The Effect of a Hospital Safety Incentive in an Employed Population

Dennis P. Scanlon*Colleen Lucas

Pennsylvania State University

Jon B. ChristiansonUniversity 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) BenefitsBefore 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

HospitalCoinsurance

0% 0% 0% 5% with 0% HSI option

Annual Out-of-Pocket 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?

Physician Choice

Health Plan Choice

Range ofServices

(lab, x-ray)

Credentials/Board

Certification

Quality Rating

Travel Time/Distance

PriorExperience

Reputation/Recommend-

ation

In HealthPlan

Network

PhysicianChoice

QualityRating

PriorExperience

Reputation/ Recommend

-ation

In Health Plan

Network

Specialty Services

Travel Time/Distance

Amenities

Physician Privileges/

Recommend-ation

Out of Pocket

Expenses

Hospital Choice

Hospital Choice

Factors Influencing Hospital Choice

Out of Pocket

Expenses

PriorExperience

QualityRating

CoveredBenefits/Services

Reputation/Recommend

-ation

PhysicianNetwork

HospitalNetwork

Health Plan

Choice

Factors Influencing Consumer’s Health Care Choices

Factors Influencing Health Plan ChoiceFactors Influencing Physician Choice

Conceptual Framework for Studying Tiered Hospital Benefit Programs

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. Non-hospitalized, Union

35,490 / 29,883 749 / 829 296 / 305

2. Non-hospitalized, 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/Non-Union- (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