
296 / 305
2. Nonhospitalized,
Non-Union
23,369 / 21,391
747 / 680
284 / 305
3. Hospitalized,
Union
1,180 / 1,558
925 / 1,008
377 / 401
4. Hospitalized,
Non-Union
654 / 929
654 / 624
275 / 269
Regression – General Form
• Yijt = βX + α(Post) + γ(Union) + δ(Hospital) +
η(Post*Union) + ξ(Hospital*Post) +
ψ(Hospital*Union) + τ(Hospital*Union*Post)+ εijt
• X = vector of demographic characteristics
– Age, propensity to seek health info, health status,
gender, race, education, income, active or early retiree,
spouse, years with Boeing
• Normalize Y - as a preference weight - to allow
attribute tradeoff:
– Norm (Yijt) = Yij / ∑j=1...10 Yijt
Timing of Awareness
• We hypothesize that learning about the HSI does not occur
until after a hospitalization, when individuals receive a bill
from the hospital and/or the explanation of benefits from
the insurance company
• We test this by:
– Estimating the probability of awareness among union beneficiaries
in the post-period as a function of individual characteristics,
including hospitalization status
– Asking questions about the need for a future hospitalization and
comparing the importance of attributes for hospitalized, union
respondents in the post period (relative to hospitalized non-union
respondents)
Models Estimated
• Awareness of HSI
– union beneficiaries in the post period -Table 7
• Attribute importance for previously hospitalization
– beneficiaries with recent hospital admission – Table 8
• Attribute importance if future hospitalization is needed
– all beneficiaries – Table 9
• Attribute tradeoff – willingness to go to a different hospital
than first preference
– all beneficiaries – Table 10
• Preference for same hospital if future hospitalization is
required
– beneficiaries with recent hospital admission – Table 11
Hospital Attribute Importance –
Actual & Future Hospital Decisions
• When deciding which hospital to use, how
important was: Your physician’s recommendation
of the hospital?
– 1 not at all important, 10 extremely important
– Hospitalized samples only
• The next time you decide which hospital to use for
inpatient services, how valuable would you find:
Your physician’s recommendation of the hospital?
– 1 not at all valuable, 10 extremely valuable
– Entire sample
Normalized Attribute Weights from
Survey Respondents – Actual Choice
Attribute
Overall –
(Mean)
Pre/Post –
(Mean)
Union/NonUnion- (Mean)
Out-of-pocket costs
0.096
0.096/0.095
0.097/0.094
Quality
0.090
0.093/0.087
0.093/0.086
Physician Recommendation
0.114
0.115/0.114
0.115/0.114
Travel Time & Distance
0.082
0.081/0.082
0.081/0.083
Plan’s Hospital Network
0.108
0.106/0.110
0.112/0.102
Family & Friends
0.082
0.078/0.085
0.081/0.082
Amenities
0.099
0.099/0.099
0.099/0.099
Specialty Med Services
0.107
0.107/0.107
0.103/0.113
Prior Experience
0.099
0.104/0.093
0.098/0.099
Hospital’s Overall Reputation
0.124
0.121/0.127
0.120/0.123
Within Person Response Variance
Conclusions
•
The HSI does not appear to have had an effect
– Hospitalized union beneficiaries not more aware than non-hospitalized
union beneficiaries
– No systematic shift in attribute importance among the recently
hospitalized union beneficiaries
– No systematic differences in reported importance of attributes for future
hospitalization among recently hospitalized union beneficiaries
•
The ‘Why’ is important for policy & insurance design
– If physician-patient relationships dominate and physician hospital
privileges are limited, then a financial incentive geared towards consumers
may have little impact (more effective alternative approaches may include
hospital or physician incentives)
– 5% may not have been enough to encourage ‘shopping’ or behavior
change
– Recently hospitalized are less likely to consider a different hospital,
suggesting the value of experience
•
Optimal timing of incentive program implementation when few providers meet
the preferred tier initially?
– Value of sending signals to the market to spur adoption vs. waiting until
enough suppliers meet the preferred criteria?
Limitations
• Few hospitals met the safety standards to qualify for the
HSI
– So effect may have been larger if beneficiaries had more
alternatives
• Stated preference may not match revealed preference
– Respondent recall and attribute tradeoff may have been cognitively
challenging
• Bad phone number information may limit generalizability
– But probably would not affect the conclusion unless the HIS had a
systematically different effect on those with bad phone numbers