1. 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

2. 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’

3. Traditional Medical Plan (TMP) Benefits Before 7/1/2004After 7/1/2004 Non-Union Salaried Employees Union Hourly Employees Non-Union Salaried Employees Union Hourly Employees Deductible (Individual - Family) $200 / $600 Hospital Coinsurance 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

6. 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?

7. Conceptual Framework for Studying Tiered Hospital Benefit Programs

8. 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.

9. 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

10. Sampling Distribution (Pre/Post) Received from Regence (Pre/Post) Sample Drawn (Pre/Post) Completed Interviews (Pre/Post) 1. Non- hospitalized, Union 35,490 / 29,883749 / 829296 / 305 2. Non- hospitalized, Non-Union 23,369 / 21,391747 / 680284 / 305 3. Hospitalized, Union 1,180 / 1,558925 / 1,008377 / 401 4. Hospitalized, Non-Union 654 / 929654 / 624275 / 269

11. Regression – General Form Y ijt = βX + α(Post) + γ(Union) + δ(Hospital) + η(Post*Union) + ξ(Hospital*Post) + ψ(Hospital*Union) + τ(Hospital*Union*Post)+ ε ijt 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 (Y ijt ) = Y ij / ∑ j=1...10 Y ijt

12. 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)

13. 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

14. 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

15. Normalized Attribute Weights from Survey Respondents – Actual Choice AttributeOverall – (Mean) Pre/Post – (Mean) Union/Non- Union- (Mean) Out-of-pocket costs0.0960.096/0.0950.097/0.094 Quality0.0900.093/0.0870.093/0.086 Physician Recommendation0.1140.115/0.114 Travel Time & Distance0.0820.081/0.0820.081/0.083 Plan’s Hospital Network0.1080.106/0.1100.112/0.102 Family & Friends0.0820.078/0.0850.081/0.082 Amenities0.0990.099/0.099 Specialty Med Services0.1070.107/0.1070.103/0.113 Prior Experience0.0990.104/0.0930.098/0.099 Hospital’s Overall Reputation0.1240.121/0.1270.120/0.123

16. Within Person Response Variance

17. 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?

18. 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